ORIGINAL_ARTICLE
Why Are Physicists Involved in the Studies on the Origin of SARS-CoV-2?
https://jbpe.sums.ac.ir/article_47696_def6a5679289611e660631c66a6f847e.pdf
2021-08-01
413
414
10.31661/jbpe.v0i0.2106-1361
Alireza
Mehdizdeh
alirezamehdizadeh@gmail.com
1
MD, PhD, Editor-in-Chief of the Journal of Biomedical Physics and Engineering, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
Joseph J
Bevelacqua
2
PhD, Bevelacqua Resources, Richland, Washington 99352, United States
AUTHOR
James S
Welsh
shermanwelsh@gmail.com
3
MD, PhD, Department of Radiation Oncology Edward Hines Jr VA Hospital Hines, Illinois, United States
AUTHOR
Seyed Ali Reza
Mortazavi
alireza.mortazavi.med@gmail.com
4
MD, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
Leila
Haghshenas
5
PhD, Postdoc association member of Harvard Medical School, Boston, MA, United States
AUTHOR
Seyed Mohammad Javad
Mortazavi
khaleghiim89@gmail.com
6
PhD, Department of Medical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
LEAD_AUTHOR
IOP. Stories from physicists helping to tackle COVID-19. IOP; 2021. Available from: https://www.iop.org/stories-physicists-helping-tackle-covid-19#gref.
1
Physics World. COVID-19: how physics is helping the fight against the pandemic. Physics World; 2020. Available from: https://physicsworld.com/a/covid-19-how-physics-is-helping-the-fight-against-the-pandemic/.
2
McKenzie J. Why COVID-19 could make physicists kick some of their habits forever. Physics World; 2020. Available from: https://physicsworld.com/a/why-covid-19-could-make-physicists-kick-some-of-their-habits-forever/.
3
APS. APS’s Efforts to Help Physics Community Recover from COVID-19 Impact. The American Physical Society (APS); 2021. Available from: https://www.aps.org/about/covid-19/.
4
Lobos I. The PHYSICS OF A DEADLY VIRUS, Immune response research hints at answers to fighting deadly coronavirus. University of Washington; 2021. Available from: https://www.washington.edu/uwit/stories/immune-response-research-coronavirus/.
5
Wade N. The origin of COVID: Did people or nature open Pandora’s box at Wuhan? Bulletin of the Atomic Scientists; 2021.
6
Kaina B. On the Origin of SARS-CoV-2: Did Cell Culture Experiments Lead to Increased Virulence of the Progenitor Virus for Humans? In Vivo. 2021;35(3):1313-1326. doi: 10.21873/invivo.12384. PubMed PMID: 33910809. PubMed PMCID: PMC8193286.
7
Ghadimi-Moghadam A, Haghani M, Bevelacqua JJ, Jafarzadeh A, et al. COVID-19 tragic pandemic: concerns over unintentional “directed accelerated evolution” of novel Coronavirus (SARS-CoV-2) and introducing a modified treatment method for ARDS. J Biomed Phys Eng. 2020;10(2):241-6. doi: 10.31661/jbpe.v0i0.2003-1085. PubMed PMID: 32337192. PubMed PMCID: PMC7166223.
8
GBevelacqua JJ, Mehdizadeh AR, Mortazavi SAR , Mortazavi SMJ. A New Look at the LDRT treatment for COVID-19 Associated Pneumonia: The Issues of Antiviral Resistance and Virus Spread-Ability. J Biomed Phys Eng. 2020;10(5):549-52. doi: 10.31661/jbpe.v0i0.2007-1151. PubMed PMID: 33134212. PubMed PMCID: PMC7557455.
9
Mortazavi SAR, Mortazavi SMJ, Sihver L. Selective Pressure-Free Treatments for COVID-19. Radiation. 2021;1(1):18-32. doi: 10.3390/radiation1010003.
10
Mortazavi SMJ, Kefayat A, Cai J. Low-dose radiation as a treatment for COVID-19 pneumonia: A threat or real opportunity? Medical Physics. 2020;47(9):3773-6. doi: 10.1002/mp.14367.
11
ORIGINAL_ARTICLE
Automated Segmentation of Abnormal Tissues in Medical Images
Nowadays, medical image modalities are almost available everywhere. These modalities are bases of diagnosis of various diseases sensitive to specific tissue type. Usually physicians look for abnormalities in these modalities in diagnostic procedures. Count and volume of abnormalities are very important for optimal treatment of patients. Segmentation is a preliminary step for these measurements and also further analysis. Manual segmentation of abnormalities is cumbersome, error prone, and subjective. As a result, automated segmentation of abnormal tissue is a need. In this study, representative techniques for segmentation of abnormal tissues are reviewed. Main focus is on the segmentation of multiple sclerosis lesions, breast cancer masses, lung nodules, and skin lesions. As experimental results demonstrate, the methods based on deep learning techniques perform better than other methods that are usually based on handy feature engineering techniques. Finally, the most common measures to evaluate automated abnormal tissue segmentation methods are reported.
https://jbpe.sums.ac.ir/article_45654_9a5b539ad72696b984bf2f3d65d619af.pdf
2021-08-01
415
424
10.31661/jbpe.v0i0.958
Skin Abnormalities
Abnormal Tissue Detection
Multiple Sclerosis
Breast cancer
Multiple Pulmonary Nodules
Automatic Segmentation
Medical Imaging
Hassan
Homayoun
khastavaneh@hotmail.com
1
PhD, Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Kashan, Kashan, Iran
LEAD_AUTHOR
Hossein
Ebrahimpour-komleh
ebrahimpour@kashanu.ac.ir
2
PhD, Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Kashan, Kashan, Iran
AUTHOR
Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, man, and Sybernetics. 1979;9:62-6. doi: 10.1109/TSMC.1979.4310076.
1
Shi J, Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000;22:888-905. doi: 10.1109/34.868688.
2
Boykov Y, Veksler O, Zabih R. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2001;23:1222-39. doi: 10.1109/34.969114.
3
Suzuki K. Pixel-based machine learning in medical imaging. Int J Biomed Imaging. 2012;2012:792079. doi: 10.1155/2012/792079. PubMed PMID: 22481907. PubMed PMCID: PMC3299341.
4
Khastavaneh H, Haron H, editors. False Positives Reduction on Segmented Multiple Sclerosis Lesions Using Fuzzy Inference System by Incorporating Atlas Prior Anatomical Knowledge: A Conceptual Model. International Conference on Computational Collective Intelligence; Springer; 2014. p. 11-9. doi: 10.1007/978-3-319-11289-3_2.
5
Shen S, Szameitat AJ, Sterr A. An improved lesion detection approach based on similarity measurement between fuzzy intensity segmentation and spatial probability maps. Magn Reson Imaging. 2010;28:245-54. doi: 10.1016/j.mri.2009.06.007. PubMed PMID: 19695812.
6
Wu Y, Warfield SK, Tan IL, Wells III WM, Meier DS, Van Schijndel RA, et al. Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI. Neuroimage. 2006;32:1205-15. doi: 10.1016/j.neuroimage.2006.04.211. PubMed PMID: 16797188.
7
Simoes R, Monninghoff C, Dlugaj M, Weimar C, Wanke I, et al. Automatic segmentation of cerebral white matter hyperintensities using only 3D FLAIR images. Magn Reson Imaging. 2013;31:1182-9. doi: 10.1016/j.mri.2012.12.004. PubMed PMID: 23684961.
8
Prastawa M, Gerig G. Automatic MS lesion segmentation by outlier detection and information theoretic region partitioning. Grand Challenge Work: Mult Scler Lesion Segm Challenge. 2008:1-8.
9
Ong KH, Ramachandram D, Mandava R, Shuaib IL. Automatic white matter lesion segmentation using an adaptive outlier detection method. Magn Reson Imaging. 2012;30:807-23. doi: 10.1016/j.mri.2012.01.007. PubMed PMID: 22578927.
10
Xie Y, Tao X, editors. White matter lesion segmentation using machine learning and weakly labeled MR images. Florida, United States: SPIE Medical Imaging; 2011. doi: 10.1117/12.878237.
11
Anbeek P, Vincken KL, Viergever MA. Automated MS-lesion segmentation by k-nearest neighbor classification. MIDAS Journal. 2008:1-8.
12
Harmouche R, Collins L, Arnold D, Francis S, Arbel T. Bayesian MS lesion classification modeling regional and local spatial information. 18th International Conference on Pattern Recognition (ICPR’06); Hong Kong, China: IEEE; 2006. p. 984-7.
13
Yamamoto D, Arimura H, Kakeda S, Magome T, Yamashita Y, Toyofuku F, et al. Computer-aided detection of multiple sclerosis lesions in brain magnetic resonance images: False positive reduction scheme consisted of rule-based, level set method, and support vector machine. Comput Med Imaging Graph. 2010;34:404-13. doi: 10.1016/j.compmedimag.2010.02.001. PubMed PMID: 20189353.
14
Schmidt P, Gaser C, Arsic M, Buck D, Forschler A, Berthele A, et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. Neuroimage. 2012;59:3774-83. doi: 10.1016/j.neuroimage.2011.11.032. PubMed PMID: 22119648.
15
Abdullah BA, Younis AA, Pattany PM, Saraf-Lavi E. Textural based SVM for MS lesion segmentation in FLAIR MRIs. Open Journal of Medical Imaging. 2011;1:26-42. doi: 10.4236/ojmi.2011.12005.
16
Cabezas M, Oliver A, Freixenet J, Lladó X, editors. A supervised approach for multiple sclerosis lesion segmentation using context features and an outlier map. Iberian conference on pattern recognition and image analysis; Springer; 2013. p. 782-9
17
Geremia E, Clatz O, Menze BH, Konukoglu E, Criminisi A, Ayache N. Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. Neuroimage. 2011;57:378-90. doi: 10.1016/j.neuroimage.2011.03.080. PubMed PMID: 21497655.
18
Anbeek P, Vincken KL, Van Bochove GS, Van Osch MJ, Van Der Grond J. Probabilistic segmentation of brain tissue in MR imaging. Neuroimage. 2005;27:795-804. doi: 10.1016/j.neuroimage.2005.05.046. PubMed PMID: 16019235.
19
Anbeek P, Vincken KL, Van Osch MJ, Bisschops RH, Van Der Grond J. Automatic segmentation of different-sized white matter lesions by voxel probability estimation. Med Image Anal. 2004;8:205-15. doi: 10.1016/j.media.2004.06.019. PubMed PMID: 15450216.
20
Zacharaki EI, Kanterakis S, Bryan RN, Davatzikos C. Measuring brain lesion progression with a supervised tissue classification system. Med Image Comput Comput Assist Interv. 2008;11:620-7.doi: 10.1007/978-3-540-85988-8_74. PubMed PMID: 18979798.
21
Ferrari RJ, Wei X, Zhang Y, Scott JN, Mitchell JR, editors. Segmentation of multiple sclerosis lesions using support vector machines. California, United States: SPIE Medical Imaging; 2003. p. 16–26. doi: 10.1117/12.481377.
22
Akselrod-Ballin A, Galun M, Basri R, Brandt A, Gomori MJ, Filippi M, et al., editors. An integrated segmentation and classification approach applied to multiple sclerosis analysis. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06); USA: IEEE; 2006. p. 1122-9. doi: 10.1109/cvpr.2006.55.
23
Akselrod-Ballin A, Galun M, Gomori JM, Filippi M, Valsasina P, Basri R, et al. Automatic segmentation and classification of multiple sclerosis in multichannel MRI. IEEE Trans Biomed Eng. 2009;56:2461-9. doi: 10.1109/TBME.2008.926671. PubMed PMID: 19758850.
24
Haralick RM, Shanmugam K. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics. 1973;6:610-21. doi: 10.1109/TSMC.1973.4309314.
25
Galloway MM. Texture analysis using grey level run lengths. Computer Graphics and Image Processing. 1975;4:172-9. doi: 10.1016/S0146-664X(75)80008-6.
26
Khastavaneh H, Ebrahimpour-Komleh H. Neural Network-Based Learning Kernel for Automatic Segmentation of Multiple Sclerosis Lesions on Magnetic Resonance Images. J Biomed Phys Eng. 2017;7:155-62. PubMed PMID: 28580337. PubMed PMCID: PMC5447252.
27
Brosch T, Yoo Y, Tang LY, Li DK, Traboulsee A, Tam R, editors. Deep convolutional encoder networks for multiple sclerosis lesion segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention; Springer; 2015. p. 3-11. doi: 10.1007/978-3-319-24574-4_1.
28
Brosch T, Tang LY, Youngjin Y, Li DK, Traboulsee A, Tam R. Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation. IEEE Trans Med Imaging. 2016;35:1229-39. doi: 10.1109/TMI.2016.2528821. PubMed PMID: 26886978.
29
Moeskops P, Viergever MA, Mendrik AM, De Vries LS, Benders MJ, Isgum I. Automatic Segmentation of MR Brain Images With a Convolutional Neural Network. IEEE Trans Med Imaging. 2016;35:1252-61. doi: 10.1109/TMI.2016.2548501. PubMed PMID: 27046893.
30
Ghafoorian M, Karssemeijer N, Heskes T, Van Uden IWM, Sanchez CI, Litjens G, et al. Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities. Sci Rep. 2017;7:5110. doi: 10.1038/s41598-017-05300-5. PubMed PMID: 28698556. PubMed PMCID: PMC5505987.
31
Bae MS, Moon WK, Chang JM, Koo HR, Kim WH, Cho N, et al. Breast cancer detected with screening US: reasons for nondetection at mammography. Radiology. 2014;270:369-77. doi: 10.1148/radiol.13130724. PubMed PMID: 24471386.
32
Henriksen EL, Carlsen JF, Vejborg IM, Nielsen MB, Lauridsen CA. The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review. Acta Radiol. 2019;60(1):13-8. doi: 10.1177/0284185118770917. PubMed PMID: 29665706.
33
Ghongade R, Wakde D, editors. Breast Cancer Diagnosis from Digital Mammograms Using RF and RF-ELM. Proceedings of International Conference on Recent Advancement on Computer and Communication; Springer; 2018. p. 365-74. doi: 10.1007/978-981-10-8198-9_38.
34
Sheba K, Gladston Raj S. An approach for automatic lesion detection in mammograms. Cogent Eng. 2018;5:1444320. doi: 10.1080/23311916.2018.1444320.
35
Shi P, Zhong J, Rampun A, Wang H. A hierarchical pipeline for breast boundary segmentation and calcification detection in mammograms. Comput Biol Med. 2018;96:178-88. doi: 10.1016/j.compbiomed.2018.03.011. PubMed PMID: 29597143.
36
Ronneberger O, Fischer P, Brox T, editors. U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention; Springer; 2015. p. 234-41. doi: 10.1007/978-3-319-24574-4_28.
37
De Moor T, Rodriguez-Ruiz A, Mann R, Teuwen J. Automated lesion detection and segmentation in digital mammography using a u-net deep learning network. 14th International Workshop on Breast Imaging (IWBI); United States: IWBI; 2018. doi: 10.1117/12.2318326.
38
Lee SLA, Kouzani AZ, Hu EJ. Automated detection of lung nodules in computed tomography images: a review. Machine Vision and Applications. 2012;23:151-63. doi: 10.1007/s00138-010-0271-2.
39
Suzuki K, Doi K. How can a massive training artificial neural network (MTANN) be trained with a small number of cases in the distinction between nodules and vessels in thoracic CT? Acad Radiol. 2005;12:1333-41. doi: 10.1016/j.acra.2005.06.017. PubMed PMID: 16179210.
40
Arimura H, Katsuragawa S, Suzuki K, Li F, Shiraishi J, Sone S, et al. Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. Acad Radiol. 2004;11:617-29. doi: 10.1016/j.acra.2004.02.009. PubMed PMID: 15172364.
41
Shi Z, Zhao M, He L, Wang Y, Zhang M, Suzuki K. A computer aided pulmonary nodule detection system using multiple massive training SVMs. Applied Mathematics & Information Sciences. 2013;7:1165. doi: 10.12785/amis/070339.
42
Hua KL, Hsu CH, Hidayati SC, Cheng WH, Chen YJ. Computer-aided classification of lung nodules on computed tomography images via deep learning technique. Onco Targets Ther. 2015;8:2015-22. doi: 10.2147/OTT.S80733. PubMed PMID: 26346558. PubMed PMCID: PMC4531007.
43
Setio AA, Ciompi F, Litjens G, Gerke P, Jacobs C, Van Riel SJ, et al. Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks. IEEE Trans Med Imaging. 2016;35:1160-9. doi: 10.1109/TMI.2016.2536809. PubMed PMID: 26955024.
44
Van Tulder G, De Bruijne M. Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines. IEEE Trans Med Imaging. 2016;35:1262-72. doi: 10.1109/TMI.2016.2526687. PubMed PMID: 26886968.
45
Pardo A, Real E, Fernandez-Barreras G, Madruga F, López-Higuera JM, Conde O, editors. Automated skin lesion segmentation with kernel density estimation. European Conference on Biomedical Optics; Germany: SPIE; 2017. p. 8. doi: 10.1117/12.2283038.
46
Nasir M, Attique Khan M, Sharif M, Lali IU, Saba T, Iqbal T. An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach. Microsc Res Tech. 2018;81:528-43. doi: 10.1002/jemt.23009. PubMed PMID: 29464868.
47
Meskini E, Helfroush MS, Kazemi K, Sepaskhah M. A New Algorithm for Skin Lesion Border Detection in Dermoscopy Images. J Biomed Phys Eng. 2018;8(1):117-26. PubMed PMID: 29732346. PubMed PMCID: PMC5928301.
48
He Y, Xie F, editors. Automatic skin lesion segmentation based on texture analysis and supervised learning. Asian Conference on Computer Vision; Springer; 2013. p. 330-41. doi: 10.1007/978-3-642-37444-9_26.
49
Lin BS, Michael K, Kalra S, Tizhoosh HR, editors. Skin lesion segmentation: U-nets versus clustering. IEEE Symposium Series on Computational Intelligence (SSCI); USA: IEEE; 2017. p. 1–7. doi: 10.1109/ssci.2017.8280804.
50
Yuan Y, Chao M, Lo YC. Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance. IEEE Trans Med Imaging. 2017;36:1876-86. doi: 10.1109/TMI.2017.2695227. PubMed PMID: 28436853.
51
ORIGINAL_ARTICLE
Evaluation of Dose Distribution in Optimized Stanford Total Skin Electron Therapy (TSET) Technique in Rando Anthropomorphic Phantom using EBT3 Gafchromatic Films
Background: The Total Skin Electron Therapy (TSET) targets the whole of skin using 6 to 10 MeV electrons in large field size and large Source to Surface Distance (SSD). Treatment in sleeping position leads to a better distribution of dose and patient comfort. Objective: This study aims to investigate the uniformity of absorbed dose in the sleeping Stanford technique on the Rando phantom using dosimetry.Material and Methods: It is an experimental study which was performed using 6 MeV electron irradiation produced by Varian accelerator in the AP and PA positions with gantry angles of 318/3, 0 and 41/5 degrees, and RAO, LAO, RPO and LPO with 291/4 gantry angle and 45 degrees of collimator angle in the sleeping position. Results: The results show that the dose uniformity achieved in this technique is in the range of (100 ± 25%) and, the dose accuracy was 6%. Conclusion: Total Skin Electron Therapy (TSET) technique in sleeping position is very suitable for elderly and disabled patients, and meets the required dose uniformity. Furthermore, the use of a flattening filter is recommended for the more dose distribution uniformity.
https://jbpe.sums.ac.ir/article_46566_eef6e3e8fb8079e8449aac30c8a7cf58.pdf
2021-08-01
425
434
10.31661/jbpe.v0i0.1035
TSET
Film Dosimeters
Phantom
Dosage Radiotherapy
Shaghayegh
Fahimi Monzari
1
MSc student, Department of Medical Physics, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
Ghazale
Geraily
ghazalegraily@yahoo.com
2
PhD, Department of Medical Physics, Tehran University of Medical Sciences, Tehran, Iran
LEAD_AUTHOR
Mehdi
Aghili
mehdiaghili@yahoo.com
3
MD, Oncology Specialist, Cancer Institute of Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
Heydar
Toolee
toolee.hedi@gmail.com
4
PhD, Department of Anatomy, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
Jones GW, Rosenthal D, Wilson LD. Total skin electron radiation for patients with erythrodermic cutaneous T-cell lymphoma (mycosis fungoides and the Sézary syndrome). Cancer: Interdisciplinary International Journal of the American Cancer Society. 1999;85:1985-95. doi: 10.1002/(sici)1097-0142(19990501)85:9%3c1985::aid- cncr16%3e3.0.co;2-o.
1
Ravi A, Nisce LZ, Nori D. Total skin electron beam therapy in the management of cutaneous malignancies. Clin Dermatol. 2001;19:354-6. doi: 10.1016/s0738-081x(01)00167-5. PubMed PMID: 11479048.
2
Burket LW, Greenberg MS, Glick M. Burket’s oral medicine: diagnosis & treatment. PMPH-USA; 2003.
3
Bagshaw MA, Schneidman HM, Farber EM, Kaplan HS. Electron beam therapy of mycosis fungoides. Calif Med. 1961;95:292.
4
Scarantino CW, Rini CJ, Aquino M, Carrea TB, Ornitz RD, Anscher MS, et al. Initial clinical results of an in vivo dosimeter during external beam radiation therapy. Int J Radiat Oncol Biol Phys. 2005;62:606-13. doi: 10.1016/j.ijrobp.2004.09.041. PubMed PMID: 15890606.
5
Wood NK, Goaz PW, Jacobs MC. Periapical radiolucencies. Differential Diagnosis of Oral and Maxillofacial Lesions. 1997:252-77.
6
Diamandidou E, Cohen PR, Kurzrock R. Mycosis fungoides and Sezary syndrome [see comments]. Blood. 1996;88:2385-409.
7
Rodriguez-Cortes J, Rivera-Montalvo T, Villasenor Navarro LF, Flores-Lopez O, Roman J, Hernandez-Oviedo JO. Thermoluminescent dosimetry in total body irradiation. Appl Radiat Isot. 2012;71 Suppl:35-9. doi: 10.1016/j.apradiso.2012.04.014. PubMed PMID: 23039951.
8
Tadros AA, Tepperman BS, Hryniuk WM, Peters VG, Rosenthal D, Roberts JT, et al. Total skin electron irradiation for mycosis fungoides: failure analysis and prognostic factors. Int J Radiat Oncol Biol Phys. 1983;9:1279-87. doi: 10.1016/0360-3016(83)90258-4. PubMed PMID: 6885540.
9
Archer BR, Glaze S, North LB, Bushong SC. Dosimeter placement in the Rando phantom. Med Phys. 1977;4:315-8. doi: 10.1118/1.594320. PubMed PMID: 882065.
10
Le JB, Bridier A, Bounik H, Schlienger M. Whole cutaneous irradiation in mycosis fungoides with 55KV x-rays. Technical study. Bull Cancer. 1977;64:313-22.
11
Scholtz W. About the influence of X-rays radiate on the skin in healthy and sick Conditions. Archive for Dermatology and Syphilis. 1902;59:421-46. doi: 10.1007/bf01930719.
12
Fuks ZY, Bagshaw MA, Farber EM. Prognostic signs and the management of the mycosis fungoides. Cancer. 1973;32:1385-95. PubMed PMID: 4202190.
13
Hoppe RT, Fuks Z, Bagshaw MA. The rationale for curative radiotherapy in mycosis fungoides. Int J Radiat Oncol Biol Phys. 1977;2:843-51. doi: 10.1016/0360-3016(77)90182-1. PubMed PMID: 591404.
14
Khan F. Clinical radiation generators. The Physics of Radiation Therapy. 2010:45-70.
15
Chen Z, Agostinelli AG, Wilson LD, Nath R. Matching the dosimetry characteristics of a dual-field Stanford technique to a customized single-field Stanford technique for total skin electron therapy. International Journal of Radiation Oncology Biology Physics. 2004;59:872-85. doi: 10.1016/j.ijrobp.2004.02.046.
16
Lucic F, Sanchez-Nieto B, Caprile P, Zelada G, Goset K. Dosimetric characterization and optimization of a customized Stanford total skin electron irradiation (TSEI) technique. J Appl Clin Med Phys. 2013;14:231-42. doi: 10.1120/jacmp.v14i5.4388. PubMed PMID: 24036877.
17
Haybittle J. The protection of multicurie strontium-yttrium (90) sources. Phys Med Biol. 1957;1:270. doi: 10.1088/0031-9155/1/3/305.
18
Deufel CL, Antolak JA. Total skin electron therapy in the lying-on-the-floor position using a customized flattening filter to accommodate frail patients. J Appl Clin Med Phys. 2013;14:115-26. doi: 10.1120/jacmp.v14i5.4309. PubMed PMID: 24036864. PubMed PMCID: PMC5714577.
19
Wu JM, Leung SW, Wang CJ, Chui CS. Lying-on position of total skin electron therapy. Int J Radiat Oncol Biol Phys. 1997;39:521-8. PubMed PMID: 9308958.
20
Baugh G, Al-Alawi T, Fletcher C, Mills J, Grieve R. A preliminary comparison of total skin electron treatment techniques to demonstrate the application of a mid-torso phantom for measurement of dose penetration. The British Journal of Radiology. 2011;84:1125-30. doi: 10.1259/bjr/52924135.
21
Karzmark CJ, Loevinger R, Steele RE, Weissbluth M. A technique for large-field, superficial electron therapy. Radiology. 1960;74:633-44. doi: 10.1148/74.4.633. PubMed PMID: 14404611.
22
Ragona R, Anglesio S, Madon E, Urgesi A, Rampino M, Monetti U. Total skin therapy with electron beams. I. Physical and geometric factors. Radiol Med. 1990;80:151-4. PubMed PMID: 2251408.
23
Monzari SF, Geraily G, Salmanian S, Toolee H, Farzin M. Fabrication of anthropomorphic phantoms for use in total body irradiations studies. Journal of Radiotherapy in Practice. 2019:1-6. doi: 10.1017/S1460396919000591.
24
Villarreal-Barajas JE, Khan RF. Energy response of EBT3 radiochromic films: implications for dosimetry in kilovoltage range. J Appl Clin Med Phys. 2014;15:4439. doi: 10.1120/jacmp.v15i1.4439. PubMed PMID: 24423839. PubMed PMCID: PMC5711253.
25
Harden SV, Routsis DS, Geater AR, Thomas SJ, Coles C, Taylor PJ, et al. Total body irradiation using a modified standing technique: a single institution 7 year experience. Br J Radiol. 2001;74:1041-7. doi: 10.1259/bjr.74.887.741041. PubMed PMID: 11709470.
26
Vollans SE, Perrin B, Wilkinson JM, Gattamaneni HR, Deakin DP. Investigation of dose homogeneity in paediatric anthropomorphic phantoms for a simple total body irradiation technique. Br J Radiol. 2000;73:317-21. doi: 10.1259/bjr.73.867.10817050. PubMed PMID: 10817050.
27
Najafi M, Geraily G, Shirazi A, Esfahani M, Teimouri J. Analysis of Gafchromic EBT3 film calibration irradiated with gamma rays from different systems: Gamma Knife and Cobalt-60 unit. Medical Dosimetry. 2017;42(3):159-68.
28
Reynard EP. Rotational Total Skin Electron Irradiation (RTSEI) with a 6 MeV Electron Linear Accelerator. McGill University Libraries; 2007.
29
Platoni K, Diamantopoulos S, Panayiotakis G, Kouloulias V, Pantelakos P, Kelekis N, et al. First application of total skin electron beam irradiation in Greece: setup, measurements and dosimetry. Phys Med. 2012;28:174-82. doi: 10.1016/j.ejmp.2011.03.007. PubMed PMID: 21515082.
30
Anacak Y, Arican Z, Bar-Deroma R, Tamir A, Kuten A. Total skin electron irradiation: evaluation of dose uniformity throughout the skin surface. Med Dosim. 2003;28:31-4. doi: 10.1016/S0958-3947(02)00235-2. PubMed PMID: 12747616.
31
Funk A, Hensley F, Krempien R, Neuhof D, Van Kampen M, Treiber M, et al. Palliative total skin electron beam therapy (TSEBT) for advanced cutaneous T-cell lymphoma. Eur J Dermatol. 2008;18:308-12. doi: 10.1684/ejd.2008.0394. PubMed PMID: 18474461.
32
Poli MER, Todo AS, Campos LL, editors. Dose Measurements in the treatment of mycosis fungoides with total skin irradiation using a 4 MeV electron beam. Proceedings of 10th International Congress of The International Radiation Protection Association. Hirishima, Japan: IRPA-10; 2000. p. 7-68.
33
Fuse H, Suzuki K, Shida K, Mori Y, Takahashi H, Kobayashi D, et al. Total skin electron beam therapy using an inclinable couch on motorized table and a compensating filter. Rev Sci Instrum. 2014;85:064301. doi: 10.1063/1.4882336. PubMed PMID: 24985829. PubMed PMCID: PMC4098054.
34
ORIGINAL_ARTICLE
Simulation and In Vitro Experimental Studies on Targeted Photothermal Therapy of Cancer using Folate-PEG-Gold Nanorods
Background: Selective targeting of malignant cells is the ultimate goal of anticancer studies around the world. There are some modalities for cancer therapy devastating tumor size and growth rate, meanwhile attacking normal cells. Utilizing appropriate ligands, like folate, allow the delivery of therapeutic molecules to cancer cells selectively. There are a variety of photosensitizers, like gold nanorods (GNRs), capable of absorbing the energy of light and converting it to heat, evidently build a photothermal procedure for cancer therapy.Objective: To develop a one-step approach for calculating the temperature distribution by solving the heat transfer equation with multiple heat sources originating from NIR laser-exposed GNRs. Material and Methods: In this experimental study, we simulated NIR laser heating process in a single cancer cell, with and without incubation with folate conjugated PEG-GNRs. This simulation was based on a real TEM image from an experiment with the same setup. An in vitro experiment based on aforesaid scenario was performed to validate the simulated model in practice. Results: According to the simplifications due to computational resource limits, the resulting outcome of simulation showed significant compatibility to the supporting experiment. Both simulation and experimental studies showed a similar trend for heating and cooling of the cells incubated with GNRs and irradiated by NIR laser (5 min, 1.8 W/cm2). It was observed that temperature of the cells in microplate reached 53.6 °C when irradiated by laser. Conclusion: This new method can be of great application in developing a planning technique for treating tumors utilizing GNP-mediated thermal therapy.
https://jbpe.sums.ac.ir/article_47088_7f46bc59a1fe929179194e8cab423435.pdf
2021-08-01
435
446
10.31661/jbpe.v0i0.1108
Computer Simulations
Hyperthermia, Induced
Theranostic Nanomedicine
GNR-PEG-Folate
Dose Enhance
cancer
Heat Transfer
Shayan
Maleki
shmaleki57@gmail.com
1
PhD, ENT and Head & Neck Research Center and Department, Hazrat Rasoul Hospital, the Five Senses Institute, Iran University of Medical Sciences, Tehran, Iran
AUTHOR
Mohammad
Farhadi
farhadi28@gmail.com
2
MD, ENT and Head & Neck Research Center and Department, Hazrat Rasoul Hospital, the Five Senses Institute, Iran University of Medical Sciences, Tehran, Iran
AUTHOR
Seyed Kamran
Kamrava
skkamrava@gmail.com
3
MD, ENT and Head & Neck Research Center and Department, Hazrat Rasoul Hospital, the Five Senses Institute, Iran University of Medical Sciences, Tehran, Iran
AUTHOR
Alimohamad
Asghari
4
MD, Skull Base Research Center, The Five Senses Health Institute, Iran University of Medical Sciences, Tehran, Iran
AUTHOR
Ahmad
Daneshi
daneshiahmad@gmail.com
5
MD, ENT and Head & Neck Research Center and Department, Hazrat Rasoul Hospital, the Five Senses Institute, Iran University of Medical Sciences, Tehran, Iran
LEAD_AUTHOR
Hosseini V, Mirrahimi M, Shakeri-Zadeh A, Koosha F, Ghalandari B, Maleki S, Komeili A, Kamrava SK. Multimodal cancer cell therapy using Au@ Fe2O3 core–shell nanoparticles in combination with photo-thermo-radiotherapy. Photodiagnosis and Photodynamic Therapy. 2018;24:129-35. doi: 10.1016/j.pdpdt.2018.08.003.
1
Mirrahimi M, Abed Z, Beik J, Shiri I, Dezfuli AS, Mahabadi VP, Kamrava SK, Ghaznavi H, Shakeri-Zadeh A. A thermo-responsive alginate nanogel platform co-loaded with gold nanoparticles and cisplatin for combined cancer chemo-photothermal therapy. Pharmacological Research. 2019;143:178-85. doi: 10.1016/j.phrs.2019.01.005.
2
Mirrahimi M, Hosseini V, Shakeri-Zadeh A, Alamzadeh Z, Kamrava SK, Attaran N, Abed Z, Ghaznavi H, Nami SH. Modulation of cancer cells’ radiation response in the presence of folate conjugated Au@ Fe 2 O 3 nanocomplex as a targeted radiosensitizer. Clinical and Translational Oncology. 2019;21(4):479-88. doi: 10.1007/s12094-018-1947-8.
3
Kurian AW, Bondarenko I, Jagsi R, Friese CR, McLeod MC, Hawley ST, Hamilton AS, Ward KC, Hofer TP, Katz SJ. Recent trends in chemotherapy use and oncologists’ treatment recommendations for early-stage breast cancer. Journal of the National Cancer Institute. 2018;110(5):493-500. doi: 10.1093/jnci/djx239.
4
Beik J, Asadi M, Mirrahimi M, Abed Z, Farashahi A, Hashemian R, Ghaznavi H, Shakeri-Zadeh A. An image-based computational modeling approach for prediction of temperature distribution during photothermal therapy. Applied Physics B. 2019;125(11):213. doi: 10.1007/s00340-019-7316-7.
5
Beik J, Asadi M, Khoei S, Laurent S, Abed Z, Mirrahimi M, Farashahi A, Hashemian R, Ghaznavi H, Shakeri-Zadeh A. Simulation-guided photothermal therapy using MRI-traceable iron oxide-gold nanoparticle. Journal of Photochemistry and Photobiology B: Biology. 2019;199:111599. doi: 10.1016/j.jphotobiol.2019.111599.
6
Zabanran M, Asadi M, Zare-Sadeghi A, Ardakani AA, Shakeri-Zadeh A, Komeili A, Kamrava SK, Ghalandari B. The effects of gold nanoparticles characteristics and laser irradiation conditions on spatiotemporal temperature pattern of an agar phantom: A simulation and MR thermometry study. Optik. 2020;202:163718. doi: 10.1016/j.ijleo.2019.163718.
7
Asadi M, Beik J, Hashemian R, Laurent S, Farashahi A, Mobini M, Ghaznavi H, Shakeri-Zadeh A. MRI-based numerical modeling strategy for simulation and treatment planning of nanoparticle-assisted photothermal therapy. Physica Medica. 2019;66:124-32. doi: 10.1016/j.ejmp.2019.10.002.
8
Hashemian AR,Eshghi H, Mansoori GA, Shakeri-Zadeh A, Mehdizadeh AR. Folate-Conjugated Gold Nanoparticles (Synthesis, Characterization and Design for Cancer Cells Nanotechnology-based Targeting). International Journal of Nanoscience and Nanotechnology. 2009;5(1):25-34.
9
Shakeri-Zadeh A, Eshghi H, Mansoori GA, Hashemian AR. Gold nanoparticles conjugated with folic acid using mercaptohexanol as the linker. Journal Nanotechnology Progress International. 2009;1(1):1-44.
10
Zeinizade E, Tabei M, Shakeri-Zadeh A, Ghaznavi H, Attaran N, Komeili A, Ghalandari B, Maleki S, Kamrava SK. Selective apoptosis induction in cancer cells using folate-conjugated gold nanoparticles and controlling the laser irradiation conditions. Artificial Cells, Nanomedicine, and Biotechnology. 2018;46(1):1026-38. doi: 10.1080/21691401.2018.1443116.
11
Movahedi MM, Mehdizadeh A, Koosha F, Eslahi N, Mahabadi VP, Ghaznavi H, Shakeri-Zadeh A. Investigating the photo-thermo-radiosensitization effects of folate-conjugated gold nanorods on KB nasopharyngeal carcinoma cells. Photodiagnosis and Photodynamic Therapy. 2018;24:324-31. doi:10.1016/j.pdpdt.2018.10.016.
12
Araya T, Kasahara K, Nishikawa S, Kimura H, Sone T, Nagae H, Ikehata Y, Nagano I, Fujimura M. Antitumor effects of inductive hyperthermia using magnetic ferucarbotran nanoparticles on human lung cancer xenografts in nude mice. Onco Targets and Therapy. 2013;6:237. doi: 10.2147/OTT.S42815. PubMed PMID: 23569387. PubMed PMCID: PMC3615880.
13
Beik J, Jafariyan M, Montazerabadi A, Ghadimi-Daresajini A, Tarighi P, Mahmoudabadi A, Ghaznavi H, Shakeri-Zadeh A. The benefits of folic acid-modified gold nanoparticles in CT-based molecular imaging: radiation dose reduction and image contrast enhancement. Artificial Cells, Nanomedicine, and Biotechnology. 2018;46(8):1993-2001. doi: 10.1080/21691401.2017.1408019.
14
Cheong KH, Yi DK, Lee JG, Park JM, Kim MJ, Edel JB, Ko C. Gold nanoparticles for one step DNA extraction and real-time PCR of pathogens in a single chamber. Lab on a Chip. 2008;8(5):810-3. doi: 10.1039/b717382b. PubMed PMID: 18432353.
15
Cao SW, Fang J, Shahjamali MM, Wang Z, Yin Z, Yang Y, Boey FY, Barber J, Loo SC, Xue C. In situ growth of Au nanoparticles on Fe 2 O 3 nanocrystals for catalytic applications. Cryst Eng Comm. 2012;14(21):7229-35. doi: 10.1039/C2CE25746G.
16
Carslow HS, Jaeger JC. Conduction of heat in solids. Oxford University Press; 1986.
17
Cheong SK, Krishnan S, Cho SH. Modeling of plasmonic heating from individual gold nanoshells for near-infrared laser-induced thermal therapy. Medical Physics. 2009;36(10):4664-71. doi: 10.1118/1.3215536.
18
Draine BT, Flatau PJ. Discrete-dipole approximation for scattering calculations. J Opt Soc Am A. 1994;11(4):1491-9. doi: 10.1364/JOSAA.11.001491.
19
Jain PK, Lee KS, El-Sayed IH, El-Sayed MA. Calculated absorption and scattering properties of gold nanoparticles of different size, shape, and composition: applications in biological imaging and biomedicine. The Journal of Physical Chemistry B. 2006;110(14):7238-48. doi: 10.1021/jp057170o.
20
Zhu X, Feng W, Chang J, Tan YW, Li J, Chen M, Sun Y, Li F. Temperature-feedback upconversion nanocomposite for accurate photothermal therapy at facile temperature. Nature Communications. 2016;7:10437. doi: 10.1038/ncomms10437.
21
Sönnichsen C, Franzl T, Wilk T, Von G, Plessen, J. Feldmann, Wilson O, Mulvaney P. Drastic reduction of plasmon damping in gold nanorods. Phys Rev Lett. 2002;88(7):077402. doi: 10.1103/PhysRevLett.88.077402. PubMed PMID: 11863939.
22
Bohren CF, Huffman DR. Absorption and scattering of light by small particles. Weinheim, Germany: Wiley Online Library; 1998. doi: 10.1002/9783527618156.
23
Xu Z. Optical Properties of Metal Clusters By Uwe Kreibig (I. Physikalisches Inst. der RWTH Aachen, Gmermany) and Michael Vollmer (Technische Physik Brandenburg, Germany). Springer: New York. 1994. xvii+ 532 pp. $69.00. ISBN 0-387-57836-6. J Am Chem Soc. 1996;118(25):6098. doi: 10.1021/ja955378p.
24
Mehdizadeh AR, Pandesh S, Shakeri-Zadeh A, Kamrava SK, Habib-Agahi M, Farhadi M, Pishghadam M, Ahmadi A, Arami S, Fedutik Y. The effects of folate-conjugated gold nanorods in combination with plasmonic photothermal therapy on mouth epidermal carcinoma cells. Lasers in Medical Science. 2014;29(3):939-48. doi: 10.1007/s10103-013-1414-2. PubMed PMID: 24013622.
25
ORIGINAL_ARTICLE
Determination of Diagnostic Reference Level (DRL) in Common Computed Tomography Examinations with the Modified Quality Control-Based Dose Survey Method in Four University Centers: A Comparison of Methods
Background: The diagnostic reference level (DRL) is measured with different methods in the common Computed tomography (CT) exams, but it has not been measured through the size-specific dose estimate (SSDE) method in Iran, yet. Objective: This study aimed to calculate the local DRL (LDRL) using the new quality control-based dose survey method (QC) and patients’ effective diameter (MQC) and compare them with a data collection method (DC) as well as local national DRLs (NDRL).Material and Methods: In this cross-sectional study, LDRL, based on the third quartile of volumetric computed tomography dose index (CTDIvol) and dose length product (DLP) values, was calculated for the four common CT examinations in four CT scan centers affiliated with Shiraz University of Medical Sciences by DC, QC and MQC methods. The CTDIvol of each patient for each CT exam calculated with three methods was compared with paired t-test. Also, the LDRL using MQC method was compared with other national DRL studies. Results: There was a significant difference between the CTDIvol values calculated with MQC and QC in all four examinations (P <0.001). The LDRL based on CTDIvol obtained by the MQC method for head, sinus, chest, abdomen, and pelvis were (50, 18, 15, 19) mGy, respectively, and the calculated DLP values were also (735, 232, 519, 984) mGy.cm. Conclusion: In MQC, LDRL based on CTDIvol was calculated with a mean difference percentage of (19.2 ± 11.6)% and (27.1 ± 8.1)% as compared to the QC and DC methods, respectively. This difference resulted from the use of the SSDE method and dose accuracy in the QC dose survey.
https://jbpe.sums.ac.ir/article_47703_45cc2cfe471fe837d49312985e7fe813.pdf
2021-08-01
447
458
10.31661/jbpe.v0i0.2105-1322
Diagnostic Reference Levels
Multidetector Computed Tomography
Quality Control-Based Dose Survey Method
Size-Specific Dose Estimate
Body mass index
Jalal
Tabesh
jalal.tabesh@yahoo.com
1
MSc, Department of Radiology, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
Maziyar
Mahdavi
mahdavi@sums.ac.ir
2
PhD, Department of Radiology, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
LEAD_AUTHOR
Gholamhasan
Hadadi
ghadadi@gmail.com
3
PhD, Department of Radiology, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
Rezvan
Ravanfar Haghighi
sravanfarr@gmail.com
4
PhD, Medical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
Reza
Jalli
jallireza@yahoo.com
5
MD, Medical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
Bushberg JT, Boone JM. The essential physics of medical imaging. Lippincott Williams & Wilkins; 2011.
1
Bolus NE. NCRP report 160 and what it means for medical imaging and nuclear medicine. J Nucl Med Technol. 2013;41(4):255-60. doi: 10.2967/jnmt.113.128728. PubMed PMID: 24179182.
2
Mahdavi M, Rahimi S, Eghlidospoor M. Evaluation of some spiral and sequential computed tomography protocols of adults used in three hospitals in Shiraz, Iran with American College of Radiology and European Commission guidelines. Pol J Radiol. 2018;83:297-305. doi: 10.5114/pjr.2018.77023. PubMed PMID: 30627250. PubMed PMCID: PMC6323603.
3
Smith-Bindman R, Lipson J, Marcus R, Kim KP, et al. Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer. Arch Intern Med. 2009;169(22):2078-86. doi: 10.1001/archinternmed.2009.427. PubMed PMID: 20008690. PubMed PMCID: PMC4635397.
4
Protection ICoR. ICRP Publication 105: Radiation protection in medicine. Ann ICRP. 2007;37:1-63. doi: 10.1016/j.icrp.2008.08.001. PubMed PMID: 18762065.
5
Bongartz G, Golding SJ, Jurik AG, et al. European guidelines on quality criteria for computed tomography, Chapter 1: Quality criteria for computed tomogrpahy. Report EUR 16262 EN; Brussels: EU publications; 1998.
6
American College of Radiology. ACR practice guideline for diagnostic reference levels in medical x-ray imaging. ACR; 2008. p. 799-804.
7
Tsapaki V, Aldrich JE, Sharma R, et al. Dose reduction in CT while maintaining diagnostic confidence: diagnostic reference levels at routine head, chest, and abdominal CT—IAEA-coordinated research project. Radiology. 2006;240(3):828-34. doi: 10.1148/radiol.2403050993. PubMed PMID: 16837668.
8
Charles MW. ICRP Publication 103: Recommendations of the ICRP. Oxford University Press; 2008.
9
Protection R. 109: Guidance on diagnostic reference levels (DRLs) for Medical Exposures. Luxembourg: European Communities; 1999.
10
Parsi M, Sohrabi M, Mianji F, Paydar R. Determination of Examination-Specific Diagnostic Reference Level in Computed Tomography by A New Quality Control-Based Dose Survey Method. Health Phys. 2018;114(3):273-81. doi: 10.1097/HP.0000000000000758. PubMed PMID: 29360705.
11
Parsi M, Sohrabi M, Mianji F, Paydar R. A “quality-control-based correction method” for displayed dose indices on CT scanner consoles in patient dose surveys. Phys Med. 2017;38:88-92. doi: 10.1016/j.ejmp.2017.05.054. PubMed PMID: 28610702.
12
Medicine AAoPi. Size-specific dose estimates (SSDE) in pediatric and adult body CT examinations. Report of AAPM Task Group 204; AAPM; 2011.
13
Vañó E, Miller D, Martin C, Rehani M, et al. ICRP Publication 135: Diagnostic reference levels in medical imaging. Ann ICRP. 2017;46(1):1-144. doi: 10.1177/0146645317717209. PubMed PMID: 29065694.
14
McLean I. Quality assurance programme for computed tomography: diagnostic and therapy applications. IAEA Human Health; 2012.
15
Brix G, Lechel U, Veit R, Truckenbrodt R, et al. Assessment of a theoretical formalism for dose estimation in CT: an anthropomorphic phantom study. Eur Radiol. 2004;14(7):1275-84. doi: 10.1007/s00330-004-2267-7. PubMed PMID: 15034744.
16
Foley SJ, McEntee MF, Rainford LA. Establishment of CT diagnostic reference levels in Ireland. Br J Radiol. 2012;85(1018):1390-7. doi: 10.1259/bjr/15839549. PubMed PMID: 22595497. PubMed PMCID: PMC3474022.
17
Najafi M, Deevband MR, Ahmadi M, Kardan MR. Establishment of diagnostic reference levels for common multi-detector computed tomography examinations in Iran. Australas Phys Eng Sci Med. 2015;38(4):603-9. doi: 10.1007/s13246-015-0388-8. PubMed PMID: 26507898.
18
Shrimpton P, Hillier M, Lewis M, Dunn M. National survey of doses from CT in the UK: 2003. Br J Radiol. 2006;79(948):968-80. doi: 10.1259/bjr/93277434. PubMed PMID: 17213302.
19
Kanal KM, Butler PF, Sengupta D, et al. US diagnostic reference levels and achievable doses for 10 adult CT examinations. Radiology. 2017;284(1):120-33. doi: 10.1148/radiol.201716191. PubMed PMID: 28221093.
20
Sohrabi M, Parsi M, Mianji F. Determination of national diagnostic reference levels in computed tomography examinations of Iran by a new quality control-based dose survey method. Radiat Prot Dosimetry. 2017;179(3):206-15. doi: 10.1093/rpd/ncx252. PubMed PMID: 29136248.
21
Deevband MR, Ghorbani M, Eshraghi A, Salimi Y, et al. Patient effective dose estimation for routine computed tomography examinations in Iran. International Journal of Radiation Research. 2021;19(1):63-73. doi:10.29252/ijrr.19.1.63.
22
Sohrabi M, Parsi M, Sina S. A New Dual-purpose Quality Control Dosimetry Protocol for Diagnostic Reference-level Determination in Computed Tomography. Health Phys. 2018;115(2):252-8. doi: 10.1097/HP.0000000000000872. PubMed PMID: 29781838.
23
McCollough C, Bakalyar DM, Bostani M, Brady S, et al. Use of water equivalent diameter for calculating patient size and size-specific dose estimates (SSDE) in CT: the report of AAPM Task Group 220. AAPM Rep. 2016;2014:6-23. PubMed PMID: 27546949. PubMed PMCID: PMC4991550.
24
McCollough C, Cody D, Edyvean S, Rich G, et al. The measurement, reporting, and management of radiation dose in CT. Report of AAPM Task Group 23; AAPM; 2008.
25
ORIGINAL_ARTICLE
Predicting the Risk of Radiation Pneumonitis and Pulmonary Function Changes after Breast Cancer Radiotherapy
Background: Radiotherapy plays an important role in the treatment of breast cancer. In the process of radiotherapy, the underling lung tissue receives higher doses from treatment field, which led to incidence of radiation pneumonitis. Objective: The present study aims to evaluate the predictive factors of radiation pneumonitis and related changes in pulmonary function after 3D-conformal radiotherapy of breast cancer.Material and Methods: In prospective basis study, thirty-two patients with breast cancer who received radiotherapy after surgery, were followed up to 6 months. Respiratory symptoms, lung radiologic changes and pulmonary function were evaluated. Radiation pneumonitis (RP) was graded according to common terminology criteria for adverse events (CTCAE) version 3.0. Dose-volume parameters, which included percentage of lung volume receiving dose of d Gy (V5-V50) and mean lung dose (MLD), were evaluated for RP prediction. Pulmonary function evaluated by spirometry test and changes of FEV1 and FVC parameters. Results: Eight patients developed RP. Among the dose-volume parameters, V10 was associated to RP incidence. When V10<40% and V10≥40% the incidences of RP were 5.26% and 61.54%, respectively. The FEV1 and FVC had a reduction 3 and 6 months after radiotherapy, while only FEV1 showed significant reduction. The FEV1 had more reduction in the patients who developed RP than patients without RP (15.25±3.81 vs. 9.2±0.93). Conclusion: Pulmonary function parameters, especially FEV1, significantly decreased at 3 and 6 months after radiotherapy. Since most patients with breast cancer who developed RP did not show obvious clinical symptoms, so spirometry test is beneficial to identify patients with risk of radiation pneumonitis.
https://jbpe.sums.ac.ir/article_46524_ff9e7bee9ab8607247af81bdfa987c8b.pdf
2021-08-01
459
464
10.31661/jbpe.v0i0.1079
Breast cancer
Radiation Pneumonitis
3-D Conformal Radiotherapy
Spirometry
Lung
Parinaz
Mehnati
parinazmehnati8@gmail.com
1
PhD, Immunology research center, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
AUTHOR
Maryam
Ghorbanipoor
a.ghorbanipoor@gmail.com
2
MSc, Department of Medical Physics, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
LEAD_AUTHOR
Mohammad
Mohammadzadeh
mohammadzadehmohammad@yahoo.com
3
MD, Department of Radiology, Emam Reza Hospital, Tabriz University of Medical Sciences, Tabriz, Iran
AUTHOR
Behnam
Nasiri Motlagh
tt_rtt@yahoo.com
4
MD, Department of Radiology, Emam Reza Hospital, Tabriz University of Medical Sciences, Tabriz, Iran
AUTHOR
Asghar
Mesbahi
amesbahi2010@gmail.com
5
PhD, Medical Radiation Sciences Research team, Department of Medical Physics, School of Medicine, Tabriz, Iran
AUTHOR
Emami RS, Aghajani H, Haghazali M, Nadali F, et al. The most common cancers in Iranian women. Iranian J Publ Health. 2009;38:109-12.
1
Alizadeh OH, Hoseini M, Mirmalek A, Ahmari H, Arab F, Mohtasham AN. Breast Sarcoma: a review article. Iranian Journal of Surgery. 2014;22:1-11.
2
Mousavi SM, Montazeri A, Mohagheghi MA, Jarrahi AM, Harirchi I, Najafi M, et al. Breast cancer in Iran: an epidemiological review. Breast J. 2007;13:383-91. doi: 10.1111/j.1524-4741.2007.00446.x. PubMed PMID: 17593043.
3
Huang EH, Tucker SL, Strom EA, McNeese MD, et al. Postmastectomy radiation improves local-regional control and survival for selected patients with locally advanced breast cancer treated with neoadjuvant chemotherapy and mastectomy. J Clin Oncol. 2004;22:4691-9. doi: 10.1200/JCO.2004.11.129. PubMed PMID: 15570071.
4
Group EBCTC. Effect of radiotherapy after breast-conserving surgery on 10-year recurrence and 15-year breast cancer death: meta-analysis of individual patient data for 10 801 women in 17 randomised trials. Lancet. 2011;378:1707-16.
5
Madani I, De Ruyck K, Goeminne H, et al. Predicting risk of radiation-induced lung injury. J Thorac Oncol. 2007;2:864-74. doi: 10.1097/JTO.0b013e318145b2c6. PubMed PMID: 17805067.
6
Mehta V. Radiation pneumonitis and pulmonary fibrosis in non-small-cell lung cancer: pulmonary function, prediction, and prevention. Int J Radiat Oncol Biol Phys. 2005;63:5-24. doi: 10.1016/j.ijrobp.2005.03.047. PubMed PMID: 15963660.
7
Marks LB, Bentzen SM, Deasy JO, Kong FM, et al. Radiation dose-volume effects in the lung. Int J Radiat Oncol Biol Phys. 2010;76:S70-6. doi: 10.1016/j.ijrobp.2009.06.091. PubMed PMID: 20171521. PubMed PMCID: PMC3576042.
8
Lind PA, Wennberg B, Gagliardi G, Fornander T. Pulmonary complications following different radiotherapy techniques for breast cancer, and the association to irradiated lung volume and dose. Breast Cancer Res Treat. 2001;68:199-210. doi: 10.1023/a:1012292019599. PubMed PMID: 11727957.
9
Jin H, Tucker SL, Liu HH, Wei X, Yom SS, Wang S, et al. Dose-volume thresholds and smoking status for the risk of treatment-related pneumonitis in inoperable non-small cell lung cancer treated with definitive radiotherapy. Radiother Oncol. 2009;91:427-32. doi: 10.1016/j.radonc.2008.09.009. PubMed PMID: 18937989. PubMed PMCID: PMC5555233.
10
Dang J, Li G, Zang S, Zhang S, Yao L. Risk and predictors for early radiation pneumonitis in patients with stage III non-small cell lung cancer treated with concurrent or sequential chemoradiotherapy. Radiat Oncol. 2014;9:172. doi: 10.1186/1748-717X-9-172. PubMed PMID: 25074618. PubMed PMCID: PMC4120001.
11
Roach 3rd M, Gandara DR, Yuo HS, et al. Radiation pneumonitis following combined modality therapy for lung cancer: analysis of prognostic factors. J Clin Oncol. 1995;13:2606-12. doi: 10.1200/JCO.1995.13.10.2606. PubMed PMID: 7595714.
12
Tsujino K, Hirota S, Endo M, Obayashi K, et al. Predictive value of dose-volume histogram parameters for predicting radiation pneumonitis after concurrent chemoradiation for lung cancer. Int J Radiat Oncol Biol Phys. 2003;55:110-5. doi: 10.1016/s0360-3016(02)03807-5. PubMed PMID: 12504042.
13
Spyropoulou D, Leotsinidis M, Tsiamita M, et al. Pulmonary function testing in women with breast cancer treated with radiotherapy and chemotherapy. In Vivo. 2009;23:867-71. PubMed PMID: 19779125.
14
Trotti A, Colevas AD, Setser A, et al. CTCAE v3. 0: development of a comprehensive grading system for the adverse effects of cancer treatment. Semin Radiat Oncol. 2003;13:176-81. doi: 10.1016/s1053-4296(03)00031-6.
15
Wang S, Liao Z, Wei X, Liu HH, et al. Analysis of clinical and dosimetric factors associated with treatment-related pneumonitis (TRP) in patients with non-small-cell lung cancer (NSCLC) treated with concurrent chemotherapy and three-dimensional conformal radiotherapy (3D-CRT). Int J Radiat Oncol Biol Phys. 2006;66:1399-407. doi: 10.1016/j.ijrobp.2006.07.1337. PubMed PMID: 16997503.
16
Zhou ZR, Han Q, Liang SX, et al. Dosimetric factors and Lyman normal-tissue complication modelling analysis for predicting radiation-induced lung injury in postoperative breast cancer radiotherapy: a prospective study. Oncotarget. 2017;8:33855-63. doi: 10.18632/oncotarget.12979. PubMed PMID: 27806340. PubMed PMCID: PMC5464917.
17
Lee TF, Chao PJ, Chang L, et al. Developing Multivariable Normal Tissue Complication Probability Model to Predict the Incidence of Symptomatic Radiation Pneumonitis among Breast Cancer Patients. PLoS One. 2015;10:e0131736. doi: 10.1371/journal.pone.0131736. PubMed PMID: 26147496. PubMed PMCID: PMC4492617.
18
Shi A, Zhu G, Wu H, Yu R, et al. Analysis of clinical and dosimetric factors associated with severe acute radiation pneumonitis in patients with locally advanced non-small cell lung cancer treated with concurrent chemotherapy and intensity-modulated radiotherapy. Radiat Oncol. 2010;5:35. doi: 10.1186/1748-717X-5-35. PubMed PMID: 20462424. PubMed PMCID: PMC2883984.
19
Erven K, Weltens C, Nackaerts K, et al. Changes in pulmonary function up to 10 years after locoregional breast irradiation. Int J Radiat Oncol Biol Phys. 2012;82:701-7. doi: 10.1016/j.ijrobp.2010.12.058. PubMed PMID: 21398052.
20
Park YH, Kim JS. Predictors of radiation pneumonitis and pulmonary function changes after concurrent chemoradiotherapy of non-small cell lung cancer. Radiat Oncol J. 2013;31:34-40. doi: 10.3857/roj.2013.31.1.34. PubMed PMID: 23620867. PubMed PMCID: PMC3633229.
21
ORIGINAL_ARTICLE
Modulation of Radiation-Induced NADPH Oxidases in Rat’s Heart Tissues by Melatonin
Background: Experimental studies have shown that infiltration of inflammatory cells as well as upregulation of some cytokines play a central role in the development of late effects of ionizing radiation in heart tissues. Evidences have shown that an increased level of TGF-β has a direct correlation with late effects of exposure to ionizing radiation such as chronic oxidative stress and fibrosis. Recent studies have shown that TGF-β, through upregulation of pro-oxidant enzymes such as NOX2 and NOX4, promotes continuous ROS production and accumulation of fibrosis. Objective: In present study, we aimed to evaluate the expression of NOX2 and NOX4 signaling pathways as well as possible modulatory effects of melatonin on the expression of these genes.Material and Methods: In this experimental study, four groups of 20 rats (5 in each) were used as follows; G1: control; G2: melatonin; G3: radiation; G4: radiation + melatonin. 100 mg/kg of melatonin was administrated before irradiation of heart tissues with 15 Gy gamma rays. 10 weeks after irradiation, heart tissues were collected for real-time PCR. Results: Results showed a significant increase in the expression of TGF-β, Smad2, NF-kB, NOX2 and NOX4. The upregulation of NOX2 was more obvious by 20-fold compared to other genes. Except for TGF-β, melatonin could attenuate the expression of other genes. Conclusion: This study indicated that exposure of rat’s heart tissues to radiation leads to upregulation of TGF-β-NOX4 and TGF-β-NOX2 pathways. Melatonin, through modulation of these genes, may be able to alleviate radiation-induced chronic oxidative stress and subsequent consequences.
https://jbpe.sums.ac.ir/article_46520_bf6bec5a2a33153f054d9bfeea4e423d.pdf
2021-08-01
465
472
10.31661/jbpe.v0i0.1094
Radiation
Melatonin
Heart
NADPH Oxidase 2
NADPH oxidase 4
Tayebeh
Aryafar
t.ariyafar86@gmail.com
1
PhD, Department of Medical Physics and Biomedical Engineering, Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
Peyman
Amini
amini.peyman91@gmail.com
2
MSc, Department of Radiology, Faculty of Paramedical, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
Saeed
Rezapoor
rezapoor.saeed10@gmail.com
3
MSc, Department of Radiology, Faculty of Paramedical, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
Dheyauldeen
Shabeeb
sazanatop5@yahoo.com
4
PhD, Department of Physiology, College of Medicine, University of Misan, Misan, Iraq
AUTHOR
Ahmed
Eleojo Musa
musahmele@yahoo.com
5
PhD, Research Center of Molecular and Cellular Imaging, Tehran University of Medical Sciences (International Campus), Tehran, Iran
AUTHOR
Masoud
Najafi
najafi_ma@yahoo.com
6
PhD, Radiology and Nuclear Medicine Department, School of Paramedical Sciences, Kermanshah University of Medical Sciences, Kermanshah, Iran
LEAD_AUTHOR
Alireza
Shirazi
shirazia@sina.tums.ac.ir
7
PhD, Department of Medical Physics and Biomedical Engineering, Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
Bostrom PJ, Soloway MS. Secondary cancer after radiotherapy for prostate cancer: should we be more aware of the risk? Eur Urol. 2007;52:973-82. doi: 10.1016/j.eururo.2007.07.002. PubMed PMID: 17644245.
1
Krivokrysenko VI, Toshkov IA, Gleiberman AS, Krasnov P, Shyshynova I, Bespalov I, et al. The Toll-like receptor 5 agonist entolimod mitigates lethal acute radiation syndrome in non-human primates. PLoS One. 2015;10:e0135388. doi: 10.1371/journal.pone.0135388.
2
Madan R, Benson R, Sharma DN, Julka PK, Rath GK. Radiation induced heart disease: Pathogenesis, management and review literature. J Egypt Natl Canc Inst. 2015;27:187-93. doi: 10.1016/j.jnci.2015.07.005. PubMed PMID: 26296945.
3
Gerber TC, Kantor B, McCollough CH. Radiation dose and safety in cardiac computed tomography. Cardiol Clin. 2009;27:665-77. doi: 10.1016/j.ccl.2009.06.006. PubMed PMID: 19766923. PubMed PMCID: PMC2749002.
4
Sun Z, Aziz YF, Ng KH. Coronary CT angiography: how should physicians use it wisely and when do physicians request it appropriately? Eur J Radiol. 2012;81:e684-7. doi: 10.1016/j.ejrad.2011.06.040. PubMed PMID: 21724353.
5
Taunk NK, Haffty BG, Kostis JB, Goyal S. Radiation-induced heart disease: pathologic abnormalities and putative mechanisms. Front Oncol. 2015;5:39. doi: 10.3389/fonc.2015.00039. PubMed PMID: 25741474. PubMed PMCID: PMC4332338.
6
Yusuf SW, Venkatesulu BP, Mahadevan LS, Krishnan S. Radiation-Induced Cardiovascular Disease: A Clinical Perspective. Front Cardiovasc Med. 2017;4:66. doi: 10.3389/fcvm.2017.00066. PubMed PMID: 29124057. PubMed PMCID: PMC5662579.
7
Shimizu Y, Kodama K, Nishi N, Kasagi F, Suyama A, Soda M, et al. Radiation exposure and circulatory disease risk: Hiroshima and Nagasaki atomic bomb survivor data, 1950-2003. BMJ. 2010;340:b5349. doi: 10.1136/bmj.b5349. PubMed PMID: 20075151. PubMed PMCID: PMC2806940.
8
Ming X, Feng Y, Yang C, Wang W, Wang P, Deng J. Radiation-induced heart disease in lung cancer radiotherapy: A dosimetric update. Medicine (Baltimore). 2016;95:e5051. doi: 10.1097/MD.0000000000005051. PubMed PMID: 27741117. PubMed PMCID: PMC5072944.
9
Sardaro A, Petruzzelli MF, D’Errico MP, Grimaldi L, Pili G, Portaluri M. Radiation-induced cardiac damage in early left breast cancer patients: risk factors, biological mechanisms, radiobiology, and dosimetric constraints. Radiother Oncol. 2012;103:133-42. doi: 10.1016/j.radonc.2012.02.008. PubMed PMID: 22391054.
10
Seyyednejad F, Rezaee A, Haghi S, Goldust M. Survey of pre-inflammation cytokines levels in radiotherapy-induced-mucositis. Pak J Biol Sci. 2012;15:1098-101. doi: 10.3923/pjbs.2012.1098.1101. PubMed PMID: 24261128.
11
Boerma M, Sridharan V, Mao XW, Nelson GA, Cheema AK, Koturbash I, et al. Effects of ionizing radiation on the heart. Mutat Res. 2016;770:319-27. doi: 10.1016/j.mrrev.2016.07.003. PubMed PMID: 27919338. PubMed PMCID: PMC5144922.
12
Khan R, Sheppard R. Fibrosis in heart disease: understanding the role of transforming growth factor-beta in cardiomyopathy, valvular disease and arrhythmia. Immunology. 2006;118:10-24. doi: 10.1111/j.1365-2567.2006.02336.x. PubMed PMID: 16630019. PubMed PMCID: PMC1782267.
13
Chai Y, Calaf GM, Zhou H, Ghandhi SA, Elliston CD, Wen G, et al. Radiation induced COX-2 expression and mutagenesis at non-targeted lung tissues of gpt delta transgenic mice. Br J Cancer. 2013;108:91-8. doi: 10.1038/bjc.2012.498. PubMed PMID: 23321513. PubMed PMCID: PMC3553512.
14
Mortezaee K, Goradel NH, Amini P, Shabeeb D, Musa AE, Najafi M, et al. NADPH oxidase as a target for modulation of radiation response; implications to carcinogenesis and radiotherapy. Curr Mol Pharmacol. 2019;12:50-60. doi: 10.2174/1874467211666181010154709.
15
Chang J, Feng W, Wang Y, Luo Y, Allen AR, Koturbash I, et al. Whole-body proton irradiation causes long-term damage to hematopoietic stem cells in mice. Radiat Res. 2015;183:240-8. doi: 10.1667/RR13887.1. PubMed PMID: 25635345. PubMed PMCID: PMC4992474.
16
Collins-Underwood JR, Zhao W, Sharpe JG, Robbins ME. NADPH oxidase mediates radiation-induced oxidative stress in rat brain microvascular endothelial cells. Free Radic Biol Med. 2008;45:929-38. doi: 10.1016/j.freeradbiomed.2008.06.024. PubMed PMID: 18640264. PubMed PMCID: PMC2603423.
17
Pazhanisamy SK, Li H, Wang Y, Batinic-Haberle I, Zhou D. NADPH oxidase inhibition attenuates total body irradiation-induced haematopoietic genomic instability. Mutagenesis. 2011;26:431-5. doi: 10.1093/mutage/ger001. PubMed PMID: 21415439. PubMed PMCID: PMC3081334.
18
Carsten RE, Bachand AM, Bailey SM, Ullrich RL. Resveratrol reduces radiation-induced chromosome aberration frequencies in mouse bone marrow cells. Radiat Res. 2008;169:633-8. doi: 10.1667/RR1190.1. PubMed PMID: 18494544. PubMed PMCID: PMC2692544.
19
Xu G, Wu H, Zhang J, Li D, Wang Y, Wang Y, et al. Metformin ameliorates ionizing irradiation-induced long-term hematopoietic stem cell injury in mice. Free Radic Biol Med. 2015;87:15-25. doi: 10.1016/j.freeradbiomed.2015.05.045. PubMed PMID: 26086617. PubMed PMCID: PMC4707049.
20
Ahmadi Z, Ashrafizadeh M. Melatonin as a potential modulator of Nrf2. Fundam Clin Pharmacol. 2020;34(1):11-19. doi: 10.1111/fcp.12498.
21
Ashrafizadeh M, Ahmadi Z, et al. Nanoparticles Targeting STATs in Cancer Therapy. Cells. 2019;8(10):1158. doi: 10.3390/cells8101158.
22
Zielinski JM, Ashmore PJ, Band PR, Jiang H, Shilnikova NS, Tait VK, et al. Low dose ionizing radiation exposure and cardiovascular disease mortality: cohort study based on Canadian national dose registry of radiation workers. Int J Occup Med Environ Health. 2009;22:27-33. doi: 10.2478/v10001-009-0001-z. PubMed PMID: 19329385.
23
Little MP. Cancer and non-cancer effects in Japanese atomic bomb survivors. J Radiol Prot. 2009;29:A43-59. doi: 10.1088/0952-4746/29/2A/S04. PubMed PMID: 19454804.
24
Burnette B, Weichselbaum RR. Radiation as an immune modulator. Semin Radiat Oncol. 2013;23:273-80. doi: 10.1016/j.semradonc.2013.05.009. PubMed PMID: 24012341.
25
Liu LK, Ouyang W, Zhao X, Su Sh F, Yang Y, Ding WJ, et al. Pathogenesis and Prevention of Radiation-induced Myocardial Fibrosis. Asian Pac J Cancer Prev. 2017;18:583-7. doi: 10.22034/APJCP.2017.18.3.583. PubMed PMID: 28440606. PubMed PMCID: PMC5464468.
26
Mathias D, Mitchel RE, Barclay M, Wyatt H, Bugden M, Priest ND, et al. Low-dose irradiation affects expression of inflammatory markers in the heart of ApoE -/- mice. PLoS One. 2015;10:e0119661. doi: 10.1371/journal.pone.0119661. PubMed PMID: 25799423. PubMed PMCID: PMC4370602.
27
Patties I, Haagen J, Dorr W, Hildebrandt G, Glasow A. Late inflammatory and thrombotic changes in irradiated hearts of C57BL/6 wild-type and atherosclerosis-prone ApoE-deficient mice. Strahlenther Onkol. 2015;191:172-9. doi: 10.1007/s00066-014-0745-7. PubMed PMID: 25200359.
28
Hoving S, Heeneman S, Gijbels MJ, Te Poele JA, Visser N, Cleutjens J, et al. Irradiation induces different inflammatory and thrombotic responses in carotid arteries of wildtype C57BL/6J and atherosclerosis-prone ApoE(-/-) mice. Radiother Oncol. 2012;105:365-70. doi: 10.1016/j.radonc.2012.11.001. PubMed PMID: 23245647.
29
Eldabaje R, Le DL, Huang W, Yang LX. Radiation-associated Cardiac Injury. Anticancer Res. 2015;35:2487-92. PubMed PMID: 25964521.
30
Murillo MM, Carmona-Cuenca I, Del Castillo G, Ortiz C, Roncero C, Sanchez A, et al. Activation of NADPH oxidase by transforming growth factor-beta in hepatocytes mediates up-regulation of epidermal growth factor receptor ligands through a nuclear factor-kappaB-dependent mechanism. Biochem J. 2007;405:251-9. doi: 10.1042/BJ20061846. PubMed PMID: 17407446. PubMed PMCID: PMC1904531.
31
Samarakoon R, Overstreet JM, Higgins PJ. TGF-β signaling in tissue fibrosis: redox controls, target genes and therapeutic opportunities. Cell Signal. 2013;25:264-8. doi: 10.1016/j.cellsig.2012.10.003.
32
Yahyapour R, Salajegheh A, Safari A, Amini P, Rezaeyan A, Amraee A, et al. Radiation-induced Non-targeted Effect and Carcinogenesis; Implications in Clinical Radiotherapy. J Biomed Phys Eng. 2018;8:435-46. PubMed PMID: 30568933. PubMed PMCID: PMC6280111.
33
Ataee R, Shokrzadeh M, Jafari-Sabet M, Nasrabadi Nasri N, Ataie A, Haghi Aminjan H. The role of melatonin and melatonin receptors in pharmacology and pharmacotherapy of cancer. Austin Oncol. 2017;2:1015.
34
Haghi-Aminjan H, Asghari MH, Farhood B, Rahimifard M, Hashemi Goradel N, Abdollahi M. The role of melatonin on chemotherapy-induced reproductive toxicity. J Pharm Pharmacol. 2018;70:291-306. doi: 10.1111/jphp.12855. PubMed PMID: 29168173.
35
Haghi-Aminjan H, Farhood B, Rahimifard M, Didari T, Baeeri M, Hassani S, et al. The protective role of melatonin in chemotherapy-induced nephrotoxicity: a systematic review of non-clinical studies. Expert Opin Drug Metab Toxicol. 2018;14:937-50. doi: 10.1080/17425255.2018.1513492. PubMed PMID: 30118646.
36
Li D, Tian Z, Tang W, Zhang J, Lu L, Sun Z, et al. The Protective Effects of 5-Methoxytryptamine-alpha-lipoic Acid on Ionizing Radiation-Induced Hematopoietic Injury. Int J Mol Sci. 2016;17. doi: 10.3390/ijms17060935. PubMed PMID: 27314327. PubMed PMCID: PMC4926468.
37
Favero G, Franceschetti L, Bonomini F, Rodella LF, Rezzani R. Melatonin as an Anti-Inflammatory Agent Modulating Inflammasome Activation. Int J Endocrinol. 2017;2017:1835195. doi: 10.1155/2017/1835195. PubMed PMID: 29104591. PubMed PMCID: PMC5643098.
38
Esposito E, Cuzzocrea S. Antiinflammatory activity of melatonin in central nervous system. Curr Neuropharmacol. 2010;8:228-42. doi: 10.2174/157015910792246155. PubMed PMID: 21358973. PubMed PMCID: PMC3001216.
39
ORIGINAL_ARTICLE
Geant4 Modeling of Cellular Dosimetry of 188Re: Comparison between Geant4 Predicted Surviving Fraction and Experimentally Surviving Fraction Determined by MTT Assay
Background: The importance of cellular dosimetry in both diagnostic and radiation therapy is becoming increasingly recognized. Objective: This study aims to compare surviving fractions, which were predicted using Geant4 and contained three types of cancer cell lines exposed to 188Re with the experimentally surviving fraction determined by MTT assay.Material and Methods: In this comparative study, Geant4 was used to simulate the transport of electrons emitted by 188Re from the cell surface, cytoplasm, nucleus or medium around the cells. The nucleus dose per decay (S-value) was computed for models of single cell and random monolayer cell. Geant4-computed survival fraction (SF) of cancer cells exposed to 188Re was compared with the experimental SF values of MTT assay. Results: For single cell model, Geant4 S-values of nucleus-to-nucleus were consistent with values reported by Goddu et al. (ratio of S-values by analytical techniques vs. Geant4 = 0.811–0.975). Geant4 S-values of cytoplasm and cell surface to nucleus were relatively comparable to the reported values (ratio =0.914–1.21). For monolayer model, the values of SCy→N and SCS→N, were greater compared to those for model of single cell (2%–25% and 4%–38% were larger than single cell, respectively). The Geant4 predicted SF for monolayer MCF7, HeLa and A549 cells was in agreement with the experimental data in 10μCi activity (relative error of 2.29%, 2.69% and 2.99%, respectively). Conclusion: Geant4 simulation with monolayer cell model showed the highest accuracy in predicting the SF of cancer cells exposed to homogeneous distribution of 188Re in the medium.
https://jbpe.sums.ac.ir/article_46522_d08f0869ea811c52744494f7d5340367.pdf
2021-08-01
473
482
10.31661/jbpe.v0i0.1050
Dosimetry
Monte Carlo Method
Cell Survival
S-Value
A549 Cells
Hela Cell
MCF7 Cell
Sara
Mohammadi
mohammadis@pnum.ac.ir
1
PhD, Department of Medical Physics, Mashhad University of Medical Sciences, Mashhad, Iran
AUTHOR
Mahdy
Ebrahimi Loushab
ebrahimi.mahdy@gmail.com
2
PhD, Department of Physics, Faculty of Rajaee, Quchan Branch, Technical and Vocational University (TVU), Khorasan Razavi, Iran
AUTHOR
Mohammad Taghi
Bahreyni Toossi
mbahreyni@gmail.com
3
PhD, Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
LEAD_AUTHOR
Chatal J-F, Hoefnagel CA. Radionuclide therapy. Lancet. 1999;354:931-5.
1
Schell S, Wilkens JJ, Oelfke U. Radiobiological effect based treatment plan optimization with the linear quadratic model. Z Med Phys. 2010;20:188-96. doi: 10.1016/j.zemedi.2010.02.003. PubMed PMID: 20832006.
2
Bousis C, Emfietzoglou D, Nikjoo H. Monte Carlo single-cell dosimetry of I-131, I-125 and I-123 for targeted radioimmunotherapy of B-cell lymphoma. Int J Radiat Biol. 2012;88:908-15. doi: 10.3109/09553002.2012.666004. PubMed PMID: 22348681.
3
Bardies M, Chatal JF. Absorbed doses for internal radiotherapy from 22 beta-emitting radionuclides: beta dosimetry of small spheres. Phys Med Biol. 1994;39:961-81. doi: 10.1088/0031-9155/39/6/004. PubMed PMID: 15551573.
4
Humm JL. Dosimetric aspects of radiolabeled antibodies for tumor therapy. J Nucl Med. 1986;27:1490-7. PubMed PMID: 3528417.
5
Humm JL. A microdosimetric model of astatine-211 labeled antibodies for radioimmunotherapy. Int J Radiat Oncol Biol Phys. 1987;13:1767-73. doi: 10.1016/0360-3016(87)90176-3. PubMed PMID: 3667382.
6
Emfietzoglou D, Bousis C, Hindorf C, Fotopoulos A, Pathak A, Kostarelos K. A Monte Carlo study of energy deposition at the sub-cellular level for application to targeted radionuclide therapy with low-energy electron emitters. Nucl Instrum Methods Phys Res B. 2007;256:547-53.
7
Bousis C, Emfietzoglou D, Hadjidoukas P, Nikjoo H. A Monte Carlo study of absorbed dose distributions in both the vapor and liquid phases of water by intermediate energy electrons based on different condensed-history transport schemes. Phys Med Biol. 2008;53:3739-61. doi: 10.1088/0031-9155/53/14/003. PubMed PMID: 18574312.
8
Bernal MA, Liendo JA. An investigation on the capabilities of the PENELOPE MC code in nanodosimetry. Med Phys. 2009;36:620-5. doi: 10.1118/1.3056457. PubMed PMID: 19292002.
9
Syme AM, Kirkby C, Riauka TA, Fallone BG, McQuarrie SA. Monte Carlo investigation of single cell beta dosimetry for intraperitoneal radionuclide therapy. Phys Med Biol. 2004;49:1959-72. doi: 10.1088/0031-9155/49/10/009. PubMed PMID: 15214535.
10
Cai Z, Pignol J-P, Chan C, Reilly RM. Cellular dosimetry of 111In using Monte Carlo N-particle computer code: comparison with analytic methods and correlation with in vitro cytotoxicity. J Nucl Med. 2010;51:462-70. doi: 10.2967/jnumed.109.063156.
11
Cai Z, Kwon YL, Reilly RM. Monte Carlo N-Particle (MCNP) Modeling of the Cellular Dosimetry of 64Cu: Comparison with MIRDcell S Values and Implications for Studies of Its Cytotoxic Effects. J Nucl Med. 2017;58:339-45. doi: 10.2967/jnumed.116.175695. PubMed PMID: 27660146.
12
Nikjoo H, Uehara S, Emfietzoglou D, Cucinotta F. Track-structure codes in radiation research. Radiat Meas. 2006;41:1052-74. doi: 10.1016/j.radmeas.2006.02.001.
13
Agostinelli S, Allison J, Amako Ka, Apostolakis J, Araujo H, Arce P, et al. GEANT4—a simulation toolkit. Nucl Instrum Methods Phys Res A. 2003;506:250-303.
14
Allison J, Amako K, Apostolakis J, Araujo H, Dubois PA, Asai M, et al. Geant4 developments and applications. IEEE Trans Nucl Sci. 2006;53:270-8.
15
Chauvie S, Francis Z, Guatelli S, Incerti S, Mascialino B, Montarou G, et al., editors. Models of biological effects of radiation in the Geant4 Toolkit. IEEE Nuclear Science Symposium Conference Record; San Diego, Calif: IEEE Service Center; 2006.
16
Kyriakou I, Emfietzoglou D, Ivanchenko V, Bordage M, Guatelli S, Lazarakis P, et al. Microdosimetry of electrons in liquid water using the low-energy models of Geant4. J Appl Phys. 2017;122:024303. doi: 10.1063/1.4992076.
17
Cirrone GP, Cuttone G, Mazzaglia SE, Romano F, Sardina D, Agodi C, et al. Hadrontherapy: a Geant4-based tool for proton/ion-therapy studies. Prog Nucl Sci Technol. 2011;2:207-12. doi: 10.15669/pnst.2.207.
18
Sefl M, Incerti S, Papamichael G, Emfietzoglou D. Calculation of cellular S-values using Geant4-DNA: The effect of cell geometry. Appl Radiat Isot. 2015;104:113-23. doi: 10.1016/j.apradiso.2015.06.027. PubMed PMID: 26159660.
19
Jiang RD, Shen H, Piao YJ. The morphometrical analysis on the ultrastructure of A549 cells. Rom J Morphol Embryol. 2010;51:663-7. PubMed PMID: 21103623.
20
Arya SK, Lee KC, Bin Dah’alan D, Daniel, Rahman AR. Breast tumor cell detection at single cell resolution using an electrochemical impedance technique. Lab Chip. 2012;12:2362-8. doi: 10.1039/c2lc21174b. PubMed PMID: 22513827.
21
Zhao L, Sukstanskii AL, Kroenke CD, Song J, Piwnica-Worms D, Ackerman JJ, et al. Intracellular water specific MR of microbead-adherent cells: HeLa cell intracellular water diffusion. Magn Reson Med. 2008;59:79-84. doi: 10.1002/mrm.21440. PubMed PMID: 18050315. PubMed PMCID: PMCPMC2730972.
22
Chen KT, Lee TW, Lo JM. In vivo examination of (188)Re(I)-tricarbonyl-labeled trastuzumab to target HER2-overexpressing breast cancer. Nucl Med Biol. 2009;36:355-61. doi: 10.1016/j.nucmedbio.2009.01.006. PubMed PMID: 19423002.
23
Carlone M, Wilkins D, Raaphorst P. The modified linear-quadratic model of Guerrero and Li can be derived from a mechanistic basis and exhibits linear-quadratic-linear behaviour. Phys Med Biol. 2005;50:L9-13. doi: 10.1088/0031-9155/50/10/l01. PubMed PMID: 15876677.
24
Goddu SM. MIRD Cellular S values: Self-absorbed dose per unit cumulated activity for selected radionuclides and monoenergetic electron and alpha particle emitters incorporated into different cell compartments. Reston: Society of Nuclear Medicine; 1997.
25
Qing Y, Yang X-Q, Zhong Z-Y, Lei X, Xie J-Y, Li M-X, et al. Microarray analysis of DNA damage repair gene expression profiles in cervical cancer cells radioresistant to 252 Cf neutron and X-rays. BMC Cancer. 2010;10:71. doi: 10.1186/1471-2407-10-71.
26
Lacoste-Collin L, Castiella M, Franceries X, Cassol E, Vieillevigne L, Pereda V, et al. Nonlinearity in MCF7 Cell Survival Following Exposure to Modulated 6 MV Radiation Fields: Focus on the Dose Gradient Zone. Dose Response. 2015;13:1559325815610759. doi: 10.1177/1559325815610759. PubMed PMID: 26740805. PubMed PMCID: PMCPMC4679192.
27
Jiang L, Xiong XP, Hu CS, Ou ZL, Zhu GP, Ying HM. In vitro and in vivo studies on radiobiological effects of prolonged fraction delivery time in A549 cells. J Radiat Res. 2013;54:230-4. doi: 10.1093/jrr/rrs093. PubMed PMID: 23090953. PubMed PMCID: PMCPMC3589931.
28
ORIGINAL_ARTICLE
Accuracy Evaluation of EPL and ETAR Algorithms in the Treatment Planning Systems using CIRS Thorax Phantom
Background: It is recommended for each set of radiation data and algorithm that subtle deliberation is done regarding dose calculation accuracy. Knowing the errors in dose calculation for each treatment plan will result in an accurate estimate of the actual dose achieved by the tumor. Objective: This study aims to evaluate the equivalent path length (EPL) and equivalent tissue air ratio (ETAR) algorithms in radiation dose calculation.Material and Methods: In this experimental study, the TEC-DOC 1583 guideline was used. Measurements and calculations were obtained for each algorithm at specific points in thorax CIRS phantom for 6 and 18 MVs and results were compared. Results: In the EPL, calculations were in agreement with measurements for 27 points and differences between them ranged from 0.1% to 10.4% at 6 MV. The calculations were in agreement with measurements for 21 points and differences between them ranged from 0.4% to 13% at 18 MV. In ETAR, calculations were also in consistent with measurements for 21 points, and differences between them ranged from 0.1% to 9% at 6 MV. Moreover, for 18 MV, the calculations were in agreement with measurements for 17 points and differences between them ranged from 0% to 11%. Conclusion: For the EPL algorithm, more dose points were in consistent with acceptance criteria. The errors in the ETAR were 1% to 2% less than the EPL. The greatest calculation error occurs in low-density lung tissue with inhomogeneities or in high-density bone. Errors were larger in shallow depths. The error in higher energy was more than low energy beam.
https://jbpe.sums.ac.ir/article_47131_4765c57429db7f5c412a48851c5620ac.pdf
2021-08-01
483
496
10.31661/jbpe.v0i0.1097
Algorithms
Dose Calculation Error
Lung Tissue
Inhomogeneities
Radiotherapy
Treatment Planning Systems
Radiation Dosage
Mansour
Zabihzadeh
zabihzadeh@gmail.com
1
PhD, Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
AUTHOR
Azizollah
Rahimi
azizrahimi91@gmail.com
2
PhD, Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
LEAD_AUTHOR
Hodjatollah
Shahbazian
shahbazian72@yahoo.com
3
MD, Department of Clinical Oncology, Faculty of Medicine, Golestan Hospital, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
AUTHOR
Sasan
Razmjoo
s.razmjoo1990@gmail.com
4
MD, Department of Clinical Oncology, Faculty of Medicine, Golestan Hospital, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
AUTHOR
Seyyed Rabie
Mahdavi
s.r.mahdavi@yahoo.com
5
PhD, Department of Medical Physics, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran
AUTHOR
Herrick AC. A comparative dosimetric analysis of the effect of heterogeneity corrections used in three treatment planning algorithms. Electronic Thesis or Dissertation; University of Toledo; 2010. Available from: https://etd.ohiolink.edu/.
1
Ahnesjo A, Aspradakis MM. Dose calculations for external photon beams in radiotherapy. Phys Med Biol. 1999;44:R99-155. PubMed PMID: 10588277.
2
Woon YL, Heng S, Wong J, Ung NM. Comparison of selected dose calculation algorithms in radiotherapy treatment planning for tissues with inhomogeneities. J Phys: Conf Ser. 2016;694(1):1-5. doi: 10.1088/1742-6596/694/1/012024.
3
Krieger T, Sauer OA. Monte Carlo- versus pencil-beam-/collapsed-cone-dose calculation in a heterogeneous multi-layer phantom. Phys Med Biol. 2005;50:859-68. doi: 10.1088/0031-9155/50/5/010. PubMed PMID: 15798260.
4
Zhu XR, Low DA, Harms WB, Purdy JA. A convolution-adapted ratio-TAR algorithm for 3D photon beam treatment planning. Med Phys. 1995;22:1315-27. doi: 10.1118/1.597516. PubMed PMID: 7476719.
5
Yorke E, Harisiadis L, Wessels B, Aghdam H, Altemus R. Dosimetric considerations in radiation therapy of coin lesions of the lung. Int J Radiat Oncol Biol Phys. 1996;34:481-7. doi: 10.1016/0360-3016(95)02036-5. PubMed PMID: 8567352.
6
Rutonjski L, Petrovic B, Baucal M, Teodorovic M, Cudic O, Gershkevitsh E, et al. Dosimetric verification of radiotherapy treatment planning systems in Serbia: national audit. Radiat Oncol. 2012;7:155. doi: 10.1186/1748-717X-7-155. PubMed PMID: 22971539. PubMed PMCID: PMC3504524.
7
Orton CG, Chungbin S, Klein EE, Gillin MT, Schultheiss TE, Sause WT. Study of lung density corrections in a clinical trial (RTOG 88-08). Radiation Therapy Oncology Group. Int J Radiat Oncol Biol Phys. 1998;41:787-94. doi: 10.1016/s0360-3016(98)00117-5. PubMed PMID: 9652839.
8
Lopes MC, Cavaco A, Jacob K, Madureira L, Germano S, Faustino S, et al. Treatment planning systems dosimetry auditing project in Portugal. Phys Med. 2014;30:96-103. doi: 10.1016/j.ejmp.2013.03.008. PubMed PMID: 23623589.
9
Klein EE, Morrison A, Purdy JA, Graham MV, Matthews J. A volumetric study of measurements and calculations of lung density corrections for 6 and 18 MV photons. Int J Radiat Oncol Biol Phys. 1997;37:1163-70. doi: 10.1016/s0360-3016(97)00110-7. PubMed PMID: 9169827.
10
Gershkevitsh E, Schmidt R, Velez G, Miller D, Korf E, Yip F, et al. Dosimetric verification of radiotherapy treatment planning systems: results of IAEA pilot study. Radiother Oncol. 2008;89:338-46. doi: 10.1016/j.radonc.2008.07.007. PubMed PMID: 18701178.
11
Engelsman M, Damen EM, Koken PW, Van‘t Veld AA, Van Ingen KM, Mijnheer BJ. Impact of simple tissue inhomogeneity correction algorithms on conformal radiotherapy of lung tumours. Radiother Oncol. 2001;60:299-309. PubMed PMID: 11514010.
12
El-Khatib EE, Evans M, Pla M, Cunningham JR. Evaluation of lung dose correction methods for photon irradiations of thorax phantoms. Int J Radiat Oncol Biol Phys. 1989;17:871-8. doi: 10.1016/0360-3016(89)90081-3. PubMed PMID: 2777679.
13
De Jaeger K, Hoogeman MS, Engelsman M, Seppenwoolde Y, Damen EM, Mijnheer BJ, et al. Incorporating an improved dose-calculation algorithm in conformal radiotherapy of lung cancer: re-evaluation of dose in normal lung tissue. Radiother Oncol. 2003;69:1-10. doi: 10.1016/s0167-8140(03)00195-6. PubMed PMID: 14597351.
14
Alam R, Ibbott GS, Pourang R, Nath R. Application of AAPM Radiation Therapy Committee Task Group 23 test package for comparison of two treatment planning systems for photon external beam radiotherapy. Med Phys. 1997;24:2043-54. doi: 10.1118/1.598119. PubMed PMID: 9434989.
15
Aarup LR, Nahum AE, Zacharatou C, Juhler-Nottrup T, Knoos T, Nystrom H, et al. The effect of different lung densities on the accuracy of various radiotherapy dose calculation methods: implications for tumour coverage. Radiother Oncol. 2009;91:405-14. doi: 10.1016/j.radonc.2009.01.008. PubMed PMID: 19297051.
16
Meredith WJ, Neary G. The production of isodose curves and the calculation of energy absorption from standard depth dose data. The British Journal of Radiology. 1944;17:75-82. doi: 10.1259/0007-1285-17-195-75.
17
Liu Q, Liang J, Stanhope CW, Yan D. The effect of density variation on photon dose calculation and its impact on intensity modulated radiotherapy and stereotactic body radiotherapy. Med Phys. 2016;43:5717. doi: 10.1118/1.4963207. PubMed PMID: 27782711.
18
Breitman K, Rathee S, Newcomb C, Murray B, Robinson D, Field C, et al. Experimental validation of the Eclipse AAA algorithm. J Appl Clin Med Phys. 2007;8:76-92. doi: 10.1120/jacmp.v8i2.2350. PubMed PMID: 17592457. PubMed PMCID: PMC5722411.
19
Craig J, Oliver M, Gladwish A, Mulligan M, Chen J, Wong E. Commissioning a fast Monte Carlo dose calculation algorithm for lung cancer treatment planning. J Appl Clin Med Phys. 2008;9:2702. PubMed PMID: 18714276. PubMed PMCID: PMC5721711.
20
International Atomic Energy Agency. Commissioning of radiotherapy treatment planning systems: Testing for typical external beam treatment techniques. IAEA Tecdoc Series No. 1583; Vienna: IAEA; 2008. p. 1-67.
21
Batho HF. Lung Corrections in Cobalt 60 Beam Therapy. J Can Assoc Radiol. 1964;15:79-83. PubMed PMID: 14173312.
22
Sontag MR, Cunningham JR. Clinical application of a CT based treatment planning system. Comput Tomogr. 1978;2:117-30. doi: 10.1016/0363-8235(78)90009-1. PubMed PMID: 699542.
23
Sontag MR, Cunningham JR. The equivalent tissue-air ratio method for making absorbed dose calculations in a heterogeneous medium. Radiology. 1978;129:787-94. doi: 10.1148/129.3.787. PubMed PMID: 725060.
24
Purdy JA. Photon Dose Calculations for Three-Dimensional Radiation Treatment Planning. Semin Radiat Oncol. 1992;2:235-45. doi: 10.1053/SRAO00200235. PubMed PMID: 10717040.
25
Cunningham JR. Scatter-air ratios. Phys Med Biol. 1972;17:42-51. PubMed PMID: 5071500.
26
Greene D, Stewart JG. Isodose Curves in Non-Uniform Phantoms. Br J Radiol. 1965;38:378-85. doi: 10.1259/0007-1285-38-449-378. PubMed PMID: 14280292.
27
Purdy J, Prasad S. Current methods and algorithms in radiation absorbed dose calculation and the role of computed tomography: A review. United States; Raven Press Publications; 1983.
28
Sundbom L. Dose planning for irradiation of thorax with 60-Co in fixed-beam teletherapy. Acta Radiol Ther Phys Biol. 1965;3:342-52. doi: 10.3109/02841866509133109. PubMed PMID: 5838014.
29
O’Connor JE. The variation of scattered x-rays with density in an irradiated body. Phys Med Biol. 1957;1:352-69. doi: 10.1088/0031-9155/1/4/305. PubMed PMID: 13452841.
30
International Atomic Energy Agency. Specification and acceptance testing of radiotherapy treatment planning systems. IAEA Tecdoc Series No. 1540; Vienna, Austria: IAEA; 2007.
31
Asnaashari K, Nodehi MR, Mahdavi SR, Gholami S, Khosravi HR. Dosimetric comparison of different inhomogeneity correction algorithms for external photon beam dose calculations. J Med Phys. 2013;38:74-81. doi: 10.4103/0971-6203.111310. PubMed PMID: 23776310. PubMed PMCID: PMC3683304.
32
ORIGINAL_ARTICLE
Can Common Lead Apron in Testes Region Cause Radiation Dose Reduction during Chest CT Scan? A Patient Study
Background: Computed tomography (CT) is a routine procedure for diagnosing using ionization radiation which has hazardous effects especially on sensitive organs. Objective: The aim of this study was to quantify the dose reduction effect of lead apron shielding on the testicular region during routine chest CT scans.Material and Methods: In this measurement study, the routine chest CT examinations were performed for 30 male patients with common lead aprons folded and positioned in testis regions. The patient’s mean body mass index (BMI) was 26.2 ± 4.6 kg/m2. To calculate the doses at testis region, three thermoluminescent dosimeters (TLD-100) were attached at the top surface of the apron as an indicator of the doses without shielding, and three TLDs under the apron for doses with shielding. The TLD readouts were compared using SPSS software (Wilcoxon test) version 16. Results: The radiation dose in the testicular regions was reduced from 0.46 ± 0.04 to 0.20 ± 0.04 mGy in the presence of lead apron shielding (p < 0.001), the reduction was equal to 56%. Furthermore, the heritable risk probability was obtained at 2.0 ×10-5 % and 4.6 ×10-5 % for the patients using the lead apron shield versus without shield, respectively. Conclusion: Applying common lead aprons as shielding in the testis regions of male patients undergoing chest CT scans can reduce the radiation doses significantly. Therefore, this shield can be recommended for routine chest CT examinations.
https://jbpe.sums.ac.ir/article_47699_a88d704a4f4c84ec3d5b9bdb5015ae51.pdf
2021-08-01
497
504
10.31661/jbpe.v0i0.2104-1307
Computed Tomography
Radiation protection
Chest CT Scan
Lead Apron
Testis
Thermoluminescent Dosimetry
Mohammad
Kiapour
mkiapour1985@gmail.com
1
MSc, Student Research Committee, Babol University of Medical Sciences, Babol, Iran
AUTHOR
Kourosh
Ebrahimnejad Gorji
kourosh_gorji@yahoo.com
2
PhD, Department of Medical Physics Radiobiology and Radiation Protection, School of Medicine, Babol University of Medical Sciences, Babol, Iran
AUTHOR
Rahele
Mehraeen
m.afkhamiardekani87@gmail.com
3
MD, Department of Pediatric Radiology, Babol University of Medical Sciences, Babol, Iran
AUTHOR
Naser
Ghaemian
nghaemian41@yahoo.com
4
MD, Department of Radiology and Radiotherapy, School of Medicine, Babol University of Medical Sciences, Babol, Iran
AUTHOR
Fatemeh
Niksirat Sustani
mprrp.mubabol@gmail.com
5
MSc, Department of Medical Physics Radiobiology and Radiation Protection, School of Medicine, Babol University of Medical Sciences, Babol, Iran
AUTHOR
Razzagh
Abedi-Firouzjah
razzaghabedi@gmail.com
6
MSc, Department of Medical Physics Radiobiology and Radiation Protection, School of Medicine, Babol University of Medical Sciences, Babol, Iran
AUTHOR
Ali
Shabestani Monfared
monfared1345@gmail.com
7
PhD, Cancer Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, Iran
LEAD_AUTHOR
Valentin J. Managing patient dose in multi-detector computed tomography (MDCT). ICRP Publication 102. Ann ICRP. 2007;1-8. doi: 10.1016/j.icrp.2007.09.001. PubMed PMID: 18069128.
1
Hart D, Wall BF, Hillier MC, Shrimpton PC. Frequency and collective dose for medical and dental X-ray examinations in the UK, 2008. HPA-CRCE-012; UK: HPA; 2010.
2
Davoudi M, Khoramian D, Abedi-Firouzjah R, Ataei G. STRATEGY OF COMPUTED TOMOGRAPHY IMAGE OPTIMISATION IN CERVICAL VERTEBRAE AND NECK SOFT TISSUE IN EMERGENCY PATIENTS. Radiat Prot Dosimetry. 2019;187(1):98-102. doi: 10.1093/rpd/ncz145. PubMed PMID: 31135908.
3
Khoramian D, Sistani S, Firouzjah RA. Assessment and comparison of radiation dose and image quality in multi-detector CT scanners in non-contrast head and neck examinations. Pol J Radiol. 2019;84(3):61-7. doi: 10.5114/pjr.2019.82743. PubMed PMID: 31019596. PubMed PMCID: PMC6479057.
4
Turner AC, Zankl M, DeMarco JJ, et al. The feasibility of a scanner-independent technique to estimate organ dose from MDCT scans: Using to account for differences between scanners. Med Phys. 2010;37(4):1816-25. doi: 10.1118/1.3368596. PubMed PMID: 20443504. PubMed PMCID: PMC2861967.
5
Cohen BL. A test of the linear-no threshold theory of radiation carcinogenesis. Environ Res. 1990;53(2):193-220. doi: 10.1016/s0013-9351(05)80119-7. PubMed PMID: 2253600.
6
Khorramian D, Sistani S, Banaei A, Bijari S. Estimation and assessment of the effective doses for radiosensitive organs in women undergoing chest CT scans with or without automatic exposure control system. Tehran Univ Med J. 2017;75(7):496-503.
7
Hart D, Hillier MC, Wall BF. Doses to patients from medical X-ray examinations in the UK-2000 review. NRPB report; UK: NRPB; 2002.
8
Edgar RG, Patel M, Bayliss S, et al. Treatment of lung disease in alpha-1 antitrypsin deficiency: a systematic review. Int J Chron Obstruct Pulmon Dis. 2017;12(1):1295-08. doi: 10.2147/COPD.S130440. PubMed PMID: 28496314. PubMed PMCID: PMC5422329.
9
Iball GR, Brettle DS. Organ and effective dose reduction in adult chest CT using abdominal lead shielding. Br J Radiol. 2011;84(1007):1020-26. doi: 10.1259/bjr/53865832. PubMed PMID: 22011831. PubMed PMCID: PMC3473701.
10
Protection R. The 2007 recommendations of the International Commission on Radiological Protection. ICRP publication 103. Ann ICRP. 2007;37(2-4):1-332. doi: 10.1016/j.icrp.2007.10.003. PubMed PMID: 18082557.
11
Hohl C, Mahnken AH, Klotz E, Das M, et al. Radiation dose reduction to the male gonads during MDCT: the effectiveness of a lead shield. Am J Roentgenol. 2005;184(1):128-30. doi: 10.2214/ajr.184.1.01840128. PubMed PMID: 15615962.
12
Shielding B. Assessing the image quality and eye lens dose reduction using bismuth shielding in computed tomography of brain. J Kerman Univ Med Sci. 2018;25(6):471-82.
13
Dauer LT, Casciotta KA, Erdi YE, Rothenberg LN. Radiation dose reduction at a price: the effectiveness of a male gonadal shield during helical CT scans. BMC Med Imaging. 2007;7(5):1-7. doi: 10.1186/1471-2342-7-5. PubMed PMID: 17367529. PubMed PMCID: PMC1831769.
14
Daniels C, Furey E. The effectiveness of surface lead shielding of gonads outside the primary X-ray beam. J Med Imaging Radiat Sci. 2008;39(4):189-91. doi: 10.1016/j.jmir.2008.09.001. PubMed PMID: 31051779.
15
Sancaktutar AA, Bozkurt Y, Önder H, et al. A new practical model of testes shield: the effectiveness during abdominopelvic computed tomography. J Androl. 2012;33(5):984-89. doi: 10.2164/jandrol.111.015560. PubMed PMID: 22207708.
16
Groves AM, Owen KE, Courtney HM, et al. 16-detector multislice CT: dosimetry estimation by TLD measurement compared with Monte Carlo simulation. Br J Radiol. 2004;77(920):662-65. doi: 10.1259/bjr/48307881. PubMed PMID: 15326044.
17
Wrixon AD. New ICRP recommendations. J Radiol Prot. 2008;28(2):161. doi: 10.1088/0952-4746/28/2/R02. PubMed PMID: 18495983.
18
De González AB, Mahesh M, Kim K-P, et al. Projected cancer risks from computed tomographic scans performed in the United States in 2007. Arch Intern Med. 2009;169(22):2071-77. doi: 10.1001/archinternmed.2009.440. PubMed PMID: 20008689. PubMed PMCID: PMC6276814.
19
Linton OW, Mettler Jr FA. National conference on dose reduction in CT, with an emphasis on pediatric patients. Am J Roentgenol. 2003;181(2):321-29. doi: 10.2214/ajr.181.2.1810321. PubMed PMID: 12876005.
20
Slovis TL. The ALARA concept in pediatric CT: myth or reality? Radiology. 2002;223(1):5-6. doi: 10.1148/radiol.2231012100. PubMed PMID: 11930041.
21
Grobe H, Sommer M, Koch A, Hietschold V, Henniger J, Abolmaali N. Dose reduction in computed tomography: the effect of eye and testicle shielding on radiation dose measured in patients with beryllium oxide-based optically stimulated luminescence dosimetry. Eur Radiol. 2009;19(5):1156-60. doi: 10.1007/s00330-008-1241-1. PubMed PMID: 19082601.
22
Zarb F, Rainford L, McEntee MF. AP diameter shows the strongest correlation with CTDI and DLP in abdominal and chest CT. Radiat Prot Dosimetry. 2010;140(3):266-73. doi: 10.1093/rpd/ncq115. PubMed PMID: 20332128.
23
Price R, Halson P, Sampson M. Dose reduction during CT scanning in an anthropomorphic phantom by the use of a male gonad shield. Br J Radiol. 1999;72(857):489-94. doi: 10.1259/bjr.72.857.10505015. PubMed PMID: 10505015.
24
Hidajat N, Schröder RJ, Vogl T, Schedel H, Felix R. The efficacy of lead shielding in patient dosage reduction in computed tomography. ROFO. 1996;165(5):462-65. doi: 10.1055/s-2007-1015790. PubMed PMID: 8998318.
25
Shah DJ, Sachs RK, Wilson DJ. Radiation-induced cancer: a modern view. Br J Radiol. 2012;85(1020):1166-73. doi: 10.1259/bjr/25026140. PubMed PMID: 23175483. PubMed PMCID: PMC3611719.
26
ICRP. Radiological protection in medicine. ICRP Publication 105. Ann ICRP. 2007;37(6):1-64.
27
ORIGINAL_ARTICLE
Screening through Temperature and Thermal Pattern Analysis in DMBA - Induced Breast Cancer in Wistar Rats
Background: Based on thermal temperatures around the breast, thermography is considered a promising approache providing information about the condition of the breast without any side effects. Objective: Using thermography, breast screening is highly dependent on the process of heat recognition. The angular effects in the process of thermal patterns recognition can increase false detection. The effect can be observed in breasts with growing mammary glands. This study aims to develop a system to identify breast conditions through analysis of temperature and thermal patterns.Material and Methods: In this experimental study, analysis of thermal patterns are performed using the Canny method, specifically detection of anomalies in the breast. Twenty-four Wistar female rats were used as experimental animal models with group 1 (normal), group 2 (induced with DMBA), group 3 (rats with growing mammary gland). At the end of 8 weeks, all rats were sacrificed and histopathology analysis was performed. The body temperature was measured every week using the Infrared Camera type TiS20 brand Fluke camera. Results: Histopathology indicated average temperature of 36.66 °C, 37.77 °C and above 38.87 °C in normal, growing mammary glands, and cancerous breasts, respectively. These results revealed significantly higher heat in breasts with cancerous lesions. In the analysis of thermal pattern recognition for breast, no curve was formed in the normal group, while cancerous and growing mammary glands demonstrated a perfectly closed curve and an imperfect curve pattern, respectively. Conclusion: Breast screening through the analysis of temperature and thermal patterns can distinguish normal, cancerous and breast with growing mammary glands.
https://jbpe.sums.ac.ir/article_46417_53a6a1088c8a618d93dfb35f1b468e06.pdf
2021-08-01
505
514
10.31661/jbpe.v0i0.1229
Rats
Hot Temperature
Breast neoplasms
Evy
Poerbaningtyas
evystudi@gmail.com
1
MT, Doctoral Program of Medical Science, Faculty of Medicine, University Brawijaya, Malang, Indonesia
LEAD_AUTHOR
Respati S
Dradjat
respatisdradjat@yahoo.com
2
PhD, Department of Orthopaedic, Saiful Anwar Hospital, Faculty of Medicine, University Brawijaya, Malang, Indonesia
AUTHOR
Agustina T
Endharti
tinapermana@yahoo.com
3
PhD, Department of Parasitology, Faculty of Medicine, University Brawijaya, Malang, Indonesia
AUTHOR
Setyawan P
Sakti
setyawansakti@gmail.com
4
PhD, Department of Physics, University Brawijaya, Malang, Indonesia
AUTHOR
Edi
Widjajanto
5
PhD, Department of Clinical Pathology, Faculty of Medicine, University Brawijaya, Malang, Indonesia
AUTHOR
Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136(5):E359-86. doi: 10.1002/ijc.29210. PubMed PMID: 25220842.
1
Opstal-Van Winden AWJ, De Haan HG, Hauptmann M, Schmidt MK, Broeks A, Russell NS, Janus CPM, Krol ADG, et al. Genetic susceptibility to radiation-induced breast cancer after Hodgkin lymphoma. Blood. 2019;133(10):1130-9. doi: 10.1182/blood-2018-07-862607. PubMed PMID: 30573632. PubMed PMCID: PMC6405334.
2
Ozmen V, Ozcinar B, Bozdogan A, Eralp Y, Yavuz E, Dincer M. The effect of internal mammary lymph node biopsy on the therapeutic decision and survival of patients with breast cancer. Eur J Surg Oncol. 2015;41(10):1368-72. doi: 10.1016/j.ejso.2015.07.005. PubMed PMID: 26210653.
3
Bick U, Diekmann F. Digital mammography: what do we and what don’t we know? Eur Radiol. 2007;17(8):1931–42. doi: 10.1007/s00330-007-0586-1.
4
Rangayyan RM, Banik S, Desautels JEL. Computer-aided detection of architectural distortion in prior mammograms of interval cancer. J Digit Imaging. 2010;23(5):611–31. doi: 10.1007/s10278-009-9257-x. PubMed PMID: 20127270. PubMed PMCID: PMC3046672.
5
Fletcher SW, Elmore JG. Clinical practice. Mammographic screening for breast cancer. N Engl J Med. 2003;348(17):1672-80. doi: 10.1056/NEJMcp021804. PubMed PMID: 12711743. PubMed PMCID: PMC3157308.
6
Sobti A, Sobti P, Keith LG. Screening and diagnostic mammograms: why the gold standard does not shine more brightly. Int J Fertil Womens Med. 2005;50:199-206. PubMed PMID: 16468469.
7
Othman E, Wang J, Sprague BL, Rounds T, Ji Y, Herschorn SD, Wood ME. Comparison of false positive rates for screening breast magnetic resonance imaging (MRI) in high risk women performed on stacked versus alternating schedules. Springerplus. 2015;4:77. doi: 10.1186/s40064-015-0793-1. PubMed PMID: 25741458. PubMed PMCID: PMC4340856.
8
Salem DS, Kamal RM, Mansour SM, Salah LA, Wessam R. Breast imaging in the young: the role of magnetic resonance imaging in breast cancer screening, diagnosis and follow-up. J Thorac Dis. 2013;5(Suppl 1):S9-S18. doi: 10.3978/j.issn.2072-1439.2013.05.02. PubMed PMID: 23819032. PubMed PMCID: PMC3695543.
9
Lozano III A, Hassanipour F. Infrared imaging for breast cancer detection: An objective review of foundational studies and its proper role in breast cancer screening. Infrared Physics & Technology. 2019;97:244-57. doi: 10.1016/j.infrared.2018.12.017.
10
Yao X, Wei W, Li J, Wang L, Xu Z, Wan Y, Li K, Sun S. A comparison of mammography, ultrasonography, and far-infrared thermography with pathological results in screening and early diagnosis of breast cancer. Asian Biomedicine. 2014;8(1):11-9. doi: 10.5372/1905-7415.0801.257.
11
Han F, Liang CW, Shi GL, Wang L, Li KY. Clinical applications of internal heat source analysis for breast cancer identification. Gent Mol Res. 2015;14(1):1450-60.
12
Salhab M, Al Sarakbi W, Mokbel K. The evolving role of the dynamic thermal analysis in the early detection of breast cancer. Int Semin Surg Oncol. 2005;2(1):8. PubMed PMID: 15819982. PubMed PMCID: PMC1084358.
13
Lashkari A, Pak F, Firouzmand M. Full intelligent cancer classification of thermal breast images to assist physician in clinical diagnostic applications. Journal of Medical Signals and Sensors. 2016;6(1):12-24. PubMed PMID: 27014608. PubMed PMCID: PMC4786959.
14
Francis SV, Sasikala M, Saranya S. Detection of breast abnormality from thermograms using curvelet transform based feature extraction. J Med Syst. 2014;38(4):23. doi: 10.1007/s10916-014-0023-3. PubMed PMID: 24659445.
15
Mamahit DJ. Detection early breast cancer by using digital infrared image based on asymmetry thermal. Jurnal Teknik Elektro Dan Komputer. 2012;23:1-8.
16
Sheeja VF, Punitha N, Sasikala M. Cancer Detection in Rotational Breast Thermography Images using Bispectral Invariant. J Chem Pharm Sci. 2019;9(4):2189-94.
17
Paramkusham S, Rao KMM, Prabhakar Rao BVVSN., editor. Early stage detection of breast cancer using novel image processing techniques, Matlab and Labview implementation. 15th International Conference on Advanced Computing Technologies (ICACT); Rajampet, India: IEEE; 2013. p. 1-5.
18
Kubatka P, Ahlersová E, Ahlers I, Bojková B, Kalická K, Adámeková E, Marková M, Chamilová M, Ermáková M. Variability of mammary carcinogenesis induction in female Sprague-Dawley and Wistar:Han rats: the effect of season and age. Physiol Res. 2002;51(6):633-40. PubMed PMID: 12511189.
19
Poerbaningtyas E., editor. Visualization of the Breast Cancer through Raw Data of Temperature on Thermal Imaging (Rat Model Animals). The 2nd International Conference on Informatics for Development 2018. UIN Sunan Kalijaga Yogyakarta; 2018.
20
Poerbaningtyas E, et al. Thermal Image Analysis Using Wavelet Method and Statistics in Ann Structure on Breast Cancer Identification (Animal Model: Rat). Int J Adv Res. 2018;6(11):178-84. doi: 10.21474/IJAR01/7984.
21
Sham FC, Chen N, Long L. Surface crack detection by flash thermography on concrete surface. Insight-Non-Destructive Testing and Condition Monitoring. 2008;50(5):240-3. doi: 10.1784/insi.2008.50.5.240.
22
ORIGINAL_ARTICLE
Assessment of Motor Cortex in Active, Passive and Imagery Wrist Movement using Functional MRI
Background: Functional Magnetic resonance imaging (fMRI) measures the small fluctuation of blood flow happening during task-fMRI in brain regions. Objective: This research investigated these active, imagery and passive movements in volunteers design to permit a comparison of their capabilities in activating the brain areas.Material and Methods: In this applied research, the activity of the motor cortex during the right-wrist movement was evaluated in 10 normal volunteers under active, passive, and imagery conditions. T2* weighted, three-dimensional functional images were acquired using a BOLD sensitive gradient-echo EPI (echo planar imaging) sequence with echo time (TE) of 30 ms and repetition time (TR) of 2000 ms. The functional data, which included 248 volumes per subject and condition, were acquired using the blocked design paradigm. The images were analyzed by the SPM12 toolbox, MATLAB software. Results: The findings determined a significant increase in signal intensity of the motor cortex while performing the test compared to the rest time (p < 0.05). It was also observed that the active areas in hand representation of the motor cortex are different in terms of locations and the number of voxels in different wrist directions. Moreover, the findings showed that the position of active centers in the brain is different in active, passive, and imagery conditions. Conclusion: Results confirm that primary motor cortex neurons play an essential role in the processing of complex information and are designed to control the direction of movement. It seems that the findings of this study can be applied for rehabilitation studies.
https://jbpe.sums.ac.ir/article_46458_5b48143a5722f1f93261fcabab16e26c.pdf
2021-08-01
515
526
10.31661/jbpe.v0i0.1034
Functional MRI
Active Movement
Passive Movement
Imaginary Movement
Motor Cortex
Rehabilitation
Brain-Computer Interfaces
Wrist Movement
Hamid
Sharini
h-sharini@razi.tums.ac.ir
1
PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Kermanshah University of Medical Sciences (KUMS), Kermanshah, Iran
AUTHOR
Shokufeh
Zolghadriha
sh.zolghadriha@gmail.com
2
MSc, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
AUTHOR
Nader
Riyahi Alam
riahinad@sina.tums.ac.ir
3
PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
LEAD_AUTHOR
Maziar
Jalalvandi
mjalalvandi@ymail.com
4
MSc, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
AUTHOR
Hamid
Khabiri
h_khabiri@razi.tums.ac.ir
5
PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
AUTHOR
Hossein
Arabalibeik
arabalibeik@tums.ac.ir
6
PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
AUTHOR
Mohadeseh
Nadimi
7
MSc, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
AUTHOR
Johnson-Frey SH. Stimulation through simulation? Motor imagery and functional reorganization in hemiplegic stroke patients. Brain Cogn. 2004;55(2):328-31. doi: 10.1016/j.bandc.2004.02.032. PubMed PMID: 15177807.
1
Kaneko F, Murakami T, Onari K, Kurumadani H, Kawaguchi K. Decreased cortical excitability during motor imagery after disuse of an upper limb in humans. Clin Neurophysiol. 2003;114(12):2397-403. doi: 10.1016/s1388-2457(03)00245-1. PubMed PMID: 14652100.
2
Jeannerod, M. The representing brain: Neural correlates of motor intention and imagery. Behavioral and Brain Sciences. 1994;17(2):187-202. doi: 10.1017/S0140525X00034026.
3
Jeannerod M, Frak V. Mental imaging of motor activity in humans. Curr Opin Neurobiol. 1999;9(6):735-9. doi: 10.1016/s0959-4388(99)00038-0. PubMed PMID: 10607647.
4
Li M, Liu Y, Wu Y, Liu S, Jia J, Zhang L. Neurophysiological substrates of stroke patients with motor imagery-based Brain-Computer Interface training. Int J Neurosci. 2014;124(6):403-15. doi: 10.3109/00207454.2013.850082. PubMed PMID: 24079396.
5
Munzert J, Zentgraf K, Stark R, Vaitl D. Neural activation in cognitive motor processes: comparing motor imagery and observation of gymnastic movements. Exp Brain Res. 2008;188(3):437-44. doi: 10.1007/s00221-008-1376-y. PubMed PMID: 18425505.
6
Decety J, Jeannerod M. Mentally simulated movements in virtual reality: does Fitts’s law hold in motor imagery? Behav Brain Res. 1995;72(1-2):127-34. doi: 10.1016/0166-4328(96)00141-6. PubMed PMID: 8788865.
7
Kranczioch C, Mathews S, Dean PJ, Sterr A. On the equivalence of executed and imagined movements: evidence from lateralized motor and nonmotor potentials. Hum Brain Mapp. 2009;30(10):3275-86. doi: 10.1002/hbm.20748. PubMed PMID: 19253343.
8
Szameitat AJ, Shen S, Sterr A. Effector-dependent activity in the left dorsal premotor cortex in motor imagery. Eur J Neurosci. 2007;26(11):3303-8. doi: 10.1111/j.1460-9568.2007.05920.x. PubMed PMID: 18005067.
9
Szameitat AJ, Shen S, Sterr A. Motor imagery of complex everyday movements. An fMRI study. NeuroImage. 2007;34(2):702-13. doi:10.1016/j.neuroimage.2006.09.033.
10
Porro CA, Francescato MP, Cettolo V, Diamond ME, et al. Primary motor and sensory cortex activation during motor performance and motor imagery: a functional magnetic resonance imaging study. J Neurosci. 1996;16(23):7688-98. PubMed PMID: 8922425. PubMed PMCID: PMC6579073.
11
Lotze M, Halsband U. Motor imagery. J Physiol Paris. 2006;99(4-6):386-95. PubMed PMID: 16716573. doi: 10.1016/j.jphysparis.2006.03.012.
12
Braun SM, Beurskens AJ, Borm PJ, Schack T, Wade DT. The effects of mental practice in stroke rehabilitation: a systematic review. Arch Phys Med Rehabil. 2006;87(6):842-52. doi: 10.1016/j.apmr.2006.02.034. PubMed PMID: 16731221.
13
Cramer SC, Lastra L, Lacourse MG, Cohen MJ. Brain motor system function after chronic, complete spinal cord injury. Brain. 2005;128(12):2941-50. doi: 10.1093/brain/awh648. PubMed PMID: 16246866.
14
Cramer SC, Orr EL, Cohen MJ, Lacourse MG. Effects of motor imagery training after chronic, complete spinal cord injury. Experimental Brain Research. 2007;177(2):233-42. doi: 10.1007/s00221-006-0662-9.
15
Feltz DL, Landers DM. The effects of mental practice on motor skill learning 17.and performance: a meta-analysis. J Sport Psychol. 1983;5:25-57.
16
Szynkiewicz SH, et al. Motor Imagery Practice and Increased Tongue Strength: A Case Series Feasibility Report. Journal of Speech, Language, and Hearing Research. 2019;62(6):1676-84. doi: 10.1044/2019_JSLHR-S-18-0128.
17
Jackson PL, Lafleur MF, Malouin F, Richards C, Doyon J. Potential role of mental practice using motor imagery in neurologic rehabilitation. Archives of Physical Medicine and Rehabilitation. 2001;82(8):1133-41. doi: 10.1053/apmr.2001.24286.
18
Jackson PL, Lafleur MF, Malouin F, Richards CL, Doyon J. Functional cerebral reorganization following motor sequence learning through mental practice with motor imagery. Neuroimage. 2003;20(2):1171-80. doi: 10.1016/S1053-8119(03)00369-0.
19
Munzert J, Lorey B, Zentgraf K. Cognitive motor processes: the role of motor imagery in the study of motor representations. Brain Research Reviews. 2009;60(2):306-26. doi: 10.1016/j.brainresrev.2008.12.024.
20
Dechaumont-Palacin S, Marque P, De Boissezon X, et al. Neural correlates of proprioceptive integration in the contralesional hemisphere of very impaired patients shortly after a subcortical stroke: an FMRI study. Neurorehabilitation and Neural Repair. 2008;22(2):154-65. doi: 10.1177/1545968307307118.
21
Lemon RN. Neural control of dexterity: what has been achieved? Experimental Brain Research. 1999;128(1-2):6-12. doi: 10.1007/s002210050811.
22
Lemon RN, Porter R. Afferent input to movement-related precentral neurones in conscious monkeys. Proceedings of the Royal Society of London. Proc R Soc Lond B Biol Sci. 1976;194(1116):313-39. PubMed PMID: 11491.
23
Naito E, Roland PE, Ehrsson HH. I feel my hand moving: a new role of the primary motor cortex in somatic perception of limb movement. Neuron. 2002;36(5):979-88. doi: 10.1016/S0896-6273(02)00980-7.
24
Terumitsu M, Ikeda K, Kwee IL, Nakada T. Participation of primary motor cortex area 4a in complex sensory processing: 3.0-T fMRI study. Neuroreport. 2009;20(7):679-83. doi: 10.1097/WNR.0b013e32832a1820.
25
Mima T, Sadato N, Yazawa S, Hanakawa T, Fukuyama H, Yonekura Y, Shibasaki H. Brain structures related to active and passive finger movements in man. Brain. 1999;122(10):1989-97. doi: 10.1093/brain/122.10.1989.
26
Krakauer JW. Motor learning: its relevance to stroke recovery and neurorehabilitation. Current Opinion in Neurology. 2006;19(1):84-90. doi: 10.1097/01.wco.0000200544.29915.cc
27
Schmidt RA, Lee TD, Winstein C, Wulf G, Zelaznik HN. Motor control and learning: A behavioral emphasis. Human kinetics; 2018.
28
Stoykov ME, Corcos DM, Madhavan S. Movement-based priming: clinical applications and neural mechanisms. Journal of Motor Behavior. 2017;49(1):88-97. doi: 10.1080/00222895.2016.1250716.
29
Lewis GN, Byblow WD. The effects of repetitive proprioceptive stimulation on corticomotor representation in intact and hemiplegic individuals. Clinical Neurophysiology. 2004;115(4):765-73. doi: 10.1016/j.clinph.2003.11.014.
30
Rizzolatti G, Sinigaglia C. The functional role of the parieto-frontal mirror circuit: interpretations and misinterpretations. Nature Reviews Neuroscience. 2010;11(4):264-74. doi: 10.1038/nrn2805.
31
Roosink M, Zijdewind I. Corticospinal excitability during observation and imagery of simple and complex hand tasks: implications for motor rehabilitation. Behavioural Brain Research. 2010;213(1):35-41. doi: 10.1016/j.bbr.2010.04.027.
32
Jalalvandi M, Riahi Alam N, Sharini H, Hashemi H, Kohzad S. Optical Imaging of the Motor Cortex in the Brain in Order to Determine the Direction of the Hand Movements Using Functional Near-Infrared Spectroscopy (fNIRS). Iranian Journal of Medical Physics. 2018;15(Special Issue-12th. Iranian Congress of Medical Physics):152. doi: 10.22038/IJMP.2018.12653.
33
Celnik P, Webster B, Glasser DM, Cohen LG. Effects of action observation on physical training after stroke. Stroke. 2008;39(6):1814-20. doi: 10.1161/STROKEAHA.107.508184.
34
Clark S, Tremblay F, Ste-Marie D. Differential modulation of corticospinal excitability during observation, mental imagery and imitation of hand actions. Neuropsychologia. 2004;42(1):105-12. doi: 10.1016/S0028-3932(03)00144-1.
35
Filimon F, Nelson JD, Hagler DJ, Sereno MI. Human cortical representations for reaching: mirror neurons for execution, observation, and imagery. Neuroimage. 2007;37(4):1315-28. doi: 10.1016/j.neuroimage.2007.06.008.
36
Iseki K, Hanakawa T, Shinozaki J, Nankaku M, Fukuyama H. Neural mechanisms involved in mental imagery and observation of gait. Neuroimage. 2008;41(3):1021-31. doi: 10.1016/j.neuroimage.2008.03.010.
37
Wang C, Wai Y, Weng Y, Yu J, Wang J. The cortical modulation from the external cues during gait observation and imagination. Neuroscience Letters. 2008;443(3):232-5. doi: 10.1016/j.neulet.2008.07.084.
38
Jalalvandi M, Sharini H, Naderi Y, Alam NR. Assessment of Brain Cortical Activation in Passive Movement during Wrist Task Using Functional Near Infrared Spectroscopy (fNIRS). Frontiers in Biomedical Technologies. 2019;6(2):99-105. doi: 10.18502/fbt.v6i2.1691.
39
Piefke M, Kramer K, Korte M, Schulte-Rüther M, Korte JM, Wohlschläger AM, Weber J, Shah NJ, Huber W, Fink GR. Neurofunctional modulation of brain regions by distinct forms of motor cognition and movement features. Human Brain Mapp. 2009;30(2):432-51. doi: 10.1002/hbm.20514.
40
Beckmann CF, Jenkinson M, Smith SM. General multilevel linear modeling for group analysis in FMRI. Neuroimage. 2003;20(2):1052-63. doi: 10.1016/S1053-8119(03)00435-X.
41
Lancaster JL, Woldorff MG, Parsons LM, Liotti M, et al. Automated Talairach atlas labels for functional brain mapping. Human Brain Mapp. 2000;10(3):120-31. doi: 10.1002/1097-0193(200007)10. PubMed PMID: 10912591.
42
Brett M, Christoff K, Cusack R, Lancaster J. Using the Talairach atlas with the MNI template. Neuroimage. 2001;13(6):85. doi: 10.1016/S1053-8119(01)91428-4.
43
Georgopoulos AP, Kalaska JF, Caminiti R, Massey JT. On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. Journal of Neuroscience. 1982;2(11):1527-37. doi: 10.1523/JNEUROSCI.02-11-01527.1982.
44
Kakei S, Hoffman DS, Strick PL. Muscle and movement representations in the primary motor cortex. Science. 1999;285(5436):2136-9. doi: 10.1126/science.285.5436.2136.
45
Caminiti R, Johnson PB, Galli C, Ferraina S, Burnod Y. Making arm movements within different parts of space: the premotor and motor cortical representation of a coordinate system for reaching to visual targets. Journal of Neuroscience. 1991;11(5):1182-97. doi: 10.1523/JNEUROSCI.11-05-01182.1991.
46
Kakei S, Hoffman DS, Strick PL. Direction of action is represented in the ventral premotor cortex. Nature Neuroscience. 2001;4(10):1020-5. doi: 10.1038/nn726.
47
Oghabian MA, Khosravi HR, Ghiasinejad M, Riahi Alam N. Functional Magnetic Resonance Imaging of Motor Cortex Stimulation Induced by Right Thumb Movement Using 1.5 Tesla Routine Mri System. Jundishapur Scientific Medical Journal. 2003:9-17.
48
Fortier PA, Kalaska JF, Smith AM. Cerebellar neuronal activity related to whole-arm reaching movements in the monkey. Journal of Neurophysiology. 1989;62(1):198-211. doi: 10.1152/jn.1989.62.1.198.
49
Cowper-Smith CD, Lau EY, Helmick CA, Eskes GA, Westwood DA. Neural coding of movement direction in the healthy human brain. PloS One. 2010;5(10):e13330. doi: 10.1371/journal.pone.0013330.
50
Eisenberg M, Shmuelof L, Vaadia E, Zohary E. Functional organization of human motor cortex: directional selectivity for movement. Journal of Neuroscience. 2010;30(26):8897-905. doi: 10.1523/JNEUROSCI.0007-10.2010.
51
ORIGINAL_ARTICLE
Liver Segmentation in MRI Images using an Adaptive Water Flow Model
Background: Identification and precise localization of the liver surface and its segments are essential for any surgical treatment. An algorithm of accurate liver segmentation simplifies the treatment planning for different types of liver diseases. Although liver segmentation turns researcher’s attention, it still has some challenging problems in computer-aided diagnosis. Objective: This study aimed to extract the potential liver regions by an adaptive water flow model and perform the final segmentation by the classification algorithm.Material and Methods: In this experimental study, an automatic liver segmentation algorithm was introduced. The proposed method designed the image by a transfer function based on the probability distribution function of the liver pixels to enhance the liver area. The enhanced image is then segmented using an adaptive water flow model in which the rainfall process is controlled by the liver location in the training images and the gray levels of pixels. The candidate liver segments are classified by a Multi-Layer Perception (MLP) neural network considering some texture, area, and gray level features. Results: The proposed algorithm efficiently distinguishes the liver region from its surrounding organs, resulting in perfect liver segmentation over 250 Magnetic Resonance Imaging (MRI) test images. The accuracy of 97% was obtained by quantitative evaluation over test images, which revealed the superiority of the proposed algorithm compared to some evaluated algorithms. Conclusion: Liver segmentation using an adaptive water flow algorithm and classifying the segmented area in MRI images yields more robust and reliable results in comparison with the classification of pixels.
https://jbpe.sums.ac.ir/article_47704_112b1bf7e24625ca7174aaeec4277132.pdf
2021-08-01
527
534
10.31661/jbpe.v0i0.2103-1293
Image Enhancement
MRI Scans
Artificial Intelligence
Image Processing
Computer-Assisted
Marjan
Heidari
mmarjan_heidari@yahoo.com
1
PhD Candidate, Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
AUTHOR
Mehdi
Taghizadeh
m.taghizadeh@kau.ac.ir
2
PhD, Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
LEAD_AUTHOR
Hassan
Masoumi
hasan.masoumi62@gmail.com
3
PhD, Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
AUTHOR
Morteza
Valizadeh
mo.valizadeh@urmia.ac.ir
4
PhD, Department of Electrical and Computer Engineering, Urmia University, Urmia, Iran
AUTHOR
Bereciartua A, Picon A, Galdran A, Iriondo P. 3D active surfaces for liver segmentation in multisequence MRI images. Comput Methods Programs Biomed. 2016;132:149-60. doi: 10.1016/j.cmpb.2016.04.028. PubMed PMID: 27282235.
1
Massoptier L, Casciaro S. Fully automatic liver segmentation through graph-cut technique. Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:5243-6, doi: 10.1109/IEMBS.2007.4353524. PubMed PMID: 18003190.
2
Prasantha HS, Shashidhara HL, Murthy KN, Madhavi LG. Medical image segmentation. International Journal on Computer Science and Engineering. 2010;2(4):1209-18.
3
Lebre MA, Vacavant A, Grand-Brochier M, Rositi H, Strand R, et al. A robust multi-variability model based liver segmentation algorithm for CT-scan and MRI modalities. Comput Med Imaging Graph. 2019;76:101635. doi: 10.1016/j.compmedimag.2019.05.003. PubMed PMID: 31301489.
4
Sojar V, Stanisavljević D, Hribernik M, Glušič M, Kreuh D, Velkavrh U, Fius T. Liver surgery training and planning in 3D virtual space. International Congress Series. 2004;1268:390-4.
5
López-Mir F, Naranjo V, Angulo J, Alcañiz M, Luna L. Liver segmentation in MRI: A fully automatic method based on stochastic partitions. Comput Methods Programs Biomed. 2014;114(1):11-28. doi: 10.1016/j.cmpb.2013.12.022. PubMed PMID: 24529637.
6
Gloger O, Kühn J, Stanski A, Völzke H, Puls R. A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MR images. Magn Reson Imaging. 2010;28(6):882-97. doi: 10.1016/j.mri.2010.03.010. PubMed PMID: 20409666.
7
Liu H, Tang P, Guo D, Liu H, Zheng Y, Dan G. Liver MRI segmentation with edge-preserved intensity inhomogeneity correction. Signal, Image and Video Processing. 2018;12(4):791-8. doi: 10.1007/s11760-017-1221-5.
8
Said S, Mostafa A, Houssein EH, Hassanien AE, Hefny H. Moth-flame optimization based segmentation for MRI liver images. International Conference on Advanced Intelligent Systems and Informatics. Cham: Springer; 2017. p. 320-30.
9
Mostafa A, Hassanien AE, Houseni M, Hefny H. Liver segmentation in MRI images based on whale optimization algorithm. Multimedia Tools and Applications. 2017;76(23):24931-54. doi: 10.1007/s11042-017-4638-5.
10
Huynh HT, Karademir I, Oto A, Suzuki K. Liver volumetry in MRI by using fast marching algorithm coupled with 3D geodesic active contour segmentation. Computational Intelligence in Biomedical; New York, NY: Springer; 2014. p. 141-57. doi: 10.1007/978-1-4614-7245-2_6.
11
Masoumi H, Behrad A, Pourmina MA, Roosta A. Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network. Biomedical Signal Processing and Control. 2012;7(5):429-37. doi: 10.1016/j.bspc.2012.01.002.
12
Yuan Z, Wang Y, Yang J, Liu Y. A novel automatic liver segmentation technique for MR images. 3rd International Congress on Image and Signal Processing; Yantai, China: IEEE; 2010. p. 1282-86. doi: 10.1109/CISP.2010.5647676.
13
Gloger O, Toennies K, Kuehn JP. Fully automatic liver volumetry using 3D level set segmentation for differentiated liver tissue types in multiple contrast MR datasets. Scandinavian Conference on Image Analysis; Berlin: Springer; 2011. p. 512-23. doi: 10.1007/978-3-642-21227-7_48.
14
Platero C, Gonzalez M, Tobar MC, Poncela JM, Sanguino J, Asensio G, Santas E. Automatic method to segment the liver on multi-phase MRI. Computer Assisted Radiology and Surgery (CARS) 22nd International Congress and Exhibition; Barcelona, España: Matemática Aplicada; 2008.
15
Takenaga T, Hanaoka S, Nomura Y, Nemoto M, Murata M, Nakao T, et al. Four-dimensional fully convolutional residual network-based liver segmentation in Gd-EOB-DTPA-enhanced MRI. Int J Comput Assist Radiol Surg. 2019;14(8):1259-66. doi: 10.1007/s11548-019-01935-z. PubMed PMID: 30929130.
16
Kim IK, Jung DW, Park RH. Document image binarization based on topographic analysis using a water flow model. Pattern Recognition. 2002;35(1):265-77. doi: 10.1016/S0031-3203(01)00027-9.
17
Oh HH, Lim KT, Chien SI. An improved binarization algorithm based on a water flow model for document image with inhomogeneous backgrounds. Pattern Recognition. 2005;38(12):2612-25. doi: 10.1016/j.patcog.2004.11.025.
18
Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics. 1979;9(1):62-6. doi: 10.1109/TSMC.1979.4310076.
19
Haralick RM, Shanmugam K, Dinstein IH. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics. 1973;SMC-3(6):610-21. doi: 10.1109/TSMC.1973.4309314.
20
Hagan MT, Demuth HB, Beale MH. Neural Network Design. Boston: PWS Publishing Company; 1995.
21
ORIGINAL_ARTICLE
Reconstructed State Space Features for Classification of ECG Signals
Background: Cardiac arrhythmias are considered as one of the most serious health conditions; therefore, accurate and quick diagnosis of these conditions is highly paramount for the electrocardiogram (ECG) signals. Moreover, are rather difficult for the cardiologists to diagnose with unaided eyes due to a close similarity of these signals in the time domain. Objective: In this paper, an image-based and machine learning method were presented in order to investigate the differences between the three cardiac arrhythmias of VF, VT, SVT and the normal signal.Material and Methods: In this simulation study, the ECG data used are collected from 3 databases, including Boston Beth University Arrhythmias Center, Creighton University, and MIT-BIH. The proposed algorithm was implemented using MATLAB R2015a software and its simulation. At first, the signal is transmitted to the state space using an optimal time delay. Then, the optimal delay values are obtained using the particle swarm optimization algorithm and normalized mutual information criterion. Furthermore, the result is considered as a binary image. Then, 19 features are extracted from the image and the results are presented in the multilayer perceptron neural network for the purpose of training and testing. Results: In order to classify N-VF, VT-SVT, N-SVT, VF-VT, VT-N-VF, N-SVT-VF, VT-VF-SVT and VT-VF-SVT-N in the conducted experiments, the accuracy rates were determined at 99.5%, 100%, 94.98%, 100%,100%, 100%, 99.5%, 96.5% and 95%, respectively. Conclusion: In this paper, a new approach was developed to classify the abnormal signals obtained from an ECG such as VT, VF, and SVT compared to a normal signal. Compared to Other related studies, our proposed system significantly performed better.
https://jbpe.sums.ac.ir/article_46650_2224acac4f4764f1ff26540c9362351a.pdf
2021-08-01
535
550
10.31661/jbpe.v0i0.1112
Ventricular Fibrillation
Tachycardia
Ventricular
Neural networks
Computer
Soheil
Pashoutan
soheil.pashootan@gmail.com
1
MSc, Department of Electrical, Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
Shahriar
Baradaran Shokouhi
bshokouhi@iust.ac.ir
2
PhD, Department of Electrical, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Theodoridis S, Koutroumbas K. Pattern recognition 2003. Elsevier; 2009.
1
Riasi A, Mohebbi M, editors. Prediction of ventricular tachycardia using morphological features of ECG signal. The International Symposium on Artificial Intelligence and Signal Processing (AISP); IEEE; 2015. p. 170-5. doi: 10.1109/aisp.2015.7123515.
2
Kelly BB, Fuster V. Promoting cardiovascular health in the developing world: a critical challenge to achieve global health. Washington: National Academies Press; 2010.
3
Dantas AP, Jimenez-Altayo F, Vila E. Vascular aging: facts and factors. Front Physiol. 2012;3:325. doi: 10.3389/fphys.2012.00325. PubMed PMID: 22934073. PubMed PMCID: PMC3429093.
4
Goldschlager N, Goldman M. Effects of drugs and electrolytes on the electrocardiogram. Principles of Clinical Electrocardiography Appleton and Lange, East Norwalk. 1989:256-71.
5
Kolettis TM. Coronary artery disease and ventricular tachyarrhythmia: pathophysiology and treatment. Curr Opin Pharmacol. 2013;13:210-7. doi: 10.1016/j.coph.2013.01.001. PubMed PMID: 23357129.
6
Joo S, Huh S-J, Choi K-J, editors. A predictor for ventricular tachycardia based on heart rate variability analysis. IEEE Biomedical Circuits and Systems Conference (BioCAS); San Diego, CA, USA: IEEE; 2011. p. 409-411. doi: 10.1109/BioCAS.2011.6107814.
7
Li Q, Rajagopalan C, Clifford GD. Ventricular fibrillation and tachycardia classification using a machine learning approach. IEEE Trans Biomed Eng. 2014;61:1607-13. doi: 10.1109/TBME.2013.2275000. PubMed PMID: 23899591.
8
Li Y, Pang Y, Wang J, Li X. Patient-specific ECG classification by deeper CNN from generic to dedicated. Neurocomputing. 2018;314:336-46. doi: 10.1016/j.neucom.2018.06.068.
9
Thakor NV, Zhu YS, Pan KY. Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm. IEEE Trans Biomed Eng. 1990;37:837-43. doi: 10.1109/10.58594. PubMed PMID: 2227970.
10
Chen S, Thakor NV, Mower MM. Ventricular fibrillation detection by a regression test on the autocorrelation function. Med Biol Eng Comput. 1987;25:241-9. doi: 10.1007/bf02447420. PubMed PMID: 3329694.
11
Zhang XS, Zhu YS, Thakor NV, Wang ZZ. Detecting ventricular tachycardia and fibrillation by complexity measure. IEEE Trans Biomed Eng. 1999;46:548-55. doi: 10.1109/10.759055. PubMed PMID: 10230133.
12
Barro S, Ruiz R, Cabello D, Mira J. Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmias and imitative artefacts: a diagnostic system. J Biomed Eng. 1989;11:320-8. doi: 10.1016/0141-5425(89)90067-8. PubMed PMID: 2755113.
13
Addison PS, Watson JN, Clegg GR, Holzer M, Sterz F, Robertson CE. Evaluating arrhythmias in ECG signals using wavelet transforms. IEEE Eng Med Biol Mag. 2000;19:104-9. doi: 10.1109/51.870237. PubMed PMID: 11016036.
14
Alonso-Atienza F, Morgado E, Fernandez-Martinez L, Garcia-Alberola A, Rojo-Alvarez JL. Detection of life-threatening arrhythmias using feature selection and support vector machines. IEEE Trans Biomed Eng. 2014;61:832-40. doi: 10.1109/TBME.2013.2290800. PubMed PMID: 24239968.
15
Zhou SH, Rautaharju PM, Calhoun HP, editors. Selection of a reduced set of parameters for classification of ventricular conduction defects by cluster analysis. Proceedings of Computers in Cardiology Conference; London, UK: IEEE; 1993. p. 879-82. doi: 10.1109/CIC.1993.378298.
16
Afonso VX, Tompkins WJ. Detecting ventricular fibrillation. IEEE Eng Med Biol Mag. 1995;14:152-9. doi: 10.1109/51.376752 .
17
Ham FM, Han S. Classification of cardiac arrhythmias using fuzzy ARTMAP. IEEE Trans Biomed Eng. 1996;43:425-30. doi: 10.1109/10.486263. PubMed PMID: 8626192.
18
Finelli CJ. The time-sequenced adaptive filter for analysis of cardiac arrhythmias in intraventricular electrograms. IEEE Trans Biomed Eng. 1996;43:811-9. doi: 10.1109/10.508543. PubMed PMID: 9216153.
19
Golrizkhatami Z, Acan A. ECG classification using three-level fusion of different feature descriptors. Expert Systems with Applications. 2018;114:54-64. doi: 10.1016/j.eswa.2018.07.030.
20
Dong X, Wang C, Si W. ECG beat classification via deterministic learning. Neurocomputing. 2017;240:1-12. doi: 10.1016/j.neucom.2017.02.056.
21
Amann A, Tratnig R, Unterkofler K. Detecting ventricular fibrillation by time-delay methods. IEEE Trans Biomed Eng. 2007;54:174-7. doi: 10.1109/TBME.2006.880909. PubMed PMID: 17260872.
22
Sarvestani RR, Boostani R, Roopaei M. VT and VF classification using trajectory analysis. Nonlinear Analysis: Theory, Methods & Applications. 2009;71:e55-e61. doi: 10.1016/j.na.2008.10.015.
23
Lloyd MA, Murphy JG. Mayo Clinic Cardiology: Board Review Questions and Answers. CRC Press; 2007. doi: 10.1201/b14443.
24
Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000;101(23):E215-20. PubMed PMID: 10851218.
25
Cao L. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena. 1997;110:43-50. doi: 10.1016/s0167-2789(97)00118-8.
26
Rafal R, Pawel L, Krzysztof K, Bogdan K, Jerzy W. Chatter identification methods on the basis of time series measured during titanium superalloy milling. International Journal of Mechanical Sciences. 2015;99:196-207. doi: 10.1016/j.ijmecsci.2015.05.013.
27
Rusinek R, Weremczuk A, Kecik K, Warminski J. Dynamics of a time delayed Duffing oscillator. International Journal of Non-Linear Mechanics. 2014;65:98-106.
28
Roopaei M, Boostani R, Sarvestani RR, Taghavi MA, Azimifar Z. Chaotic based reconstructed phase space features for detecting ventricular fibrillation. Biomedical Signal Processing and Control. 2010;5:318-27. doi: 10.1016/j.bspc.2010.05.003.
29
Khan A, Rehman S, Abbas M, Ahmad A. On the mutual information of relaying protocols. Physical Communication. 2018;30:33-42. doi: 10.1016/j.phycom.2018.07.005.
30
Ayatollahi F, Shokouhi SB, Ayatollahi A. A new hybrid particle swarm optimization for multimodal brain image registration. Journal of Biomedical Science and Engineering. 2012;5:153. doi: 10.4236/jbise.2012.54020.
31
Wachowiak MP, Smolíková R, Zheng Y, Zurada JM, Elmaghraby AS. An approach to multimodal biomedical image registration utilizing particle swarm optimization. IEEE Transactions on Evolutionary Computation. 2004;8:289-301. doi: 10.1109/tevc.2004.826068.
32
Öztürk S, Akdemir B. Application of feature extraction and classification methods for histopathological image using GLCM, LBP, LBGLCM, GLRLM and SFTA. Proc Comput Sci. 2018;132:40-6. doi: 10.1016/j.procs.2018.05.057.
33
Tatar A, Naseri S, Bahadori M, Hezave AZ, Kashiwao T, Bahadori A, et al. Prediction of carbon dioxide solubility in ionic liquids using MLP and radial basis function (RBF) neural networks. Journal of the Taiwan Institute of Chemical Engineers. 2016;60:151-64. doi: 10.1016/j.jtice.2015.11.002.
34
Ruiz J, Aramendi E, De Gauna SR, Lazkano A, Leturiondo L, Gutierrez J, editors. Distinction of ventricular fibrillation and ventricular tachycardia using cross correlation. Computers in Cardiology; Greece: IEEE; 2003. doi: 10.1109/CIC.2003.1291259.
35
Li X, Dong Z. Detection and prediction of the onset of human ventricular fibrillation: an approach based on complex network theory. Physical Review E. 2011;84(6):062901. doi: 10.1103/PhysRevE.84.062901. PubMed PMID: 22304137.
36
ORIGINAL_ARTICLE
The Design and Evaluation of a Mobile based Application to Facilitate Self-care for Pregnant Women with Preeclampsia during COVID-19 Prevalence
Preeclampsia is one of the most common complications of pregnancy that is very difficult to control and manage during the outbreak of COVID-19. One way to control and manage this disease is to use self-care applications. Therefore, the aim of this study was to design and develop a mobile-based application to facilitate self-care for women, who suffer from pregnancy poisoning in the COVID-19 pandemic. This study was conducted in two stages: In the first stage, according to the opinion of 20 obstetricians and pregnant women, a needs assessment was performed. In the second stage, based on the identified needs, the application prototype was designed and then evaluated. For evaluation, 20 pregnant women were asked to use the application for 10 days. QUIS questionnaire version 5.5 was used for evaluation. Descriptive statistics and mann-whitney test in SPSS software version 23 were used for data analysis. Out of the 66 information needs that were identified via the questionnaire, 58 were considered in designing the application. Features of the designed application were placed in 5 categories: User’s profile, lifestyle, disease prevention and control, application capabilities and user’s satisfaction. The capabilities of the application consist of introducing specialized COVID-19 medical centers, search for the location of medical centers and doctors’ offices, drug management, drug allergies, self-assessment, stress reduction and control, nutrition and diet management, sleep management, doctor’s appointment reminders, communication with other patients and physicians, application settings. Pregnant women rated the usability of the application at a good level. The designed application can reduce the anxiety and stress due to preeclampsia feel and also improve their knowledge as well as attitude towards the COVID-19 pandemic and preeclampsia.
https://jbpe.sums.ac.ir/article_47705_635f51dbe5bedc5ac5cfc2f3eba2dbd6.pdf
2021-08-01
551
560
10.31661/jbpe.v0i0.2103-1294
Pregnancy
COVID-19
Pre-eclampsia
Mobile Applications
Self-care
Khadijeh
Moulaei
moulaei.kh91@gmail.com
1
PhD Candidate, Student Research Committee, Kerman University of Medical Sciences, Kerman, Iran
AUTHOR
Kambiz
Bahaadinbeigy
kambizb321@gmail.com
2
MD, PhD, Medical Informatics Research Centre, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
AUTHOR
Zahra
Ghaffaripour
z.ghaffaripour2019@gmail.com
3
MSc student, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
AUTHOR
Mohammad Mehdi
Ghaemi
dr.mghaemi@gmail.com
4
PhD, Department of Health Information Management, Kerman University of Medical Sciences, Kerman, Iran
LEAD_AUTHOR
Rana S, Lemoine E, Granger JP, et al. Preeclampsia: Pathophysiology, Challenges, and Perspectives. Circ Res. 2019;124(7):1094-112. doi: 10.1161/CIRCRESAHA.118.313276. PubMed PMID: 30920918.
1
Bokslag A, Van Weissenbruch M, Mol BW, et al. Preeclampsia; short and long-term consequences for mother and neonate. Early Hum Dev. 2016;102:47-50. doi: 10.1016/j.earlhumdev.2016.09.007. PubMed PMID: 27659865.
2
Azarkish F, Sheikhi F, Mirkazehi Z, et al. Preeclampsia and the crucial postpartum period for Covid-19 infected mothers: A case report. Pregnancy Hypertens. 2021;23:136-9. doi: 10.1016/j.preghy.2020.10.012. PubMed PMID: 33388729. PubMed PMCID: PMC7604163.
3
Mehraeen E, Hayati B, Saeidi S, et al. Self-care instructions for people not requiring hospitalization for coronavirus disease 2019 (COVID-19). Arch Clin Infect Dis. 2020;15(COVID-19):e102978. doi: 10.5812/archcid.102978.
4
McIntyre P, et al. Pregnant adolescents: delivering on global promises of hope. World Health Organization; 2006. p. 2-28.
5
Waris Nawaz M, Imtiaz S, Kausar E. Self-care of Frontline Health Care Workers: During COVID-19 Pandemic. Psychiatr Danub. 2020;32(3-4):557-62. doi: 10.24869/psyd.2020.557. PubMed PMID: 33370766.
6
Riegel B, Jaarsma T, Lee CS, et al. Integrating Symptoms In to the Middle-Range Theory of Self-Care of Chronic Illness. ANS Adv Nurs Sci. 2019;42(3):206-15. doi: 10.1097/ANS.0000000000000237. PubMed PMID: 30475237. PubMed PMCID: PMCID: PMC6686959.
7
Chan KL, Chen M. Effects of Social Media and Mobile Health Apps on Pregnancy Care: Meta-Analysis. JMIR Mhealth Uhealth. 2019;7(1):e11836. doi: 10.2196/11836. PubMed PMID: 30698533. PubMed PMCID: PMC6372934.
8
Lee Y, Moon M. Utilization and Content Evaluation of Mobile Applications for Pregnancy, Birth, and Child Care. Healthc Inform Res. 2016;22(2):73-80. doi: 10.4258/hir.2016.22.2.73. PubMed PMID: 27200216. PubMed PMCID: PMC4871848.
9
Turner-McGrievy GM, Beets MW, Moore JB, et al. Comparison of traditional versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an mHealth weight loss program. J Am Med Inform Assoc. 2013;20(3):513-8. doi: 10.1136/amiajnl-2012-001510. PubMed PMID: 23429637. PubMed PMCID: PMC3628067.
10
Moradi F, Ghadiri-Anari A, Enjezab B. COVID-19 and self-care strategies for women with gestational diabetes mellitus. Diabetes Metab Syndr. 2020;14(5):1535-9. doi: 10.1016/j.dsx.2020.08.004. PubMed PMID: 32947751. PubMed PMCID: PMC7837010.
11
Yang Z, Wang M, Zhu Z, et al. Coronavirus disease 2019 (COVID-19) and pregnancy: a systematic review. J Matern Fetal Neonatal Med. 2020:1-4. doi: 10.1080/14767058.2020.1759541. PubMed PMID: 32354293.
12
Chen H, Guo J, Wang C, et al. Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: a retrospective review of medical records. Lancet. 2020;395(10226):809-15. doi: 10.1016/S0140-6736(20)30360-3. PubMed PMID: 32151335. PubMed PMCID: PMC7159281.
13
Kazemi-Arpanahi H, Moulaei K, Shanbehzadeh M. Design and development of a web-based registry for Coronavirus (COVID-19) disease. Med J Islam Repub Iran. 2020;34:68. doi: 10.34171/mjiri.34.68. PubMed PMID: 32974234. PubMed PMCID: PMC7500427.
14
Liang H, Acharya G. Novel corona virus disease (COVID-19) in pregnancy: What clinical recommendations to follow? Acta Obstet Gynecol Scand. 2020;99(4):439-42. doi: 10.1111/aogs.13836. PubMed PMID: 32141062.
15
Panahi L, Amiri M, Pouy S. Risks of Novel Coronavirus Disease (COVID-19) in Pregnancy; a Narrative Review. Arch Acad Emerg Med. 2020;8(1):e34. PubMed PMID: 32232217. PubMed PMCID: PMC7092922.
16
Coronado-Arroyo JC, Concepción-Zavaleta MJ, Zavaleta-Gutiérrez FE, et al. Is COVID-19 a risk factor for severe preeclampsia? Hospital experience in a developing country. Eur J Obstet Gynecol Reprod Biol. 2021;256:502-3. doi: 10.1016/j.ejogrb.2020.09.020. PubMed PMID: 32958322. PubMed PMCID: PMC7489262.
17
Rolnik DL. Can COVID-19 in pregnancy cause pre-eclampsia? BJOG. 2020;127(11):1381. doi: 10.1111/1471-0528.16369. PubMed PMID: 32570284. PubMed PMCID: PMC7361765.
18
World Health Organization. Self care during COVID-19. WHO; 2020 [cited 2020 June 16]. Available from: https://www.who.int/news-room/photo-story/photo-story-detail/self-care-during-covid-19.
19
Centers for Disease Control and Prevention. Coping with Stress. CDC; 2020 [cited 2020 June 16]. Available from: https://www.cdc.gov/coronavirus/2019-ncov/daily-life-coping/managing-stress-anxiety.html.
20
International OCD Foundation. Obsessive Compulsive Disorder. OCD: 2020 [cited 2020 July 10]. Available from: https://iocdf.org/covid19/self-care-during-covid-19.
21
Chin JP, Diehl VA, Norman KL. Development of an instrument measuring user satisfaction of the human-computer interface. Proceedings of the SIGCHI conference on Human factors in computing systems; USA: Association for Computing Machinery; 1988. p. 213-8. doi: 10.1145/57167.57203.
22
Raghunath N, Dahmen J, Brown K, Cook D, Schmitter-Edgecombe M. Creating a digital memory notebook application for individuals with mild cognitive impairment to support everyday functioning. Disabil Rehabil Assist Technol. 2020;15(4):421-431. doi: 10.1080/17483107.2019.1587017. PubMed PMID: 30907223. PubMed PMCID: PMC7314313.
23
Saeidnia H, Mohammadzadeh Z, Saeidnia M, et al. Identifying Requirements of a Self-care System on Smartphones for Preventing Coronavirus Diseas. Iran J Med Microbiol. 2020;14(3):241-51. doi: 10.30699/ijmm.14.3.241.
24
Chaudhry BM, Faust L, Chawla NV. From Design to Development to Evaluation of a Pregnancy App for Low-Income Women in a Community-Based Setting. Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services; USA: Association for Computing Machinery; 2019. p. 1-11. doi: 10.1145/3338286.3340118.
25
Sajjad UU, Shahid S. Baby+ a mobile application to support pregnant women in Pakistan. Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct; USA: Association for Computing Machinery; 2016. p. 667-74. doi: 10.1145/2957265.2961856.
26
Tripp N, Hainey K, Liu A, Poulton A, Peek M, Kim J, Nanan R. An emerging model of maternity care: smartphone, midwife, doctor? Women Birth. 2014;27(1):64-7. doi: 10.1016/j.wombi.2013.11.001. PubMed PMID: 24295598.
27
Rezaeean SM, Abedian Z, Latifnejad-Roudsari R, Mazloum SR, Abbasi Z. The effect of prenatal self-care based on orem’s theory on preterm birth occurrence in women at risk for preterm birth. Iran J Nurs Midwifery Res. 2020;25(3):242. doi: 10.4103/ijnmr.IJNMR_207_19. PubMed PMID: 32724771. PubMed PMCID: PMC7299423.
28