Document Type : Original Research
Authors
1 Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
2 Department of Radiology, Faculty of Alliance Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
3 Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
4 Department of Radiology and Radiotherapy, School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
5 Medical Radiation Sciences Research Group, Tabriz University of Medical Sciences, Tabriz, Iran
Abstract
Background: Radiomics with single Region of Interest (ROI) and single-sequence Magnetic Resonance Imaging (MRI) may facilitate the segmentation reproducibility and radiomics workflow due to a time-consuming and complicated delineation of that in multi-sequence MRI images.
Objective: This study aimed to evaluate the performance of the radiomics approach in grading glioma based on a single-ROI delineation as Gross Tumor Volume (GTV) in a single – sequence as contrast-enhanced T1-weighted MRI.
Material and Methods: This retrospective study was conducted on contrast-enhanced T1 weighted (CE T1W) MRI images of 60 grade II and 60 grade III glioma patients. The GTV regions were manually delineated. Radiomics features were extracted per patient. The segmentation reproducibility of the robust features was evaluated in several repetitions of GTV delineation. Finally, a linear Support Vector Machine (SVM) assessed the classification performance of the robust features.
Results: Four significant robust features were selected for training the model (P-value<0.05). The average Intraclass Correlation Coefficient (ICC) of the four features was 0.96 in several repetitions of GTV delineation. The linear SVM model differentiated grades II and III of glioma with an Area Under the Curve (AUC) of 0.9 in the training group.
Conclusion: High predicting power for glioma grading can be achieved with radiomics analysis by a single-ROI delineated on a single-sequence MRI image (CE T1W). In addition, single-ROI segmentation can increase radiomics reproducibility.
Highlights
Yunus Soleymani (Google Scholar)
Davood Khezerloo (Google Scholar)
Keywords
- Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. 2016;278(2):563-77. doi: 10.1148/radiol.2015151169. PubMed PMID: 26579733. PubMed PMCID: PMC4734157.
- Soleymani Y, Jahanshahi AR, Hefzi M, Fazel Ghaziani M, Pourfarshid A, Khezerloo D. Evaluation of textural-based radiomics features for differentiation of COVID-19 pneumonia from non-COVID pneumonia. Egyptian Journal of Radiology and Nuclear Medicine. 2021;52:1-7.
- Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, Van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441-6. doi: 10.1016/j.ejca.2011.11.036. PubMed PMID: 22257792. PubMed PMCID: PMC4533986.
- Haarburger C, Müller-Franzes G, Weninger L, Kuhl C, Truhn D, Merhof D. Radiomics feature reproducibility under inter-rater variability in segmentations of CT images. Sci Rep. 2020;10(1):12688. doi: 10.1038/s41598-020-69534-6. PubMed PMID: 32728098. PubMed PMCID: PMC7391354.
- Soleymani Y, Jahanshahi AR, Pourfarshid A, Khezerloo D. Reproducibility assessment of radiomics features in various ultrasound scan settings and different scanner vendors. J Med Imaging Radiat Sci. 2022;53(4):664-71. doi: 10.1016/j.jmir.2022.09.018. PubMed PMID: 36266173.
- Cho HH, Lee SH, Kim J, Park H. Classification of the glioma grading using radiomics analysis. 2018;6:e5982. doi: 10.7717/peerj.5982. PubMed PMID: 30498643. PubMed PMCID: PMC6252243.
- Jang K, Russo C, Di Ieva A. Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis. Neuroradiology. 2020;62(7):771-90. doi: 10.1007/s00234-020-02403-1. PubMed PMID: 32249351.
- Singh G, Manjila S, Sakla N, True A, Wardeh AH, Beig N, et al. Radiomics and radiogenomics in gliomas: a contemporary update. Br J Cancer. 2021;125(5):641-57. doi: 10.1038/s41416-021-01387-w. PubMed PMID: 33958734. PubMed PMCID: PMC8405677.
- Xie T, Chen X, Fang J, Kang H, Xue W, Tong H, et al. Textural features of dynamic contrast-enhanced MRI derived model-free and model-based parameter maps in glioma grading. J Magn Reson Imaging. 2018;47(4):1099-111. doi: 10.1002/jmri.25835. PubMed PMID: 28845594.
- Kobayashi K, Miyake M, Takahashi M, Hamamoto R. Observing deep radiomics for the classification of glioma grades. Sci Rep. 2021;11(1):10942. doi: 10.1038/s41598-021-90555-2. PubMed PMID: 34035410. PubMed PMCID: PMC8149679.
- Chaddad A, Sabri S, Niazi T, Abdulkarim B. Prediction of survival with multi-scale radiomic analysis in glioblastoma patients. Med Biol Eng Comput. 2018;56(12):2287-300. doi: 10.1007/s11517-018-1858-4. PubMed PMID: 29915951.
- Bae S, Choi YS, Ahn SS, Chang JH, Kang SG, Kim EH, et al. Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction. 2018;289(3):797-806. doi: 10.1148/radiol.2018180200. PubMed PMID: 30277442.
- Qin JB, Liu Z, Zhang H, Shen C, Wang XC, Tan Y, et al. Grading of Gliomas by Using Radiomic Features on Multiple Magnetic Resonance Imaging (MRI) Sequences. Med Sci Monit. 2017;23:2168-78. doi: 10.12659/msm.901270. PubMed PMID: 28478462. PubMed PMCID: PMC5436423.
- Jeong J, Wang L, Ji B, Lei Y, Ali A, Liu T, et al. Machine-learning based classification of glioblastoma using delta-radiomic features derived from dynamic susceptibility contrast enhanced magnetic resonance images: Introduction. Quant Imaging Med Surg. 2019;9(7):1201-13. doi: 10.21037/qims.2019.07.01. PubMed PMID: 31448207. PubMed PMCID: PMC6685811.
- Das IJ, Andersen A, Chen ZJ, Dimofte A, Glatstein E, Hoisak J, et al. State of dose prescription and compliance to international standard (ICRU-83) in intensity modulated radiation therapy among academic institutions. Pract Radiat Oncol. 2017;7(2):e145-55. doi: 10.1016/j.prro.2016.11.003. PubMed PMID: 28274405.
- Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013;26(6):1045-57. doi: 10.1007/s10278-013-9622-7. PubMed PMID: 23884657. PubMed PMCID: PMC3824915.
- Beer JC, Tustison NJ, Cook PA, Davatzikos C, Sheline YI, Shinohara RT, et al. Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data. 2020;220:117129. doi: 10.1016/j.neuroimage.2020.117129. PubMed PMID: 32640273. PubMed PMCID: PMC7605103.
- Da-Ano R, Masson I, Lucia F, Doré M, Robin P, Alfieri J, et al. Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies. Sci Rep. 2020;10(1):10248. doi: 10.1038/s41598-020-66110-w. PubMed PMID: 32581221. PubMed PMCID: PMC7314795.
- Bettinelli A, Branchini M, De Monte F, Scaggion A, Paiusco M. Technical Note: An IBEX adaption toward image biomarker standardization. Med Phys. 2020;47(3):1167-73. doi: 10.1002/mp.13956. PubMed PMID: 31830303.
- Di Leo G, Sardanelli F. Statistical significance: p value, 0.05 threshold, and applications to radiomics-reasons for a conservative approach. Eur Radiol Exp. 2020;4(1):18. doi: 10.1186/s41747-020-0145-y. PubMed PMID: 32157489. PubMed PMCID: PMC7064671.
- Toubiana D, Maruenda H. Guidelines for correlation coefficient threshold settings in metabolite correlation networks exemplified on a potato association panel. BMC Bioinformatics. 2021;22(1):116. doi: 10.1186/s12859-021-03994-z. PubMed PMID: 33691629. PubMed PMCID: PMC7945624.
- Xiong Z, Cui Y, Liu Z, Zhao Y, Hu M, Hu J. Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation. Computational Materials Science. 2020;171:109203. doi: 10.1016/j.commatsci.2019.109203.
- Togao O, Hiwatashi A, Yamashita K, Kikuchi K, Mizoguchi M, Yoshimoto K, et al. Differentiation of high-grade and low-grade diffuse gliomas by intravoxel incoherent motion MR imaging. Neuro Oncol. 2016;18(1):132-41. doi: 10.1093/neuonc/nov147. PubMed PMID: 26243792. PubMed PMCID: PMC4677415.
- Zhao SS, Feng XL, Hu YC, Han Y, Tian Q, Sun YZ, et al. Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist’s reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images. BMC Neurol. 2020;20(1):48. doi: 10.1186/s12883-020-1613-y. PubMed PMID: 32033580. PubMed PMCID: PMC7007642.
- Zacharaki EI, Wang S, Chawla S, Soo Yoo D, Wolf R, et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med. 2009;62(6):1609-18. doi: 10.1002/mrm.22147. PubMed PMID: 19859947. PubMed PMCID: PMC2863141.
- Sun L, Zhang S, Chen H, Luo L. Brain Tumor Segmentation and Survival Prediction Using Multimodal MRI Scans With Deep Learning. Front Neurosci. 2019;13:810. doi: 10.3389/fnins.2019.00810. PubMed PMID: 31474816. PubMed PMCID: PMC6707136.
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