Document Type : Original Research

Authors

1 MT, Doctoral Program of Medical Science, Faculty of Medicine, University Brawijaya, Malang, Indonesia

2 MT, Department of Informatic, STIKI, Malang, Indonesia

3 PhD, Department of Orthopaedic, Saiful Anwar Hospital, Faculty of Medicine, University Brawijaya, Malang, Indonesia

4 PhD, Department of Parasitology, Faculty of Medicine, University Brawijaya, Malang, Indonesia

5 PhD, Department of Physics, University Brawijaya, Malang, Indonesia

6 PhD, Department of Clinical Pathology, Faculty of Medicine, University Brawijaya, Malang, Indonesia

Abstract

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.

Keywords

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. Mamahit DJ. Detection early breast cancer by using digital infrared image based on asymmetry thermal. Jurnal Teknik Elektro Dan Komputer. 2012;23:1-8.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.