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Feasibility of sparse contact thermometry as a method for breast cancer detection
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Paper Abstract

It has been widely reported that breast tumors produce surface temperature signatures due to an increased metabolic heat generation rate and angiogenesis (the generation of new blood vessels around a tumor). The present work provides an assessment of the feasibility of using sparse contact thermometry to detect tumors in the breast. The surface temperatures at positions approximately corresponding to the sensor locations in a proposed sensor array were obtained from infrared thermography images of 123 healthy patients and 27 patients with breast cancer. A Support Vector Machine was trained and tested through Leave One Out Cross Validation. The model obtained an AUC ROC of 0.914, with a sensitivity of 92.6% and specificity of 82.1%. These results are close to the gold standard and even higher in women with high breast density. The present work shows promise for sparse contact thermometry. It is imperative to conduct further research with larger sample sizes and with data collected with sparse contact thermometry devices to determine the effectiveness of the method.

Paper Details

Date Published: 6 September 2019
PDF: 12 pages
Proc. SPIE 11139, Applications of Machine Learning, 1113910 (6 September 2019); doi: 10.1117/12.2526814
Show Author Affiliations
Raymundo González Leal, Eva Technologies (Mexico)
Francisco Javier González Contreras, Eva Technologies (Mexico)


Published in SPIE Proceedings Vol. 11139:
Applications of Machine Learning
Michael E. Zelinski; Tarek M. Taha; Jonathan Howe; Abdul A. S. Awwal; Khan M. Iftekharuddin, Editor(s)

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