An article by Dr. Oshrit Hoffer and Dr. Yair Zimmer
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The study was conducted in collaboration with Dr. Tatiana Rabin of Sourasky Medical Center, Dr. Rony-Reuven Nir of Meuhedet Health Services, Dr. Rafael Y. Brzezinski, and Prof. Israel Gannot of Tel Aviv University
ABSTRACT: Cervical cancer is the fourth most common cancer among women worldwide. Malignant tumors have high metabolic rates that result in a unique temperature distribution as compared to healthy tissues. In this study, we sought to characterize the thermal response of the cervix following brachytherapy (oncological radiation therapy) in women with advanced cervical carcinoma. The purpose of the study was to develop a noninvasive, affordable, and readily available method for evaluating the efficacy of oncological treatment, thus enabling personalized care. Study patients underwent imaging with a thermal camera before a brachytherapy treatment session and after a 7-day follow-up period. A designated algorithm was developed to calculate and store the texture parameters of the examined tissues across all time points. These parameters were fed into a machine learning algorithm. Our algorithms achieved a 100% detection rate for physiological changes in cervical tumors before and after brachytherapy.
The study results showed that thermal imaging combined with image processing and machine learning could be used to detect tissue-specific changes in the cervix in response to local brachytherapy for cervical cancer.
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