Predictive features of breast cancer on Mexican screening mammography patients

Juan Rodriguez-Rojas; Margarita Garza-Montemayor; Victor Trevino-Alvarado; José Gerardo Tamez-Pena

doi:10.1117/12.2007978

26 February 2013 Predictive features of breast cancer on Mexican screening mammography patients

Juan Rodriguez-Rojas, Margarita Garza-Montemayor, Victor Trevino-Alvarado, José Gerardo Tamez-Pena

Author Affiliations +

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 867023 (2013) https://doi.org/10.1117/12.2007978
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Breast cancer is the most common type of cancer worldwide. In response, breast cancer screening programs are becoming common around the world and public programs now serve millions of women worldwide. These programs are expensive, requiring many specialized radiologists to examine all images. Nevertheless, there is a lack of trained radiologists in many countries as in Mexico, which is a barrier towards decreasing breast cancer mortality, pointing at the need of a triaging system that prioritizes high risk cases for prompt interpretation. Therefore we explored in an image database of Mexican patients whether high risk cases can be distinguished using image features. We collected a set of 200 digital screening mammography cases from a hospital in Mexico, and assigned low or high risk labels according to its BIRADS score. Breast tissue segmentation was performed using an automatic procedure. Image features were obtained considering only the segmented region on each view and comparing the bilateral di erences of the obtained features. Predictive combinations of features were chosen using a genetic algorithms based feature selection procedure. The best model found was able to classify low-risk and high-risk cases with an area under the ROC curve of 0.88 on a 150-fold cross-validation test. The features selected were associated to the differences of signal distribution and tissue shape on bilateral views. The model found can be used to automatically identify high risk cases and trigger the necessary measures to provide prompt treatment.

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Juan Rodriguez-Rojas, Juan Rodriguez-Rojas, Margarita Garza-Montemayor, Margarita Garza-Montemayor, Victor Trevino-Alvarado, Victor Trevino-Alvarado, José Gerardo Tamez-Pena, José Gerardo Tamez-Pena, "Predictive features of breast cancer on Mexican screening mammography patients", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867023 (26 February 2013); doi: 10.1117/12.2007978; https://doi.org/10.1117/12.2007978

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