Automated breast cancer risk estimation on routine CT thorax scans by deep learning segmentation
Stijn De Buck, Jeroen Bertels, Chelsey Vanbilsen, Tanguy Dewaele, Chantal Van Ongeval, Hilde Bosmans, Jan Erik Vandevenne, Paul L. Suetens · Medical Imaging 2020: Computer-Aided Diagnosis · 2020
Automation of systematic scoring of breast glandularity on CT thorax examinations performed for another clinical reason could aid in detecting postmenopausal women with increased breast cancer risk. We propose a novel method that combines automated deep learning based breast segmentation from CT thorax examinations with computation of breast glandularity based on radiodensity and volumetric breast density. Reasonable segmentation Dice scores were found as well as very strong correlation between the risk measures computed on the ground truth and with the proposed approach. Hence, the proposed method can offer reliable breast cancer risk measures with limited additional workload for the radiologist.