Evaluation of Deep Learning-Based Auto-Segmentation of Target Volume and Organs-at-Risk in Breast Cancer Patients

This study evaluated the feasibility of using AVIEW RT-ACS, a deep learning-based auto-segmentation software developed by Coreline Soft, for target and organs-at-risk (OAR) segmentation in breast cancer patients undergoing radiotherapy (RT). The software was used to auto-segment clinical target volumes (CTV) and OARs of 61 patients who underwent breast-conserving surgery. The study found good correlation between the auto-segmented and manual contours for most OARs and CTVs, except for the right coronary artery and left anterior descending artery. The study suggests that auto-segmentation can be an expedient tool to assist radiation oncologists and improve the quality control of RT in breast cancer patients.