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

S.Y. Chung, J.S. Chang, Y. Chang, B.S. Choi, J. Chun, J.S. Kim, Y.B. Kim
International journal of radiation oncology biology physics
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Date Published
2020. 11

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.


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