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

Authors
S.Y. Chung, J.S. Chang, Y. Chang, B.S. Choi, J. Chun, J.S. Kim, Y.B. Kim
Journal
International journal of radiation oncology biology physics
Related Product

RT ACS

Date Published
2020. 11
doi
https://doi.org/10.1016/j.ijrobp.2020.07.235
Summary

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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