Evaluation of Deep Learning-based Auto-segmentation in Breast Cancer Radiotherapy

Hwa Kyung Byun, Jee suk Chang, Min Seo Choi, Jaehee Chun, Jinhong Jung, Chiyoung Jeong, Jin Sung Kim, Yongjin Chang, Seung Yeun Chung, Seungryul Lee, Yong Bae Kim
Radiation Oncology, 2021
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The study evaluates the performance of Coreline Soft's AVIEW RT-ACS, a deep learning-based autocontouring system, in delineating organs at risk (OARs) during breast radiotherapy. Eleven experts were initially tasked with manually contouring nine OARs in 10 cases of adjuvant radiotherapy post breast-conserving surgery, which were then corrected using the autocontouring system. Comparisons between manual contours, corrected autocontours, and autocontours were made using Dice similarity coefficient (DSC) and Hausdorff distance (HD). The results demonstrated that the autocontouring system's accuracy was comparable to that of the experts' manual contouring, with an average DSC value of 0.90. Furthermore, the system significantly reduced interphysician variations and decreased contouring time from 37 minutes (manual) to 6 minutes (corrected autocontours). The AVIEW RT-ACS system demonstrated good user satisfaction, highlighting its potential to enhance the quality of breast radiotherapy and reduce variability in clinical practice.


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