Clinical feasibility of deep learning-based auto-segmentation of target volumes and organs-at-risk in breast cancer patients after breast-conserving surgery

Seung Yeun Chung, Jee Suk Chang, Min Seo Choi, Yongjin Chang, Byong Su Choi, Jaehee Chun, Ki Chang Keum, Jin Sung Kim, Yong Bae Kim
Radiation Oncology, 2021
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Date Published
2021. 02

This study compared manual and deep learning-based auto-segmentation (AVIEW RT-ACS, Coreline Soft) for delineating target volumes and organs-at-risk (OARs) in breast cancer patients receiving radiotherapy. The algorithm demonstrated a strong correlation with manual contours, achieving a Dice similarity coefficient of over 0.80 for all OARs and over 0.70 for all breast and regional lymph node clinical target volumes. The study also found minor differences in dosimetric parameters between the auto-segmented and manual contours. While not replacing radiation oncologists, deep learning-based auto-segmentation proved to be a potential tool to assist them, potentially reducing workload and inter-physician variability. The study concludes that auto-segmentation using AVIEW RT-ACS is feasible and beneficial in breast radiotherapy planning.


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