Evaluation of deep learning-based autosegmentation 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

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Purpose: To study the performance of a proposed deep learning-based autocontouring system in delineating organs at risk (OARs) in breast radiotherapy with a group of experts. Methods: Eleven experts from two institutions delineated nine OARs in 10 cases of adjuvant radiotherapy after breast-conserving surgery. Autocontours were then provided to the experts for correction. Overall, 110 manual contours, 110 corrected autocontours, and 10 autocontours of each type of OAR were analyzed. The Dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to compare the degree of agreement between the best manual contour (chosen by an independent expert committee) and each autocontour, corrected autocontour, and manual contour. Higher DSCs and lower HDs indicated a better geometric overlap. The amount of time reduction using the autocontouring system was examined. User satisfaction was evaluated using a survey. Results: Manual contours, corrected autocontours, and autocontours had a similar accuracy in the average DSC value (0.88 vs. 0.90 vs. 0.90). The accuracy of autocontours ranked the second place, based on DSCs, and the first place, based on HDs among the manual contours. Interphysician variations among the experts were reduced in corrected autocontours, compared to variations in manual contours (DSC: 0.89–0.90 vs. 0.87–0.90; HD: 4.3–5.8 mm vs. 5.3–7.6 mm). Among the manual delineations, the breast contours had the largest variations, which improved most significantly with the autocontouring system. The total mean times for nine OARs were 37 min for manual contours and 6 min for corrected autocontours. The results of the survey revealed good user satisfaction. Conclusions: The autocontouring system had a similar performance in OARs as that of the experts’ manual contouring. This system can be valuable in improving the quality of breast radiotherapy and reducing interphysician variability in clinical practice.

Original languageEnglish
Article number203
JournalRadiation Oncology
Issue number1
Publication statusPublished - 2021 Dec

Bibliographical note

Funding Information:
This work was supported by a National Research Foundation of Korea (NRF) grant, funded by the Korea government (MSIT; Grant No. 2019R1C1C1009359).

Funding Information:
We thank Minji Koh, Young Seob Shin, and Jesang Yu of the Department of Radiation Oncology at Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea, and Jina Kim, Taehyung Kim, Jason Joon Bok Lee, Jin Young Moon, and Ryeong Hwang Park of the Department of Radiation Oncology at Yonsei University College of Medicine, Seoul, Korea for participating in contouring.

Publisher Copyright:
© 2021, The Author(s).

All Science Journal Classification (ASJC) codes

  • Oncology
  • Radiology Nuclear Medicine and imaging


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