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Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy

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Abstract
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.
Author(s)
변화경장지석최민서전재희정진홍정치영김진성장용진정승연이성률김용배
Issued Date
2021
Type
Article
Keyword
AutocontouringBreastOrgans at riskRadiotherapy
DOI
10.1186/s13014-021-01923-1
URI
https://oak.ulsan.ac.kr/handle/2021.oak/7429
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_6271a3efee73416d825437dfc3e439ef&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Evaluation%20of%20deep%20learning-based%20autosegmentation%20in%20breast%20cancer%20radiotherapy&offset=0&pcAvailability=true
Publisher
RADIATION ONCOLOGY
Location
영국
Language
영어
ISSN
1748-717X
Citation Volume
16
Citation Number
1
Citation Start Page
203
Citation End Page
203
Appears in Collections:
Medicine > Medicine
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