Evaluation of deep learning-based autosegmentation in breast cancer radiotherapy.

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 ge

SUBMITTER: Byun HK 

PROVIDER: S-EPMC8518257 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature