Abstract
PurposeTo 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.MethodsEleven 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.ResultsManual 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.ConclusionsThe 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.
Highlights
In breast cancer radiotherapy, techniques have evolved from two-dimensional (2D) radiotherapy planning to conformal radiotherapy planning and intensity-modulatedByun et al Radiat Oncol (2021) 16:203With recent advances in big data collection and computing power, deep learning algorithms and procedures have increasingly been used in many different fields [2]
We evaluated the performance of a proposed autocontouring system (ACS) in delineating Organ at risk (OAR) for breast radiotherapy with a group of experts from multiple institutions
Accuracy We collected 110 manual contours, 110 corrected autocontours, and 10 autocontours of each type of OAR. When these contours were compared to the consensus ground truth contours, 100 Dice similarity coefficient (DSC) and 100 Hausdorff distance (HD) were created for each type of OAR for the manual contours and corrected autocontours, and 10 DSCs and 10 HDs were created for the autocontours
Summary
Techniques have evolved from two-dimensional (2D) radiotherapy planning to conformal radiotherapy planning and intensity-modulatedByun et al Radiat Oncol (2021) 16:203With recent advances in big data collection and computing power, deep learning algorithms and procedures have increasingly been used in many different fields [2]. Unlike other image segmentation used in surgical and radiologic fields, normal tissue contouring in radiation oncology, known as “OAR delineation,” has been defined and standardized through expert consensus with regard to better quantification of dose-volume histogram–toxicity relationships [4]. Men et al [5] previously developed deep learning-based target volume for breast radiotherapy, while Feng et al [6] developed deep learning-based segmentation of OARs for thoracic radiotherapy. Our group previously demonstrated the potential of deep learning-based autosegmentation of target volumes and OARs in breast cancer radiotherapy [7, 8]. A training set for a proposed deep learning-based autocontouring system (ACS) is generally generated by a single expert or a small group of experts [9]. Generalization is often discussed as an issue of external validity
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