Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy

Background and purpose: Artificial intelligence advances have stimulated a new generation of autosegmentation, however clinical evaluations of these algorithms are lacking. This study assesses the clinical utility of deep learning-based autosegmentation for MR-based prostate radiotherapy planning. Materials and methods: Data was collected prospectively for patients undergoing prostate-only radiation at our institution from June to December 2019. Geometric indices (volumetric Dice-Sorensen Coefficient, VDSC; surface Dice-Sorensen Coefficient, SDSC; added path length, APL) compared automated to final contours. Physicians reported contouring time and rated autocontours on 3-point protocol deviation scales. Descriptive statistics and univariable analyses evaluated relationships between the aforementioned metrics. Results: Among 173 patients, 85% received SBRT. The CTV was available for 167 (97%) with median VDSC, SDSC, and APL for CTV (prostate and SV) 0.89 (IQR 0.83-0.95), 0.91 (IQR 0.75-0.96), and 1801 mm (IQR 1140-2703), respectively. Physicians completed surveys for 43/55 patients (RR 78%). 33% of autocontours (14/43) required major "clinically significant" edits. Physicians spent a median of 28 min contouring (IQR 20-30), representing a 12-minute (30%) time savings compared to historic controls (median 40, IQR 25- 68, n = 21, p < 0.01). Geometric indices correlated weakly with contouring time, and had no relationship with quality scores. Conclusion: Deep learning-based autosegmentation was implemented successfully and improved efficiency. Major "clinically significant" edits are uncommon and do not correlate with geometric indices. APL was supported as a clinically meaningful quantitative metric. Efforts are needed to educate and generate consensus among physicians, and develop mechanisms to flag cases for quality assurance. (c) 2021 Elsevier B.V. All rights reserved. Radiotherapy and Oncology 159 (2021) 1-7