Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy Journal Article
| Authors: | Cha, E.; Elguindi, S.; Onochie, I.; Gorovets, D.; Deasy, J. O.; Zelefsky, M.; Gillespie, E. F. |
| Article Title: | Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy |
| Abstract: | 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-Sørensen Coefficient, VDSC; surface Dice-Sørensen 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. © 2021 Elsevier B.V. |
| Keywords: | prostatic neoplasms; program evaluation; radiation oncology; deep learning; radiologic technology |
| Journal Title: | Radiotherapy and Oncology |
| Volume: | 159 |
| ISSN: | 0167-8140 |
| Publisher: | Elsevier Inc. |
| Date Published: | 2021-06-01 |
| Start Page: | 1 |
| End Page: | 7 |
| Language: | English |
| DOI: | 10.1016/j.radonc.2021.02.040 |
| PUBMED: | 33667591 |
| PROVIDER: | scopus |
| PMCID: | PMC9444280 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 1 April 2021 -- Source: Scopus |
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