Evaluating the effectiveness of deep learning contouring across multiple radiotherapy centres
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Authors
Walker, Z.Bartley, G.
Hague, Christina
Kelly, D.
Navarro, C.
Rogers, J.
South, C.
Temple, S.
Whitehurst, Philip
Chuter, Robert
Affiliation
Medical Physics, University Hospitals Coventry and Warwickshire NHS Trust, Clifford Bridge Road, Coventry CV2 2DX, UKIssue Date
2022
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Background and purpose: Deep learning contouring (DLC) has the potential to decrease contouring time and variability of organ contours. This work evaluates the effectiveness of DLC for prostate and head and neck across four radiotherapy centres using a commercial system. Materials and methods: Computed tomography scans of 123 prostate and 310 head and neck patients were evaluated. Besides one head and neck model, generic DLC models were used. Contouring time using centres' existing clinical methods and contour editing time after DLC were compared. Timing was evaluated using paired and non-paired studies. Commercial software or in-house scripts assessed dice similarity coefficient (DSC) and distance to agreement (DTA). One centre assessed head and neck inter-observer variability. Results: The mean contouring time saved for prostate structures using DLC compared to the existing clinical method was 5.9 ± 3.5 min. The best agreement was shown for the femoral heads (median DSC 0.92 ± 0.03, median DTA 1.5 ± 0.3 mm) and the worst for the rectum (median DSC 0.68 ± 0.04, median DTA 4.6 ± 0.6 mm). The mean contouring time saved for head and neck structures using DLC was 16.2 ± 8.6 min. For one centre there was no DLC time-saving compared to an atlas-based method. DLC contours reduced inter-observer variability compared to manual contours for the brainstem, left parotid gland and left submandibular gland. Conclusions: Generic prostate and head and neck DLC models can provide time-savings which can be assessed with paired or non-paired studies to integrate with clinical workload. Reducing inter-observer variability potential has been shown.Citation
Walker Z, Bartley G, Hague C, Kelly D, Navarro C, Rogers J, et al. Evaluating the Effectiveness of Deep Learning Contouring across Multiple Radiotherapy Centres. Physics and imaging in radiation oncology. 2022 Oct;24:121-8. PubMed PMID: 36405563. Pubmed Central PMCID: PMC9668733. Epub 2022/11/22. eng.Journal
Physics and Imaging in Radiation OncologyDOI
10.1016/j.phro.2022.11.003PubMed ID
36405563Additional Links
https://dx.doi.org/10.1016/j.phro.2022.11.003Type
ArticleLanguage
enae974a485f413a2113503eed53cd6c53
10.1016/j.phro.2022.11.003
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