An evaluation of MR based deep learning auto-contouring for planning head and neck radiotherapy
Authors
Hague, ChristinaMcPartlin, Andrew J
Lee, Lip W
Hughes, Christopher
Mullan, Damian
Beasley, William J
Green, Andrew
Price, Gareth J
Whitehurst, Philip
Slevin, Nicholas J
van Herk, Marcel
West, Catharine M L
Chuter, Robert
Affiliation
Department of Head and Neck Clinical Oncology, The Christie NHS Foundation Trust, Wilmslow Road, Manchester, United KingdomIssue Date
2021
Metadata
Show full item recordAbstract
Introduction: Auto contouring models help consistently define volumes and reduce clinical workload. This study aimed to evaluate the cross acquisition of a Magnetic Resonance (MR) deep learning auto contouring model for organ at risk (OAR) delineation in head and neck radiotherapy. Methods: Two auto contouring models were evaluated using deep learning contouring expert (DLCExpert) for OAR delineation: a CT model (modelCT) and an MR model (modelMRI). Models were trained to generate auto contours for the bilateral parotid glands and submandibular glands. Auto-contours for modelMRI were trained on diagnostic images and tested on 10 diagnostic, 10 MR radiotherapy planning (RTP), eight MR-Linac (MRL) scans and, by modelCT, on 10 CT planning scans. Goodness of fit scores, dice similarity coefficient (DSC) and distance to agreement (DTA) were calculated for comparison. Results: ModelMRI contours improved the mean DSC and DTA compared with manual contours for the bilateral parotid glands and submandibular glands on the diagnostic and RTP MRs compared with the MRL sequence. There were statistically significant differences seen for modelMRI compared to modelCT for the left parotid (mean DTA 2.3 v 2.8 mm), right parotid (mean DTA 1.9 v 2.7 mm), left submandibular gland (mean DTA 2.2 v 2.4 mm) and right submandibular gland (mean DTA 1.6 v 3.2 mm). Conclusion: A deep learning MR auto-contouring model shows promise for OAR auto-contouring with statistically improved performance vs a CT based model. Performance is affected by the method of MR acquisition and further work is needed to improve its use with MRL images.Citation
Hague C, McPartlin A, Lee LW, Hughes C, Mullan D, Beasley W, et al. An evaluation of MR based deep learning auto-contouring for planning head and neck radiotherapy. Radiother Oncol. 2021;158:112-7.Journal
Radiotherapy and OncologyDOI
10.1016/j.radonc.2021.02.018PubMed ID
33636229Additional Links
https://dx.doi.org/10.1016/j.radonc.2021.02.018Type
ArticleLanguage
enae974a485f413a2113503eed53cd6c53
10.1016/j.radonc.2021.02.018