- Title
- Automatic radiotherapy delineation quality assurance on prostate MRI with deep learning in a multicentre clinical trial
- Creator
- Min, Hang; Dowling, Jason; De Leon, Jeremiah; Berry, Megan; Pryor, David; Greer, Peter; Vinod, Shalini K.; Holloway, Lois; Jameson, Michael G.; Cloak, Kirrily; Faustino, Joselle; Sidhom, Mark; Martin, Jarad; Ebert, Martin A.; Haworth, Annette; Chlap, Phillip
- Relation
- NHMRC.1102198 http://purl.org/au-research/grants/nhmrc/1102198
- Relation
- Physics in Medicine and Biology Vol. 66, Issue 19, no. 195008
- Publisher Link
- http://dx.doi.org/10.1088/1361-6560/ac25d5
- Publisher
- Institute of Physics Publishing Ltd
- Resource Type
- journal article
- Date
- 2021
- Description
- Volume delineation quality assurance (QA) is particularly important in clinical trial settings where consistent protocol implementation is required, as outcomes will affect future as well current patients. Currently, where feasible, this is conducted manually, which is time consuming and resource intensive. Although previous studies mostly focused on automating delineation QA on CT, magnetic resonance imaging (MRI) is being increasingly used in radiotherapy treatment. In this work, we propose to perform automatic delineation QA on prostate MRI for both the clinical target volume (CTV) and organs-at-risk (OARs) by using delineations generated by 3D Unet variants as benchmarks for QA. These networks were trained on a small gold standard atlas set and applied on a multicentre radiotherapy clinical trial dataset to generate benchmark delineations. Then, a QA stage was designed to recommend 'pass', 'minor correction' and 'major correction' for each manual delineation in the trial set by thresholding its Dice similarity coefficient to the network generated delineation. Among all 3D Unet variants explored, the Unet with anatomical gates in an AtlasNet architecture performed the best in delineation QA, achieving an area under the receiver operating characteristics curve of 0.97, 0.92, 0.89 and 0.97 for identifying unacceptable (major correction) delineations with a sensitivity of 0.93, 0.73, 0.74 and 0.90 at a specificity of 0.93, 0.86, 0.86 and 0.95 for bladder, prostate CTV, rectum and gel spacer respectively. To the best of our knowledge, this is the first study to propose automated delineation QA for a multicentre radiotherapy clinical trial with treatment planning MRI. The methods proposed in this work can potentially improve the accuracy and consistency of CTV and OAR delineation in radiotherapy treatment planning.
- Subject
- deep learning; delineation quality assurance; MRI; multicentre clinical trial; multicentre clinical trial; radiotherapy
- Identifier
- http://hdl.handle.net/1959.13/1448939
- Identifier
- uon:43531
- Identifier
- ISSN:0031-9155
- Language
- eng
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