Automatic radiotherapy delineation quality assurance on prostate MRI with deep learning in a multicentre clinical trial

作者：Hang Min, Jason Dowling, Michael Jameson, Kirrily Cloak, Joselle Faustino, Mark Sidhom, Jarad Martin, Martin A. Ebert, Annette Haworth, Phillip Chlap, Jeremiah de Leon, Megan Berry, David Pryor, Peter B. Greer, Shalini Vinod, Lois Holloway · 发表于：Physics in Medicine and Biology · 年份：2021 · 被引用次数：16 · 研究领域：Advanced Radiotherapy Techniques、Medical Imaging and Analysis、Prostate Cancer Diagnosis and Treatment

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...