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

Abstract

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.

Review of Deep Learning Based Autosegmentation for Clinical Target Volume: Current Status and Future Directions

“…Overall, the DSCs for CTV prostate ranged from 0.65 to 0.92. 56 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 Most DLAS models had DSC >0.7, with the exception of U-Net in one study. 64 In intact patients with prostate cancer, the use of DLAS models demonstrated superiority, as blind physician evaluation resulted in selection of DLAS more often than manual contouring.…”

“… 56 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 Most DLAS models had DSC >0.7, with the exception of U-Net in one study. 64 In intact patients with prostate cancer, the use of DLAS models demonstrated superiority, as blind physician evaluation resulted in selection of DLAS more often than manual contouring. 65 For patients who received radiation after prostatectomy, DLAS models either outperformed or performed similarly to manual contouring.…”

Harnessing uncertainty in radiotherapy auto-segmentation quality assurance

“…An increasing number of studies have begun to apply uncertainty estimation to the quality assurance of radiotherapy-related auto-segmentation [10] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] . The study by Outeiral et al serves as a cornerstone contribution to this crucial literature.…”