Deep learning‐based dominant index lesion segmentation for MR‐guided radiation therapy of prostate cancer

Abstract

AbstractBackgroundDose escalation radiotherapy enables increased control of prostate cancer (PCa) but requires segmentation of dominant index lesions (DIL). This motivates the development of automated methods for fast, accurate, and consistent segmentation of PCa DIL.PurposeTo construct and validate a model for deep‐learning‐based automatic segmentation of PCa DIL defined by Gleason score (GS) ≥3+4 from MR images applied to MR‐guided radiation therapy. Validate generalizability of constructed models across scanner and acquisition differences.MethodsFive deep‐learning networks were evaluated on apparent diffusion coefficient (ADC) MRI from 500 lesions in 365 patients arising from internal training Dataset 1 (156 lesions in 125 patients, 1.5Tesla GE MR with endorectal coil), testing using Dataset 1 (35 lesions in 26 patients), external ProstateX Dataset 2 (299 lesions in 204 patients, 3Tesla Siemens MR), and internal inter‐rater Dataset 3 (10 lesions in 10 patients, 3Tesla Philips MR). The five networks include: multiple resolution residually connected network (MRRN) and MRRN regularized in training with deep supervision implemented into the last convolutional block (MRRN‐DS), Unet, Unet++, ResUnet, and fast panoptic segmentation (FPSnet) as well as fast panoptic segmentation with smoothed labels (FPSnet‐SL). Models were evaluated by volumetric DIL segmentation accuracy using Dice similarity coefficient (DSC) and the balanced F1 measure of detection accuracy, as a function of lesion aggressiveness and size (Dataset 1 and 2), and accuracy with respect to two‐raters (on Dataset 3). Upon acceptance for publication segmentation models will be made available in an open‐source GitHub repository.ResultsIn general, MRRN‐DS more accurately segmented tumors than other methods on the testing datasets. MRRN‐DS significantly outperformed ResUnet in Dataset2 (DSC of 0.54 vs. 0.44, p < 0.001) and the Unet++ in Dataset3 (DSC of 0.45 vs. p = 0.04). FPSnet‐SL was similarly accurate as MRRN‐DS in Dataset2 (p = 0.30), but MRRN‐DS significantly outperformed FPSnet and FPSnet‐SL in both Dataset1 (0.60 vs. 0.51 [p = 0.01] and 0.54 [p = 0.049] respectively) and Dataset3 (0.45 vs. 0.06 [p = 0.002] and 0.24 [p = 0.004] respectively). Finally, MRRN‐DS produced slightly higher agreement with experienced radiologist than two radiologists in Dataset 3 (DSC of 0.45 vs. 0.41).ConclusionsMRRN‐DS was generalizable to different MR testing datasets acquired using different scanners. It produced slightly higher agreement with an experienced radiologist than that between two radiologists. Finally, MRRN‐DS more accurately segmented aggressive lesions, which are generally candidates for radiative dose ablation.