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For prostate cancer detection on prostate multiparametric MRI (mpMRI), the Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) and computer-aided diagnosis (CAD) systems aim to widely improve standardization across radiologists and centers. Our goal was to evaluate CAD assistance in prostate cancer detection compared with conventional mpMRI interpretation in a diverse dataset acquired from five institutions tested by nine readers of varying experience levels, in total representing 14 globally spread institutions.Index lesion sensitivities of mpMRI-alone were 79% (whole prostate (WP)), 84% (peripheral zone (PZ)), 71% (transition zone (TZ)), similar to CAD at 76% (WP, p=0.39), 77% (PZ, p=0.07), 79% (TZ, p=0.15). Greatest CAD benefit was in TZ for moderately-experienced readers at PI-RADSv2 <3 (84% vs mpMRI-alone 67%, p=0.055). Detection agreement was unchanged but CAD-assisted read times improved (4.6 vs 3.4 minutes, p<0.001). At PI-RADSv2 ≥ 3, CAD improved patient-level specificity (72%) compared to mpMRI-alone (45%, p<0.001).PI-RADSv2 and CAD-assisted mpMRI interpretations have similar sensitivities across multiple sites and readers while CAD has potential to improve specificity and moderately-experienced radiologists’ detection of more difficult tumors in the center of the gland. The multi-institutional evidence provided is essential to future prostate MRI and CAD development.

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The groups both showed satisfactory clinical outcomes and the improvements were comparable; however, the group with preserved ligamentum flavum showed significantly less local fibrosis at 6 months postoperatively. The authors speculate that this surgical technique provides a physical protective barrier that can reduce or even eliminate fibrosis-related complications after lumbar disc surgery.

One proposed solution is to use artificial intelligence (AI)-based detection systems.With the help of machine learning, classification algorithms can be trained to predict results and outcomes, provided that enough training data are available. In 2017, we at the National Cancer Institute [7] proposed an AI system based on intensity and texture analysis and a random forest classification algorithm. This system was validated in a large multireader multicenter study in 2018 [8]. Results of that study revealed an increase in detection of transition zone lesions among moderately experienced readers only. Overall, however, the AI system was equivalent to conventional MRI interpretation [8]. In that study, color-coded prediction maps were used to draw attention to AI-detected lesions. Feedback from the study suggested that prediction maps compromised the interaction between the radiologists and the AI system with resultant decreased accuracy for some readers. To address this issue a new AI detection system with more expert annotated