Fergus V. Coakley

To determine whether the unsupervised domain adaptation (UDA) method with generated images improves the performance of a supervised learning (SL) model for prostate cancer (PCa) detection using multisite biparametric (bp) MRI datasets. This retrospective study included data from 5150 patients (14 191 samples) collected across nine different imaging centers. A novel UDA method using a unified generative model was developed for PCa detection using multisite bpMRI datasets. This method translates diffusion-weighted imaging (DWI) acquisitions, including apparent diffusion coefficient (ADC) and individual diffusion-weighted (DW) images acquired using various b values, to align with the style of images acquired using b values recommended by Prostate Imaging Reporting and Data System (PI-RADS) guidelines. The generated ADC and DW images replace the original images for PCa detection. An independent set of 1692 test cases (2393 samples) was used for evaluation. The area under the receiver operating characteristic curve (AUC) was used as the primary metric, and statistical analysis was performed via bootstrapping. For all test cases, the AUC values for baseline SL and UDA methods were 0.73 and 0.79 (P < .001), respectively, for PCa lesions with PI-RADS score of 3 or greater and 0.77 and 0.80 (P < .001) for lesions with PI-RADS scores of 4 or greater. In the 361 test cases under the most unfavorable image acquisition setting, the AUC values for baseline SL and UDA were 0.49 and 0.76 (P < .001) for lesions with PI-RADS scores of 3 or greater and 0.50 and 0.77 (P < .001) for lesions with PI-RADS scores of 4 or greater. UDA with generated images improved the performance of SL methods in PCa lesion detection across multisite datasets with various b values, especially for images acquired with significant deviations from the PI-RADS-recommended DWI protocol (eg, with an extremely high b value). Keywords: Prostate Cancer Detection, Multisite, Unsupervised Domain Adaptation, Diffusion-weighted Imaging, b Value Supplemental material is available for this article. © RSNA, 2024

Diagnostic multiparametric MRI of the prostate has steadily evolved over the last three decades and can now reliably depict the dominant tumor in most men with prostate cancer. In response, several methods of targeted biopsy to direct tissue sampling of suspected tumor foci seen at multiparametric MRI have been developed and successfully tested in recent years, including software-assisted MRI-ultrasound (US) fusion biopsy and direct MRI-guided in-bore biopsy. These advances are leading to a sea change in the approach to prostate cancer diagnosis, with the traditional approach of blind systematic biopsy increasingly being replaced by MRI directed targeted biopsy. This review aims to describe the current status of targeted biopsy, with an emphasis on the relative accuracy of different techniques. The results of several critical large multicenter trials are presented, while unanswered questions that require more research are highlighted.

We describe early ex vivo proof-of-concept testing of a novel system composed of a disposable endorectal coil and converging multichannel needle guide with a reusable clamp stand, embedded electronics, and baseplate to allow for endorectal magnetic resonance (MR) imaging and in-bore MRI-targeted biopsy of the prostate as a single integrated procedure. Using prostate phantoms imaged with standard T₂-weighted sequences in a Siemens 3T Prisma MR scanner, we measured the signal-to-noise ratio in successive 1-cm distances from the novel coil and from a commercially available inflatable balloon coil and measured the lateral and longitudinal deviation of the tip of a deployed MR compatible needle from the intended target point. Signal-to-noise ratio obtained with the novel system was significantly better than the inflatable balloon coil at each of five 1-cm intervals, with a mean improvement of 78% (P < 0.05). In a representative sampling of 15 guidance channels, the mean lateral deviation for MR imaging–guided needle positioning was 1.7 mm and the mean longitudinal deviation was 2.0 mm. Our ex vivo results suggest that our novel system provides significantly improved signal-to-noise ratio when compared with an inflatable balloon coil and is capable of accurate MRI-guided needle deployment.

Wide variation in prostate cancer detection is seen across all Prostate Imaging Reporting and Data System scores for men with suspected or biopsy-proven untreated prostate cancer who undergo MRI. Background Prostate MRI is used widely in clinical care for guiding tissue sampling, active surveillance, and staging. The Prostate Imaging Reporting and Data System (PI-RADS) helps provide a stand...