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Updated: Jun 16, 2025
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Deep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite
Hao Li1, Han Liu1, Heinrich von Busch1
1From Digital Technology and Innovation, Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540 (H. Li, H. Liu, D.C., A.K., B.L.); Diagnostic Imaging, Siemens Healthineers, Erlangen, Bavaria, Germany (H.v.B., R.G.); Vanderbilt University, Nashville, Tenn (H. Li, H. Liu, I.O.); Radboud University Medical Center, Nijmegen, the Netherlands (H.H.); New York University, New York, NY (A.T.); Universitätsspital Basel, Basel, Switzerland (D.W.); Charité, Universitätsmedizin Berlin, Berlin, Germany (T.P.); Patero Clinic, Moscow, Russia (I.S.); Eunpyeong St. Mary's Hospital, Catholic University of Korea, Seoul, Republic of Korea (M.H.C.); Department of Radiology, Changhai Hospital of Shanghai, Shanghai, China (Q.Y.); Diagnostikum Graz Süd-West, Graz, Austria (D.S.); Department of Radiology, Loyola University Medical Center, Maywood, Ill (S.S.); Department of Diagnostic Radiology, Oregon Health and Science University School of Medicine, Portland, Ore (F.C.); and Massachusetts General Hospital, Boston, Mass (M.H.).
None:
Purpose 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. Materials and Methods 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. Results 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. Conclusion 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.
