This retrospective analysis was performed in a previously unreported cohort of men undergoing MRI–transrectal US (MR/TRUS) fusion biopsy. The institutional ethics committee approved the study and waived written informed consent (S-156/2018) to allow analysis of a complete consecutive cohort. All men had clinical indication for biopsy based on prostate-specific antigen (PSA) elevation, clinical examination, or participation in our active surveillance program; were biopsied between January 2017 and December 2017; and were included if they met the following criteria: (a) imaging performed at our main institutional 3-T MRI system and (b) MRI/TRUS-fusion biopsy performed at our institution. Exclusion criteria were (a) history of treatment for prostate cancer (antihormonal therapy, radiation therapy, focal therapy, prostatectomy); (b) biopsy within 6 months prior to MRI; and (c) incomplete sequences or severe MRI artifacts. sPC was defined as International Society of Urological Pathology (ISUP) grade ≥ 2 . Details on image preprocessing are given in Supplement S1.
T2-weighted, diffusion-weighted (DWI), and dynamic contrast-enhanced MRI were acquired on a single 3-T MRI system (Prisma, Siemens Healthineers) in accordance with European Society of Urogenital Radiology guidelines, by using the standard multichannel body coil and integrated spine phased-array coil. The institutional prostate MRI protocol is given in Supplementary Table 1.
PI-RADS interpretation of mpMRI was performed by 8 board-certified radiologists during clinical routine (using PI-RADS version 2) , with 85% of the studies being interpreted by radiologists with at least 3 years of experience in prostate MRI. For quality assurance, prior to biopsy, all examinations were reviewed in an interdisciplinary conference and radiologists participated in regular retrospective review of MRI reports and biopsy results.
All men underwent grid-directed transperineal biopsy under general anesthesia using rigid or elastic software registration (BiopSee, MEDCOM and UroNav, Philips Invivo, respectively). First, MRI-suspicious lesions received fusion-targeted biopsy (FTB) (inter-quartile range (IQR) 3–5 cores, median 4 per lesion), followed by systematic saturation biopsy (22–27 cores, median 24 cores), as previously described [14, 15]. This combined biopsy approach of FTBs and transperineal systematic saturation biopsies (SBs) has been validated against and its concordance with radical prostatectomy (RP) specimen has been confirmed . A median of 32 biopsies (IQR 28–37) were taken per patient, with the number of biopsies adjusted to prostate volume . Histopathological analyses were performed under supervision of one dedicated uropathologist (A.S., 17 years of experience) according to the International Society of Urological Pathology standards.
Lesion segmentation was retrospectively performed based on clinical reports and their accompanying sector map diagrams by one investigator (X.W.), a board-certified radiologist with 5 years of experience in body imaging and 6 months of focused expertise in prostate MRI under supervision and in consensus with a board-certified radiologist (D.B.) with 11 years of experience in prostate MRI interpretation, using the polygon tool from open-source MITK software (www.mitk.org, version 2018.04) to draw the three-dimensional volumes of interest (VOI) separately on axial T2-weighted and apparent diffusion coefficient (ADC)/DWI images.
Application of deep learning algorithm
The previously trained and validated two-dimensional 16-member U-Net ensemble  utilizes T2-weighted, b-value 1500 s/mm2 and ADC maps to classify each voxel as either tumor, normal-appearing prostate, or background. For each U-Net in the ensemble, output probabilities for the three classes sum up to one per voxel. The ensemble probability map is the mean of the ensemble member U-Net probability maps. For each examination, the ensemble was applied to each of the rigid, affine, and b-spline registration schemes and the map with the highest tumor probability used for further processing. Deep learning was implemented in PyTorch (version 1.2.0; https://pytorch.org) .
Combined histopathological mapping
To utilize all available histopathological information including that of sPC outside of PI-RADS lesions, sextant-specific systematic and targeted lesion histopathology were fused into a combined histological reference (Supplementary Material S-2).
Threshold adjustment and statistical analysis
Receiver operating characteristic (ROC) curves were calculated from U-Net probability predictions. U-Net probability thresholds yielding patient-based working points most closely matching PI-RADS ≥ 3 and ≥ 4 performance were obtained as outlined in Supplementary Material S-3. For application to the current cohort, three U-Net thresholds were determined: fixed, dynamic, and limit. Fixed thresholds represent the most straightforward application of the published U-Net to new examinations and are determined from the 300 most recent examinations of the published cohort. Dynamic thresholds are readjusted in regular intervals to keep U-Net and PI-RADS closely matched on the most recent examinations. These are initially set to the values of the fixed thresholds, applied to the 50 following examinations, then repeatedly readjusted using the most recent 300 examinations. Each patient is evaluated in a simulated prospective manner using only the dynamic threshold resulting from the most recent adjustment. Limit thresholds represent the theoretical limit of best dynamic threshold performance by producing the closest possible match between U-Net and PI-RADS performance and are determined from the current cohort. Only fixed and dynamic thresholds can be applied prospectively to new patients, while limit thresholds are an a posteriori reference to judge the success of threshold selection.
Sensitivity, specificity, and positive and negative predictive value were calculated and compared using the McNemar test . We examined the effect of co-occurrent detection of sPC-positive men, biopsy sextants, and PI-RADS lesions by U-Net and radiologists on the positive (PPV) and negative predictive value (NPV) using a test based on relative predictive values implemented in the R package DTComPair [19, 20]. Statistical analyses were implemented in Python (Python Software Foundation, version 3.7.3, http://www.python.org) and R (R version 3.6.0, R Foundation for Statistical Computing) with details given in Supplementary Material S-4. A p value of 0.05 or less was considered statistically significant. All p values were adjusted for multiple comparisons using Holm’s method . We used the Dice coefficient , a commonly used spatial overlap index, to compare manual and U-Net-derived lesion segmentations separately for DWI, T2w, and their combination. The mean Dice coefficient was calculated from all biopsy sPC–positive clinical lesions and U-Net-derived lesions (Supplementary Material S-5).