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Minimally invasive alternatives to traditional prostate surgery are increasingly utilized to treat benign prostatic hyperplasia and localized prostate cancer in select patients. Advantages of these treatments over prostatectomy include lower risk of complication, shorter length of hospital stay, and a more favorable safety profile. Multiparametric magnetic resonance imaging (mpMRI) has become a widely accepted imaging modality for evaluation of the prostate gland and provides both anatomical and functional information. As prostate mpMRI and minimally invasive prostate procedure volumes increase, it is important for radiologists to be familiar with normal post-procedure imaging findings and potential complications. This paper reviews the indications, procedural concepts, common post-procedure imaging findings, and potential complications of prostatic artery embolization, prostatic urethral lift, irreversible electroporation, photodynamic therapy, high-intensity focused ultrasound, focal cryotherapy, and focal laser ablation., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Purpose To develop a deep learning model to delineate the transition zone (TZ) and peripheral zone (PZ) of the prostate on MR images. Materials and Methods This retrospective study was composed of patients who underwent a multiparametric prostate MRI and an MRI/transrectal US fusion biopsy between January 2013 and May 2016. A board-certified abdominal radiologist manually segmented the prostate, TZ, and PZ on the entire data set. Included accessions were split into 60% training, 20% validation, and 20% test data sets for model development. Three convolutional neural networks with a U-Net architecture were trained for automatic recognition of the prostate organ, TZ, and PZ. Model performance for segmentation was assessed using Dice scores and Pearson correlation coefficients. Results A total of 242 patients were included (242 MR images; 6292 total images). Models for prostate organ segmentation, TZ segmentation, and PZ segmentation were trained and validated. Using the test data set, for prostate organ segmentation, the mean Dice score was 0.940 (interquartile range, 0.930-0.961), and the Pearson correlation coefficient for volume was 0.981 (95% CI: 0.966, 0.989). For TZ segmentation, the mean Dice score was 0.910 (interquartile range, 0.894-0.938), and the Pearson correlation coefficient for volume was 0.992 (95% CI: 0.985, 0.995). For PZ segmentation, the mean Dice score was 0.774 (interquartile range, 0.727-0.832), and the Pearson correlation coefficient for volume was 0.927 (95% CI: 0.870, 0.957). Conclusion Deep learning with an architecture composed of three U-Nets can accurately segment the prostate, TZ, and PZ. Keywords: MRI, Genital/Reproductive, Prostate, Neural Networks Supplemental material is available for this article. © RSNA, 2021.

Objective: Prostate cancer is the most commonly diagnosed cancer in men in the United States with more than 200,000 new cases in 2018. Multiparametric MRI (mpMRI) is increasingly used for prostate cancer evaluation. Prostate organ segmentation is an essential step of surgical planning for prostate fusion biopsies. Deep learning convolutional neural networks (CNNs) are the predominant method of machine learning for medical image recognition. In this study, we describe a deep learning approach, a subset of artificial intelligence, for automatic localization and segmentation of prostates from mpMRI., Materials and Methods: This retrospective study included patients who underwent prostate MRI and ultrasound-MRI fusion transrectal biopsy between September 2014 and December 2016. Axial T2-weighted images were manually segmented by two abdominal radiologists, which served as ground truth. These manually segmented images were used for training on a customized hybrid 3D-2D U-Net CNN architecture in a fivefold cross-validation paradigm for neural network training and validation. The Dice score, a measure of overlap between manually segmented and automatically derived segmentations, and Pearson linear correlation coefficient of prostate volume were used for statistical evaluation., Results: The CNN was trained on 299 MRI examinations (total number of MR images = 7774) of 287 patients. The customized hybrid 3D-2D U-Net had a mean Dice score of 0.898 (range, 0.890-0.908) and a Pearson correlation coefficient for prostate volume of 0.974., Conclusion: A deep learning CNN can automatically segment the prostate organ from clinical MR images. Further studies should examine developing pattern recognition for lesion localization and quantification.

Objectives: To assess the utility of multiparametric MRI in detecting clinically significant prostate cancer (csPCa) by comparing PI-RADSv2 scores with International Society of Urological Pathology (ISUP) pathologic grading criteria., Methods: Data from 137 patients were retrospectively analyzed. PI-RADSv2 scores were compared with pathologic grade using ISUP criteria. Pathologic grades were divided into clinically significant (groups 3-5) and clinically insignificant lesions (groups 1-2). Chi-squared analysis was performed for to assess correlation., Results: Sensitivity and specificity of PI-RADSv2 score 3-5 lesions for detecting csPCa was 100% and 18.5%, respectively. Negative predictive value (NPV) is 100% for these lesions. When considering only PI-RADSv2 score 4-5 lesions, sensitivity decreases to 90% and specificity increases to 67.5%, with a NPV of 98.5%. When only PI-RADSv2 score 5 lesions are considered, sensitivity decreases to 50% and specificity increases to 90%, with a NPV of 95%., Conclusions: Multiparametric MRI has excellent sensitivity for detecting csPCa. Specificity is poor for PI-RADSv2 score 3 lesions but improves significantly for PI-RADSv2 score 4 and 5 lesions. Overall, mpMRI is an excellent screening tool for csPCa, as designated by the recently validated ISUP criteria., Advances in Knowledge: Multiple limitations of the longstanding Gleason pathologic scoring system have led to the development of new ISUP pathologic criteria, which is more focused on the clinical significance of lesions. There are currently insufficient studies evaluating and validating the ISUP criteria with PIRADS v2 evaluation of the prostate., (Copyright © 2019 Elsevier Inc. All rights reserved.)

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 standardized probabilistic approach for identifying clinically significant prostate cancer. Despite widespread use, the variability in performance of prostate MRI across practices remains unknown. Purpose To estimate the positive predictive value (PPV) of PI-RADS for the detection of high-grade prostate cancer across imaging centers. Materials and Methods This retrospective cross-sectional study was compliant with the HIPAA. Twenty-six centers with members in the Society of Abdominal Radiology Prostate Cancer Disease-focused Panel submitted data from men with suspected or biopsy-proven untreated prostate cancer. MRI scans were obtained between January 2015 and April 2018. This was followed with targeted biopsy. Only men with at least one MRI lesion assigned a PI-RADS score of 2-5 were included. Outcome was prostate cancer with Gleason score (GS) greater than or equal to 3+4 (International Society of Urological Pathology grade group ≥2). A mixed-model logistic regression with institution and individuals as random effects was used to estimate overall PPVs. The variability of observed PPV of PI-RADS across imaging centers was described by using the median and interquartile range. Results The authors evaluated 3449 men (mean age, 65 years ± 8 [standard deviation]) with 5082 lesions. Biopsy results showed 1698 cancers with GS greater than or equal to 3+4 (International Society of Urological Pathology grade group ≥2) in 2082 men. Across all centers, the estimated PPV was 35% (95% confidence interval [CI]: 27%, 43%) for a PI-RADS score greater than or equal to 3 and 49% (95% CI: 40%, 58%) for a PI-RADS score greater than or equal to 4. The interquartile ranges of PPV at these same PI-RADS score thresholds were 27%-44% and 27%-48%, respectively. Conclusion The positive predictive value of the Prostate Imaging and Reporting Data System was low and varied widely across centers. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Milot in this issue.

Purpose: Use of magnetic resonance imaging (MRI)/transrectal ultrasound fusion biopsies to determine the accuracy of multiparametric MRI (mpMRI), using Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2), for detecting clinically significant prostate cancer in the overall gland and specifically the peripheral zone (PZ) and transitional zone (TZ)., Methods: A retrospective analysis of patients who underwent fusion biopsy identified 137 men with 231 prostate lesions was approved by the Institutional Review Board. Subjects initially classified under PI-RADSv1 criteria were regraded using PI-RADSv2 by a radiologist blinded to PI-RADSv1 score and biopsy results. Spearman correlation, chi-squared, and logistic regression analysis were performed., Results: There was positive correlation between PI-RADSv2 and Gleason scores (P < 0.001). In the PZ, mpMRI demonstrated 100% sensitivity, 100% negative predictive value, and 35.9% positive predictive value, compared to 100%, 100%, and 27.1%, respectively, for TZ lesions. When predicting clinically significant prostate cancer, the PI-RADSv2 area under the curve for TZ lesions was 0.844 (95% CI: 0.753-0.935, P < 0.001) and 0.769 (95% CI: 0.684-0.854, P < 0.001) for PZ lesions. Combining PI-RADSv2 with additional risk factors (body mass index, prostate-specific antigen density, digital rectal examination) improved the area under curve., Conclusions: PI-RADSv2 achieves excellent sensitivity and negative predictive value for both PZ and TZ lesions., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Objective: The aim of this study is to evaluate the utility of quantitative apparent diffusion coefficient (ADC) measurements and normalized ADC ratios in multiparametric MRI for the diagnosis of clinically significant peripheral zone (PZ) prostate cancer particularly among equivocally suspicious prostate lesions., Methods: A retrospective analysis of 95 patients with PZ lesions by PI-RADSv2 criteria, and who underwent subsequent MRI-US fusion biopsy, was approved by an institutional review board. Two radiologists independently measured ADC values in regions of interest (ROIs) of PZ lesions and calculated normalized ADC ratio based on ROIs in the bladder lumen. Diagnostic performance was evaluated using ROC. Inter observer variability was assessed using intraclass correlation coefficient (ICC)., Results: Mean ADC and normalized ADC ratios for clinically significant and non-clinically significant lesions were 0.763 × 10-3 mm 2  s -1 , 29.8%; and 1.135 × 10-3 mm 2  s -1 , 47.2% (p < 0.001), respectively. Area under the ROC curve (AUC) was 0.880 [95% CI (0.816-0.944) and 0.885 (95% CI (0.814-0.955)] for ADC and ADC ratio, respectively. Optimal AUC threshold for ADC was 0.843 × 10-3 mm 2  s -1 (Sn 70.5%, Sp 88.2%) and for normalized ADC was 33.1% (Sn 75.0%, Sp 95.7%). intraclass correlation coefficient was high at 0.889., Conclusion: Quantitative ADC measurement in PZ prostate lesions demonstrates excellent diagnostic performance in differentiating clinically significant from non-clinically significant prostate cancer with high inter observer correlation. Advances In knowledge: Quantitative ADC is presented as an additional method to evaluate lesions in mpMRI of the prostate. This technique may be incorporated in new and existing methods to improve detection and discrimination of clinically significant prostate cancer.

Objectives: Multi-parametric prostate magnetic resonance imaging (MRI) is considered the current imaging standard for detection and staging of prostate cancer. The combination of anatomical and functional imaging provided in this exam significantly increases the accuracy of prostate cancer detection. Computed tomography (CT) imaging has so far been found to be lacking in this regard, however observations at our academic institution as well as evidence present in the literature support the proposition that CT could indeed be helpful in detecting prostate abnormalities that correspond to neoplasm. The purpose of this study was to prove that areas of focal mass-like enhancement on CT imaging directly correlate with prostate neoplasms as revealed on multi-parametric MRI and follow-up targeted biopsy., Materials and Methods: This was a single institution retrospective study with 27 male subjects. Inclusion criteria required subjects to have a multi-parametric MRI of the prostate between January 1, 2014 and June 1, 2015 and a pelvic venous phase contrast-enhanced CT study between January 1, 2000 and June 1, 2015. Two blinded Radiologists read subjects' CT scans for any abnormalities of the prostate. CT and multi-parametric MRI results were compared and were considered concordant if focal or mass like enhancement to a greater degree than the background parenchyma was detected in the same areas of the prostate on CT scan as areas of decreased T2 signal, perfusion abnormalities, and restricted diffusion on multi-parametric MRI., Results: CT results were directly compared to multi-parametric MRI findings and biopsy results. The overall agreement of MRI and CT is 85.19% (95% CI: 67.52-94.08%). The positive percent agreement is 78.95% (95% CI: 54.43-93.95%) and the negative percent agreement is 100.0% (95% CL: 63.06-100.0%). When CT results are directly compared to biopsy results, sensitivity and specificity of CT are 63.64% (95% CI: 30.79-89.07%) and 100.0% (95% CI: 47.82-100.0%). The positive predictive value (PPV) is 100.0% (95% CI: 59.04-100.0%) and the negative predictive value (NPV) is 55.56% (95% CI: 21.2-86.3%). When compared to MRI, CT has a lower sensitivity and a higher specificity, as well as a higher PPV and NPV. Logistic regression analysis did not show a significant relationship between concordance of MRI and CT and Gleason score, time between studies, age, and Prostate-specific antigen (PSA) level., Conclusion: Incidental focal areas of mass-like enhancement in the peripheral prostate detected on venous phase contrast-enhanced CT imaging may indeed correlate with prostate neoplasm and it would be prudent to suggest further work-up with PSA and perhaps multi-parametric MRI, especially in high-risk patients., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)