Characterisation of prostate cancer using texture analysis for diagnostic and prognostic monitoring.

Automated classification of significant prostate cancer (PCa) using MRI plays a potential role in assisting in clinical decision-making. Multiparametric MRI using a machine-aided approach is a better step to improve the overall accuracy of diagnosis of PCa. The objective of this study was to develop and validate a framework for differentiating Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) grades (grade 2 to grade 5) of PCa using texture features and machine learning (ML) methods with diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC). The study cohort included an MRI dataset of 59 patients with clinically proven PCa. Regions of interest (ROIs) for a total of 435 lesions were delineated from the segmented peripheral zones of DWI and ADC. Six texture methods comprising 98 texture features in total (49 each of DWI and ADC) were extracted from lesion ROIs. Random forest (RF) and correlation-based feature selection methods were applied on feature vectors to select the best features for classification. Two ML classifiers, support vector machine (SVM) and K-nearest neighbour, were used and validated by 10-fold cross-validation. The proposed framework achieved high diagnostic performance with a sensitivity of 85.25% ± 3.84%, specificity of 95.71% ± 1.96%, accuracy of 84.90% ± 3.37% and area under the receiver-operating characteristic curve of 0.98 for PI-RADS v2 grades (2 to 5) classification using the RF feature selection method and Gaussian SVM classifier with combined features of DWI + ADC. The proposed computer-assisted framework can distinguish between PCa lesions with different aggressiveness based on PI-RADS v2 standards using texture analysis to improve the efficiency of PCa diagnostic performance.

How does PI-RADS v2.1 impact patient classification? A head-to-head comparison between PI-RADS v2.0 and v2.1.

BACKGROUND: PI-RADS classification has recently been updated, with the magnitude of changes implemented currently unknown. PURPOSE: To quantify the categorization shifts between PI-RADS v2.0 and v2.1. MATERIAL AND METHODS: Retrospective review of 535 consecutive diagnostic magnetic resonance imaging (MRI) studies performed over 18 months, assigning to each case a PI-RADS category in the peripheral zone (PZ), the transition zone (TZ), and the whole gland using both PI-RADS v2.0 and v2.1. Significance of changes in category assignments and of differences in the number of positive or negative MRIs were evaluated using the McNemar test. RESULTS: Comparing v2.0 to v2.1 for the whole gland, 11.2% of PI-RADS 2 categories shifted to PI-RADS 1 (6.9% in the PZ, 56.8% in the TZ), 16.1% of PI-RADS 3 categories shifted to PI-RADS 2 (15.0% in the PZ, 20.0% in the TZ), and 2.1% of PI-RADS 2 categories shifted to PI-RADS 3 (0.3% in the PZ, 1.9% in the TZ). The proportion of PI-RADS 1 significantly increased from 0.6% to 7.3%, PI-RADS 2 significantly decreased from 60.0% to 53.8%, and PI-RADS 3 non-significantly decreased from 11.6% to 11.0%. The total number of positive exams (PI-RADS 3-5) did not change significantly (39.4% versus 38.8%). CONCLUSION: The most prominent change between v2.0 and v2.1 was observed in the TZ with the downgrading of typical benign prostatic hyperplasia nodules from category 2 into category 1. Overall, there were no significant changes in the number of positive and negative MRI results, with an expected low influence in clinical management.

Effects of the addition of quantitative apparent diffusion coefficient data on the diagnostic performance of the PI-RADS v2 scoring system to detect clinically significant prostate cancer.

PURPOSE: To evaluate the impact of the addition of quantitative apparent diffusion coefficient (ADC) data into the diagnostic performance of the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) scoring system to predict clinically significant prostate cancer (CSPCa). METHODS: We retrospectively included 91 consecutive patients who underwent prostate multiparametric magnetic resonance imaging (mp-MRI) and histopathological evaluation. Mp-MRI images were reported by the PI-RADSv2 scoring system and patients were divided into groups considering the likelihood of CSPCa. ADC value and ratio were obtained. Findings were correlated with histopathological data. RESULTS: CSPCa was found in 41.8% of cases (n = 38). PI-RADSv2 score 3-5 yielded a sensitivity of 97.4% (95% confidence intervals 86.5-99.5), a specificity of 50.9% (37.9-63.9), and AUC of 0.74 (0.67-0.81) to predict CSPCa. ADC value < 750 µm2/s and an ADC ratio < 0.62 were the most accurate thresholds for differentiation of CSPCa, with AUC of 0.81 and 0.76, respectively. Combined PI-RADSv2 score 3-5 and ADC value < 750 µm2/s yielded a specificity of 84.9 (72.9-92.2), sensitivity of 70.3 (54.2-82.5), and AUC of 0.77 (0.68-0.86). Combined PI-RADSv2 score 3-5 and ADC ratio < 0.62 yielded a specificity of 86.5 (74.7-93.3), sensitivity of was 64.9 (48.8-78.2), and AUC of 0.75 (0.66-0.84). CONCLUSION: Quantitative ADC data might not be beneficial to be used routinely in mp-MR imaging as criteria to detect clinically significant lesions due to the reduced sensitivity. Instead, when prostate lesions present a PI-RADSv2 score ≥ 3, additional quantitative ADC criteria can be helpful to increase the PI-RADS score specificity.

A Systematic Review of the Existing Prostate Imaging Reporting and Data System Version 2 (PI-RADSv2) Literature and Subset Meta-Analysis of PI-RADSv2 Categories Stratified by Gleason Scores.

OBJECTIVE: The objective of this study was to quantitatively and qualitatively assess the methodologic heterogeneity of the current Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) literature and estimate the proportions of Gleason scores (GSs) diagnosed across PI-RADSv2 categories. MATERIALS AND METHODS: This study was a systematic review and meta-analysis and was performed in concordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only English-language studies and studies published before April 1, 2018, were assessed. The primary outcome of the meta-analysis was the estimated percentage of patients with GS ≥ 3 + 4 within each individual PI-RADSv2 score. We calculated the pooled estimates and 95% CIs on the basis of a random-effects model using the meta-analysis routine of Stata (version 13.1). RESULTS: Our search revealed 434 titles, and 59 of these studies were selected. These studies were remarkable for their technical and terminological diverseness. Thirteen studies had sufficient data to be included in the meta-analysis. The prevalence of ≥ GS 3 + 4 in lesions assigned a PI-RADSv2 score of 3 or higher was approximately 45%. Lesions assigned PI-RADSv2 scores 1 or 2, 3, 4, and 5 represented high-grade disease in approximately 6%, 12%, 48%, and 72% of patients. CONCLUSION: The data available in the literature are highly heterogeneous and challenging to analyze because of variations in terminology, patient cohort selection, criteria, imaging parameters, and reference standards. In spite of this heterogeneity, our meta-analysis shows that PI-RADSv2 has good sensitivity when a score of ≥ 3 is considered as a positive test.

Detection rate of clinically significant prostate cancer in magnetic resonance imaging and ultrasonography-fusion transperineal targeted biopsy for lesions with a prostate imaging reporting and data system version 2 score of 3-5.

OBJECTIVES: To evaluate the detection rates of clinically significant prostate cancer classified according to the prostate imaging reporting and data system scoring system using magnetic resonance imaging/ultrasound rigid fusion targeted biopsy. METHODS: A total of 339 patients underwent transperineal magnetic resonance imaging/ultrasound rigid fusion targeted biopsy in our institution between January 2015 and July 2017. Patients with prostate imaging reporting and data system category 1 or 2 and those with a pre-biopsy prostate-specific antigen value of >30 ng/mL were excluded from this study. Finally, 310 patients were recruited. RESULTS: The detection rates of clinically significant prostate cancer with prostate imaging reporting and data system category 3, 4, and 5 were 1.0% (1/98), 35.1% (47/134) and 73.1% (57/78), respectively. The factors affecting the detection of clinically significant prostate cancer with prostate imaging reporting and data system categories 4 and 5 were: (i) prostate imaging reporting and data system category 5; (ii) prostate volume <40 cc; (iii) no previous biopsy; (iv) lesion located in the peripheral zone; and (v) prostate-specific antigen density >0.35 ng/mL/mL. CONCLUSIONS: The detection rate of clinically significant prostate cancer on magnetic resonance imaging/ultrasound rigid fusion targeted biopsy is very low in patients with prostate imaging reporting and data system category 3; therefore, patients with this classification should not undergo targeted biopsy. Prostate-specific antigen density, prostate volume, locations of suspected cancer and history of biopsy should be considered to predict the detection rate of clinically significant prostate cancer with prostate imaging reporting and data system categories 4 and 5.

Is Prostate Imaging Reporting and Data System Version 2 Sufficiently Discovering Clinically Significant Prostate Cancer? Per-Lesion Radiology-Pathology Correlation Study.

OBJECTIVE: The objective of our study was to evaluate the performance of multiparametric MRI with Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) for detecting prostate cancer (PCA) and clinically significant PCA through this per-lesion one-to-one correlation study between pathologically proven lesions and MRI-visible lesions. MATERIALS AND METHODS: A total of 93 PCA lesions from 44 patients who underwent radical prostatectomy were included in this retrospective study. Two radiologists scored every visible lesion with a PI-RADSv2 score of 3, 4, or 5 in each patient's multiparametric MRI examination using PI-RADSv2. A per-lesion one-to-one correlation between MRI-visible lesions and pathologically confirmed PCA lesions was conducted during regular radiology-pathology meetings at our center. The detection rates of clinically significant PCA and the proportions of clinically significant PCAs from MRI-visible and MRI-invisible PCAs were calculated. The performance of PI-RADSv2 for detecting clinically significant PCA was evaluated using the positive predictive value (PPV), negative predictive value (NPV), and area under the ROC curve (AUC) value. RESULTS: Using a PI-RADSv2 score of 3, 4, or 5 as an MRI-visible lesion, 46.88% of clinically significant PCA lesions were detected. The PPV, NPV, and AUC were 96.77%, 45.16%, and 0.72, respectively. Tumor volume and secondary Gleason grade showed a statistically significant difference between MRI-visible and MRI-invisible clinically significant PCAs. CONCLUSION: Multiparametric MRI with PI-RADSv2 missed a considerable number of clinically significant PCA lesions in this per-lesion analysis, causing a relatively low NPV and diagnostic performance compared with previous per-patient studies. However, the high PPV indicates that multiparametric MRI with PI-RADSv2 may be useful for follow-up of active surveillance and planning focal therapy.

Simplified Prostate Imaging Reporting and Data System for Biparametric Prostate MRI: A Proposal.

OBJECTIVE: We describe our institutional experience using a simplified Prostate Imaging Reporting and Data System (PI-RADS) based on biparametric prostate MRI. We discuss two important controversies: the use of gadolinium-based contrast agents and the management of PI-RADS category 3 lesions. CONCLUSION: Our simplified PI-RADS identifies four categories and suggests management strategies for each. The simplified PI-RADS can be an effective system to facilitate multidisciplinary cooperation and to improve the management of suspected prostate cancer.

Impact of the Prostate Imaging Reporting and Data System, Version 2, on MRI Diagnosis for Extracapsular Extension of Prostate Cancer.

OBJECTIVE: The purpose of this study is to validate the Prostate Imaging Reporting and Data System, version 2 (PI-RADSv2), in assessing extracapsular extension (ECE), compared with PI-RADS, version 1 (PI-RADSv1). MATERIALS AND METHODS: A total of 210 patients with clinically localized prostate cancer underwent MRI and radical prostatectomy. Two readers independently interpreted the MR images. In PI-RADSv1, 5-point ECE risk scoring was used. In PI-RADSv2, ECE criteria included morphologic features and a tumor-capsule contact length (CL) greater than 10 mm. The diagnostic performance of each PI-RADS version and the cutoff CL were evaluated. RESULTS: ECE was found in 56 patients (26.7%). In PI-RADSv1, maximal accuracy was achieved with a risk score of 3 or greater. At this threshold, positive findings on PI-RADSv1 and PI-RADSv2 were identified in 21.0-34.3% and 49.0-51.4% of patients, respectively. Compared with PI-RADSv1, PI-RADSv2 had higher negative predictive values (84.9-89.1% vs 96.3-97.1%, respectively; p = 0.003 and 0.021, for each reader). PI-RADSv1 and PI-RADSv2 had positive predictive values of 56.9-70.5% and 49.1-50.5%, respectively (p = 0.025 and 0.300, respectively). Interobserver kappa values for PI-RADSv1 and PI-RADSv2 were 0.511 and 0.781, respectively. The best cutoff CL was greater than 10 mm among patients without morphologic features of ECE. For patients positive for ECE on the basis of PI-RADSv2 but not PI-RADSv1, 73.3-74.1% of prostate cancer cases with a biopsy Gleason score of 7 or less and 35.7-44.4% of cases with a biopsy Gleason score of 8 or higher were overstaged. CONCLUSION: PI-RADSv2 reduces understaging and improves interobserver agreement in ECE assessment. However, overstaging is a concern, and the biopsy Gleason score may have a complementary role in reducing overstaging.

Evaluation of Multiparametric Magnetic Resonance Imaging and Correlation with Radical Prostatectomy Histopathology Specimen in Prostate Cancer.

Magnetic resonance imaging (MRI) has shown a great potential in the evaluation and management of prostate cancer. In this study, we would like to evaluate the benefit of multiparametric MRI in the detection and localization of prostate cancer by comparing it with the gold standard of histopathology from radical prostatectomy. In this single-centre prospective study, 90 consecutive patients underwent radical prostatectomy from November 2016 to May 2018. All patients first underwent multiparametric (mp)-MRI, and all suspicious regions of interest were delineated and recorded on a 5-point scale as defined in prostate imaging reporting and data system version 2 (PI-RADS V2) score. All radical prostatectomy specimens, acquired after robotic radical prostatectomy with extended pelvic lymphadenectomy, were sent for histopathological examination (HPE). The mean age of the 90 patients was 65.3 years, and the mean serum prostate-specific antigen (PSA) was 16.9 ng/ml. The sensitivity and specificity of mp-MRI in the detection of the corresponding region of interest (ROI) on HPE were 67.4% and 89.3% respectively. Positive predictive value (PPV), negative predictive value (NPV), and accuracy of mp-MRI in the detection of corresponding ROI on HPE were 86.3%, 73.3%, and 78.3% respectively. The mp-MRI detected 96.8% solitary lesions and 61.7% multifocal lesions on the corresponding ROI on HPE. Multiparametric MRI has an excellent specificity and reasonable sensitivity for the diagnosis of prostate cancer. It is a good modality for the detection of solitary tumours, higher-grade tumours, detection of seminal vesicle invasion and extracapsular extension and helps in the decision-making process before radical prostatectomy, focal therapy or selecting an appropriate candidate for active surveillance.

Machine learning-based analysis of a semi-automated PI-RADS v2.1 scoring for prostate cancer.

Background: Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) was developed to standardize the interpretation of multiparametric MRI (mpMRI) for prostate cancer (PCa) detection. However, a significant inter-reader variability among radiologists has been found in the PI-RADS assessment. The purpose of this study was to evaluate the diagnostic performance of an in-house developed semi-automated model for PI-RADS v2.1 scoring using machine learning methods. Methods: The study cohort included an MRI dataset of 59 patients (PI-RADS v2.1 score 2 = 18, score 3 = 10, score 4 = 16, and score 5 = 15). The proposed semi-automated model involved prostate gland and zonal segmentation, 3D co-registration, lesion region of interest marking, and lesion measurement. PI-RADS v2.1 scores were assessed based on lesion measurements and compared with the radiologist PI-RADS assessment. Machine learning methods were used to evaluate the diagnostic accuracy of the proposed model by classification of PI-RADS v2.1 scores. Results: The semi-automated PI-RADS assessment based on the proposed model correctly classified 50 out of 59 patients and showed a significant correlation (r = 0.94, p < 0.05) with the radiologist assessment. The proposed model achieved an accuracy of 88.00% ± 0.98% and an area under the receiver-operating characteristic curve (AUC) of 0.94 for score 2 vs. score 3 vs. score 4 vs. score 5 classification and accuracy of 93.20 ± 2.10% and AUC of 0.99 for low score vs. high score classification using fivefold cross-validation. Conclusion: The proposed semi-automated PI-RADS v2.1 assessment system could minimize the inter-reader variability among radiologists and improve the objectivity of scoring.

Risk Stratification of Patients Candidate to Radical Prostatectomy Based on Clinical and Multiparametric Magnetic Resonance Imaging Parameters: Development and External Validation of Novel Risk Groups.

BACKGROUND: Despite the key importance of magnetic resonance imaging (MRI) parameters, risk classification systems for biochemical recurrence (BCR) in prostate cancer (PCa) patients treated with radical prostatectomy (RP) are still based on clinical variables alone. OBJECTIVE: We aimed at developing and validating a novel classification integrating clinical and radiological parameters. DESIGN, SETTING, AND PARTICIPANTS: A retrospective multicenter cohort study was conducted between 2014 and 2020 across seven academic international referral centers. A total of 2565 patients treated with RP for PCa were identified. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Early BCR was defined as two prostate-specific antigen (PSA) values of ≥0.2 ng/ml within 3 yr after RP. Kaplan-Meier and Cox regressions tested time and predictors of BCR. Development and validation cohorts were generated from the overall patient sample. A model predicting early BCR based on Cox-derived coefficients represented the basis for a nomogram that was validated externally. Predictors consisted of PSA, biopsy grade group, MRI stage, and the maximum diameter of lesion at MRI. Novel risk categories were then identified. The Harrel's concordance index (c-index) compared the accuracy of our risk stratification with the European Association of Urology (EAU), Cancer of the Prostate Risk Assessment (CAPRA), and International Staging Collaboration for Cancer of the Prostate (STAR-CAP) risk groups in predicting early BCR. RESULTS AND LIMITATIONS: Overall, 200 (8%), 1834 (71%), and 531 (21%) had low-, intermediate-, and high-risk disease according to the EAU risk groups. The 3-yr overall BCR-free survival rate was 84%. No differences were observed in the 3-yr BCR-free survival between EAU low- and intermediate-risk groups (88% vs 87%; p = 0.1). The novel nomogram depicted optimal discrimination at external validation (c-index 78%). Four new risk categories were identified based on the predictors included in the Cox-based nomogram. This new risk classification had higher accuracy in predicting early BCR (c-index 70%) than the EAU, CAPRA, and STAR-CAP risk classifications (c-index 64%, 63%, and 67%, respectively). CONCLUSIONS: We developed and externally validated four novel categories based on clinical and radiological parameters to predict early BCR. This novel classification exhibited higher accuracy than the available tools. PATIENT SUMMARY: Our novel and straightforward risk classification outperformed currently available preoperative risk tools and should, therefore, assist physicians in preoperative counseling of men candidate to radical treatment for prostate cancer.

Predictors of Clinically Significant Prostate Cancer in Patients with PIRADS Categories 3-5 Undergoing Magnetic Resonance Imaging-Ultrasound Fusion Biopsy of the Prostate.

Prostate biopsy is recommended in cases of positive magnetic resonance imaging (MRI), defined as Prostate Imaging Reporting and Data System (PIRADS) category ≥ 3. However, most men with positive MRIs will not be diagnosed with clinically significant prostate cancer (csPC). Our goal was to evaluate pre-biopsy characteristics that influence the probability of a csPC diagnosis in these patients. We retrospectively analyzed 740 consecutive men with a positive MRI and no prior PC diagnosis who underwent MRI-ultrasound fusion biopsies of the prostate in three centers. csPC detection rates (CDRs) for each PIRADS category were calculated. Patient, disease, and lesion characteristics were studied for interdependencies with the csPC diagnosis. The CDR in patients with PIRADS categories 3, 4, and 5 was 10.5%, 30.7%, and 54.6%, respectively. On both uni- and multivariable regression models, older age, being biopsy-naïve, prostate specific antigen ≥ 10 ng/mL, smaller prostate volume, PIRADS > 3, a larger maximum lesion size, a lesion in the peripheral zone, and a positive digital rectal examination were associated with csPC. In this large, multicenter study, we provide new data regarding CDRs in particular PIRADS categories. In addition, we present several strong predictors that further alter the risk of csPC in MRI-positive patients. Our results could help in refining individual risk assessment, especially in PIRADS 3 patients, in whom the risk of csPC is substantially low.

Association between Incidental Pelvic Inflammation and Aggressive Prostate Cancer.

The impact of pelvic inflammation on prostate cancer (PCa) biology and aggressive phenotype has never been studied. Our study objective was to evaluate the role of pelvic inflammation on PCa aggressiveness and its association with clinical outcomes in patients following radical prostatectomy (RP). This study has been conducted on a retrospective single-institutional consecutive cohort of 2278 patients who underwent robot-assisted laparoscopic prostatectomy (RALP) between 01/2013 and 10/2019. Data from 2085 patients were analyzed to study the association between pelvic inflammation and adverse pathology (AP), defined as Gleason Grade Group (GGG) > 2 and ≥ pT3 stage, at resection. In a subset of 1997 patients, the association between pelvic inflammation and biochemical recurrence (BCR) was studied. Alteration in tumor transcriptome and inflammatory markers in patients with and without pelvic inflammation were studied using microarray analysis, immunohistochemistry, and culture supernatants derived from inflamed sites used in functional assays. Changes in blood inflammatory markers in the study cohort were analyzed by O-link. In univariate analyses, pelvic inflammation emerged as a significant predictor of AP. Multivariate cox proportional-hazards regression analyses showed that high pelvic inflammation with pT3 stage and positive surgical margins significantly affected the time to BCR (p ≤ 0.05). PCa patients with high inflammation had elevated levels of pro-inflammatory cytokines in their tissues and in blood. Genes involved in epithelial-to-mesenchymal transition (EMT) and DNA damage response were upregulated in patients with pelvic inflammation. Attenuation of STAT and IL-6 signaling decreased tumor driving properties of conditioned medium from inflamed sites. Pelvic inflammation exacerbates the progression of prostate cancer and drives an aggressive phenotype.

Synthetic magnetic resonance imaging for primary prostate cancer evaluation: Diagnostic potential of a non-contrast-enhanced bi-parametric approach enhanced with relaxometry measurements.

PURPOSE: Bi-parametric magnetic resonance imaging (bpMRI) with diffusion-weighted images has wide utility in diagnosing clinically significant prostate cancer (csPCa). However, bpMRI yields more false-negatives for PI-RADS category 3 lesions than multiparametric (mp)MRI with dynamic-contrast-enhanced (DCE)-MRI. We investigated the utility of synthetic MRI with relaxometry maps for bpMRI-based diagnosis of csPCa. METHODS: One hundred and five treatment-naïve patients who underwent mpMRI and synthetic MRI before prostate biopsy for suspected PCa between August 2019 and December 2020 were prospectively included. Three experts and three basic prostate radiologists evaluated the diagnostic performance of conventional bpMRI and synthetic bpMRI for csPCa. PI-RADS version 2.1 category 3 lesions were identified by consensus, and relaxometry measurements (T1-value, T2-value, and proton density [PD]) were performed. The diagnostic performance of relaxometry measurements for PI-RADS category 3 lesions in peripheral zone was compared with that of DCE-MRI. Histopathological evaluation results were used as the reference standard. Statistical analysis was performed using the areas under the receiver operating characteristic curve (AUC) and McNemar test. RESULTS: In 102 patients without significant MRI artefacts, the diagnostic performance of conventional bpMRI was not significantly different from that of synthetic bpMRI for all readers (p = 0.11-0.79). The AUCs of the combination of T1-value, T2-value, and PD (T1 + T2 + PD) for csPCa in peripheral zone for PI-RADS category 3 lesions were 0.85 for expert and 0.86 for basic radiologists, with no significant difference between T1 + T2 + PD and DCE-MRI for both expert and basic radiologists (p = 0.29-0.45). CONCLUSION: Synthetic MRI with relaxometry maps shows promise for contrast media-free evaluation of csPCa.

Positive Predictive Value of Prostate Imaging Reporting and Data System Version 2 for the Detection of Clinically Significant Prostate Cancer: A Systematic Review and Meta-analysis.

CONTEXT: The variability of the positive predictive value (PPV) represents a significant factor affecting the diagnostic performance of multiparametric magnetic resonance imaging (mpMRI). OBJECTIVE: To analyze published studies reporting mpMRI PPV and the reasons behind the variability of clinically significant prostate cancer (csPCa) detection rates on targeted biopsies (TBx) according to Prostate Imaging Reporting and Data System (PI-RADS) version 2 categories. EVIDENCE ACQUISITION: A search of PubMed, Cochrane library's Central, EMBASE, MEDLINE, and Scopus databases, from January 2015 to June 2020, was conducted. The primary and secondary outcomes were to evaluate the PPV of PI-RADS version 2 in detecting csPCa and any prostate cancer (PCa), respectively. Individual authors' definitions for csPCa and PI-RADS thresholds for positive mpMRI were accepted. Detection rates, used as a surrogate of PPV, were pooled using random-effect models. Preplanned subgroup analyses tested PPV after stratification for PI-RADS scores, previous biopsy status, TBx technique, and number of sampled cores. PPV variation over cancer prevalence was evaluated. EVIDENCE SYNTHESIS: Fifty-six studies, with a total of 16 537 participants, were included in the quantitative synthesis. The PPV of suspicious mpMRI for csPCa was 40% (95% confidence interval 36-43%), with large heterogeneity between studies (I2 94%, p < 0.01). PPV increased according to PCa prevalence. In subgroup analyses, PPVs for csPCa were 13%, 40%, and 69% for, respectively, PI-RADS 3, 4, and 5 (p < 0.001). TBx missed 6%, 6%, and 5% of csPCa in PI-RADS 3, 4, and 5 lesions, respectively. In biopsy-naïve and prior negative biopsy groups, PPVs for csPCa were 42% and 32%, respectively (p = 0.005). Study design, TBx technique, and number of sampled cores did not affect PPV. CONCLUSIONS: Our meta-analysis underlines that the PPV of mpMRI is strongly dependent on the disease prevalence, and that the main factors affecting PPV are PI-RADS version 2 scores and prior biopsy status. A substantially low PPV for PI-RADS 3 lesions was reported, while it was still suboptimal in PI-RADS 4 and 5 lesions. Lastly, even if the added value of a systematic biopsy for csPCa is relatively low, this rate can improve patient risk assessment and staging. PATIENT SUMMARY: Targeted biopsy of Prostate Imaging Reporting and Data System 3 lesions should be considered carefully in light of additional individual risk assessment corroborating the presence of clinically significant prostate cancer. On the contrary, the positive predictive value of highly suspicious lesions is not high enough to omit systematic prostate sampling.

Multiparametric Magnetic Resonance Imaging for the Detection of Clinically Significant Prostate Cancer: What Urologists Need to Know. Part 3: Targeted Biopsy.

BACKGROUND: After a lesion has been assessed adequately on multiparametric magnetic resonance imaging (mpMRI), magnetic resonance (MR)-guided biopsy (MRGB) is the logical next step. The choice of the MRGB technique, however, is difficult. OBJECTIVE: To show the advantages and disadvantages of the three commonly used MRGB techniques-MRI-ultrasound fusion MRGB (fus-MRGB), direct in-bore MRGB (inbore-MRGB), and cognitive MRGB (cog-MRGB), and to determine when each of the techniques can be used. DESIGN, SETTING, AND PARTICIPANTS: Based on expert opinion and literature overview, the advantages, disadvantages, and challenges of fus-MRGB, inbore-MRGB, and cog-MRGB are evaluated. Further, the clinical setting of each biopsy strategy is assessed. SURGICAL PROCEDURE: Based on expert opinion and literature data, the three biopsy procedures are evaluated, and the important pros and cons are determined. MEASUREMENTS: The basic concept of each biopsy technique is reviewed, which would result in a clinical recommendation. This will be shown in individual patients. RESULTS AND LIMITATIONS: The accompanying video shows how fus-MRGB and inbore-MRGB are performed in our hospital. An important advantage of fus-MRGB is its generally availability; however, it has fusion-error limitations. Although not supported by evidence, inbore-MRGB seems to be better suited for smaller lesions, but is rather expensive. Cog-MRGB is easy to use and inexpensive, but is more operator dependent as it requires knowledge about both ultrasound and MR images. Readers should be aware that our MRGB approach is largely based on expert opinion and, where possible, supported by evidence. CONCLUSIONS: This article and the accompanying video show different MRGB techniques. The advantages and disadvantages of the three biopsy techniques, as well as the clinical setting in which each biopsy strategy is being used in our hospital, are discussed. Fus-MRGB is our first choice for prostate biopsy. Direct inbore-MRGB is used in difficult lesions but is mainly used as a "problem solver" (eg, a negative biopsy with a high suspicion for clinically significant prostate cancer). In our opinion, cog-MRGB is best for sampling larger and diffuse lesions. PATIENT SUMMARY: This third surgery in motion contribution shows our approach in magnetic resonance (MR)-guided biopsy (MRGB). Fusion MRGB is our first choice for prostate biopsy. In-bore MRGB is used in selected, difficult cases, mainly as a problem solver. In our point of view, cognitive MRGB seems to be best for sampling larger lesions and diffuse processes.

Multiparametric Magnetic Resonance Imaging for the Detection of Clinically Significant Prostate Cancer: What Urologists Need to Know. Part 2: Interpretation.

BACKGROUND: There is large variability among radiologists in their detection of clinically significant (cs) prostate cancer (PCa) on multiparametric magnetic resonance imaging (mpMRI). OBJECTIVE: To reduce the interpretation variability and achieve optimal accuracy in assessing prostate mpMRI. DESIGN, SETTING, AND PARTICIPANTS: How the interpretation of mpMRI can be optimized is demonstrated here. Whereas part 1 of the "surgery-in-motion" paper focused on acquisition, this paper shows the correlation between (ab)normal prostate anatomical structures and image characteristics on mpMRI, and how standardized interpretation according to Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) should be performed. This will be shown in individual patients. SURGICAL PROCEDURE: To detect csPCa, three mpMRI "components" are used: "anatomic" T2-weighted imaging, "cellular-density" diffusion-weighted imaging, and "vascularity" dynamic contrast-enhanced MRI. MEASUREMENTS: Based on PI-RADS v2, the accompanying video shows how mpMRI interpretation is performed. Finally, the role of mpMRI in detecting csPCa is briefly discussed and the main features of the recently introduced PI-RADS v2.1 are evaluated. RESULTS AND LIMITATIONS: With PI-RADS v2, it is possible to quantify normal and abnormal anatomical structures within the prostate based on its imaging features of the three mpMRI "components." With this knowledge, a more objective evaluation of the presence of a csPCa can be performed. However, there still remains quite some space to reduce interobserver variability. CONCLUSIONS: For understanding the interpretation of mpMRI according to PI-RADS v2, knowledge of the correlation between imaging and (ab)normal anatomical structures on the three mpMRI components is needed. PATIENT SUMMARY: This second surgery-in-motion contribution shows what structures can be recognized on prostate magnetic resonance imaging (MRI). How a radiologist performs his reading according to the so-called Prostate Imaging Reporting and Data System criteria is shown here. The main features of these criteria are summarized, and the role of prostate MRI in detecting clinically significant prostate cancer is discussed briefly.

Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2.

The Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) was developed with a consensus-based process using a combination of published data, and expert observations and opinions. In the short time since its release, numerous studies have validated the value of PI-RADS v2 but, as expected, have also identified a number of ambiguities and limitations, some of which have been documented in the literature with potential solutions offered. To address these issues, the PI-RADS Steering Committee, again using a consensus-based process, has recommended several modifications to PI-RADS v2, maintaining the framework of assigning scores to individual sequences and using these scores to derive an overall assessment category. This updated version, described in this article, is termed PI-RADS v2.1. It is anticipated that the adoption of these PI-RADS v2.1 modifications will improve inter-reader variability and simplify PI-RADS assessment of prostate magnetic resonance imaging even further. Research on the value and limitations on all components of PI-RADS v2.1 is strongly encouraged.

Comparison of PI-RADS version 2 and PI-RADS version 2.1 for the detection of transition zone prostate cancer.

PURPOSE: To compare the diagnostic performance of PI-RADS v2 and v2.1 for detecting transition zone prostate cancer (TZPC) on multiparametric prostate MRI (mpMRI). METHOD: Fifty-eight patients with elevated PSA levels underwent mpMRI at 3 T including T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI), and subsequent MRI-transrectal ultrasonography fusion-guided prostate-targeted biopsy (MRGB). The standard of reference was MRGB-derived histopathology. Two readers independently assessed each TZ lesion, assigning a score of 1-5 for T2WI, a score of 1-5 for DWI, and the overall PI-RADS assessment category according to PI-RADS v2 and v2.1. The diagnostic performance of the two methods was compared in terms of inter-reader agreement, diagnostic sensitivity, diagnostic specificity, and area under the ROC curve (AUC). RESULTS: Of the 58 patients, 26 were diagnosed with PC (GS = 3 + 3, n = 9; GS = 3 + 4, n = 9; GS = 3 + 5, n = 1; GS = 4 + 3, n = 4; GS = 4 + 4, n = 3) and 32 with benign lesions. Regarding inter-reader agreement of overall PI-RADS assessment category, the kappa value was 0.580 for v2 and 0.645 for v2.1. For both readers, there was no difference in diagnostic sensitivity between the versions (p ≥ 0.500). For reader 1, the diagnostic specificity was higher for v2.1 (p = 0.002), and was similar for reader 2 (p = 1.000). For both readers, AUC tended to be higher for v2.1 than for v2, but the difference was not significant (0.786 vs. 0.847 for reader 1, p = 0.052; and 0.808 vs. 0.858 for reader 2, p = 0.197). CONCLUSIONS: These results suggest that compared with PI-RADS v2, PI-RADS v2.1 could be preferable for evaluating TZ lesions.

Prostate cancer detection with biparametric magnetic resonance imaging (bpMRI) by readers with different experience: performance and comparison with multiparametric (mpMRI).

PURPOSE: To study the detection of clinically significant prostate cancer (PCa) by readers with different experience, comparing performance with biparametric magnetic resonance imaging (bmMRI) and with the reference multiparametric (mpMRI). METHODS: Retrospective analysis of 68 patients with mpMRI of the prostate at 1.5 Tesla using a 32 phased-array coil. Forty-five patients (cases) underwent radical prostatectomy, whereas 23 (controls) had a negative prostate biopsy and ≥ 2.5 years of negative follow-up. Six observers (two with 1000 cases interpreted, two with 300, two with 100) performed the analysis first with bpMRI including diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps and T2-weighted (T2W) imaging in three planes and, after 1 month, with mpMRI, adding dynamic contrast enhancement (DCE). The performance was quantified by sensitivity (SNS), specificity (SPC) and area under the curve (AUC) of the ROC (Receiver Operating Characteristics) procedure. RESULTS: Concordance within observers of equivalent experience was good (weighted Cohen's k ≈ 0.7). The two expert readers performed as well in bpMRI as in mpMRI (SNS = 0.91-0.96, AUC = 0.86-0.93; p ≥ 0.10); readers with 300 cases performed well in mpMRI, but significantly worse in bpMR: SNS = 0.58 versus 0.91 (p < 0.0001) and AUC = 0.73 versus 0.86 (p = 0.01); the limited experience of readers with 100 cases showed in mpMRI (SNS = 0.71; AUC = 0.77) and even more in bpMRI (SNS = 0.50; AUC = 0.68). CONCLUSION: The study revealed the impact of the readers' experience when using bpMRI. The bpMRI without contrast media was a valid alternative for expert readers, whereas less experienced ones needed DCE to significantly boost SNS and AUC. Results indicate 700-800 cases as threshold for reliable interpretation with bpMRI.