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Multiparametric magnetic resonance imaging (mpMRI) and targeted biopsy (TB) facilitate accurate detection of clinically significant prostate cancer (csPC). However, it remains unclear how targeted cores should be applied for accurate diagnosis of csPC.To assess csPC detection rates for two target-directed MRI/transrectal ultrasonography (TRUS) fusion biopsy approaches, conventional TB and target saturation biopsy (TS).This was a prospective single-center study of outcomes for transperineal MRI/TRUS fusion biopsies for 170 men. Half of the men (n = 85) were randomized to conventional TB with four cores per lesion and half (n = 85) to TS with nine cores. Biopsies were performed by three experienced board-certified urologists.PC and csPC (International Society of Urological Pathology grade group ≥2) detection rates for systematic biopsy (SB), TB, and TS were analyzed using McNemar's test for intrapatient comparisons and Fisher's exact test for TS versus TB. A combination of targeted biopsy (TS or TB) and SB served as the reference.According to the reference, csPC was diagnosed for 57 men in the TS group and 36 men in the TB group. Of these, TS detected 57/57 csPC cases and TB detected 33/36 csPC cases (p = 0.058). Detection of Gleason grade group 1 disease was 10/12 cases with TS and 8/17 cases with TB (p = 0.055). In addition, TS detected 97% of 63 csPC lesions, compared to 86% with TB (p = 0.1). Limitations include the single-center design, the limited generalizability owing to the transperineal biopsy route, the lack of central review of pathology and radical prostatectomy correlation, and uneven distributions of csPC prevalence, Prostate Imaging-Reporting and Data System (PI-RADS) 5 lesions, men with two or more PI-RADS ≥3 lesions, and prostate-specific antigen density between the groups, which may have affected the results.In our study, rates of csPC detection did not significantly differ between TS and TB.In this study, we investigated two targeted approaches for taking prostate biopsy samples after observation of suspicious lesions on prostate scans. We found that the rates of detection of prostate cancer did not significantly differ between the two approaches.

Background Currently, interpretation of prostate MRI is performed qualitatively. Quantitative assessment of the mean apparent diffusion coefficient (mADC) is promising to improve diagnostic accuracy while radiomic machine learning (RML) allows to probe complex parameter spaces to identify the most promising multi-parametric models. We have previously developed quantitative RML and ADC classifiers for prediction of clinically significant prostate cancer (sPC) from prostate MRI, however these have not been combined with radiologist PI-RADS assessment. Purpose To propose and evaluate diagnostic algorithms combining quantitative ADC or RML and qualitative PI-RADS assessment for prediction of sPC. Methods and population The previously published quantitative models (RML and mADC) were utilized to construct four algorithms: 1) Down(ADC) and 2) Down(RML): clinically detected PI-RADS positive prostate lesions (defined as either PI-RADS≥3 or ≥4) were downgraded to MRI negative upon negative quantitative assessment; and 3) Up(ADC) and 4) Up(RML): MRI-negative lesions were upgraded to MRI-positive upon positive assessment of quantitative parameters. Analyses were performed at the individual lesion level and the patient level in 133 consecutive patients with suspicion for clinically significant prostate cancer (sPC, International Society of Urological Pathology (ISUP) grade group≥2), the test set subcohort of a previously published patient population. McNemar test was used to compare differences in sensitivity, specificity and accuracy. Differences between lesions of different prostate zones were assessed using ANOVA. Reduction in false positive assessments was assessed as ratios. Results Compared to clinical assessment at the PI-RADS≥4 cut-off alone, algorithms Down(ADC/RML) improved specificity from 43% to 65% (p = 0.001)/62% (p = 0.003), while sensitivity did not change significantly at 89% compared to 87% (p = 1.0)/89% (unchanged) on the patient level. Reduction of false positive lesions was 50% [26/52] in the PZ and 53% [15/28] in the TZ. Algorithms Up(ADC/RML) led, on a patient basis, to an unfavorable loss of specificity from 43% to 30% (p = 0.039)/32% (p = 0.106), with insignificant increase of sensitivity from 89% to 96%/96% (both p = 1.0). Compared to clinical assessment at the PI-RADS≥3 cut-off alone, similar results were observed for Down(ADC) with significantly increased specificity from 2% to 23% (p 0.001) and unchanged sensitivity on the lesion level; patient level specificity increased only non-significantly. Conclusion Downgrading PI-RADS≥3 and ≥ 4 lesions based on quantitative mADC measurements or RML classifiers can increase diagnostic accuracy by enhancing specificity and preserving sensitivity for detection of sPC and reduce false positives.

Background Multiparametric magnetic resonance imaging (mpMRI) is gaining widespread acceptance in prostate cancer (PC) diagnosis and improves significant PC (sPC; Gleason score≥3+4) detection. Decision making based on European Randomised Study of Screening for PC (ERSPC) risk-calculator (RC) parameters may overcome prostate-specific antigen (PSA) limitations. Objective We added pre-biopsy mpMRI to ERSPC-RC parameters and developed risk models (RMs) to predict individual sPC risk for biopsy-naive men and men after previous biopsy. Design, setting, and participants We retrospectively analyzed clinical parameters of 1159 men who underwent mpMRI prior to MRI/transrectal ultrasound fusion biopsy between 2012 and 2015. Outcome measurements and statistical analysis Multivariate regression analyses were used to determine significant sPC predictors for RM development. The prediction performance was compared with ERSPC-RCs, RCs refitted on our cohort, Prostate Imaging Reporting and Data System (PI-RADS) v1.0, and ERSPC-RC plus PI-RADSv1.0 using receiver-operating characteristics (ROCs). Discrimination and calibration of the RM, as well as net decision and reduction curve analyses were evaluated based on resampling methods. Results and limitations PSA, prostate volume, digital-rectal examination, and PI-RADS were significant sPC predictors and included in the RMs together with age. The ROC area under the curve of the RM for biopsy-naive men was comparable with ERSPC-RC3 plus PI-RADSv1.0 (0.83 vs 0.84) but larger compared with ERSPC-RC3 (0.81), refitted RC3 (0.80), and PI-RADS (0.76). For postbiopsy men, the novel RM's discrimination (0.81) was higher, compared with PI-RADS (0.78), ERSPC-RC4 (0.66), refitted RC4 (0.76), and ERSPC-RC4 plus PI-RADSv1.0 (0.78). Both RM benefits exceeded those of ERSPC-RCs and PI-RADS in the decision regarding which patient to receive biopsy and enabled the highest reduction rate of unnecessary biopsies. Limitations include a monocentric design and a lack of PI-RADSv2.0. Conclusions The novel RMs, incorporating clinical parameters and PI-RADS, performed significantly better compared with RMs without PI-RADS and provided measurable benefit in making the decision to biopsy men at a suspicion of PC. For biopsy-naive patients, both our RM and ERSPC-RC3 plus PI-RADSv1.0 exceeded the prediction performance compared with clinical parameters alone. Patient summary Combined risk models including clinical and imaging parameters predict clinically relevant prostate cancer significantly better than clinical risk calculators and multiparametric magnetic resonance imaging alone. The risk models demonstrate a benefit in making a decision about which patient needs a biopsy and concurrently help avoid unnecessary biopsies.

Mean ADC has high predictive value for the presence of clinically significant prostate cancer (sPC). Measurement variability is introduced by different scanners, protocols, intra-and inter-patient variation. Internal calibration by ADC ratios can address such fluctuations however can potentially lower the biological value of quantitative ADC determination by being sensitive to deviations in reference tissue signal.To better understand the predictive value of quantitative ADC measurements in comparison to internal reference ratios when measured in a single scanner, single protocol setup.284 consecutive patients who underwent 3 T MRI on a single scanner followed by MRI-transrectal ultrasound fusion biopsy were included. A board-certified radiologist retrospectively reviewed all MRIs blinded to clinical information and placed regions of interest (ROI) on all focal lesions and the following reference regions: normal-appearing peripheral zone (PZNL) and transition zone (TZNL), the urinary bladder (BLA), and right and left internal obturator muscle (RIOM, LIOM). ROI-based mean ADC and ADC ratios to the reference regions were compared regarding their ability to predict the aggressiveness of prostate cancer. Spearman's rank correlation coefficient was used to estimate the correlation between ADC parameters, Gleason score (GS) and ADC ratios. The primary endpoint was presence of sPC, defined as a GS ≥ 3 + 4. Univariable and multivariable logistic regression models were constructed to predict sPC. Receiver operating characteristics curves (ROC) were used for visualization; DeLong test was used to evaluate the differences of the area under the curve (AUC). Bias-corrected AUC values and corresponding 95 %-CI were calculated using bootstrapping with 100 bootstrap samples.After exclusion of patients who received prior treatment, 259 patients were included in the final cohort of which 220 harbored 351 MR lesions. Mean ADC and ADC ratios demonstrated a negative correlation with the GS. Mean ADC had the strongest correlation with ρ of -0.34, followed by ADCratioPZNL (ρ=-0.32). All ADC parameters except ADCratioLIOM (p = 0.07) were associated with sPC p0.05). Mean ADC and ADCratioPZNL had the highest ROC AUC of all parameters (0.68). Multivariable models with mean ADC improve predictive performance.A highly standardized single-scanner mean ADC measurement could not be improved upon using any of the single ADC ratio parameters or combinations of these parameters in predicting the aggressiveness of prostate cancer.