Background:Transrectal prostate biopsy has limited diagnostic accuracy. Prostate Imaging Compared to Transperineal Ultrasound-guided biopsy for significant prostate cancer Risk Evaluation (PICTURE) was a paired-cohort confirmatory study designed to assess diagnostic accuracy of multiparametric magnetic resonance imaging (mpMRI) in men requiring a repeat biopsy.Methods:All underwent 3 T mpMRI and transperineal template prostate mapping biopsies (TTPM biopsies). Multiparametric MRI was reported using Likert scores and radiologists were blinded to initial biopsies. Men were blinded to mpMRI results. Clinically significant prostate cancer was defined as Gleason ⩾4+3 and/or cancer core length ⩾6 mm.Results:Two hundred and forty-nine had both tests with mean (s.d.) age was 62 (7) years, median (IQR) PSA 6.8 ng ml (4.98–9.50), median (IQR) number of previous biopsies 1 (1–2) and mean (s.d.) gland size 37 ml (15.5). On TTPM biopsies, 103 (41%) had clinically significant prostate cancer. Two hundred and fourteen (86%) had a positive prostate mpMRI using Likert score ⩾3; sensitivity was 97.1% (95% confidence interval (CI): 92–99), specificity 21.9% (15.5–29.5), negative predictive value (NPV) 91.4% (76.9–98.1) and positive predictive value (PPV) 46.7% (35.2–47.8). One hundred and twenty-nine (51.8%) had a positive mpMRI using Likert score ⩾4; sensitivity was 80.6% (71.6–87.7), specificity 68.5% (60.3–75.9), NPV 83.3% (75.4–89.5) and PPV 64.3% (55.4–72.6).Conclusions:In men advised to have a repeat prostate biopsy, prostate mpMRI could be used to safely avoid a repeat biopsy with high sensitivity for clinically significant cancers. However, such a strategy can miss some significant cancers and overdiagnose insignificant cancers depending on the mpMRI score threshold used to define which men should be biopsied.

Objective The purpose of this study was: To test whether machine learning classifiers for transition zone (TZ) and peripheral zone (PZ) can correctly classify prostate tumors into those with/without a Gleason 4 component, and to compare the performance of the best performing classifiers against the opinion of three board-certified radiologists. Methods A retrospective analysis of prospectively acquired data was performed at a single center between 2012 and 2015. Inclusion criteria were (i) 3-T mp-MRI compliant with international guidelines, (ii) Likert ≥ 3/5 lesion, (iii) transperineal template ± targeted index lesion biopsy confirming cancer ≥ Gleason 3 + 3. Index lesions from 164 men were analyzed (119 PZ, 45 TZ). Quantitative MRI and clinical features were used and zone-specific machine learning classifiers were constructed. Models were validated using a fivefold cross-validation and a temporally separated patient cohort. Classifier performance was compared against the opinion of three board-certified radiologists. Results The best PZ classifier trained with prostate-specific antigen density, apparent diffusion coefficient (ADC), and maximum enhancement (ME) on DCE-MRI obtained a ROC area under the curve (AUC) of 0.83 following fivefold cross-validation. Diagnostic sensitivity at 50% threshold of specificity was higher for the best PZ model (0.93) when compared with the mean sensitivity of the three radiologists (0.72). The best TZ model used ADC and ME to obtain an AUC of 0.75 following fivefold cross-validation. This achieved higher diagnostic sensitivity at 50% threshold of specificity (0.88) than the mean sensitivity of the three radiologists (0.82). Conclusions Machine learning classifiers predict Gleason pattern 4 in prostate tumors better than radiologists. Key Points • Predictive models developed from quantitative multiparametric magnetic resonance imaging regarding the characterization of prostate cancer grade should be zone-specific. • Classifiers trained differently for peripheral and transition zone can predict a Gleason 4 component with a higher performance than the subjective opinion of experienced radiologists. • Classifiers would be particularly useful in the context of active surveillance, whereby decisions regarding whether to biopsy are necessitated.

Transperineal template prostate mapping biopsy causes a high urinary retention rate and a detrimental impact on genitourinary functional outcomes, including deterioration in urinary flow and sexual function. Our findings can be used to ensure adequate counseling about transperineal template prostate mapping biopsies. The results point to a need for strategies such as multiparametric magnetic resonance imaging and targeted biopsies to minimize the harms of transperineal template prostate mapping biopsy.

We present a pituitary cyst discovered on MRI in an amenorrheic patient that regressed over months. Although the precise etiology of the cyst is unproven, documentation of pituitary cyst regression has not to our knowledge been described previously.