Our consensus statements demonstrate a set of criteria that are required for the practical dissemination of consistently high-quality prostate mpMRI as a diagnostic test before biopsy in men at risk.

espite the merits of the apparent diffusion coefficient (ADC), reporting quantitative ADC values is not a routine part of clinical practice. This is partially due to lack of biologic specificity (1). Recently, our group presented the feasibility of Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumors (VERDICT) MRI as a quantitative microstructural imaging tool for prostate cancer (2). VERDICT combines a diffusion-weighted MRI acquisition with a mathematical model and assigns the diffusion-weighted MRI signal to three principal components: (a) intracellular water, (b) water in the extracellular extravascular space, and (c) water in the microvasculature. Because the fraction of each of these compartments differs between each Gleason grade (3), we hypothesized that

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.

The VERDICT framework for modelling diffusion MRI data aims to relate parameters from a biophysical model to histological features used for tumour grading in prostate cancer. Validation of the VERDICT model is necessary for clinical use. This study compared VERDICT parameters obtained ex vivo with histology in five specimens from radical prostatectomy. A patient‐specific 3D‐printed mould was used to investigate the effects of fixation on VERDICT parameters and to aid registration to histology. A rich diffusion data set was acquired in each ex vivo prostate before and after fixation. At both time points, data were best described by a two‐compartment model: the model assumes that an anisotropic tensor compartment represents the extracellular space and a restricted sphere compartment models the intracellular space. The effect of fixation on model parameters associated with tissue microstructure was small. The patient‐specific mould minimized tissue deformations and co‐localized slices, so that rigid registration of MRI to histology images allowed region‐based comparison with histology. The VERDICT estimate of the intracellular volume fraction corresponded to histological indicators of cellular fraction, including high values in tumour regions. The average sphere radius from VERDICT, representing the average cell size, was relatively uniform across samples. The primary diffusion direction from the extracellular compartment of the VERDICT model aligned with collagen fibre patterns in the stroma obtained by structure tensor analysis. This confirmed the biophysical relationship between ex vivo VERDICT parameters and tissue microstructure from histology.

Background Chemical exchange saturation transfer (CEST) can potentially support cancer imaging with metabolically derived information. Multiparametric prostate MRI has improved diagnosis but may benefit from additional information to reduce the need for biopsies. Purpose To optimize an acquisition and postprocessing protocol for 3.0 T multipool CEST analysis of prostate data and evaluate the repeatability of the technique. Study Type Prospective. Subjects Five healthy volunteers (age range: 24–47 years; median age: 28 years) underwent two sessions (interval range: 7–27 days; median interval: 20 days) and two biopsy‐proven prostate cancer patients were evaluated once. Patient 1 (71 years) had a Gleason 3 + 4 transition zone (TZ) tumor and patient 2 (55 years) had a Gleason 4 + 3 peripheral zone (PZ) tumor. Field Strength 3.0 T. Sequences run: T2‐weighted turbo‐spin‐echo (TSE); diffusion‐weighted imaging; CEST; WASABI (for B0 determination). Assessment Saturation, readout, and fit‐model parameters were optimized to maximize in vivo amide and nuclear Overhauser effect (NOE) signals. Repeatability (intrasession and intersession) was evaluated in healthy volunteers. Subsequently, preliminary evaluation of signal differences was made in patients. Regions of interest were drawn by two post‐FRCR board‐certified readers, both with over 5 years of experience in multiparametric prostate MRI. Statistical Tests Repeatability was assessed using Bland–Altman analysis, coefficient of variation (CV), and 95% limits of agreement (LOA). Statistical significance of CEST contrast was calculated using a nonparametric Mann–Whitney U‐test. Results The optimized saturation scheme was found to be 60 sinc‐Gaussian pulses with 40 msec pulse duration, at 50% duty‐cycle with continuous‐wave pulse equivalent B1 power (B1CWPE) of 0.92 μT. The magnetization transfer (MT) contribution to the fit‐model was centered at –1.27 ppm. Intersession coefficients of variation (CVs) of the amide, NOE, and magnetization transfer (MT) and asymmetric magnetization transfer ratio (MTRasym) signals of 25%, 23%, 18%, and 200%, respectively, were observed. Fit‐metric and MTRasym CVs agreed between readers to within 4 and 10 percentage points, respectively. Data Conclusion Signal differences of 0.03–0.10 (17–43%) detectable depending upon pool, with MT the most repeatable (signal difference of 17–22% detectable). Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1238–1250.