R ecent highest-level evidence is mounting that there is an unequivocal benefit of MRI-targeted biopsies replacing or complementing systematic biopsies for diagnosis of prostate cancer (1-3). Realizing the integral role of MRI in diagnosis of prostate cancer, the Prostate Imaging Reporting and Data System (PI-RADS) continues to be developed (4-7). PI-RADS allows standardization of prostate MRI interpretation, which is a difficult task due to heterogeneous signal changes from benign prostatic hyperplasia, inflammation, and scarring after biopsy mimicking or hiding the appearance of prostate cancer. Even with PI-RADS, the high level of expertise required for accurate interpretation and persistent interobserver variability (8) limit consistency and availability while the demand for prostate MRI interpretation is at unprecedented levels. As convolutional neural networks approach or exceed human performance in natural image analysis (9), they promise to revolutionize computer-aided diagnosis and are increasingly evaluated in prostate MRI (10-15). U-Net is a convolutional neural network architecture optimized for image segmentation. It consists of an encoder and decoder

Stejskal and Tanner's ingenious pulsed field gradient design from 1965 has made diffusion NMR and MRI the mainstay of most studies seeking to resolve microstructural information in porous systems in general and biological systems in particular. Methods extending beyond Stejskal and Tanner's design, such as double diffusion encoding (DDE) NMR and MRI, may provide novel quantifiable metrics that are less easily inferred from conventional diffusion acquisitions. Despite the growing interest on the topic, the terminology for the pulse sequences, their parameters, and the metrics that can be derived from them remains inconsistent and disparate among groups active in DDE. Here, we present a consensus of those groups on terminology for DDE sequences and associated concepts. Furthermore, the regimes in which DDE metrics appear to provide microstructural information that cannot be achieved using more conventional counterparts (in a model-free fashion) are elucidated. We highlight in particular DDE's potential for determining microscopic diffusion anisotropy and microscopic fractional anisotropy, which offer metrics of microscopic features independent of orientation dispersion and thus provide information complementary to the standard, macroscopic, fractional anisotropy conventionally obtained by diffusion MR. Finally, we discuss future vistas and perspectives for DDE. Magn Reson Med 75:82-87, 2016. V C 2015 Wiley Periodicals, Inc.

Purpose To compare biparametric contrast-free radiomic machine learning (RML), mean apparent diffusion coefficient (ADC), and radiologist assessment for characterization of prostate lesions detected during prospective MRI interpretation. Materials and Methods This single-institution study included 316 men (mean age ± standard deviation, 64.0 years ± 7.8) with an indication for MRI-transrectal US fusion biopsy between May 2015 and September 2016 (training cohort, 183 patients; test cohort, 133 patients). Lesions identified by prospective clinical readings were manually segmented for mean ADC and radiomics analysis. Global and zone-specific random forest RML and mean ADC models for classification of clinically significant prostate cancer (Gleason grade group ≥ 2) were developed on the training set and the fixed models tested on an independent test set. Clinical readings, mean ADC, and radiomics were compared by using the McNemar test and receiver operating characteristic (ROC) analysis. Results In the test set, radiologist interpretation had a per-lesion sensitivity of 88% (53 of 60) and specificity of 50% (79 of 159). Quantitative measurement of the mean ADC (cut-off 732 mm/sec) significantly reduced false-positive (FP) lesions from 80 to 60 (specificity 62% [99 of 159]) and false-negative (FN) lesions from seven to six (sensitivity 90% [54 of 60]) (P = .048). Radiologist interpretation had a per-patient sensitivity of 89% (40 of 45) and specificity of 43% (38 of 88). Quantitative measurement of the mean ADC reduced the number of patients with FP lesions from 50 to 43 (specificity 51% [45 of 88]) and the number of patients with FN lesions from five to three (sensitivity 93% [42 of 45]) (P = .496). Comparison of the area under the ROC curve (AUC) for the mean ADC (AUC = 0.84; AUC ≤ 0.87) vs the RML (AUC = 0.88, P = .176; AUC ≤ 0.89, P ≥ .493) showed no significantly different performance. Conclusion Quantitative measurement of the mean apparent diffusion coefficient (ADC) improved differentiation of benign versus malignant prostate lesions, compared with clinical assessment. Radiomic machine learning had comparable but not better performance than mean ADC assessment. © RSNA, 2018 Online supplemental material is available for this article.

Objective To evaluate diffusion kurtosis imaging (DKI) and magnetisation transfer imaging (MTI) compared to standard MRI for prostate cancer assessment in a re-biopsy population. Methods Thirty-patients were imaged at 3 T including DKI (K app and D app) with b-values 150/450/800/1150/1500 s/mm 2 and MTI performed with and without MT saturation. Patients underwent transperineal biopsy based on prospectively defined MRI targets. Receiver-operating characteristic (ROC) analyses assessed the parameters and Wilcoxon-signed ranked test assessed relationships between metrics. Results Twenty patients had ≥ 1 core positive for cancer in a total of 26 MRI targets (Gleason 3+3 in 8, 3+4 in 12, ≥ 4+3 in 6): 13 peripheral (PZ) and 13 transition zone (TZ). The apparent diffusion coefficient (ADC) and D app were significantly lower and the K app and MT ratio (MTR) significantly higher in tumour versus benign tissue (all p ≤ 0.005); ROC values 0.767-1.000. Normal TZ had: lower ADC and D app and higher K app and MTR compared to normal PZ. MTR showed a moderate correlation to K app (r = 0.570) and D app (r =-0.537) in normal tissue but a poor correlation in tumours. No parameter separated low-grade (Gleason 3+3) from high-grade (≥ 3+4) disease for either PZ (p = 0.414-0.825) or TZ (p = 0.148-0.825). Conclusion ADC, D app , K app and MTR all distinguished benign tissue from tumour, but none reliably differentiated low-from high-grade disease. Key Points • MTR was significantly higher in PZ and TZ tumours versus normal tissue • K app was significantly lower and D app higher for PZ and TZ tumours • There was no incremental value for DKI/MTI over mono-exponential ADC parameters • No parameter could consistently differentiate low-grade (Gleason 3+3) from high-grade (≥ 3+4) disease • Divergent MTR/DKI values in TZ tumours suggests they offer different functional information