Histopathological to multiparametric MRI spatial mapping of extended systematic sextant and MR/TRUS-fusion-targeted biopsy of the prostate.

PURPOSE: MRI has limited ability to detect multifocal disease or the full extent of prostate involvement with clinically significant prostate cancer (sPC). We compare the spatial co-localization at sextant resolution of MRI lesions and histopathological mapping by combined targeted and extended systematic biopsies. MATERIALS AND METHODS: Sextants were mapped for sPC (ISUP group ≥ 2) by 24-core transperineal systematic biopsies in 316 patients with suspicion for sPC and by MR lesions of PI-RADS score of ≥ 3. The gold standard is combined systematic (median 23 cores) and targeted biopsies. RESULTS: Of 316 men, 121 (38%) harbored sPC. Of these 121 patients, 4 (3%) had a negative MRI. MRI correctly identified 117/121 (97%) patients with sPC. In these patients, mpMRI missed no additional sPC in 96 (82%), while MRI-negative sPC lesions were present in 21 patients (18%). Of 1896 sextants, 379 (20%) harbored sPC. MR-positive sextants contained sPC in 26% (337/1275), compared to 7% (42/621) in MR-negative sextants. On a patient basis, sensitivity was 0.97, specificity 0.22, positive predictive value 0.43, and negative predictive value 0.91. On a sextant basis, sensitivity was 0.73, specificity 0.38, positive predictive value 0.26, and negative predictive value 0.93. CONCLUSION: MpMRI mapping agreed well with histopathology with, at the observed sPC prevalence and on a patient basis, excellent sensitivity and negative predictive value, and acceptable specificity and positive predictive value for sPC. However, 18% of sPC was outside the mpMRI mapped region, quantifying limitations of MRI for complete localization of disease extent. KEY POINTS: • Currently, exclusive MRI mapping of the prostate for focal treatment planning cannot be recommended, as significant prostate cancer may remain untreated in a substantial number of cases. • At the observed sPC prevalence and on a patient basis, mpMRI has excellent sensitivity and NPV, and acceptable specificity and PPV for detection of prostate cancer, supporting its use to detect suspicious lesions before biopsy. • Despite the excellent global performance, 18% of sPC was outside the mpMRI mapped region even when a security margin of 10 mm was considered, indicating that prostate MRI has limited ability to completely map all cancer foci within the prostate.

Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values.

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 mm2/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 (AUCglobal = 0.84; AUCzone-specific ≤ 0.87) vs the RML (AUCglobal = 0.88, P = .176; AUCzone-specific ≤ 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.