ObjectiveTo develop and externally validate a predictive model for detection of significant prostate cancer. Patients and MethodsDevelopment of the model was based on a prospective cohort including 393 men who underwent multiparametric magnetic resonance imaging (mpMRI) before biopsy. External validity of the model was then examined retrospectively in 198 men from a separate institution whom underwent mpMRI followed by biopsy for abnormal prostate-specific antigen (PSA) level or digital rectal examination (DRE). A model was developed with age, PSA level, DRE, prostate volume, previous biopsy, and Prostate Imaging Reporting and Data System (PIRADS) score, as predictors for significant prostate cancer (Gleason 7 with >5% grade 4, ≥20% cores positive or ≥7 mm of cancer in any core). Probability was studied via logistic regression. Discriminatory performance was quantified by concordance statistics and internally validated with bootstrap resampling. ResultsIn all, 393 men had complete data and 149 (37.9%) had significant prostate cancer. While the variable model had good accuracy in predicting significant prostate cancer, area under the curve (AUC) of 0.80, the advanced model (incorporating mpMRI) had a significantly higher AUC of 0.88 (P < 0.001). The model was well calibrated in internal and external validation. Decision analysis showed that use of the advanced model in practice would improve biopsy outcome predictions. Clinical application of the model would reduce 28% of biopsies, whilst missing 2.6% significant prostate cancer. ConclusionsIndividualised risk assessment of significant prostate cancer using a predictive model that incorporates mpMRI PIRADS score and clinical data allows a considerable reduction in unnecessary biopsies and reduction of the risk of overdetection of insignificant prostate cancer at the cost of a very small increase in the number of significant cancers missed.

Multiparametric magnetic resonance imaging reported by expert radiologists achieved an excellent negative predictive value and a moderate positive predictive value for significant prostate cancer at 1.5 and 3.0 Tesla.

In men with an abnormal prostate specific antigen/digital rectal examination, multiparametric magnetic resonance imaging detected significant prostate cancer with an excellent negative predictive value and moderate positive predictive value. The use of multiparametric magnetic resonance imaging to diagnose significant prostate cancer may result in a substantial number of unnecessary biopsies while missing a minimum of significant prostate cancers.

Background: Prostate cancer (PCa) influences its surrounding habitat, which tends to manifest as different phenotypic appearances on magnetic resonance imaging (MRI). This region surrounding the PCa lesion, or the peri-tumoral region, may encode useful information that can complement intra-tumoral information to enable better risk stratification. Purpose: To evaluate the role of peri-tumoral radiomic features on bi-parametric MRI (T2-weighted and Diffusion-weighted) to distinguish PCa risk categories as defined by D’Amico Risk Classification System. Materials and Methods: We studied a retrospective, HIPAA-compliant, 4-institution cohort of 231 PCa patients (n = 301 lesions) who underwent 3T multi-parametric MRI prior to biopsy. PCa regions of interest (ROIs) were delineated on MRI by experienced radiologists following which peri-tumoral ROIs were defined. Radiomic features were extracted within the intra- and peri-tumoral ROIs. Radiomic features differentiating low-risk from: (1) high-risk (L-vs.-H), and (2) (intermediate- and high-risk (L-vs.-I + H)) lesions were identified. Using a multi-institutional training cohort of 151 lesions (D1, N = 116 patients), machine learning classifiers were trained using peri- and intra-tumoral features individually and in combination. The remaining 150 lesions (D2, N = 115 patients) were used for independent hold-out validation and were evaluated using Receiver Operating Characteristic (ROC) analysis and compared with PI-RADS v2 scores. Results: Validation on D2 using peri-tumoral radiomics alone resulted in areas under the ROC curve (AUCs) of 0.84 and 0.73 for the L-vs.-H and L-vs.-I + H classifications, respectively. The best combination of intra- and peri-tumoral features resulted in AUCs of 0.87 and 0.75 for the L-vs.-H and L-vs.-I + H classifications, respectively. This combination improved the risk stratification results by 3–6% compared to intra-tumoral features alone. Our radiomics-based model resulted in a 53% accuracy in differentiating L-vs.-H compared to PI-RADS v2 (48%), on the validation set. Conclusion: Our findings suggest that peri-tumoral radiomic features derived from prostate bi-parametric MRI add independent predictive value to intra-tumoral radiomic features for PCa risk assessment.

Purpose: To investigate whether the loss of corticomedullary differentiation (CMD) on T1-weighted MR images due to renal insufficiency can be attributed to changes in T1 values of the cortex, medulla, or both. Materials and Methods:Study subjects included 10 patients (serum creatinine range 0.6 -3.0 mg/dL) referred for suspected renovascular disease who underwent 99m Tc-diethylene triamine pentaacetic acid (DTPA) renography to determine single kidney glomerular filtration rate (SKGFR) and same-day MRI, which included T1 measurements and unenhanced T1-weighted gradient echo imaging. Corticomedullary differentiation on T1-weighted images was assessed qualitatively and quantitatively.Results: SKGFR values ranged from 3.5 to 89.4 mL/minute based on radionuclide studies. T1 relaxation times of the medulla exceeded those of renal cortex by 147.9 Ϯ 176.0 msec (mean Ϯ standard deviation [SD]). Regression analysis showed a negative correlation between cortex T1 and SKGFR (r ϭ -0.5; P ϭ 0.03), whereas there was no significant correlation between medullary T1 and SKGFR. The difference between medullary and cortical T1s correlated significantly with SKGFR (r ϭ 0.58; P Ͻ 0.01). In all five kidneys with a corticomedullary contrast-to-noise ratio (CNR) Ͻ5.0 on T1-weighted images, SKGFR was less than 20 mL/minute. Conclusion:In our subject population, loss of CMD with decreasing SKGFR can be attributed primarily to an increased T1 relaxation time of the cortex. Medullary T1 values vary but do not appear to correlate with degree of renal insufficiency. ON T1-WEIGHTED MR IMAGES of the normal healthy kidney, the cortex can be clearly differentiated from the medulla, a characteristic referred to as corticomedullary differentiation (CMD). CMD reflects the T1 differences between the cortex and medulla, where the cortex, due to its shorter T1 relaxation time, appears hyperintense with respect to the medulla. Loss of CMD has been observed in renal insufficiency, secondary to a variety of etiologies, including glomerulonephritis, acute tubular necrosis, end-stage chronic renal failure, obstructive hydronephrosis, Fabry's disease, and acute allograft rejection (1-8). While average T1 values in normal adult kidneys at 1.5 T have been reported to be 882 Ϯ 59 msec (mean Ϯ standard deviation [SD]) for cortex and 1163 Ϯ 118 msec for medulla (9), to our knowledge, the underlying changes in T1 at 1.5 T that result in loss of CMD in renal insufficiency have not been determined. Our purpose was to investigate whether the loss of CMD is attributable to changes in T1 values of the cortex, medulla, or both. MATERIALS AND METHODS PatientsThis study included a total of 10 patients (five female, five male) ranging from 38 to 90 years of age (69.8 Ϯ 16.9 years) who were referred for suspected renovascular disease. Patients had underlying diagnoses of chronic renal failure and hypertension (N ϭ 1) and hypertension alone (N ϭ 9) and were being evaluated for renal artery stenosis. Serum creatinine levels in five patients were less than 1.0 mg/dL (range ϭ 0...