To evaluate the performance of a multi-parametric MRI (mp-MRI)-based radiomics signature for discriminating between clinically significant prostate cancer (csPCa) and insignificant PCa (ciPCa). Two hundred and eighty patients with pathology-proven PCa were enrolled and were randomly divided into training and test cohorts. Eight hundred and nineteen radiomics features were extracted from mp-MRI for each patient. The minority group in the training cohort was balanced via the synthetic minority over-sampling technique (SMOTE) method. We used minimum-redundancy maximum-relevance (mRMR) selection and the LASSO algorithm for feature selection and radiomics signature building. The classification performance of the radiomics signature for csPCa and ciPCa was evaluated by receiver operating characteristic curve analysis in the training and test cohorts. Nine features were selected for the radiomics signature building. Significant differences in the radiomics signature existed between the csPCa and ciPCa groups in both the training and test cohorts (p < 0.01 for both). The AUC, sensitivity and specificity of the radiomics signature were 0.872 (95% CI: 0.823-0.921), 0.883, and 0.753, respectively, in the training cohort, and 0.823 (95% CI: 0.669-0.976), 0.841, and 0.727, respectively, in the test cohort. Mp-MRI-based radiomics signature have the potential to noninvasively discriminate between csPCa and ciPCa.

OBJECTIVE. The objective of our study was to evaluate the diagnostic accuracy of abbreviated biparametric MRI (bpMRI) versus standard multiparametric MRI (mpMRI) for prostate cancer (PCa) using guided biopsy or prostatectomy histopathology results as the reference standard. MATERIALS AND METHODS. A comprehensive literature search of PubMed, Web of Science, and Cochrane Library databases was performed by two researchers independently and the relevant references were assessed. Original research studies comparing bpMRI with mpMRI in diagnosing PCa were included. The methodologic quality of eligible studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Data necessary to complete 2 × 2 contingency tables were obtained to calculate the diagnostic performance of bpMRI and mpMRI using Stata (version 14). RESULTS. Ten studies were included, and a total of 1705 patients and 3419 lesions were analyzed. Sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagn...

Background: Prostate Imaging Reporting and Data System (PI-RADS) is a globally acceptable standardization for multiparametric magnetic resonance imaging (mp-MRI) in prostate cancer (PCa) diagnosis. The American College of Radiology revised the PI-RADS to address the limitations of version 1 in December 2014. This study aimed to determine whether the PI-RADS version 2 (PI-RADS v2) scoring system improves the diagnostic accuracy of mp-MRI of the prostate compared with PI-RADS v1. Methods: This retrospective study was approved by the institutional review board. A total of 401 consecutive patients, with clinically suspicious PCa undergoing 3.0 T mp-MRI (T2-weighted imaging + diffusion-weighted imaging + DCE) before transrectal ultrasound-guided biopsy between June 2013 and July 2015, were included in the study. All patients were scored using the 5-point PI-RADS scoring system based on either PI-RADS v1 or v2. Receiver operating characteristics were calculated for statistical analysis. Sensitivity, specificity, and diagnostic accuracy were compared using McNemar's test. Results: PCa was present in 150 of 401 (37.41%) patients. When we pooled data from both peripheral zone (PZ) and transition zone (TZ), the areas under the curve were 0.889 for PI-RADS v1 and 0.942 for v2 (P = 0.0001). Maximal accuracy was achieved with a score threshold of 4. At this threshold, in the PZ, similar sensitivity, specificity, and accuracy were achieved with v1 and v2 (all P > 0.05). In the TZ, sensitivity was higher for v2 than for v1 (96.36% vs. 76.36%, P = 0.003), specificity was similar for v2 and v1 (90.24% vs. 84.15%, P = 0.227), and accuracy was higher for v2 than for v1 (92.70% vs. 81.02%, P = 0.002). Conclusions: Both v1 and v2 showed good diagnostic performance for the detection of PCa. However, in the TZ, the performance was better with v2 than with v1.

Objective: To evaluate the performance of a T2-weighted image (T2WI)-based radiomics signature for differentiating between seminomas and nonseminomas. Materials and Methods: In this retrospective study, 39 patients with testicular germ-cell tumors (TGCTs) confirmed by radical orchiectomy were enrolled, including 19 cases of seminomas and 20 cases of nonseminomas. All patients underwent 3T magnetic resonance imaging (MRI) before radical orchiectomy. Eight hundred fifty-one radiomics features were extracted from the T2WI of each patient. Intra- and interclass correlation coefficients were used to select the features with excellent stability and repeatability. Then, we used the minimum-redundancy maximum-relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithms for feature selection and radiomics signature development. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance of the radiomics signature. Results: Five features were selected to build the radiomics signature. The radiomics signature was significantly different between the seminomas and nonseminomas (p < 0.01). The area under the curve (AUC), sensitivity, and specificity of the radiomics signature for discriminating between seminomas and nonseminomas were 0.979 (95% CI: 0.873–1.000), 90.00 (95% CI: 68.3–98.8), and 100.00 (95% CI: 82.4–100.0), respectively. Conclusion: The T2WI-based radiomics signature has the potential to non-invasively discriminate between seminomas and nonseminomas.

Yin X, et al. Korean J Radiol. 2020 Aug;21(8):998-1006. https://doi.org/10.3348/kjr.2020.0423