Nakai, Hirotsugu

Abstract Purpose To assess if PSA density (PSAD) velocity is a predictor of clinically significant prostate cancer (csPCa). Methods Patients undergoing prostate MR for suspected csPCa between 2017 and 2023 were included. Of these, patients who underwent two or more MR exams within 0.3 to 2 years to each other and pathological confirmation within 1 year after the last MR were identified. PI-RADS score, patient age, screening or active surveillance (AS), PSA, PSAD, PSA-velocity and PSAD-velocity were assessed if they were predictor of csPCa. For univariate analysis, all variables were assessed using student t or chi-square tests. When variables were considered significant, variables were entered into multivariate logistic regression model. Results 23,244 MRs were performed (17,703 screening, 5,541AS). 1,174 patients (mean age: 65.9) met the inclusion criteria, and 550 patients (47%) were diagnosed with csPCa. On univariate analysis, PI-RADS score (p<.0001), age (67.0 vs 64.9, p<.0001), AS exam (67% vs 59%, p = 0.004), PSA (7.5 vs 6.6, p<.001 ), PSAD (0.16 vs 0.12, p<.0001) and PSAD-velocity (0.023 vs 0.012, p = 0.005 ) were predictors of csPCa but PSA-velocity (1.4 vs 1.2, p=.4) was not. On multivariate analysis, PI-RADS score (p<.0001), age (p<.0001), AS exam (p<.0001), PSA (p<.0001) and PSAD (p<.0001) remained significant predictors, but PSAD-velocity was not (p=.1). Conclusions Higher PSAD-velocity was associated with presence of csPCa on univariate analysis but not on multivariate analysis when combined with other predictors such PI-RADS score, age, and PSAD.

Objectives: The aims of the study are to develop a prostate cancer risk prediction model that combines clinical and magnetic resonance imaging (MRI)–related findings and to assess the impact of adding Prostate Imaging-Reporting and Data System (PI-RADS) ≥3 lesions-level findings on its diagnostic performance. Methods: This 3-center retrospective study included prostate MRI examinations performed with clinical suspicion of clinically significant prostate cancer (csPCa) between 2018 and 2022. Pathological diagnosis within 1 year after the MRI was used to diagnose csPCa. Seven clinical, 3 patient-level MRI-related, and 4 lesion-level MRI-related findings were extracted. After feature selection, 2 logistic regression models with and without lesions-level findings were created using data from facility I and II (development cohort). The area under the receiver operating characteristic curve (AUC) between the 2 models was compared in the PI-RADS ≥3 population in the development cohort and Facility III (validation cohort) using the Delong test. Interfacility differences of the selected predictive variables were evaluated using the Kruskal-Wallis test or chi-squared test. Results: Selected lesion-level features included the peripheral zone involvement and apparent diffusion coefficient (ADC) values. The model with lesions-level findings had significantly higher AUC than the model without in 655 examinations in the development cohort (0.81 vs 0.79, respectively, P = 0.005), but not in 553 examinations in the validation cohort (0.77 vs 0.76, respectively). Large interfacility differences were seen in the ADC distribution (P < 0.001) and csPCa proportion in PI-RADS 3–5 (P < 0.001). Conclusions: Adding lesions-level findings improved the csPCa discrimination in the development but not the validation cohort. Interfacility differences impeded model generalization, including the distribution of reported ADC values and PI-RADS score-level csPCa proportion.

Multifaceted risk stratification is required for management of upper tract urothelial carcinoma, and imaging plays an important integrated role in diagnosis and management.

The performance of a deep learning model was not different from that of radiologists in the detection of clinically significant prostate cancer at MRI, and gradient-weighted class activation maps localized the tumor.