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.
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.
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).
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.
Journal of computer assisted tomography. 2024 Nov 13 [Epub ahead of print]
Hirotsugu Nakai, Hiroaki Takahashi, Jordan D LeGout, Akira Kawashima, Adam T Froemming, Jason R Klug, Panagiotis Korfiatis, Derek J Lomas, Mitchell R Humphreys, Chandler Dora, Naoki Takahashi
From the Department of Radiology, Mayo Clinic, Rochester, MN., Department of Radiology, Mayo Clinic, Jacksonville, FL., Department of Radiology, Mayo Clinic, Scottsdale, AZ., Department of Urology, Mayo Clinic, Rochester, MN., Department of Urology, Mayo Clinic, Scottsdale, AZ., Department of Urology, Mayo Clinic, Jacksonville, FL.