Purpose To evaluate the performance of an artificial intelligence (AI) model in detecting overall and clinically significant prostate cancer (csPCa)-positive lesions on paired external and in-house biparametric MRI (bpMRI) scans and assess performance differences between each dataset. Materials and Methods This single-center retrospective study included patients who underwent prostate MRI at an external institution and were rescanned at the authors' institution between May 2015 and May 2022. A genitourinary radiologist performed prospective readouts on in-house MRI scans following the Prostate Imaging Reporting and Data System (PI-RADS) version 2.0 or 2.1 and retrospective image quality assessments for all scans. A subgroup of patients underwent an MRI/US fusion-guided biopsy. A bpMRI-based lesion detection AI model previously developed using a completely separate dataset was tested on both MRI datasets. Detection rates were compared between external and in-house datasets with use of the paired comparison permutation tests. Factors associated with AI detection performance were assessed using multivariable generalized mixed-effects models, incorporating features selected through forward stepwise regression based on the Akaike information criterion. Results The study included 201 male patients (median age, 66 years [IQR, 62-70 years]; prostate-specific antigen density, 0.14 ng/mL2 [IQR, 0.10-0.22 ng/mL2]) with a median interval between external and in-house MRI scans of 182 days (IQR, 97-383 days). For intraprostatic lesions, AI detected 39.7% (149 of 375) on external and 56.0% (210 of 375) on in-house MRI scans (P < .001). For csPCa-positive lesions, AI detected 61% (54 of 89) on external and 79% (70 of 89) on in-house MRI scans (P < .001). On external MRI scans, better overall lesion detection was associated with a higher PI-RADS score (odds ratio [OR] = 1.57; P = .005), larger lesion diameter (OR = 3.96; P < .001), better diffusion-weighted MRI quality (OR = 1.53; P = .02), and fewer lesions at MRI (OR = 0.78; P = .045). Better csPCa detection was associated with a shorter MRI interval between external and in-house scans (OR = 0.58; P = .03) and larger lesion size (OR = 10.19; P < .001). Conclusion The AI model exhibited modest performance in identifying both overall and csPCa-positive lesions on external bpMRI scans. Keywords: MR Imaging, Urinary, Prostate Supplemental material is available for this article. © RSNA, 2024.
Radiology. Imaging cancer. 2024 Nov [Epub]
Enis C Yilmaz, Stephanie A Harmon, Yan Mee Law, Erich P Huang, Mason J Belue, Yue Lin, David G Gelikman, Kutsev B Ozyoruk, Dong Yang, Ziyue Xu, Jesse Tetreault, Daguang Xu, Lindsey A Hazen, Charisse Garcia, Nathan S Lay, Philip Eclarinal, Antoun Toubaji, Maria J Merino, Bradford J Wood, Sandeep Gurram, Peter L Choyke, Peter A Pinto, Baris Turkbey
From the Molecular Imaging Branch (E.C.Y., S.A.H., M.J.B., Y.L., D.G.G., K.B.O., N.S.L., P.E., P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and Diagnosis (E.P.H.), Center for Interventional Oncology (L.A.H., C.G., B.J.W.), Department of Radiology, Clinical Center (L.A.H., C.G., B.J.W.), Laboratory of Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182, Bldg 10, Rm B3B85, Bethesda, MD 20892; Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.); and NVIDIA Corporation, Santa Clara, Calif (D.Y., Z.X., J.T., D.X.).