Purpose To simulate an artificial intelligence (AI)-driven triaging workflow in which an AI system, using high-confidence thresholds, assesses a subset of prostate MRI examinations for clinically significant prostate cancer (csPCa), compare the assessment with stand-alone radiologists, and evaluate the number of examinations triaged by the AI to estimate potential workload reduction. Materials and Methods Data from an international AI confirmatory study (February 2022-November 2023) were used in this retrospective study. MRI examinations of 500 men with suspected csPCa from four European centers were included. Exclusion criteria were prior prostate treatment, prior csPCa, or considerable imaging artifacts. AI-triaging thresholds were calibrated on 100 examinations. The AI system assessed examinations exceeding high-specificity or high-sensitivity thresholds, with the remaining examinations deferred to radiologists. The workflow was simulated on 400 examinations, including examinations from an external site, incorporating assessments from 62 radiologists. Reference standards were histopathology and/or 3 or more years of follow-up. Sensitivity and specificity of the triaging workflow were compared with the conventional workflow using multireader, multicase analysis of variance. Results Among the 400 patients (median age, 66 years; IQR, 60-69 years) included for testing, radiologists achieved a sensitivity of 89.4% (95% CI: 85.8, 93.1) and specificity of 57.7% (95% CI: 52.3, 63.1). The AI-driven pathway maintained comparable sensitivity (89.0%; 95% CI: 85.0, 93.0; P = .36) but improved specificity by 11.5%, reaching 69.2% (95% CI: 64.4, 74.0; P Keywords: Prostate, MRI, Localization, Oncology, Comparative Studies, Diagnosis Supplemental material is available for this article. ClinicalTrials.gov registration no. NCT05489341 © RSNA, 2026.
Twilt et al. (Fri,) studied this question.