Artificial intelligence-assisted reading of non-contrast prostate MRI: application and concordance with expert interpretation in a screening population within the PROSA trial.
Authors
Affiliations (6)
Affiliations (6)
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy.
- Department of Mechanical and Aerospace Engineering, Sapienza University, Rome, Italy.
- Institute for Liver and Digestive Health, University College London/Royal Free Campus, London, UK.
- Department of Maternal-Infant and Urological Sciences, Sapienza University/Policlinico Umberto I, Rome, Italy.
- Medical Oncology Unit, Policlinico Umberto I, University of Rome, Rome, Italy.
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy. [email protected].
Abstract
Non-contrast MRI (bi-parametric MRI-bpMRI) has been investigated as a potential tool to be integrated in clinically significant prostate cancer (csPCa) screening. Moreover, artificial intelligence (AI) is emerging too as a potential support, especially for less-experienced radiologists. Therefore, the aim of this study was to evaluate the effectiveness of an AI-based software in csPCa screening using bpMRI, with a focus on supporting less-experienced radiologists. A retrospective analysis was conducted within the PROSA-trial, a randomized, single-center study involving 759 men eligible for PCa screening. BpMRI were acquired using prostate imaging reporting and data system (PI-RADS) v2.1-compliant protocols and evaluated independently by an expert radiologist, a less-experienced reader, AI-based software, and the less-experienced reader with AI support. Diagnostic performance was assessed using ROC curves and inter-reader agreement (Cohen's kappa), using expert interpretation as the reference standard. Four hundred ninety-nine bpMRI were analyzed. The AI-assisted less-experienced reader achieved the highest diagnostic performance (sensitivity 76.5%, specificity 97.2%, accuracy 95.8%, AUC 0.969), surpassing both AI-alone (sensitivity 58.8%, specificity 96.6%, accuracy 94.0%, AUC 0.952) and unaided less-experienced reader (sensitivity 67.6%, specificity 95.1%, accuracy 93.2%, AUC 0.932). Inter-reader agreement improved with AI assistance (from Îșâ=â0.64 to 0.84). AI assistance reduced equivocal PI-RADS 3 cases (from 77 to 53) and improved exact agreement with the expert from 32.5% to 54.5%, while also reducing diagnostic discordance. AI can support less experienced radiologists and enhance consistency in bpMRI interpretation, especially considering equivocal cases. Moreover, integrating AI into radiology workflows can alleviate reporting burden and help prioritize suspicious cases, offering critical advantages in high-volume PCa screening settings. Question Can AI improve the diagnostic performance and consistency of less experienced radiologists interpreting non-contrast prostate MRI in a screening setting? Findings AI assistance significantly improved effectiveness and inter-reader agreement in prostate MRI interpretation, particularly for less experienced radiologists within a screening population. Clinical relevance Integrating AI into prostate MRI workflows may enhance screening efficiency, reduce variability, and support equitable early detection of csPCa, with a positive impact on prioritization of reporting.