Abstract
INTRODUCTION: The incidence of renal cell carcinoma has steadily been on the increase due to the increased use of imaging to identify incidental masses. Although survival has also improved because of early detection, overdiagnosis and overtreatment of benign renal masses are associated with significant morbidity, as patients with a suspected renal malignancy on imaging undergo invasive and risky procedures for a definitive diagnosis. Therefore, accurately characterising a renal mass as benign or malignant on imaging is paramount to improving patient outcomes. Artificial intelligence (AI) poses an exciting solution to the problem, augmenting traditional radiological diagnosis to increase detection accuracy. This report aims to investigate and summarise the current evidence about the diagnostic accuracy of AI in characterising renal masses on imaging. METHODS AND ANALYSIS: This will involve systematically searching PubMed, MEDLINE, Embase, Web of Science, Scopus and Cochrane databases. Publications of research that have evaluated the use of automated AI, fully or to some extent, in cross-sectional imaging for diagnosing or characterising malignant renal tumours will be included if published between July 2016 and June 2025 and in English. The protocol adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols 2015 checklist. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) score will be used to evaluate the quality and risk of bias across included studies. Furthermore, in line with Checklist for Artificial Intelligence in Medical Imaging recommendations, studies will be evaluated for including the minimum necessary information on AI research reporting. ETHICS AND DISSEMINATION: Ethical clearance will not be necessary for conducting this systematic review, and results will be disseminated through peer-reviewed publications and presentations at both national and international conferences. PROSPERO REGISTRATION NUMBER: CRD42024529929.