Missed Diagnosis of Clear Cell Renal Cell Carcinoma on Noncontrast Computed Tomography: The Potential Role of Artificial Intelligence in Reducing Radiological Diagnostic Error

Background

Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of malignant renal neoplasms, with an increasing incidence in Western countries. Early diagnosis is a critical determinant of prognosis, as advanced-stage disease with metastatic dissemination entails a significantly reduced median survival. Characterizing complex cystic renal lesions presents a recognized diagnostic challenge on noncontrast computed tomography (CT), which often leads to underestimation and missed diagnoses. Artificial intelligence (AI) applied to diagnostic imaging is emerging as a supplementary decision-support tool, but its integration must be critically evaluated to understand its potential to reduce diagnostic errors as well as its limitations.