Differentiating Fat-Poor Angiomyolipoma from Renal Cell Carcinoma Using Contrast-Enhanced CT

Fat-poor angiomyolipoma (fp-AML), a common benign renal mass, closely mimics renal cell carcinoma (RCC) on preoperative computed tomography (CT), frequently resulting in unnecessary surgical intervention. This multicenter retrospective study aimed to develop and externally validate an AI-assisted radiomics model based on triphasic contrast-enhanced CT to accurately distinguish fp-AML from RCC. A total of 655 eligible patients with sporadic solid renal lesions were enrolled and divided into a training cohort (n = 364), an internal test cohort (n = 156), and an independent external validation cohort (n = 135), with a stable fp-AML to RCC ratio of approximately 1:4. Tumor segmentation was performed using an nnU-Net-assisted workflow with radiologist refinement, followed by cross-phase image registration, radiomic feature extraction, LASSO feature reduction, and random forest classifier construction. The established model achieved favorable and stable diagnostic performance across all cohorts, with AUCs of 0.868 in the training and internal test sets and 0.803 in the external validation set, maintaining reliable discrimination even in the small renal mass subgroup (≤4 cm). This externally validated AI radiomics model demonstrates promising diagnostic performance across centers and may serve as a preoperative decision-support tool for differentiating fp-AML from RCC; however, prospective multicenter validation is required before routine clinical implementation.