Differentiating Fat-Poor Angiomyolipoma from Renal Cell Carcinoma Using Contrast-Enhanced CT.
Authors
Affiliations (8)
Affiliations (8)
- Department of Urology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, China.
- Xiamen Clinical Research Center for Cancer Therapy, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, China.
- Clinical Research Center for Precision Medicine of Abdominal Tumor of Fujian Province, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, China.
- Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350009, China.
- Clinical Medical College, Chengdu University, Chengdu 610086, China.
- Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, China.
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200000, China.
- Department of Urology, Zhongshan Hospital, Fudan University, Shanghai 200000, China.
Abstract
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 (<i>n</i> = 364), an internal test cohort (<i>n</i> = 156), and an independent external validation cohort (<i>n</i> = 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.