This multi-center retrospective study aims to develop a multimodal artificial intelligence diagnostic model using preoperative contrast-enhanced CT images and routine laboratory parameters from patients with renal tumors. The model is designed to assist clinicians in accurately predicting the pathological subtypes of renal tumors preoperatively, enabling detailed diagnoses and advancing precision medicine.

Inclusion Criteria:

• Underwent renal tumor resection with a complete postoperative pathological report, and the pathological diagnosis is one of the following types: clear cell renal cell carcinoma, papillary renal cell carcinoma, chromophobe renal cell carcinoma, renal angiomyolipoma, or renal oncocytoma.
• Complete and available four-phase contrast-enhanced CT scans prior to surgery.
• Complete and available routine laboratory test results prior to surgery.

Exclusion Criteria:

• Incomplete CT data or poor image quality that affects diagnostic analysis.
• A time interval of more than three months between imaging or laboratory testing and pathological diagnosis.
• Patients diagnosed with fat-rich renal angiomyolipoma (AML).
• Pathological diagnosis indicating the coexistence of two or more pathological types of renal tumors.

The study population consists of patients who underwent renal tumor resection at multiple centers, with complete preoperative imaging and laboratory data. Eligible participants include those with pathological diagnoses of clear cell renal cell carcinoma, papillary renal cell carcinoma, chromophobe renal cell carcinoma, renal angiomyolipoma, or renal oncocytoma. Patients with incomplete imaging data, poor-quality CT images, or coexistence of multiple pathological types of renal tumors were excluded.

Data will be made available to interested research partners upon reasonable request to Wei Zhai or Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.; the prerequisite for this is a data transfer agreement, approved by the legal departments of the requesting researcher and by all legal departments of the institutions that provided data for the study, and an ethics clearance.