A multiomics analysis-assisted machine learning model identifies renal hamartoma without visible fat and homogeneous clear cell renal cell carcinoma: A retrospective cohort study

Preoperative differentiation between benign renal hamartoma without visible fat (RH-WVF) and malignant homogeneous clear cell renal cell carcinoma (hm-ccRCC) is radiologically challenging, often requiring biopsy or surgery. This study aimed to develop a noninvasive preoperative diagnostic model for distinguishing the two lesions. A retrospective analysis included 371 patients with RH-WVF or hm-ccRCC (confirmed by surgical specimens) from two Jingzhou hospitals (Jan 2015-Feb 2024). Patients were randomly divided into training (70%) and validation (30%) cohorts by tumor stage. Radiomics features were extracted from CT corticomedullary and parenchymal phases; urinary proteomic markers were also included. LASSO regression screened predictive features, and nomogram/decision tree models were built, evaluated via ROC curves and decision curve analysis. Eight radiomics and three urinary proteomic features were selected. The nomogram showed robust performance (all P < .05) with area under the curve (AUC) 0.889 (95% CI: 0.832-0.946) in the training cohort and 0.895 (95% CI: 0.838-0.952) in the validation cohort, outperforming the decision tree (training AUC 0.821; validation AUC 0.808). In the validation cohort, ~32% (n = 36) low-risk patients could avoid unnecessary surgery, with a 4.7% (n = 5) false negative rate. The multi-omics model integrating CT radiomics and uromics is a reliable noninvasive tool for distinguishing RH-WVF from hm-ccRCC, facilitating precise treatment and reducing unnecessary surgeries.