A MRI-based radiomics nomogram for differentiation of fat-poor renal angiomyolipoma from nodular clear cell renal cell carcinoma

Objectives This study aims to assess the effectiveness of a radiomics nomogram based on multiparametric MRI in distinguishing between fat-poor renal angiomyolipoma (fp-AML) and renal clear cell carcinoma (ccRCC) in small renal tumors (≤ 4 cm). Methods A retrospective analysis was conducted from July 2017 to July 2023, including 65 ccRCC and 23 fp-AML patients with confirmed pathology. T2WI, DWI and ADC sequences were used to extract radiomics features. A t-test and LASSO were used to select features. A radiomic signature was created after calculating a radiomic score (Rad-score). To create a clinical model, clinical characteristics and subject MRI features were evaluated. Then, the Rad scores and clinical characteristics were integrated to develop a radiomics nomogram. The nomogram's performance was assessed regarding calibration, discrimination, and clinical utility. Results In the study, 88 renal masses from 86 patients were included. Each patient had their 3948 radiomics features retrieved, and 13 of those features were used to create the radiomics signature. The radiomics nomogram (AUC, 0.833; 95% CI, 0.675–0.992) showed superior discriminating capacity in the validation set compared to the clinical model (AUC, 0.774; 95% CI, 0.554–0.995) and radiomics signature (AUC, 0.778; 95% CI, 0.571–0.985) alone. Decision curve analysis indicated that the nomogram outperformed the clinical model in terms of clinical utility. Conclusions The multiparametric MRI-based radiomics nomograms effectively use Rad scores and clinical factors as non-invasive preoperative predictors, successfully differentiating fp-AML from ccRCC.