Objective: We aimed to improve preoperative accuracy of tumor staging by evaluating the ability of qualitative and quantitative CT imaging features to predict perirenal fat invasion (PFI) in renal cell carcinoma (RCC). Methods: This retrospective case-control study included 86 patients with pathologically proven PFI and 169 controls matched for tumor size without PFI who were treated by nephrectomy between January 2016 and December 2020. Two radiologists independently evaluated the qualitative imaging features of tumor complexity, shape, margin, tumor-fascia contact, perirenal vascularity, fascial thickening, septation, stranding, and nodules. We also compared tumor contact length and protruding distance between the groups. Multivariate logistic regression analyses identified significant predictors of PFI, and diagnostic performance metrics of all predictors were assessed to create a combined model that included all significant predictors. Results: Lobulated shapes and irregular margins were more prevalent in the group with than without PFI ( P <0.05). Perirenal increased vascularity, fascial thickening, septation, stranding, and nodularity were also significantly more prevalent in the PFI group ( P <0.05 for all). Tumor contact length and protruding distance were significantly greater in the PFI group ( P =0.002). Multivariate analysis identified the following independent predictors of PFI: lobulated tumors [odds ratio (OR): 2.03; P =0.042], irregular margin (OR: 3.40; P =0.007), perirenal fascial thickening (OR: 4.20; P <0.001), and contact length >154.2 mm (OR: 3.82; P =0.019). The diagnostic performance of these combined predictors was moderate, with 61.6% sensitivity, 79.3% specificity, and 73.3% accuracy. Conclusions: Qualitative CT features (lobulated tumors, irregular margins, perirenal thickened fascia) and an objective quantitative parameter (threshold 154.2 mm tumor contact length) were significant independent predictors of perirenal fat invasion in RCC. These findings emphasize the complementary value of combining subjective and objective imaging features to enhance preoperative staging accuracy.
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