Predictive quantitative multidetector computed tomography models for characterization of renal cell carcinoma subtypes and differentiation from renal oncocytoma: nomogram algorithmic approach analysis

Abstract

Our objective is to develop an algorithmic approach using predictive models to discriminate between common solid renal masses, including renal cell carcinoma [RCC] subtypes and renal oncocytoma [RO], using multiphase computed tomography [CT]. We retrospectively analyzed a group of solid renal masses between January 2011 and January 2023 regarding the CT attenuation values using a multiphase multidetector CT and clinical parameters. Inclusion criteria included patients who had four phases of CT with a partial or radical nephrectomy. Exclusion criteria were patients with biphasic or one-phase CT, poor imaging quality, patients under surveillance, radiofrequency ablation, or indeterminate pathology findings as oncocytic tumor variants. We divided our cohort into training and internal validation sets. Our results revealed that a total of 467 cases, 351 patients assigned for the training cohort and 116 cases assigned for validation cohort. There is a significant difference between hypervascular clear RCC [CRCC and RO] and hypovascular chromophobe and papillary [ChRCC and PRCC] masses in both training and validation sets, AUC = 0.95, 0.98, respectively. The predictive model for differentiation between CRCC and RO showed AUC = 0.83, 0.85 in both training and validation sets, respectively. At the same time, the discrimination of ChRCC from PRCC showed AUC = 0.94 in the training set and 0.93 in the validation cohort. Using the largest sample to our knowledge, we developed a three-phase analytical approach to initiate a practical method to discriminate between different solid renal masses that can be used in daily clinical practice.