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

Abstract

Background: 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]. Methods: 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. Results: 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. Conclusions: 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.