Development and Validation of a Diagnostic Model for Identifying Clear Cell Renal Cell Carcinoma in Small Renal Masses Based on CT Radiological Features: A Multicenter Study

Abstract

Rationale and ObjectivesThis study aimed to develop a diagnostic model based on clinical and CT features for identifying clear cell renal cell carcinoma (ccRCC) in small renal masses (SRMs). Material and methodsThis retrospective multi-center study enrolled patients with pathologically confirmed SRMs. Data from three centers were used as training set (n=229), with data from one center serving as an independent test set (n=81). Univariate and multivariate logistic regression analyses were utilized to screen independent risk factors for ccRCC and build the classification and regression tree (CART) diagnostic model. The area under the curve (AUC) was used to evaluate the performance of the model. To demonstrate the clinical utility of the model, three radiologists were asked to diagnose the SRMs in the test set based on professional experience and re-evaluated with the aid of the CART model. ResultsThere were 310 SRMs in 309 patients and 71% (220/310) were ccRCC. In the testing cohort, the AUC of the CART model was 0.90 (95% CI: 0.81, 0.97). For the radiologists' assessment, the AUC of the three radiologists based on the clinical experience were 0.78(95% CI:0.66,0.89), 0.65(95% CI:0.53,0.76), and 0.68(95% CI:0.57,0.79). With the CART model support, the AUC of the three radiologists were 0.93(95% CI:0.86,0.97), 0.87(95% CI:0.78,0.95) and 0.87(95% CI:0.78,0.95). Interobserver agreement was improved with the CART model aids (0.323 vs 0.654, P<0.001). ConclusionThe CART model can identify ccRCC with better diagnostic efficacy than that of experienced radiologists and improve diagnostic performance, potentially reducing the number of unnecessary biopsies.