Selecting the Best Elements from Previous Kidney Tumor Scoring Systems to Restructure Efficient Predictive Models for Surgery Type

Abstract

Objective: The aim of this work was to select the best elements from previous scoring systems to restructure efficient predictive models for surgery type. Methods: Sixteen elements were selected from 7 systems (RENAL, PADUA, DAP, ZS, NephRO, ABC, and CI). They were divided into 6 categories (tumor max. size, exophytic/endophytic, correlation with collecting system or sinus, tumor location, contact situation with the parenchyma, invasion depth). Three elements, selected from 3 different categories, were integrated to establish a total of 320 new models. According to AUC rank, optimized models were developed, and these models were divided into 3 sections. An analysis of the distribution of the 6 categories was made to explore the predictive capacities of the models. Results: A total of 166 consecutive patients were included. Seventy-five patients underwent radical nephrectomy operations. The AUC of the 7 systems ranged from 0.81 to 0.844. Three optimized models (AUC 0.88) were developed to predict surgery type. These optimized models were composed of DAP (D), PADUA, (sinus), and ABC; DAP (D), RENAL (N), and ABC; NePhRO (O), PADUA (UCS), and ABC. Two categories (“exophytic/endophytic,” p < 0.001; “correlation with collecting system or sinus,” p = 0.001) were nonuniformly distributed. Conclusions: Seven systems held good predictive power for surgery type. Three optimized models were developed. “Correlation with collecting system or sinus” is a critical factor for predicting surgery type.