AI-generated R.E.N.A.L.+ Score Surpasses Human-generated Score in Predicting Renal Oncologic Outcomes

Objectives To determine whether we can surpass the traditional R.E.N.A.L. nephrometry score (H-score) prediction ability of pathologic outcomes by creating artificial intelligence(AI)-generated R.E.N.A.L.+ score(AI+score) with continuous rather than ordinal components. We also assessed the AI+ score components’ relative importance with respect to outcome odds. Methods This is a retrospective study of 300 consecutive patients with preoperative CT scans showing suspected renal cancer at a single institution from 2010-2018. H-score was tabulated by three trained medical personnel. Deep neural network approach automatically generated kidney segmentation masks of parenchyma and tumor. Geometric algorithms were used to automatically estimate score components as ordinal and continuous variables. Multivariate logistic regression of continuous R.E.N.A.L. components was used to generate AI+score. Predictive utility was compared between AI+, AI, and H-scores for variables of interest, and AI+score components’ relative importance was assessed. Results Median age was 60 years(IQR 51-68), and 40% were female. Median tumor size was 4.2 cm(2.6-6.12), and 92% were malignant, including 27%, 37%, and 23% with high-stage, high-grade, and necrosis, respectively. AI+score demonstrated superior predictive ability over AI and H-scores for predicting malignant(AUC 0.69 vs.0.67 vs.0.64, respectively), high-stage(AUC 0.82 vs.0.65 vs.0.71, respectively), high-grade(AUC 0.78 vs.0.65 vs.0.65, respectively), pathologic tumor necrosis(AUC 0.81 vs.0.72 vs.0.74, respectively), and partial nephrectomy approach(AUC 0.88 vs.0.74 vs.0.79, respectively). Of AI+score components, the maximal tumor diameter (“R”) was the most important outcomes predictor. Conclusions AI+ score was superior to AI-score and H-score in predicting oncologic outcomes. Time-efficient AI+score can be used at the point of care, surpassing validated clinical scoring systems.