Abstract
BACKGROUND: To develop and validate a CT-based artificial intelligence (AI) score model integrating the R.E.N.A.L. nephrometry and contact surface area (CSA) for efficient, accurate prediction of perioperative outcomes in renal cell carcinoma (RCC) patients undergoing nephron-sparing surgery (NSS), addressing the subjectivity and inefficiency of manual score. METHODS: Retrospectively collected data from two NSS cohorts (n1 = 500, n2 = 50): 90% of cases in Cohort n1 (450 cases) were randomly assigned to the training set (315 cases), validation set (45 cases), and test set (90 cases) at a ratio of 7:1:2, which were used to develop and validate the automated kidney/tumor segmentation models, as well as to derive the AI-calculated R.E.N.A.L. score (with the "A" parameter excluded) and AI-calculated CSA score; the remaining 10% of cases in Cohort n1 (50 cases) were combined with all 50 cases in Cohort n2 to form a mixed validation set (100 cases), which was used for risk stratification prediction of NSS perioperative outcomes via AI scores. Manual image annotation/scoring was conducted by experienced radiologists and urologists. Interrater consistency was evaluated via weighted kappa coefficients; risk stratification was performed via Kruskal-Wallis tests and Mann-Whitney U tests. RESULTS: A total of 550 patients were included in this study (median age, 56 [IQR: 46-66] years; 341 males). The segmentation model exhibited excellent performance: Dice similarity coefficient (DSC) was 0.95 for kidneys and 0.80 for tumors; normalized surface distance (NSD) was 0.923 ± 0.082 and 0.892 ± 0.096, respectively; 95th percentile Hausdorff distance (HD95) was 9.78 ± 0.63 mm and 12.65 ± 0.84 mm, respectively. The R, E, N, L, R.E.N.A.L., and CSA score models had good consistency compared with the manual score, and the kappa coefficients were 0.82, 0.49, 0.63, 0.60, 0.65, and 0.69, respectively (all P < 0.01). Risk stratification by AI score significantly predicted warm ischemia time, surgical duration, intraoperative blood loss, serum creatinine changes, pathological T stage, and nuclear grade (all P < 0.05). CONCLUSIONS: This study successfully developed a CT-based automated kidney/tumor segmentation model, and on this basis constructed the AI-R.E.N.A.L. and AI-CSA scoring models, providing an efficient and objective preoperative risk assessment tool for the perioperative outcomes of NSS.