CT-based AI score associates with perioperative outcomes in nephron-sparing surgery for renal cell carcinoma.

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)

SUBMITTER: Shengfa L 

PROVIDER: S-EPMC12751408 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature