Pre-operatively predicting kidney stone recurrence: integrating radiomic features and clinical variables using machine learning.

ABSTRACT:

Background

Radiomics and artificial intelligence have shown strong predictive capabilities in urinary stone research, particularly concerning stone composition, characteristics, and treatment outcomes. However, the association of stone radiomics and recurrence has not been well studied. This study aims to develop a machine learning model that combines radiomic features and clinical variables to pre-operatively predict kidney stones recurrence.

Methods

A total of 540 patients with kidney stones from the First Affiliated Hospital of Guangzhou Medical University were randomly divided into an internal training set (n = 378) and an internal test set (n = 162) in a 7:3 ratio. Additionally, 141 patients from Zhongda Hospital Southeast University served as an external test set. Clin

SUBMITTER: Lei Y 

PROVIDER: S-EPMC12649047 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature