An interpretable machine learning model integrating computed tomography radiomics and clinical features for predicting the urosepsis after percutaneous nephrolithotomy.

ABSTRACT:

Background

The urosepsis after percutaneous nephrolithotomy (PCNL) is a critical health risk necessitating prompt medical identification and intervention. Nevertheless, a deficiency exists in the availability of a tool for precise and timely predictive analysis. The purpose is to establish a machine learning (ML) model using radiomic features and clinical data to predict urosepsis following PCNL.

Method

This study retrospectively included 401 patients with kidney stones from two centers who underwent PCNL. To enhance the dataset's equilibrium, the synthetic minority over-sampling technique for regression with Gaussian noise (SMOGN) was used to resample the training set. The screening of radiomics features and the construction of radiomics scores were completed by applying th

SUBMITTER: Zeng S 

PROVIDER: S-EPMC12538728 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature