Preoperative noninvasive prediction of Rex <i>vs.</i> Warren shunt selection in children with extrahepatic portal vein obstruction: a machine learning model based on serology and ultrasound.

ABSTRACT:

Background

Extrahepatic portal vein obstruction (EHPVO) is a leading cause of pediatric portal hypertension. While invasive portography remains the diagnostic gold standard, its risks highlight the need for non-invasive alternatives. This study aims to integrate ultrasound imaging features and serological markers to establish a machine learning model for noninvasive, simplified preoperative assessment of the portal system in pediatric patients with EHPVO. The model will serve as a reference for selecting optimal surgical strategies.

Methods

A total of 103 pediatric EHPVO patients who underwent surgery were enrolled, including 81 Rex shunt and 22 Warren shunt cases. In the training set, the least absolute shrinkage and selection operator (LASSO) algorithm identified potential

SUBMITTER: Huang M 

PROVIDER: S-EPMC12683456 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature