Machine Learning-Based CT Radiomics Method for Identifying the Stage of Wilms Tumor in Children.

ABSTRACT:

Purpose

To develop and validate a machine learning-based CT radiomics method for preoperatively predicting the stages (stage I and non-stage I) of Wilms tumor (WT) in pediatric patients.

Methods

A total of 118 patients with WT, who underwent contrast-enhanced computed tomography (CT) scans in our center between 2014 and 2021, were studied retrospectively and divided into two groups: stage I and non-stage I disease. Patients were randomly divided into training cohorts (n = 94) and test cohorts (n = 24). A total of 1,781 radiomic features from seven feature classes were extracted from preoperative portal venous-phase images of abdominal CT. Synthetic Minority Over-Sampling Technique (SMOTE) was used to handle imbalanced datasets, followed by a t-test and L

SUBMITTER: Ma XH 

PROVIDER: S-EPMC9168275 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature