Development and validation of machine learning-based MRI radiomics models for preoperative lymph node staging in T3 rectal cancer.

ABSTRACT:

Objective

The present research aimed to evaluate the diagnostic performance of a magnetic resonance imaging (MRI)-based radiomics model for predicting lymph node staging in patients with stage T3 rectal cancer (RC).

Methods

This retrospective study included 225 patients with RC who underwent surgical resection without neoadjuvant therapy treatment. Radiomics features were extracted from high-resolution T2-weighted imaging (T2WI) of primary tumor. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) algorithm. Five machine learning classifiers were employed to construct radiomics signatures differentiating between N0/N1 (low nodal burden) and N2 (high nodal burden) stages prediction in the training cohort. The predictive performanc

SUBMITTER: Qubie X 

PROVIDER: S-EPMC12450691 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature