Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer.

ABSTRACT:

Objective

To establish and validate a radiomics nomogram based on the features of the primary tumor for predicting preoperative pathological extramural venous invasion (EMVI) in rectal cancer using machine learning.

Methods

The clinical and imaging data of 281 patients with primary rectal cancer from April 2012 to May 2018 were retrospectively analyzed. All the patients were divided into a training set (n = 198) and a test set (n = 83) respectively. The radiomics features of the primary tumor were extracted from the enhanced computed tomography (CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. One optimal radiomics signature extracted from each modal image was generated by receiver operating characteristi

SUBMITTER: Liu S 

PROVIDER: S-EPMC8033032 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature