Identifying factors that may influence the classification performance of radiomics models using contrast-enhanced mammography (CEM) images.

ABSTRACT:

Background

Radiomics plays an important role in the field of oncology. Few studies have focused on the identification of factors that may influence the classification performance of radiomics models. The goal of this study was to use contrast-enhanced mammography (CEM) images to identify factors that may potentially influence the performance of radiomics models in diagnosing breast lesions.

Methods

A total of 157 women with 161 breast lesions were included. Least absolute shrinkage and selection operator (LASSO) regression and the random forest (RF) algorithm were employed to construct radiomics models. The classification result for each lesion was obtained by using 100 rounds of five-fold cross-validation. The image features interpreted by the radiologists were used in the

SUBMITTER: Sun Y 

PROVIDER: S-EPMC9101829 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature