Leveraging MRI radiomics signature for predicting the diagnosis of CXCL9 in breast cancer.

ABSTRACT:

Objective

A non-invasive predictive model was developed using radiomic features to forecast CXCL9 expression level in breast cancer patients.

Methods

CXCL9 expression data and MRI images of breast cancer patients were obtained from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) databases, respectively. Local tissue samples from 20 breast cancer patients were collected to measure CXCL9 expression levels. Radiomic features were extracted from MRI images using 3DSlicer, and the minimum Redundancy Maximum Relevance and Recursive Feature Elimination (mRMR_RFE) method was employed to select the most pertinent radiomic features associated with CXCL9 expression levels. Support vector machine (SVM) and Logistic Regression (LR) models were utilized to construct t

SUBMITTER: Yan L 

PROVIDER: S-EPMC11490775 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature