Radiomics-based model for prediction of TGF-β1 expression in head and neck squamous cell carcinoma.

ABSTRACT:

Objective

To explore the connection between TGF-β1 expression and the survival of patients with head and neck squamous cell carcinoma (HNSCC), as well as whether non-invasive CT-based Radiomics can predict TGF-β1 expression in HNSCC patients.

Methods

Data on transcriptional profiling and clinical information were acquired from the TCGA database and subsequently categorized based on the TGF-β1 expression cutoff value. Based on the completeness of enhanced arterial phase CT scans, 139 HNSCC patients were selected. The PyRadiomics package was used to extract radiomic features, and the 3D Slicer software was used for image segmentation. Using the mRMR_RFE and Repeat LASSO algorithms, the optimal features for establishing the corresponding gradient enhancement prediction models w

SUBMITTER: Qin K 

PROVIDER: S-EPMC11411193 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature