Integrating deep learning and multi-omics features in radiation pneumonitis prediction for lung cancer patients using PET/CT.

ABSTRACT:

Background

To investigate the feasibility and accuracy of PET radiomics features, along with their combination with CT radiomics, dosiomics, and deep learning (DL) features, in predicting radiation pneumonitis (RP) in lung cancer patients treated with volumetric modulated arc therapy (VMAT).

Methods

A total of 206 and 27 lung cancer patients who underwent VMAT with pre-treatment PET/CT imaging were enrolled from Hospital One and Hospital Two for model training and external validation, respectively. Four machine learning (ML) methods were applied to build radiomics models with features extracted from CT (R_CT), PET (R_PET), radiomics features fused PET/CT (R_fFU) and fused PET/CT images (R_ iFU), as well dosiomics features (D). Three DL models were built to extract features f

SUBMITTER: Ai Y 

PROVIDER: S-EPMC12557991 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature