Integrating CT radiomics and clinical data with machine learning to predict fibrosis progression in coalworker pneumoconiosis

Integrating CT radiomics and clinical data with machine learning to predict fibrosis progression in coalworker pneumoconiosis

ObjectiveThis study aims to develop a machine learning (ML) model that integrates computed tomography (CT) radiomics with clinical features to predict the progression of pulmonary interstitial fibrosis in patients with coalworker pneumoconiosis (CWP).MethodsClinical and imaging data from 297 patient...

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Bibliographic Details
Main Authors: Xiaobing Li, Qian Li, Xinyi Xie, Wei Wang, Xuemei Li, Tingqiang Zhang, Li Zhang, Yongsheng Liu, Li Wang, Wutao Xie
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-07-01
Series:Frontiers in Medicine
Subjects:
coalworker pneumoconiosis
pulmonary interstitial fibrosis
CT radiomics
clinical features
machine learning
predictive model
Online Access:https://www.frontiersin.org/articles/10.3389/fmed.2025.1599739/full
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https://www.frontiersin.org/articles/10.3389/fmed.2025.1599739/full

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