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A novel computed tomography radiomic nomogram for early evaluation of small airway dysfunction development
Sijia Cui1, Zhenyu Shu1, Yanqing Ma1
1Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
This study introduces radiomic analysis of CT scans for early detection of small airway dysfunction (fSAD). Radiomic scores, along with age, smoking, and asthma history, predict fSAD progression, aiding clinical strategies.
Area of Science:
- Radiology
- Pulmonology
- Medical Imaging Analysis
Background:
- Small airway dysfunction (fSAD) is a common respiratory issue often overlooked.
- Prognostic factors, prevalence, and risk factors for fSAD remain incompletely understood.
Purpose of the Study:
- To explore early detection of fSAD using radiomic analysis of computed tomography (CT) images.
- To develop a predictive model for fSAD progression.
Main Methods:
- Patients were categorized into fSAD and non-fSAD groups.
- A training (n=190) and validation (n=82) cohort were established.
- Lung kit software automated region of interest (ROI) delineation on CT scans, and radiomic features were analyzed to create a risk score.
Main Results:
- Multivariate logistic regression identified age, radiomic score, smoking, and asthma history as significant predictors of fSAD (P < 0.05).
- A radiomic nomogram model was developed for risk assessment.
Conclusions:
- Radiomic analysis of CT images shows promise for early fSAD detection and prediction of progression.
- The developed radiomic nomogram model offers valuable data for clinical decision-making and treatment strategies for fSAD.
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