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Updated: Sep 10, 2025

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

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Published on: April 12, 2024

Chest CT imaging for differentiating normal, PRISm, and COPD in comparison with pulmonary function tests.

Zongjing Ma¹, Yingli Sun¹, Zhuangxuan Ma¹

¹Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China.

La Radiologia Medica

|August 21, 2025

Summary

This study developed a CT radiomics model integrating clinical data to accurately classify normal lung function, preserved ratio impaired spirometry (PRISm), and chronic obstructive pulmonary disease (COPD). The lung fusion model showed high accuracy in distinguishing these respiratory conditions.

Keywords:

Chronic obstructive pulmonary disease Multinomial logistic regression Preserved ratio impaired spirometry Radiomics

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Area of Science:

Pulmonary Medicine
Radiology
Data Science

Background:

Preserved ratio impaired spirometry (PRISm) and chronic obstructive pulmonary disease (COPD) are progressive respiratory diseases causing lung function decline.
These conditions are associated with systemic comorbidities and accelerated pulmonary function decline.

Purpose of the Study:

To develop a three-category classification model using clinical variables and thoracic CT radiomics.
To differentiate between normal pulmonary function, PRISm, and COPD.

Main Methods:

Retrospective analysis of 797 participants from three centers with chest CT and pulmonary function tests.
Extraction of 2436 radiomic features from lung and airway regions using PyRadiomics.
Development of five models, including a lung fusion model combining clinical predictors (age, gender, BMI) with lung radiomic features.

Main Results:

The lung fusion model, integrating clinical data and lung radiomics, achieved superior performance in an external test set.
This model demonstrated high AUCs for classifying PFT-normal (0.939), PRISm (0.830), and COPD (0.904), with an overall accuracy of 83.59% in external validation.
The lung fusion model outperformed four other models across all datasets.

Conclusions:

Integrating clinical predictors (age, gender, BMI) with lung radiomic features significantly enhances the detection of PRISm and COPD.
CT-based radiomics holds potential for early identification and risk stratification of abnormal pulmonary function.