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Related Experiment Video

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Interpretable chronic obstructive pulmonary disease identification using chest X-ray radiomics: a multicenter study.

Qian Zhou1,2, Weihao Zhai1,2,3, Taohu Zhou1

  • 1Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.

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Summary

A new combined model using chest X-ray (CXR) radiomic features and clinical data accurately identifies chronic obstructive pulmonary disease (COPD). SHAP analysis improved interpretability, highlighting radiomic features, age, sex, and smoking history as key factors for COPD diagnosis.

Keywords:
Chest X-ray (CXR)Chronic obstructive pulmonary disease (COPD)Radiomics

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Pulmonary Medicine

Background:

  • Chronic Obstructive Pulmonary Disease (COPD) screening rates are insufficient.
  • Accurate COPD identification is crucial for timely intervention and management.
  • Existing diagnostic methods may lack comprehensive predictive capabilities.

Purpose of the Study:

  • To develop and validate a combined model for COPD identification using chest X-ray (CXR) radiomic features and clinical data.
  • To enhance the interpretability of the COPD identification model using SHapley Additive exPlanations (SHAP).
  • To assess the model's performance across multicenter training, internal, and external validation cohorts.

Main Methods:

  • Collected paired CXR images and clinical data from 2433 participants across 17 hospitals.
  • Constructed three models: radiomic, clinical, and a combined radiomic-clinical model.
  • Employed SHAP analysis for model interpretation and identification of key predictive factors.

Main Results:

  • The combined model significantly outperformed the clinical model in COPD identification across all cohorts (AUCs ranging from 0.754 to 0.764).
  • SHAP analysis identified radiomic features, age, smoking history, and sex as critical predictors of COPD.
  • The model demonstrated robust performance in multicenter validation, indicating broad applicability.

Conclusions:

  • A CXR-based combined radiomic-clinical model offers high accuracy for COPD identification.
  • SHAP-enhanced interpretability increases the clinical utility and trustworthiness of the model.
  • This approach advances interpretable and widely applicable COPD screening in clinical radiology.