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Development and Validation of an Interpretable Chest X-Ray Radiomic Model for Identifying PRISm in Non-COPD
Qian Zhou1, Weihao Zhai2, Taohu Zhou3
1Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China (Q.Z., W.Z., T.Z., Y.W., X.Z., Q.J., R.W., L.F.); College of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, China (Q.Z. W.Z.).
Rationale And Objectives:
Chest X-ray (CXR) is low-cost and widely accessible but lacks effective tools for identifying preserved ratio impaired spirometry (PRISm). This study aimed to develop and validate a CXR-based radiomics-clinical combined model for PRISm screening.
Materials And Methods:
Paired CXR images and clinical variables from 9 hospitals were retrospectively collected between January 2017 and December 2023. Bilateral lung fields on CXR were segmented with a fully automated segmentation algorithm. PyRadiomics was adopted to extract radiomic features. Clinical, radiomic, and combined models were established. Model performance was assessed using receiver operating characteristic analysis with DeLong's test for intermodel area under the curve (AUC) comparison. Global and local SHapley Additive exPlanations (SHAP) analyses were performed to interpret the model.
Results:
A total of 1563 participants (mean age, 63.1 ± 12.0 years; 857 males; 422 PRISm cases) were included. The combined model achieved optimal performance, with AUCs of 0.761, 0.753, and 0.759 across the training, internal, and external validation cohorts. In the external validation cohort, the combined model (AUC = 0.759, 95% confidence interval [CI]: 0.658-0.854) significantly outperformed individual radiomic (AUC = 0.618, 95% CI: 0.505-0.732) and clinical models (AUC = 0.655, 95% CI: 0.549-0.760; all p < 0.001). SHAP analysis revealed sex as the top predictor in the combined model, followed by radiomic features, body mass index, age, and smoking status. Local SHAP visualizations illustrated variable feature contributions for individual PRISm and normal spirometry cases.
Conclusions:
The CXR-based combined model exhibits favorable stability and diagnostic performance for PRISm identification, supporting its utility as a primary-screening tool. The SHAP method enhanced the model's interpretability and clinical applicability.
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