Single-inspiratory quantitative CT nomogram for enhanced PRISm and COPD differentiation: a cross-sectional study with interpretable diagnostic boundaries
Abstract
Background Differentiating preserved ratio impaired spirometry (PRISm) from chronic obstructive pulmonary disease (COPD) is challenging. Traditional biphasic CT scans are limited by radiation exposure, while single-inspiratory CT-based deep learning lacks interpretability. This study aimed to develop a single-inspiratory quantitative computed tomography (QCT) nomogram integrating parenchymal, airway, and vascular parameters to redefine imaging definition boundaries. Methods This retrospective study (approved by Ethics Committee YJ-NBEY-KY-2023-107-01) screened 1,265 patients from Ningbo No. 2 Hospital (January 2021–December 2023), yielding 658 eligible participants (Normal: 135, PRISm: 328, COPD: 195) based on predefined inclusion/exclusion criteria. Single-inspiratory CT metrics (parenchymal, airway, vascular) were quantified using the Aview® system. Four logistic regression models distinguished PRISm from normal and COPD group receiver operating characteristic-area under the curve (ROC-AUC) evaluated performance. Results Progressive deterioration in age (COPD: 73.3 vs . PRISm: 69.1 vs . Normal: 64.1 years), male predominance (84.6% COPD vs . 57.9% PRISm), pulmonary function (FEV1%, FEV1/FVC), and CT markers (Pi10: PRISm 3.65 vs . Normal 3.26, P < 0.001) were observed. PRISm showed reduced superficial vessel diameter (AVD9: 2.64 mm vs . Normal 2.95 mm, P < 0.001). Diagnostic models achieved AUCs up to 0.984 (PRISm vs . severe COPD) and 0.853 (PRISm vs . all COPD). Conclusion The QCT nomogram robustly differentiates PRISm from COPD, highlighting reduced superficial vessel diameter as a key biomarker. This radiation-efficient approach enables early COPD stratification via interpretable structural-functional metrics.
Fields of Study & Subjects
Chronic Obstructive Pulmonary Disease (COPD) Research
Lung Cancer Diagnosis and Treatment
Radiomics and Machine Learning in Medical Imaging
Publication details
- Journal / source
- PeerJ
- Volume
- 14
- Pages
- e20575–e20575
- Publication date
- 14 January 2026
- Language
- EN