Quantitative CT Imaging Features Associated with Stable PRISm using Machine Learning.

Rationale and Objectives: The structural lung features that characterize individuals with preserved ratio impaired spirometry (PRISm) that remain stable overtime are unknown. The objective of this study was to use machine learning models with computed tomography (CT) imaging to classify stable PRISm from stable controls and stable COPD and identify discriminative features.
Materials and Methods: A total of 596 participants that did not transition between control, PRISm and COPD groups at baseline and 3-year follow-up were evaluated: n = 274 with normal lung function (stable control), n = 22 stable PRISm, and n = 300 stable COPD. Investigated features included: quantitative CT (QCT) features (n = 34), such as total lung volume (%TLC _CT ) and percentage of ground glass and reticulation (%GG+Reticulation _texture ), as well as Radiomic (n = 102) features, including varied intensity zone distribution grainy texture (GLDZM _ZDV ). Logistic regression machine learning models were trained using various feature combinations (Base, Base+QCT, Base+Radiomic, Base+QCT+Radiomic). Model performances were evaluated using area under receiver operator curve (AUC) and comparisons between models were made using DeLong test; feature importance was ranked using Shapley Additive Explanations values.
Results: Machine learning models for all feature combinations achieved AUCs between 0.63-0.84 for stable PRISm vs. stable control, and 0.65-0.92 for stable PRISm vs. stable COPD classification. Models incorporating imaging features outperformed those trained solely on base features (p < 0.05). Compared to stable control and COPD, those with stable PRISm exhibited decreased %TLC _CT and increased %GG+Reticulation _texture and GLDZM _ZDV .
Conclusion: These findings suggest that reduced lung volumes, and elevated high-density and ground glass/reticulation patterns on CT imaging are associated with stable PRISm.
Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Wan C. Tan MD reports financial support was provided by Canadian Institute of Heath Research(CIHR), Astra Zeneca Canada Ltd., Boehringer Ingelheim Canada Ltd., GlaxoSmithKline Canada Ltd., Merck Canada Inc., Novartis Pharmaceuticals Canada Inc., Nycomed Canada Inc., Pfizer Canada Ltd. Jean Bourbeau MD reports financial support was provided by Canadian Institute of Heath Research (CIHR), Réseau en santé respiratoire du FRQS, McGill University, McGill University Health Centre, AstraZeneca Canada Ltd., Boehringer Ingelheim Canada Ltd., GlaxoSmithKline Canada Ltd., Grifols Canada Ltd., Novartis Pharmaceuticals Canada Inc., Sanofi, Trudell Canada Ltd., COVIS Pharma Canada Ltd. Miranda Kirby PhD reports financial support was provided by Natural Sciences and Engineering Research Council (NSERC) Discovery Grant, The Early Researchers Award Program, The Canada Research Chair Program (Tier II). Miranda Kirby PhD reports a relationship with VIDA Diagnostics Inc that includes consulting or advisory. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)