Logo image
Back
Sex-specific machine learning improves prediction of incident and prevalent COPD
Journal article   Open access   Peer reviewed

Sex-specific machine learning improves prediction of incident and prevalent COPD

Kalysta Makimoto, Benjamin M Smith, Joseph M Reinhardt, Cameron J Hague, James C Hogg, Jean Bourbeau, Wan C Tan and Miranda Kirby
Chest
07/25/2026
DOI: 10.1016/j.chest.2026.07.5207
PMID: 42501941
url
https://doi.org/10.1016/j.chest.2026.07.5207View
Published (Version of record) Open Access

Abstract

Computed tomography imaging with machine learning can predict incident and prevalent chronic obstructive pulmonary disease (COPD), however, it is unknown if sex-specific models improve performance. Do sex-specific machine learning models using computed tomography (CT) derived disease features improve prediction of incident and prevalent chronic obstructive pulmonary disease (COPD) and identify sex-specific predictors? Canadian Cohort Obstructive Lung Disease (CanCOLD) study participants underwent baseline CT imaging, and spirometry at baseline/follow-up. Models predicted incident and prevalent COPD using demographics and CT features (lung density/texture/shape, airway shape) for the combined-sex, male-only, and female-only datasets, and externally tested in Subpopulations and Intermediate Outcome Measures in COPD (SPIROMICS). Performance was evaluated using area under the receiver operating characteristic curve (AUC). 1283 CanCOLD and 1840 SPIROMICS participants were included. For incident COPD, the female-only model outperformed the male-only and combined-sex models in internal (AUC=0.86 vs. 0.76 and 0.78; p<0.05) and external tests (AUC=0.83 vs. 0.71 and 0.77; p<0.05). For prevalent COPD, the female-only model again outperformed the male-only and combined-sex model in internal (AUC=0.84 vs. 0.78 and 0.78; p<0.01) and external tests (AUC=0.84 vs. 0.70 and 0.73; p<0.05). Female-only models selected parenchymal texture and lung-shape features not selected in combined-sex models, whereas male-only models selected airway-based features. Sex-specific models outperform combined-sex models for identifying those with and at risk of COPD, particularly in females, by capturing different disease-relevant features. These findings highlight that combined-sex models can obscure sex-specific biology and adopting sex-specific prediction strategies may improve early detection, risk stratification, and allow for treatment targeting.
 Machine Learning CT Imaging Sex Differences COPD

Details

Metrics

1 Record Views
Logo image