Chest · 2026
Sex-Specific Machine Learning Improves Prediction of Incident and Prevalent COPD.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Makimoto Kalysta, Smith Benjamin M, Reinhardt Joseph M, Hague Cameron J, Hogg James C, Bourbeau Jean, Tan Wan C, Kirby Miranda
Аннотация
BACKGROUND: CT imaging with machine learning can predict incident and prevalent COPD; however, it is unknown if sex-specific models improve performance.
RESEARCH QUESTION: Do sex-specific machine learning models using CT imaging-derived disease features improve prediction of incident and prevalent COPD and identify sex-specific predictors?
STUDY DESIGN AND METHODS: Canadian Cohort Obstructive Lung Disease (CanCOLD) study participants underwent baseline CT imaging and spirometry at baseline and follow-up. Models predicted incident and prevalent COPD using demographics and CT imaging features (lung density, texture, and shape and airway shape) for the combined-sex, male-only, and female-only data sets and externally tested in the Subpopulations and Intermediate Outcome Measures in COPD (SPIROMICS) study. Performance was evaluated using area under the receiver operating characteristic curve (AUC).
RESULTS: The study included 1,283 participants from the CanCOLD study and 1,840 participants from the SPIROMICS study. For incident COPD, the female-only model outperformed the male-only and combined-sex models in internal tests (AUC, 0.86 vs 0.76 and 0.78; P < .05) and external tests (AUC, 0.83 vs 0.71 and 0.77; P < .05). For prevalent COPD, the female-only model again outperformed the male-only and combined-sex models in internal tests (AUC, 0.84 vs 0.78 and 0.78; P < .01) and external tests (AUC, 0.84 vs 0.70 and 0.73; P < .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.
INTERPRETATION: Our results show that sex-specific models outperform combined-sex models for identifying those with and at risk of COPD, particularly in female patients, 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 and risk stratification and may allow for treatment targeting.
RESEARCH QUESTION: Do sex-specific machine learning models using CT imaging-derived disease features improve prediction of incident and prevalent COPD and identify sex-specific predictors?
STUDY DESIGN AND METHODS: Canadian Cohort Obstructive Lung Disease (CanCOLD) study participants underwent baseline CT imaging and spirometry at baseline and follow-up. Models predicted incident and prevalent COPD using demographics and CT imaging features (lung density, texture, and shape and airway shape) for the combined-sex, male-only, and female-only data sets and externally tested in the Subpopulations and Intermediate Outcome Measures in COPD (SPIROMICS) study. Performance was evaluated using area under the receiver operating characteristic curve (AUC).
RESULTS: The study included 1,283 participants from the CanCOLD study and 1,840 participants from the SPIROMICS study. For incident COPD, the female-only model outperformed the male-only and combined-sex models in internal tests (AUC, 0.86 vs 0.76 and 0.78; P < .05) and external tests (AUC, 0.83 vs 0.71 and 0.77; P < .05). For prevalent COPD, the female-only model again outperformed the male-only and combined-sex models in internal tests (AUC, 0.84 vs 0.78 and 0.78; P < .01) and external tests (AUC, 0.84 vs 0.70 and 0.73; P < .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.
INTERPRETATION: Our results show that sex-specific models outperform combined-sex models for identifying those with and at risk of COPD, particularly in female patients, 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 and risk stratification and may allow for treatment targeting.