ArticleThe British journal of radiology2025
A generalized health index: automated thoracic CT-derived biomarkers predict life expectancy.
Article in The British journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Predicting Primary Graft Dysfunction in Systemic Sclerosis Lung Transplantation Using Machine-Learning and CT Features.Clinical transplantation · 2025Article
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7 authors.
Funding
Abstract
objectiveTo identify image biomarkers associated with overall life expectancy from low-dose CT and integrate them as an index for assessing an individual's health.
methodsTwo categories of CT image features, body composition tissues and cardiopulmonary vasculature characteristics, were quantified from LDCT scans in the Pittsburgh Lung Screening Study cohort (n = 3635). Cox proportional-hazards models identified significant image features which were integrated with subject demographics to predict the subject's overall hazard. Subjects were stratified using composite model predictions and feature-specific risk stratification thresholds. The model's performance was validated extensively, including 5-fold cross-validation on PLuSS baseline, PLuSS follow-up examinations, and the National Lung Screening Trial (NLST).
resultsThe composite model had significantly improved prognostic ability compared to the baseline model (P < .01) with AUCs of 0.774 (95% CI: 0.757-0.792) on PLuSS, 0.723 (95% CI: 0.703-0.744) on PLuSS follow-up, and 0.681 (95% CI: 0.651-0.710) on the NLST cohort. The identified high-risk stratum were several times more likely to die, with mortality rates of 79.34% on PLuSS, 76.47% on PLuSS follow-up, and 46.74% on NLST. Two cardiopulmonary structures (intrapulmonary artery-vein ratio, intrapulmonary vein density) and two body composition tissues (SM density, bone density) identified high-risk patients.
conclusionsBody composition and pulmonary vasculatures are predictive of an individual's health risk; their integrations with subject demographics facilitate the assessment of an individual's overall health status or susceptibility to disease. ADVANCES IN KNOWLEDGE: CT-computed body composition and vasculature biomarkers provide improved prognostic value. The integration of CT biomarkers and patient demographic information improves subject risk stratification.
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