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A generalized health index: automated thoracic CT-derived biomarkers predict life expectancy.

About this Publication
Title
A generalized health index: automated thoracic CT-derived biomarkers predict life expectancy.
Pubmed ID
39535867 (View this publication on the PubMed website)
Digital Object Identifier
Publication
Br J Radiol. 2025 Mar 1; Volume 98 (Issue 1167): Pages 412-421
Authors
Beeche C, Yu T, Wang J, Wilson D, Chen P, Duman E, Pu J
Affiliations
  • Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, United States.
  • Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, United States.
Abstract

OBJECTIVE: To identify image biomarkers associated with overall life expectancy from low-dose CT and integrate them as an index for assessing an individual's health.

METHODS: Two 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).

RESULTS: The 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.

CONCLUSIONS: Body 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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