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

Cameron Beeche; Tong Yu; Jing Wang; David Wilson; Pengyu Chen; Emrah Duman; Jiantao Pu

doi:10.1093/bjr/tqae234

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

Br J Radiol. 2024 Nov 13:tqae234. doi: 10.1093/bjr/tqae234. Online ahead of print.

Authors

Cameron Beeche¹, Tong Yu¹, Jing Wang¹, David Wilson^{2

3}, Pengyu Chen¹, Emrah Duman¹, Jiantao Pu^{1

3

4}

Affiliations

¹ Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
² Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
³ Cancer Epidemiology and Prevention Program, UPMC Hillman Cancer Center.
⁴ Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, 15213, USA.

PMID: 39535867
DOI: 10.1093/bjr/tqae234

Abstract

Objective: To identify image biomarkers associated with overall life expectancy from low-dose computed tomography 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 = 3,635). 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 < 0.01) 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.

Keywords: image biomarker; life expectancy; low dose computed tomography; risk stratification.