Objectives To investigate if body composition can serve as a biomarker for assessing the risk of developing lung cancer. Materials and methods We conducted a retrospective study using low-dose computed tomography (LDCT) scans from the Pittsburgh lung screening study (PLuSS) (n = 3635, 22 follow-up years) and the NLST-ACRIN (n = 16,360, 8 follow-up years) cohort. Five types of body tissues, including subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), intramuscular adipose tissue (IMAT), skeletal muscle (SM), and bone, were automatically segmented by our previously developed algorithms. Volume and density metrics were computed. Cause-specific Cox proportional hazards models were utilized to assess hazard ratios (HRs). Time-dependent area under the receiver operating characteristic curve (AUC-ROC) was used to evaluate model performance. The cumulative incidence function was estimated for different risk groups. Results The final composite models were formed by age (HR = 1.30 (95% CI: 1.17–1.43)), current smoking status (HR = 1.85 (1.49–2.28)), bone volume (HR = 1.38 (1.25–1.52)), bone density (HR = 0.80 (0.71–0.89)), SM density (HR = 0.62 (0.58–0.66)), IMAT ratio (HR = 0.65 (0.58–0.73)), and SAT volume (HR = 0.76 (0.67–0.87)). The model trained on the PLuSS cohort achieved a mean AUC of 0.77 (0.75–0.79) over 21 years and 0.71 (0.68–0.74) over the first 7 years for lung cancer prediction. External validation on the NLST cohort yielded AUC values ranging from 0.63 to 0.66 over a 7-year follow-up period. The model trained on a combined dataset of PLuSS and NLST achieved a mean AUC of 0.71 (0.7–0.72) over 21 years. Conclusion Three-dimensional body composition metrics assessed through LDCT are a significant predictor of lung cancer risk. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Is body composition a biomarker for lung cancer risk assessment? <Emphasis Type="BoldItalic">Findings</Emphasis> Body composition metrics derived from low-dose CT scans, including volumes and densities of fat, bone, and muscle, are strong predictors of lung cancer risk. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> Lung cancer risk stratification can be improved by body composition features, providing guidance for personalized lung cancer screening strategies. Graphical Abstract

Beyond nodules: body composition as a biomarker for future lung cancer

期刊论文
发表日期：

2025年6月6日

查看全文

Jing Wang,
Joseph K. Leader,
Xin Meng,
Tong Yu,
Renwei Wang,
Jian-min Yuan,
David Wilson,
Jiantao Pu

摘要

Objectives

To investigate if body composition can serve as a biomarker for assessing the risk of developing lung cancer.

Materials and methods

We conducted a retrospective study using low-dose computed tomography (LDCT) scans from the Pittsburgh lung screening study (PLuSS) (n = 3635, 22 follow-up years) and the NLST-ACRIN (n = 16,360, 8 follow-up years) cohort. Five types of body tissues, including subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), intramuscular adipose tissue (IMAT), skeletal muscle (SM), and bone, were automatically segmented by our previously developed algorithms. Volume and density metrics were computed. Cause-specific Cox proportional hazards models were utilized to assess hazard ratios (HRs). Time-dependent area under the receiver operating characteristic curve (AUC-ROC) was used to evaluate model performance. The cumulative incidence function was estimated for different risk groups.

Results

The final composite models were formed by age (HR = 1.30 (95% CI: 1.17–1.43)), current smoking status (HR = 1.85 (1.49–2.28)), bone volume (HR = 1.38 (1.25–1.52)), bone density (HR = 0.80 (0.71–0.89)), SM density (HR = 0.62 (0.58–0.66)), IMAT ratio (HR = 0.65 (0.58–0.73)), and SAT volume (HR = 0.76 (0.67–0.87)). The model trained on the PLuSS cohort achieved a mean AUC of 0.77 (0.75–0.79) over 21 years and 0.71 (0.68–0.74) over the first 7 years for lung cancer prediction. External validation on the NLST cohort yielded AUC values ranging from 0.63 to 0.66 over a 7-year follow-up period. The model trained on a combined dataset of PLuSS and NLST achieved a mean AUC of 0.71 (0.7–0.72) over 21 years.

Conclusion

Three-dimensional body composition metrics assessed through LDCT are a significant predictor of lung cancer risk.

Key Points

Question Is body composition a biomarker for lung cancer risk assessment?

Findings Body composition metrics derived from low-dose CT scans, including volumes and densities of fat, bone, and muscle, are strong predictors of lung cancer risk.

Clinical relevance Lung cancer risk stratification can be improved by body composition features, providing guidance for personalized lung cancer screening strategies.

Graphical Abstract