Body composition as a biomarker for assessing future lung cancer risk

PurposeTo investigate if body composition is a biomarker for assessing the risk of developing lung cancer. Materials and MethodsLow-dose computed tomography (LDCT) scans from the Pittsburgh Lung Screening Study (PLuSS) (n=3,635, 22 follow-up years) and NLST-ACRIN (n=16,435, 8 follow-up years) cohorts were used in the study. Artificial intelligence (AI) algorithms were developed to automatically segment and quantify subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), intramuscular adipose tissue (IMAT), skeletal muscle (SM), and bone. Cause-specific Cox proportional hazards models were used to evaluate the hazard ratios (HRs). Standard time-dependent receiver operating characteristic (ROC) analysis was used to evaluate the prognostic ability of different models over time. ResultsThe final composite models were formed by seven variables: age (HR=1.20), current smoking status (HR=1.59), bone volume (HR=1.79), SM density (HR=0.29), IMAT ratio (HR=0.33), IMAT density (HR=0.56), and SAT volume (HR=0.56). The models trained on the PLuSS cohort achieved a mean AUC of 0.76 (95% CI: 0.74-0.79) over 21 follow-up years and 0.70 (95% CI: 0.66-0.74) over the first 7 follow-up years for predicting lung cancer development within the PLuSS cohort. In contrast, models trained on the PLuSS cohort alone, as well as in combination with the NLST cohorts, achieved an AUC ranging from 0.61 to 0.68 in the NLST cohort over a 7-year follow-up period. ConclusionBody composition assessed on LDCT is a significant predictor of lung cancer risk and could improve the effectiveness of LDCT lung cancer screening by optimizing the screening eligibility and frequency. Summary statementBody composition assessed on LDCT is a significant predictor of lung cancer risk and could improve the effectiveness of LDCT lung cancer screening by optimizing the screening eligibility and frequency. Key PointsO_LIThis study unveils the significant associations between body tissues and lung cancer risk. C_LIO_LIThe prediction models based on body composition alone, as well as the combination of demographics and body composition features can effectively identify patients at higher risk of developing lung cancer. C_LI