Deep Learning to Quantify Body Composition in Lung Cancer Screening-Eligible Individuals.

PURPOSE: Most low-dose computed tomography (LDCT) scans in lung cancer (LC) screening are negative, yet contain prognostic information. We developed a fully automated 3D deep learning model for thoracic body composition (BC) quantification from LDCT and evaluated its prognostic value for overall and cause-specific mortality. MATERIALS AND METHODS: Baseline and first follow-up LDCTs from the National Lung Screening Trial were analyzed. A 3D convolutional neural network was trained for full thoracic volumetric segmentation of subcutaneous adipose tissue (SAT) and skeletal muscle (SM) and extraction of attenuation values. BC metrics were categorized into sex-specific percentile groups (<20%, 20%-80%, >80%). Kaplan-Meier and Cox regression assessed the association between baseline BC/1-year BC changes and all-cause mortality (primary end point) and atherosclerotic cardiovascular disease (ASCVD) and LC mortality (secondary end points) adjusted for age, sex, ethnicity, BMI, smoking status, diabetes, hypertension, and history of stroke or heart disease. RESULTS: Among 23,195 participants (mean age 61.4 ± 5 years; 41.5% women), 1,601 (6.9%) deaths occurred over 6.5 years. Low SM volume and density were independently associated with increased all-cause mortality (adjusted hazard ratio [aHR], 1.40 [95% CI, 1.25 to 1.57]; aHR, 1.80 [95% CI, 1.60 to 2.02]; both P < .001). Similar associations were found for low SAT volume, high SAT density, and low SM density with ASCVD and LC mortality. A >10% 1-year decline in SAT or SM measures further increased mortality risk, strongest for reduced SAT density (aHR, 2.13 [95% CI, 1.54 to 2.95]; P < .001). CONCLUSION: Automated 3D thoracic BC quantification from LDCT enables opportunistic, artificial intelligence-driven risk stratification beyond traditional factors, supporting personalized prevention in high-risk populations.