Evaluating CT-derived body composition and colorectal cancer risk: A group-based trajectory modeling morphomics study.

Abstract

e22530 Background: Obesity is an known risk factor for colorectal cancer (CRC), yet body mass index (BMI) incompletely captures metabolic risk, particularly in females and racial and ethnic minorities. Body composition is a more precise measure, as it describes visceral, subcutaneous, and intramuscular fat, and lean and skeletal muscle mass, which have different implications for CRC risk, but trajectories of body composition are under-investigated. CT-based analytic morphomics can quantify body composition and may enable “opportunistic” risk stratification using routine imaging. Methods: We conducted a retrospective cohort study within the Veterans Health Administration (VHA) using paired CT scans with morphomic measures at L3. We included CRC patients with age- and sex-matched controls with at least 2 CT scans > 1 year prior to CRC diagnosis or last known follow-up. Scan phases were selected using a standardized preference hierarchy: non-contrast, delayed, venous, arterial (all patients analyzed had matching phases). Two group-based trajectory modeling (GBTM) strategies were evaluated: age as the time scale (GBTM-AGE) and CT timepoint while adjusting for age. The primary outcome was time to CRC from the first CT. Associations between trajectory group membership and CRC were estimated using Cox proportional hazards models adjusted for age, sex, and BMI. Results: The final cohort included 39 patients (22 no CRC; 17 CRC), predominantly male and non-Hispanic White, with median age 60 years at first CT (Table 1). Among CRC cases, most cancers were colon primaries (64.7%), and over half presented with stage III/IV disease (64.7%). In GBTM-AGE models, membership in the second trajectory group for mean skeletal muscle attenuation, a marker of myosteatosis, was associated with significantly higher CRC hazard (HR 4.21, 95% CI 1.38–12.91; p = 0.012), independent of age, sex, and BMI. Other evaluated trajectory groupings were not significantly associated with CRC risk in adjusted models. Conclusions: In this VHA cohort with longitudinal CT morphomics, a trajectory class of skeletal muscle attenuation identified patients at elevated future CRC risk independently from age and BMI. These findings suggest that CT-based morphomics may be a feasible avenue for CRC risk stratification and emphasize the need for further collection and evaluation of morphomics data for opportunistic screening for CRC. Demographic characteristics. Variable No CRC (n = 22) CRC (n = 17) P-value Age at 1st CT Scan; Median [Q1,Q3] 60.0 [50.3, 66.5] 60.0 [52.0, 71.0] 0.403 Age at 2nd CT Scan; Median [Q1,Q3] 62.0 [52.8, 69.0] 63.0 [54.0, 72.0] 0.505 Age at CRC; Median [Q1,Q3] 69.0 [57.0, 79.0] Male; n (%) 18 (81.8%) 16 (94.1%) 0.363 Non-Hispanic White; n (%) 22 (100%) 17 (100%) 0.423 BMI at 1st CT Scan; Median [Q1,Q3] 29.4 [26.1, 34.4] 28.9 [25.7, 32.7] 0.865 BMI at 2nd CT Scan; Median [Q1,Q3] 29.1 [26.3, 34.0] 28.6 [24.4, 36.2] 0.821