# CT Body Composition Changes Predict Survival in Immunotherapy-Treated Cancer Patients: A Retrospective Cohort Study

## TL;DR

Changes in body composition seen on CT scans during cancer immunotherapy treatment can predict patient survival better than initial scans.

## Contribution

This study introduces a fully automated CT-based tool (CompoCT) and shows that longitudinal changes in body composition are stronger mortality predictors than baseline measures in immunotherapy-treated cancer patients.

## Key findings

- Longitudinal decreases in skeletal muscle and subcutaneous fat were strong independent predictors of mortality in immunotherapy-treated cancer patients.
- Automated CT-based body composition analysis provides standardized, quantitative markers for tracking body composition dynamics during treatment.

## Abstract

Body composition indices related to skeletal muscle and body fat can be derived from computed tomography (CT) imaging. However, longitudinal changes in these indices during the course of cancer treatment may be better predictors of outcomes than these indices at baseline. Here, we used a novel, fully automated software (CompoCT) to measure composition indices from CT imaging and showed that in patients treated with immunotherapy for non-small cell lung cancer, renal cell carcinoma, or melanoma, longitudinal decreases in skeletal muscle and subcutaneous fat were strong independent predictors of death, with prognostic value exceeding that of baseline measures. Moreover, these indices remained statistically significant after adjusting for age, sex, and tumor type. Therefore, automated CT-based body composition analysis may enhance objective risk stratification and support treatment decisions during immunotherapy. Furthermore, the integration of such automated imaging tools may also support metabolic research by providing standardized quantitative markers of body composition dynamics.

Background: Computed tomography (CT)-derived body composition parameters, including skeletal muscle and fat indices, are prognosticators in oncology. Most studies focus on baseline body-composition parameters; however, changes during treatment may provide better prognostic value. Standardized methods for measuring/reporting these parameters remain limited. Methods: This retrospective study included patients who were treated with immunotherapy for non-small cell lung cancer (NSCLC), renal cell carcinoma (RCC), or melanoma between 2017 and 2024 and had technically adequate baseline and follow-up CT scans. Body composition was analyzed using a novel, fully automated software (CompoCT) for L3 slice selection and segmentation. Body composition indices (e.g., skeletal muscle index [SMI]) were calculated by dividing the cross-sectional area by the patient’s height squared. Results: The cohort included 376 patients (mean [SD] age 66.4 [11.4] years, 67.3% male, 72.6% NSCLC, 14.6% RCC, and 12.8% melanoma). During a median follow-up of 21 months, 220 (58.5%) died. Baseline body composition parameters were not associated with mortality, except for a weak protective effect of higher SMI (HR = 0.98, p = 0.043). In contrast, longitudinal decreases were strongly associated with increased mortality. Relative decreases in SMI (HR, 1.17; 95% CI, 1.07–1.27) or subcutaneous fat index (SFI) (HR, 1.11; 95% CI, 1.07–1.15) significantly increased mortality risk. Multivariate models showed similar concordance (0.65) and identified older age, NSCLC tumor type, and relative decreases in SMI and SFI (per 5% units) as independent predictors of mortality. Conclusions: Longitudinal decreases in skeletal muscle and subcutaneous fat were independent predictors of mortality in immunotherapy-treated patients. Automated CT-based body composition analysis may support treatment decisions during immunotherapy.