PhD Scientific Days 2025

Theoretical and Translational Medicine III.

CT-Derived Body Composition as a Predictor of Acute Pancreatitis Severity: A Post-Hoc Analysis of a Multicenter Study of 437 Patients

Name of the presenter

Borbély Ruben Zsolt

Institute/workplace of the presenter

Semmelweis University

Authors

Borbély Ruben Zsolt¹, Molnár Dorottya¹, Katalin Márta¹, Vivien Vass¹, Brigitta Teutsch¹, Bálint Erőss¹, Péter Jenő Hegyi¹, Eszter Ágnes Szalai¹, Andrea Szentesi¹, Péter Hegyi¹, Nándor Faluhelyi¹

1: Semmelweis University, Centre for Translational Medicine

Text of the abstract

Aims
Accurate early prediction of acute pancreatitis (AP) severity remains a major clinical challenge. Although abdominal computed tomography (CT) is routinely used for diagnosis, its potential to assess body composition is underexplored. This study investigated whether CT-derived body composition metrics can predict AP severity in a multicenter cohort.

Methods
We performed a post-hoc analysis of 437 patients who underwent CT within 24 hours of admission for AP. Using the 3D Slicer TotalSegmentator tool, we measured visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and skeletal muscle area (SMA) at the third lumbar vertebra (L3). These values were normalized for height to derive visceral (VATI), subcutaneous (SATI), and skeletal muscle (SMI) indices. Muscle radiodensity (Hounsfield Units) was assessed to evaluate myosteatosis, and the fat-to-muscle ratio (FMR) was calculated. The Modified CT Severity Index (mCTSI) defined disease severity. Receiver operating characteristic (ROC) curves, with area under the curve (AUC), assessed predictive accuracy.

Results
FMR and VATI showed the highest predictive value for severe AP, with AUCs of 0.68 and 0.65 (p<0.05), suggesting moderate discrimination. In contrast, SMA and SMI had AUCs of 0.52 and 0.54, indicating muscle mass alone does not strongly predict severity. These findings imply that increased visceral fat and an unfavorable FMR, potentially reflecting inflammatory and metabolic derangements, are associated with worse outcomes. Incorporating these measurements into routine CT analyses may strengthen early risk stratification and guide clinical decisions in AP management.

Conclusion
CT-based body composition metrics appear vital in predicting AP severity. Prospective studies are needed to validate these findings and define optimal thresholds for clinical integration. Furthermore, incorporating these metrics into standard imaging protocols could enhance patient triage, resource allocation, and individualized treatment, potentially reducing morbidity and mortality by facilitating earlier, more targeted interventions. Ultimately, personalized care may enhance outcomes and reduce costs.

Funding
SUPPORTED BY THE 2024-2.1.1-EKÖP-2024-00004 UNIVERSITY RESEARCH SCHOLARSHIP PROGRAMME OF THE MINISTRY FOR CULTURE AND INNOVATION FROM THE SOURCE OF THE NATIONAL RESEARCH, DEVELOPMENT AND INNOVATION FUND.