Skeletal muscle and visceral fat density are predictive imaging biomarkers for overall survival in patients with pancreatic adenocarcinoma: A retrospective multicenter analysis

Maike Theis; Wei Hong; Belinda Lee; Sebastian Nowak; Julian Luetkens; Stephen Stuckey; Peter Gibbs; Benjamin Thomson; Michael Michael; Alois Martin Sprinkart; Hyun Soo Ko

doi:10.1016/j.suronc.2025.102251

Skeletal muscle and visceral fat density are predictive imaging biomarkers for overall survival in patients with pancreatic adenocarcinoma: A retrospective multicenter analysis

Surg Oncol. 2025 Jun 20:61:102251. doi: 10.1016/j.suronc.2025.102251. Online ahead of print.

Authors

Affiliations

¹ Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany.
² Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia; Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Australia.
³ Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia; Department of Medical Oncology, The Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; Department of Medical Oncology, Northern Health, Epping, VIC, Australia; Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia.
⁴ Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Australia; School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia; Department of Medical Imaging and Radiation Sciences, Monash University, Clayton, Victoria, Australia.
⁵ Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia; Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, VIC, Australia; Department of Medical Oncology, Western Health, Melbourne, VIC, Australia.
⁶ Department of Surgical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; University of Melbourne, Department of Surgery, Royal Melbourne Hospital, Melbourne, VIC, Australia.
⁷ Department of Medical Oncology, The Peter MacCallum Cancer Centre, Melbourne, VIC, Australia; The Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia.
⁸ Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany; Department of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, Australia; The Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia. Electronic address: Hyun.Ko@petermac.org.

PMID: 40570701
DOI: 10.1016/j.suronc.2025.102251

Abstract

Rationale and objectives: Utilizing a fully automated AI-generated body composition analysis (BCA) from PDAC staging computed tomography (CT) imaging to discover predictive imaging biomarkers for overall survival (OS).

Material and methods: Routine PDAC staging CTs (07/2012-12/2020) and clinicopathological data (Eastern Cooperative Oncology Group (ECOG) performance status, resection status, chemotherapy, age, CA19-9, Charlson Comorbidity Index, BMI) from four tertiary centers were collected retrospectively. Using a 3:1 split (training:holdout), we fitted Cox regression OS using every possible combination of 7 clinicopathological and 9 BCA variables: skeletal muscle index (SMI), area and density of total muscle compartment (TMC), skeletal muscle (SM), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT) and selected the combination with the lowest information complexity (ICOMP). The added value of BCA was calculated by comparing the BCA model with the base model (without BCA variables).

Results: Analysis included 472 PDAC patients (213 female, mean age 67.9 ± 11.5 years, resectable n = 170, unresectable n = 106, metastatic n = 196). Four clinicopathological (ECOG, resection status, chemotherapy, CA19-9) and 5 BCA variables (SMI, SM density, VAT density, TMC area, VAT area) were selected. Decreased SM density (myosteatosis) and increased VAT density showed strong association with OS (p = 0.0094 and 0.0019, respectively). The BCA model showed superior performance compared to the base model in all subgroups (AUC: resectable 0.76 versus 0.70, unresectable 0.76 versus 0.69, and metastatic 0.80 versus 0.75).

Conclusion: BCA-identified myosteatosis and increased VAT density to be predictive imaging biomarkers for OS in all PDAC subgroups, potentially adding value to upfront risk stratification.

Keywords: Body composition; Computed tomography; Pancreatic carcinoma.