Leveraging Sarcopenia index by automated CT body composition analysis for pan cancer prognostic stratification
This study evaluates the CT-based volumetric sarcopenia index (SI) as a baseline prognostic factor for overall survival (OS) in 10,340 solid tumor patients (40% female). Automated body composition analysis was applied to internal baseline abdomen CTs and to thorax CTs. SI's prognostic value was assessed using multivariable Cox proportional hazards regression, accelerated failure time models, and gradient-boosted machine learning. External validation included 439 patients (40% female). Higher SI was associated with prolonged OS in the internal abdomen (HR 0.56, 95% CI 0.52-0.59; P < 0.001) and thorax cohorts (HR 0.40, 95% CI 0.37-0.43; P < 0.001), as well as in the external validation cohort (HR 0.56, 95% CI 0.41-0.79; P < 0.001). Machine learning models identified SI as the most important factor in survival prediction. Our results demonstrate SI's potential as a fully automated body composition feature for standard oncologic workflows.
Authors
- Jürgen Treckmann (ORCID: https://orcid.org/0000-0002-9817-9365)
- Stefan Kasper (ORCID: https://orcid.org/0000-0002-5947-8733)
- Eugen Malamutmann (ORCID: https://orcid.org/0000-0003-4624-5890)
- Benedikt M. Schaarschmidt (ORCID: https://orcid.org/0000-0001-6331-7679)
- Jens Kleesiek (ORCID: https://orcid.org/0000-0001-8686-0682)
- René Hosch (ORCID: https://orcid.org/0000-0003-1760-2342)
- Stephan Lang (ORCID: https://orcid.org/0009-0000-4420-9664)
- Sebastian Bauer (ORCID: https://orcid.org/0000-0001-5949-8120)
- Johannes Haubold (ORCID: https://orcid.org/0000-0003-4843-5911)
- Dirk Schadendorf (ORCID: https://orcid.org/0000-0003-3524-7858)
- Sara Schramm (ORCID: https://orcid.org/0000-0001-6493-6009)
- Servet Bölükbas (ORCID: https://orcid.org/0000-0001-8275-5957)
- Rainer Kimmig (ORCID: https://orcid.org/0000-0003-1118-2199)
- Arzu Oezcelik (ORCID: https://orcid.org/0000-0002-8353-9532)
- Lale Umutlu (ORCID: https://orcid.org/0000-0001-5215-7171)
- Moon Kim (ORCID: https://orcid.org/0000-0002-7546-3910)
- Anja Merkel‐Jens
- Christoph M. Friedrich (ORCID: https://orcid.org/0000-0001-7906-0038)
- Giulia Baldini (ORCID: https://orcid.org/0000-0002-5929-0271)
- Maria Antonietta Bali (ORCID: https://orcid.org/0000-0002-2697-1329)
- Martin Stuschke (ORCID: https://orcid.org/0000-0001-6836-0940)
- Jens Thomas Siveke (ORCID: https://orcid.org/0000-0002-8772-4778)
- Boris Hadaschik (ORCID: https://orcid.org/0000-0002-1052-2692)
- Lennard Kroll (ORCID: https://orcid.org/0000-0001-8102-6146)
- Andreas Stang (ORCID: https://orcid.org/0000-0001-6363-9061)
- Julius Keyl (ORCID: https://orcid.org/0000-0002-5617-091X)
- Marcel Wiesweg (ORCID: https://orcid.org/0000-0002-9698-9559)
- Katarzyna Borys (ORCID: https://orcid.org/0000-0001-6987-6041)
- Felix Nensa (ORCID: https://orcid.org/0000-0002-5811-7100)
- Nicolas Coquelet
- Judith Kohnke (ORCID: https://orcid.org/0009-0009-8826-3481)
- Riccardo De Angelis
- Ken Herrmann
- Martin Schuler
- Michael Forsting
- Viktor Gruenwald
- Kévin Brou Boni
Institutions
- Université Libre de Bruxelles (BE)
- Dortmund University of Applied Sciences and Arts (DE)
- German Cancer Research Center (DE)
- Heidelberg University (DE)
- Institut Jules Bordet (BE)
- University Hospital of Basel (CH)
- Institut für Medizinische Informatik, Biometrie und Epidemiologie (DE)
- Ruhrlandklinik (DE)
- National Center for Tumor Diseases (DE)
- Essen University Hospital (DE)
- University of Duisburg-Essen (DE)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2025-10-14
- DOI
- https://doi.org/10.1038/s41746-025-02016-z
- Citations
- 2
- Primary Topic
- Nutrition and Health in Aging
- Type
- article
- Field-Weighted Citation Impact
- 2.33