Leveraging Sarcopenia index by automated CT body composition analysis for pan cancer prognostic stratification.
Authors
Affiliations (23)
Affiliations (23)
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany. [email protected].
- Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Essen, Germany. [email protected].
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
- Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen, Essen, Germany.
- Department of Medical Oncology, West German Cancer Center, University Hospital Essen, University Duisburg-Essen, Essen, Germany.
- Université Libre de Bruxelles (ULB), University Hospital Brussels, Institut Jules Bordet, Department of Radiology, Brussels, Belgium.
- Université Libre de Bruxelles (ULB), Radiophysics and MRI Physics Laboratory, Brussels, Belgium.
- Université Libre de Bruxelles (ULB), University Hospital Brussels, Institut Jules Bordet, Department of Medical Physics, Brussels, Belgium.
- Department for Radiology and Nuclear Medicine, University Hospital Basel, Basel, Switzerland.
- Institute for Medical Informatics, Biometry and Epidemiology (IMIBE), University Hospital Essen, Essen, Germany.
- Department of General, Visceral and Transplantation Surgery, University Hospital Essen, Essen, Germany.
- Bridge Institute of Experimental Tumor Therapy, West German Cancer Center, University Hospital Essen, Essen, Germany.
- Division of Solid Tumor Translational Oncology, German Cancer Consortium (DKTK, Partner Site Essen) and German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Department of Thoracic Surgery, University Medicine Essen - Ruhrlandklinik, University Duisburg-Essen, Essen, Germany.
- Department of Nuclear Medicine and German Cancer Consortium (DKTK), University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
- National Center for Tumor Diseases (NCT) West, Essen, Germany.
- Department of Gynaecology and Obstetrics, West German Cancer Center, University Hospital Essen, Essen, Germany.
- Department of Otorhinolaryngology, University Hospital Essen, University Hospital Duisburg-Essen, Essen, Germany.
- Department of General, Visceral and Transplant Surgery, West German Cancer Center, University Hospital Essen (AöR), Essen, Germany.
- Department of Radiotherapy, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
- Department of Urology, Urological Oncology and Pediatric Urology, University of Duisburg-Essen, University Hospital Essen, Essen, Germany.
- Department of Dermatology, West German Cancer Center, University Hospital Essen, University Duisburg-Essen, Essen, Germany.
- Department of Computer Science, University of Applied Sciences and Arts Dortmund, Dortmund, Germany.
Abstract
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.