ArticleNPJ digital medicine2025
Leveraging Sarcopenia index by automated CT body composition analysis for pan cancer prognostic stratification.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A machine learning-derived sarcopenia index is associated with survival and nonrelapse mortality in DLBCL.Blood advances · 2026Article
- Radiation dose has no significant impact on CT-based bone mineral density measurements in a large-animal model.Scientific reports · 2026Article
- AI-Based Imaging Assessment of Body Composition in Oncology: A Step Toward Routine Clinical Practice Integration.Healthcare (Basel, Switzerland) · 2026Article
- Artificial intelligence in nutritional oncology: From isolated screening tools to agentic intervention systems.Oncotarget · 2026Article
- Multidimensional modeling and stratification of nutritional risk in non-small cell lung cancer based on artificial intelligence-derived CT body composition phenotypes.Frontiers in nutrition · 2026Article
- Differential analysis of urinary stable downregulated proteins in male patients after colorectal cancer surgery.Frontiers in oncology · 2026Article
- The ageing microenvironment in sarcopenia: early alterations and targeted therapeutic strategies.Frontiers in immunology · 2026Review
- Malnutrition and Sarcopenia in Patients with Neuroendocrine Tumors: A Comprehensive Review of Evidence.Biomolecules · 2025Review
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Authors and funding
37 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.