ArticleJournal of translational medicine2025
Fully volumetric body composition analysis for prognostic overall survival stratification in melanoma patients.
Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- PET/CT-derived whole-body composition and survival in resectable NSCLC: double machine learning-based adjusted association analysis of intermuscular adiposity burden and sex-specific metabolic phenotypes.European journal of nuclear medicine and molecular imaging · 2026Article
- A machine learning-derived sarcopenia index is associated with survival and nonrelapse mortality in DLBCL.Blood advances · 2026Article
- Automated 3D chest CT muscle segmentation-derived model development and explanation in discrimination of normal, preserved ratio impaired spirometry, and COPD: a multicenter study.Insights into imaging · 2026Article
- Prediction of Overall Survival in Patients With Oesophageal Cancer Using AI-Based 3D CT Body Composition Analysis.Journal of cachexia, sarcopenia and muscle · 2026Article
- Body Composition and Melanoma Outcomes in Patients on Immunotherapy or Targeted Therapy: An Analysis from Canadian Melanoma Research Network.Current oncology (Toronto, Ont.) · 2026Article
- Correction: Fully volumetric body composition analysis for prognostic overall survival stratification in melanoma patients.Journal of translational medicine · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate assessment of expected survival in melanoma patients is crucial for treatment decisions. This study explores deep learning-based body composition analysis to predict overall survival (OS) using baseline Computed Tomography (CT) scans and identify fully volumetric, prognostic body composition features.
methodsA deep learning network segmented baseline abdomen and thorax CTs from a cohort of 495 patients. The Sarcopenia Index (SI), Myosteatosis Fat Index (MFI), and Visceral Fat Index (VFI) were derived and statistically assessed for prognosticating OS. External validation was performed with 428 patients.
resultsSI was significantly associated with OS on both CT regions: abdomen (P ≤ 0.0001, HR: 0.36) and thorax (P ≤ 0.0001, HR: 0.27), with lower SI associated with prolonged survival. MFI was also associated with OS on abdomen (P ≤ 0.0001, HR: 1.16) and thorax CTs (P ≤ 0.0001, HR: 1.08), where higher MFI was linked to worse outcomes. Lastly, VFI was associated with OS on abdomen CTs (P ≤ 0.001, HR: 1.90), with higher VFI linked to poor outcomes. External validation replicated these results.
conclusionsSI, MFI, and VFI showed substantial potential as prognostic factors for OS in malignant melanoma patients. This approach leveraged existing CT scans without additional procedural or financial burdens, highlighting the seamless integration of DL-based body composition analysis into standard oncologic staging routines.
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