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. 2025 May 12;23(1):532.
doi: 10.1186/s12967-025-06507-1.

Fully volumetric body composition analysis for prognostic overall survival stratification in melanoma patients

Katarzyna Borys  1   2 , Georg Lodde  3 , Elisabeth Livingstone  3 , Carsten Weishaupt  4 , Christian Römer  5 , Marc-David Künnemann  5 , Anne Helfen  5 , Lisa Zimmer  3 , Wolfgang Galetzka  6 , Johannes Haubold  7   8 , Christoph M Friedrich  6   9 , Lale Umutlu  8 , Walter Heindel  5 , Dirk Schadendorf  3 , René Hosch #  7   8 , Felix Nensa #  7   8
Affiliations

Fully volumetric body composition analysis for prognostic overall survival stratification in melanoma patients

Katarzyna Borys et al. J Transl Med. .

Erratum in

Abstract

Background: Accurate 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.

Methods: A 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.

Results: SI 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.

Conclusions: SI, 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.

Keywords: Biomarkers; Body composition; Cancer; Computed tomography; Melanoma; Overall survival; Prognostication.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: All procedures performed in this study complied with relevant laws and institutional guidelines and were approved by the Ethics Committee of the University Hospital Essen (approval number 21–10204-BO) and the University Hospital Münster (approval number 2023-425-b-S). Due to the study's retrospective nature, the Ethics Committee waived the requirement of written informed consent. All data were fully anonymized before being included in the study. Consent for publication: Not applicable. Competing Interests: LZ served as a consultant and has received honoraria from BMS, MSD, Novartis, Pierre Fabre, Sanofi, and Sunpharma and travel support from MSD, BMS, Pierre Fabre, Sanofi, Sunpharma, and Novartis outside the submitted work. Declaration of generative AI and AI-assisted technologies During the preparation of this work, the authors used Grammarly to improve readability and language. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Figures

Fig. 1
Fig. 1
Schematic Acquisition Process for the Internal Cohort: Flowchart of the acquisition process, including image data filtering, clinical information Screening, time range adjustment, and meta-information filtering
Fig. 2
Fig. 2
Univariate Cox regression for internal and external abdomen CT scans: Univariate Cox-Regression results obtained from the internal (A) and external (B) cohort’s abdominal CT scans showing P-Values, HRs, and 95% CIs for the SI, MFI, and VFI. HR Hazard Ratio, CI Confidence Interval, SI Sarcopenia Index, MFI Myosteatosis Fat Index, VFI Visceral Fat Index
Fig. 3
Fig. 3
Multivariate Cox regression for internal and external abdomen CT scans: Multivariate Cox regression results obtained from the internal (A) and external (B) cohort’s abdominal CT scans showing P-Values, HRs, and 95% CIs for the SI, MFI, and VFI. HR Hazard Ratio, CI Confidence Interval, SI Sarcopenia Index, MFI Myosteatosis Fat Index, VFI Visceral Fat Index
Fig. 4
Fig. 4
Multivariate machine learning results for internal and external abdomen CTs: Kaplan–Meier curves on the internal (left and middle column) and external (right column) abdomen CTs for the SI (A), MFI (B), and VFI (C) using the predicted risk scores of the multivariable abdomen-based BCA models trained and tested on patients with known M status (M0 + M1). Also, the averaged feature importance is shown for each model. SI Sarcopenia Index, MFI Myosteatosis Fat Index, VFI Visceral Fat Index, BCA Body Composition Analysis, M status Distant metastatic status
Fig. 5
Fig. 5
Restricted mean survival time differences between abdomen-based BCA models and the baseline model: The baseline ML model included only age at diagnosis, sex, and M status (M0 + M1), while the other models incorporated a respective BCA index in addition to these features. The RMST differences between models are presented over a 0 to 60-month range. BCA Body Composition Analysis, ML Machine Learning, SI Sarcopenia Index, MFI Myosteatosis Fat Index, VFI Visceral Fat Index, RMST Restricted Mean Survival Time
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