Fully Volumetric Body Composition Analysis for Prognostic Overall Survival Stratification in Melanoma Patients

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 Com‑ puted 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 worse 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 Body composition, Melanoma, Computed tomography, Overall survival, Prognostication, Cancer, Biomarkers

Katarzyna Borys

Researcher in the first cohort

My research interests include Deep Learning, Computer Vision, Radiomics, and Explainable AI.

Georg C. Lodde

Clinician Scientist

My research interests include Dermatology, Medical Research, and Digitalization.

Elisabeth Livingstone

Principal Investigator

My research interests include Medical Research, Dermatology, and Digitalization.

Wolfgang Galetzka

Researcher in the first cohort

My research interests include Deep Learning, Computer Vision, Radiomics, and Explainable AI.

Dirk Schadendorf

Principal Investigator

My research interests include Dermatology, Medical Research, and Digitalization.

René Hosch

Associated Researcher

My research interests include distributed Computer Vision, Generative Adversarial Networks and Image-to-Image translation.

Felix Nensa

Speaker

My research interests include medical digitalization, computer vision and radiology.