ArticleDiagnostics (Basel, Switzerland)2021
Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Prognostic value of body adipose tissue parameters in cancer patients treated with immune checkpoint inhibitors.Frontiers in immunology · 2025Pooled it
- Imaging-based adipose biomarkers for predicting clinical outcomes of cancer patients treated with immune checkpoint inhibitors: a systematic review.Frontiers in oncology · 2023Pooled it
- Prognostic value of body composition on survival outcomes in melanoma patients receiving immunotherapy.Frontiers in immunology · 2023Pooled it
- From Pixels to Prediction: Reviewing the Role of Artificial Intelligence in Body Composition Analysis.Journal of cachexia, sarcopenia and muscle · 2026Review
- The Prognostic Effect of Sarcopenia in Solid Cancers Treated with Immunotherapy: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2026Review
- CT Body Composition Changes Predict Survival in Immunotherapy-Treated Cancer Patients: A Retrospective Cohort Study.Cancers · 2026Article
- The impact of sarcopenia on the progression of chronic non-bacterial osteomyelitis.Insights into imaging · 2025Article
- Optimizing Immunotherapy: The Synergy of Immune Checkpoint Inhibitors with Artificial Intelligence in Melanoma Treatment.Biomolecules · 2025Review
- Artificial intelligence-assisted rectus femoris ultrasound vs. L3 computed tomography for sarcopenia assessment in oncology patients: establishing diagnostic cut-offs for muscle mass and quality.Frontiers in nutrition · 2025Article
- Impact of body composition on pathological response to neoadjuvant immunotherapy in dMMR/MSI-H colorectal cancer.Frontiers in immunology · 2025Article
- Immunometabolism in cancer: a systemic perspective.Frontiers in immunology · 2025Review
- Predicting 3-year all-cause mortality in rectal cancer patients based on body composition and machine learning.Frontiers in nutrition · 2025Article
- Deep learning for opportunistic, end-to-end automated assessment of epicardial adipose tissue in pre-interventional, ECG-gated spiral computed tomography.Insights into imaging · 2024Article
- Deep learning-based assessment of CT markers of sarcopenia and myosteatosis for outcome assessment in patients with advanced pancreatic cancer after high-intensity focused ultrasound treatment.European radiology · 2024Article
- Prognostic value of obesity in patients with cancer treated with immune checkpoint inhibitors: An updated meta‑analysis and systematic review.Molecular and clinical oncology · 2024Article
- Review
- Multimodal predictors for precision immunotherapy.Immuno-oncology technology · 2022Review
- Clinical Applications of Artificial Intelligence-An Updated Overview.Journal of clinical medicine · 2022Review
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Previous studies suggest an impact of body composition on outcome in melanoma patients. We aimed to determine the prognostic value of CT-based body composition assessment in patients receiving immune checkpoint inhibitor therapy for treatment of metastatic disease using a deep learning approach. One hundred seven patients with staging CT examinations prior to initiation of checkpoint inhibition between January 2013 and August 2019 were retrospectively evaluated. Using an automated deep learning-based body composition analysis pipeline, parameters for estimation of skeletal muscle mass (skeletal muscle index, SMI) and adipose tissue compartments (visceral adipose tissue index, VAI; subcutaneous adipose tissue index, SAI) were derived from staging CT. The cohort was binarized according to gender-specific median cut-off values. Patients below the median were defined as having low SMI, VAI, or SAI, respectively. The impact on outcome was assessed using the Kaplan-Meier method with log-rank tests. A multivariable logistic regression model was built to test the impact of body composition parameters on 3-year mortality. Patients with low SMI displayed significantly increased 1-year (25% versus 9%,
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