ArticleESMO open2024
Prognostic value of deep learning-derived body composition in advanced pancreatic cancer-a retrospective multicenter study.
Article in ESMO open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 17 citations in OpenAlex.
- Pretherapeutic radiological sarcopenia in pancreatic ductal adenocarcinoma: a narrative review.Future science OA · 2026Review
- Large language models enable prognostic stratification of cancer patients using real-world clinical notes.PLOS digital health · 2026Article
- Computed Tomography-Based Assessment of Sarcopenia and Disease Progression in Pancreatic Ductal Adenocarcinoma: A Radiomics and Machine Learning Approach.Gastroenterology research · 2026Article
- Automated Pretreatment Thoracic CT-Based Body Composition Analysis Predicts Progression-Free Survival in Head and Neck Cancer.Journal of clinical medicine · 2026Article
- Prognostic Impact of Ectopic Fat Deposition Within the Psoas Muscle in Patients with Stage IV Pancreatic Cancer Receiving Systemic Chemotherapy.Journal of clinical medicine · 2026Article
- Association Between Computed Tomography-Based AI-Derived Body Composition and Survival in Patients With Pancreatic Ductal Adenocarcinoma.The American journal of gastroenterology · 2026Article
- Prognostic value of body composition on early recurrence and long-term survival of resectable pancreatic ductal adenocarcinoma.European radiology · 2026Article
- A LASSO-based nomogram for predicting sarcopenia-related nutritional risk in esophageal cancer patients: model development, validation, and an interactive clinical decision tool.Frontiers in oncology · 2026Article
- Muscle matters: automated CT-based body composition analysis predicts survival in patients with head and neck cancer treated with immunotherapy.Frontiers in oncology · 2026Article
- Prognostic value of body composition out of PSMA-PET/CT in prostate cancer patients undergoing PSMA-therapy.European journal of nuclear medicine and molecular imaging · 2025Article
- Clinical utility of artificial intelligence models in radiology: a systemic scoping review of diagnostic and endovascular applications.CVIR endovascular · 2025Review
- 3D deep learning-based muscle volume quantification from thoracic CT as a surrogate for DXA-Derived appendicular muscle mass in older adults.Aging clinical and experimental research · 2025Article
- Automated multi-regional IHC scoring enhances prognostication in colorectal cancer.The journal of pathology. Clinical research · 2025Article
- Validation of body composition parameters extracted via deep learning-based segmentation from routine computed tomographies.Scientific reports · 2025Article
- Influence of myosteatosis on survival of patients with pancreatic cancer: A systematic review and meta-analysis.iScience · 2024Article
- AI-derived body composition parameters as prognostic factors in patients with HCC undergoing TACE in a multicenter study.JHEP reports : innovation in hepatology · 2024Article
- Pancreatic cancer and sarcopenia: a narrative review of the current status.International journal of clinical oncology · 2024Review
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Authors and funding
27 authors at 10 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDespite the prognostic relevance of cachexia in pancreatic cancer, individual body composition has not been routinely integrated into treatment planning. In this multicenter study, we investigated the prognostic value of sarcopenia and myosteatosis automatically extracted from routine computed tomography (CT) scans of patients with advanced pancreatic ductal adenocarcinoma (PDAC). PATIENTS AND
methodsWe retrospectively analyzed clinical imaging data of 601 patients from three German cancer centers. We applied a deep learning approach to assess sarcopenia by the abdominal muscle-to-bone ratio (MBR) and myosteatosis by the ratio of abdominal inter- and intramuscular fat to muscle volume. In the pooled cohort, univariable and multivariable analyses were carried out to analyze the association between body composition markers and overall survival (OS). We analyzed the relationship between body composition markers and laboratory values during the first year of therapy in a subgroup using linear regression analysis adjusted for age, sex, and American Joint Committee on Cancer (AJCC) stage.
resultsDeep learning-derived MBR [hazard ratio (HR) 0.60, 95% confidence interval (CI) 0.47-0.77, P < 0.005] and myosteatosis (HR 3.73, 95% CI 1.66-8.39, P < 0.005) were significantly associated with OS in univariable analysis. In multivariable analysis, MBR (P = 0.019) and myosteatosis (P = 0.02) were associated with OS independent of age, sex, and AJCC stage. In a subgroup, MBR and myosteatosis were associated with albumin and C-reactive protein levels after initiation of therapy. Additionally, MBR was also associated with hemoglobin and total protein levels.
conclusionsOur work demonstrates that deep learning can be applied across cancer centers to automatically assess sarcopenia and myosteatosis from routine CT scans. We highlight the prognostic role of our proposed markers and show a strong relationship with protein levels, inflammation, and anemia. In clinical practice, automated body composition analysis holds the potential to further personalize cancer treatment.
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