Trial reportJournal of cachexia, sarcopenia and muscle2023
Prognostic role of radiomics-based body composition analysis for the 1-year survival for hepatocellular carcinoma patients.
Trial report in Journal of cachexia, sarcopenia and muscle, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The trial behind it
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Who cites it
18 citing papers in PubMed, 26 citations in OpenAlex.
- Prognostic role of radiomics-based body composition analysis for the 1-year survival for hepatocellular carcinoma patients.Journal of cachexia, sarcopenia and muscle · 2023Trial
- Integrated Assessment of Sarcopenia in Patients with Gastric Cancer Using Deep Learning and Radiomics.Journal of cachexia, sarcopenia and muscle · 2026Article
- Automated three-dimensional radiomic body composition analysis enhances survival prediction in resectable non‑small cell lung cancer.European radiology experimental · 2026Article
- Beyond weight: a stigma-free, integrated approach to obesity in patients affected by cancer.Reviews in endocrine & metabolic disorders · 2026Review
- Article
- Muscle and fat matter: Automated CT-based body composition analysis predicts survival inEuropean journal of radiology open · 2026Article
- Predicting Prognosis for Gastric Cancer Patients Receiving Neoadjuvant Treatment With Body Composition-Based Deep Learning.Cancer medicine · 2026Article
- Body composition radiomics combined with machine learning for early recurrence prediction in intrahepatic cholangiocarcinoma following curative surgery: A Multi-Center study.European journal of nuclear medicine and molecular imaging · 2026Article
- Prognostic potential of radiomics evaluation of lung artery thrombus for pulmonary embolism patients.International journal of computer assisted radiology and surgery · 2026Article
- Artificial intelligence-driven body composition analysis and its association with survival in patients with colorectal liver metastases.Contemporary oncology (Poznan, Poland) · 2026Article
- Machine learning based on body composition radiomics for predicting early recurrence in colorectal cancer: a multicenter study.Frontiers in nutrition · 2026Article
- Global trends in sarcopenia and cancer over the past 10 years: a bibliometric analysis.Discover oncology · 2025Article
- Skeletal muscle index/systemic immune-inflammation index (SMI/SII) ratio predicts prognosis in patients with hepatocellular carcinoma.World journal of surgical oncology · 2025Article
- Article
- IntegratingBMC cancer · 2024Article
- Short-term mortality prediction in acute pulmonary embolism: Radiomics values of skeletal muscle and intramuscular adipose tissue.Journal of cachexia, sarcopenia and muscle · 2024Article
- Review
- Fully automated 3D body composition analysis and its association with overall survival in head and neck squamous cell carcinoma patients.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
28 authors at 18 institutions in 9 countries.
Funding
Abstract
backgroundParameters of body composition have prognostic potential in patients with oncologic diseases. The aim of the present study was to analyse the prognostic potential of radiomics-based parameters of the skeletal musculature and adipose tissues in patients with advanced hepatocellular carcinoma (HCC).
methodsRadiomics features were extracted from a cohort of 297 HCC patients as post hoc sub-study of the SORAMIC randomized controlled trial. Patients were treated with selective internal radiation therapy (SIRT) in combination with sorafenib or with sorafenib alone yielding two groups: (1) sorafenib monotherapy (n = 147) and (2) sorafenib and SIRT (n = 150). The main outcome was 1-year survival. Segmentation of muscle tissue and adipose tissue was used to retrieve 881 features. Correlation analysis and feature cleansing yielded 292 features for each patient group and each tissue type. We combined 9 feature selection methods with 10 feature set compositions to build 90 feature sets. We used 11 classifiers to build 990 models. We subdivided the patient groups into a train and validation cohort and a test cohort, that is, one third of the patient groups.
resultsWe used the train and validation set to identify the best feature selection and classification model and applied it to the test set for each patient group. Classification yields for patients who underwent sorafenib monotherapy an accuracy of 75.51% and area under the curve (AUC) of 0.7576 (95% confidence interval [CI]: 0.6376-0.8776). For patients who underwent treatment with SIRT and sorafenib, results are accuracy = 78.00% and AUC = 0.8032 (95% CI: 0.6930-0.9134).
conclusionsParameters of radiomics-based analysis of the skeletal musculature and adipose tissue predict 1-year survival in patients with advanced HCC. The prognostic value of radiomics-based parameters was higher in patients who were treated with SIRT and sorafenib.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.