ArticleScientific reports2022
CT-derived body composition analysis could possibly replace DXA and BIA to monitor NET-patients.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
28 citing papers in PubMed, 32 citations in OpenAlex.
- A Multi-Model, Pixel-Native Framework for Automated Computed Tomography Series Labeling and Characterization: Proof-of-Concept Study.JMIR medical informatics · 2026Article
- Topogram-based anatomical labelling of CT series: anatomy-aware CT data processing using deep learning.European radiology · 2026Article
- Type 1 Diabetes Mellitus as a Model of Body Composition Transformation: Pathogenetic Unity of Combined Phenotypes.International journal of molecular sciences · 2026Review
- Exploring Adipose Tissue Behavior in CT: Impact of Age, Sex, and Contrast Media on Body Composition, Liver and Skeletal Muscle.Journal of imaging · 2026Article
- From Pixels to Prediction: Reviewing the Role of Artificial Intelligence in Body Composition Analysis.Journal of cachexia, sarcopenia and muscle · 2026Review
- Applications of bioimpedance measurement in clinical and allied fields: a comprehensive review.Biomedical engineering online · 2026Review
- [Digital transformation in endocrinology/diabetology, pneumology, and hematology/oncology : Where do we stand today-and what lies ahead?]Innere Medizin (Heidelberg, Germany) · 2026Review
- Psoas muscle volume as a diagnostic indicator for sarcopenia: criteria development and comparison with traditional diagnostic approaches.Aging clinical and experimental research · 2026Article
- Body composition-based nutritional status during neoadjuvant chemotherapy and its association with relative dose intensity and hematologic toxicity in patients with gastric cancer.Frontiers in nutrition · 2026Article
- Malnutrition and Sarcopenia in Patients with Neuroendocrine Tumors: A Comprehensive Review of Evidence.Biomolecules · 2025Review
- Concordance between single-slice abdominal computed tomography-based and bioelectrical impedance-based analysis of body composition in a prospective study.European radiology · 2025Article
- 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
- Leveraging Sarcopenia index by automated CT body composition analysis for pan cancer prognostic stratification.NPJ digital medicine · 2025Article
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- Utilizing Opportunistic Computed Tomography Imaging to Refine Lean Body Weight Estimates in Patients with Obesity.Clinical pharmacokinetics · 2025Article
- Association of Parental Feeding Styles with Body Composition Among Children in Two Regions in China.Nutrients · 2025Article
- Exchange of Quantitative Computed Tomography Assessed Body Composition Data Using Fast Healthcare Interoperability Resources as a Necessary Step Toward Interoperable Integration of Opportunistic Screening Into Clinical Practice: Methodological Development Study.Journal of medical Internet research · 2025Article
- Updates on Methods for Body Composition Analysis: Implications for Clinical Practice.Current obesity reports · 2025Review
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Corrections and comments
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patients with neuroendocrine tumors of gastro-entero-pancreatic origin (GEP-NET) experience changes in fat and muscle composition. Dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) are currently used to analyze body composition. Changes thereof could indicate cancer progression or response to treatment. This study examines the correlation between CT-based (computed tomography) body composition analysis (BCA) and DXA or BIA measurement. 74 GEP-NET-patients received whole-body [68Ga]-DOTATOC-PET/CT, BIA, and DXA-scans. BCA was performed based on the non-contrast-enhanced, 5 mm, whole-body-CT images. BCA from CT shows a strong correlation between body fat ratio with DXA (r = 0.95, ρC = 0.83) and BIA (r = 0.92, ρC = 0.76) and between skeletal muscle ratio with BIA: r = 0.81, ρC = 0.49. The deep learning-network achieves highly accurate results (mean Sørensen-Dice-score 0.93). Using BCA on routine Positron emission tomography/CT-scans to monitor patients' body composition in the diagnostic workflow can reduce additional exams whilst substantially amplifying measurement in slower progressing cancers such as GEP-NET.
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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.