ArticleThe British journal of radiology2024
External validation of a deep learning model for automatic segmentation of skeletal muscle and adipose tissue on abdominal CT images.
Article in The British journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Computed Tomography-Derived Sarcopenia and Two- Versus Three-Dimensional Body Composition for Prognostication in Colorectal Cancer Patients Receiving Chemoradiotherapy: An Automated Deep Learning Segmentation Study with External Reproducibility Assessment.Healthcare (Basel, Switzerland) · 2026Article
- Association between low skeletal muscle mass assessed by deep learning-based CT and MASLD: a prospective cohort study.Diabetology & metabolic syndrome · 2026Article
- Prediction of Overall Survival in Patients With Oesophageal Cancer Using AI-Based 3D CT Body Composition Analysis.Journal of cachexia, sarcopenia and muscle · 2026Article
- A multitask deep learning framework for the automated quantification of abdominal tissue morphology on ultrasound and the therapeutic monitoring of diastasis recti abdominis.Quantitative imaging in medicine and surgery · 2026Article
- Automated L3 Skeletal Muscle Segmentation for the Evaluation of Sarcopenia: Development and Independent Validation of an Ensemble-Based 2D nnU-Net Pipeline in a Complex Liver Disease Cohort.Muscles (Basel, Switzerland) · 2026Article
- CT Body Composition Changes Predict Survival in Immunotherapy-Treated Cancer Patients: A Retrospective Cohort Study.Cancers · 2026Article
- Deep-learning pipeline for automated skeletal muscle segmentation and sarcopenia detection.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2026Article
- Multimodal AI Model for Sarcopenia Detection: Integrating Chest CT and Clinical Parameters in Older Adults.Clinical interventions in aging · 2026Article
- Skeletal Muscle Mass Estimation from Lower Leg Digital Images Using Machine Learning.Progress in rehabilitation medicine · 2026Article
- Independent validation of the Mosamatic deep learning automated skeletal muscle and adipose tissue segmentation tool in an external Chinese cancer patient cohort.BJR artificial intelligence · 2026Article
- Intramuscular Fat as a Biomarker of Muscle Health: Strengths, Limitations and Open Challenges.Journal of cachexia, sarcopenia and muscle · 2026Review
- Article
- Subcutaneous Adipose Tissue Radiation Attenuation Is Associated With Increased 1-Year Mortality in Polytrauma Patients.Journal of cachexia, sarcopenia and muscle · 2025Article
- Loss of Skeletal Muscle Mass Is Associated With Reduced Cytotoxic T Cell Abundance and Poor Survival in Advanced Lung Cancer.Journal of cachexia, sarcopenia and muscle · 2025Article
- Beyond the tumor: towards a cachexia-based host phenotype through body composition analysis in patients with resectable lung cancer.Translational lung cancer research · 2025Review
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15 authors.
Funding
Abstract
objectivesBody composition assessment using CT images at the L3-level is increasingly applied in cancer research and has been shown to be strongly associated with long-term survival. Robust high-throughput automated segmentation is key to assess large patient cohorts and to support implementation of body composition analysis into routine clinical practice. We trained and externally validated a deep learning neural network (DLNN) to automatically segment L3-CT images.
methodsExpert-drawn segmentations of visceral and subcutaneous adipose tissue (VAT/SAT) and skeletal muscle (SM) of L3-CT-images of 3187 patients undergoing abdominal surgery were used to train a DLNN. The external validation cohort was comprised of 2535 patients with abdominal cancer. DLNN performance was evaluated with (geometric) dice similarity (DS) and Lin's concordance correlation coefficient.
resultsThere was a strong concordance between automatic and manual segmentations with median DS for SM, VAT, and SAT of 0.97 (IQR: 0.95-0.98), 0.98 (IQR: 0.95-0.98), and 0.95 (IQR: 0.92-0.97), respectively. Concordance correlations were excellent: SM 0.964 (0.959-0.968), VAT 0.998 (0.998-0.998), and SAT 0.992 (0.991-0.993). Bland-Altman metrics indicated only small and clinically insignificant systematic offsets; SM radiodensity: 0.23 Hounsfield units (0.5%), SM: 1.26 cm2.m-2 (2.8%), VAT: -1.02 cm2.m-2 (1.7%), and SAT: 3.24 cm2.m-2 (4.6%).
conclusionA robustly-performing and independently externally validated DLNN for automated body composition analysis was developed. ADVANCES IN KNOWLEDGE: This DLNN was successfully trained and externally validated on several large patient cohorts. The trained algorithm could facilitate large-scale population studies and implementation of body composition analysis into clinical practice.
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