ArticleJournal of cachexia, sarcopenia and muscle2023
Deep learning-based assessment of body composition and liver tumour burden for survival modelling in advanced colorectal cancer.
Article 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 33 papers.
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33 citing papers in PubMed.
- Prognostic value of body composition analysis in cancer patients treated with immunotherapy: a systematic evaluation of TotalSegmentator-based biomarkers.La Radiologia medica · 2026Article
- Association between low skeletal muscle mass assessed by deep learning-based CT and MASLD: a prospective cohort study.Diabetology & metabolic syndrome · 2026Article
- Automated Body Composition from Computed Tomography Scans Improves Survival Prediction in Colorectal Cancer Patients.Journal of imaging informatics in medicine · 2026Article
- Large language models enable prognostic stratification of cancer patients using real-world clinical notes.PLOS digital health · 2026Article
- Automated Pretreatment Thoracic CT-Based Body Composition Analysis Predicts Progression-Free Survival in Head and Neck Cancer.Journal of clinical medicine · 2026Article
- Predictive value of primary gross tumor volume measured by MRI in response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.Journal of applied clinical medical physics · 2026Article
- Advances in Artificial Intelligence-Based Liver-Related Semantic Segmentation Techniques and Applications Using CT Imaging.Cancer medicine · 2026Review
- Multi-omics fusion network for prediction of early recurrence in colorectal liver metastases.NPJ precision oncology · 2026Article
- Artificial intelligence-driven body composition analysis and its association with survival in patients with colorectal liver metastases.Contemporary oncology (Poznan, Poland) · 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
- Muscle matters: automated CT-based body composition analysis predicts survival in patients with head and neck cancer treated with immunotherapy.Frontiers in oncology · 2026Article
- SALT: Introducing a framework for hierarchical segmentations in medical imaging using label trees.Scientific reports · 2025Article
- 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
- 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
- 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
- The Impact of Radiation Dose on CT-Based Body Composition Analysis: A Large-Animal Study.Journal of cachexia, sarcopenia and muscle · 2025Article
- Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence.Nature cancer · 2025Article
- Using a new artificial intelligence-aided method to assess body composition CT segmentation in colorectal cancer patients.Journal of medical radiation sciences · 2024Article
- The development of an attention mechanism enhanced deep learning model and its application for body composition assessment with L3 CT images.Scientific reports · 2024Article
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17 authors.
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Abstract
backgroundPersonalized therapy planning remains a significant challenge in advanced colorectal cancer care, despite extensive research on prognostic and predictive markers. A strong correlation of sarcopenia or overall body composition and survival has been described. Here, we explore whether automated assessment of body composition and liver metastases from standard of care CT images can add to clinical parameters in personalized survival risk prognostication.
methodsWe retrospectively analysed clinical imaging data from 85 patients (50.6% female, mean age 58.9 SD 12.2 years) with colorectal cancer and synchronous liver metastases. Pretrained deep learning models were used to assess body composition and liver metastasis geometry from abdominal CT images before the initiation of systemic treatment. Abdominal muscle-to-bone ratio (MBR) was calculated by dividing abdominal muscle volume by abdominal bone volume. MBR was compared with body mass index (BMI), abdominal muscle volume, and abdominal muscle volume divided by height squared. Differences in overall survival based on body composition and liver metastasis parameters were compared using Kaplan-Meier survival curves. Results were correlated with clinical and biomarker data to develop a machine learning model for survival risk prognostication.
resultsThe MBR, unlike abdominal muscle volume or BMI, was significantly associated with overall survival (HR 0.39, 95% CI: 0.19-0.80, P = 0.009). The MBR (P = 0.022), liver metastasis surface area (P = 0.01) and primary tumour sidedness (P = 0.007) were independently associated with overall survival in multivariate analysis. Body composition parameters did not correlate with KRAS mutational status or primary tumour sidedness. A prediction model based on MBR, liver metastasis surface area and primary tumour sidedness achieved a concordance index of 0.69.
conclusionsAutomated segmentation enables to extract prognostic parameters from routine imaging data for personalized survival modelling in advanced colorectal cancer patients.
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