ArticleJournal of cachexia, sarcopenia and muscle2024
Three-dimensional body composition parameters using automatic volumetric segmentation allow accurate prediction of colorectal cancer outcomes.
Article in Journal of cachexia, sarcopenia and muscle, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic impact of myosteatosis on postoperative outcomes and survival in colorectal cancer: a systematic review.International journal of colorectal disease · 2026Pooled it
- SpyGlass Direct Visualization System: A Cost-Effective Approach Enhancing Nutritional and Immune Recovery in Difficult Bile Duct Stone Management.Yonsei medical journal · 2026Trial
- AI-derived 3D body composition predicts prognosis of chemoimmunotherapy in small cell lung cancer: a multicenter study.Journal for immunotherapy of cancer · 2026Article
- AutoLMISeg: A Fully Automated Pipeline for 3D Lumbar Muscle Volumetry and Densitometry on CT - Technical Validation Against an Operator-Driven Workflow.Journal of imaging informatics in medicine · 2026Article
- Novel plasma protein signatures for early diagnosis and prognostic stratification in colorectal cancer.iScience · 2026Article
- Body composition as a predictor of cancer-related death in colon cancer: an AI-based volumetric analysis.La Radiologia medica · 2026Article
- Construction of a prognostic model for colorectal cancer based on clinical parameters and quantitative body composition using CT imaging.BMC medical imaging · 2026Article
- Skeletal Muscle Gauge and Complementary Pan-Immune-Inflammation Value for Risk Stratification of 30-Day Major Complications After Colorectal Cancer Surgery.Biomedicines · 2026Article
- Myosteatosis mediates the link between specific dietary components and colorectal carcinogenesis: from PPLSS multi-center study.Asia Pacific journal of clinical nutrition · 2026Article
- A nomogram model based on CT-assessed body composition parameters for predicting postoperative recurrence in advanced gastric cancer.Abdominal radiology (New York) · 2026Article
- Changes of bone, adipose, and muscle-related body compositions in gastric cancers after gastrectomy using deep learning based automatic segmentation.BMC gastroenterology · 2025Article
- Integrative deep learning analysis of 2D and 3D body composition features for predicting postoperative pancreatic fistula after distal pancreatectomy.Annals of medicine · 2025Article
- Artificial Intelligence-Derived Intramuscular Adipose Tissue Assessment Predicts Perineal Wound Complications Following Abdominoperineal Resection.World journal of surgery · 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
- Sarcopenia as a Prognostic Factor for the Outcomes of Surgical Treatment of Colorectal Carcinoma.Healthcare (Basel, Switzerland) · 2025Review
- Artificial Intelligence-Driven Volumetric Analysis of Muscle Mass as a Predictor of Tumor Response to Neoadjuvant Chemoradiotherapy in Patients with Rectal Cancer.Journal of clinical medicine · 2024Article
- Three-dimensional body composition parameters using automatic volumetric segmentation allow accurate prediction of colorectal cancer outcomes.Journal of cachexia, sarcopenia and muscle · 2024Article
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Authors and funding
16 authors.
Funding
Abstract
backgroundParameters obtained from two-dimensional (2D) cross-sectional images have been used to determine body composition. However, data from three-dimensional (3D) volumetric body images reflect real body composition more accurately and may be better predictors of patient outcomes in cancer. This study aimed to assess the 3D parameters and determine the best predictive factors for patient prognosis.
methodsPatients who underwent surgery for colorectal cancer (CRC) between 2010 and 2016 were included in this study. Preoperative computed tomography images were analysed using an automatic segmentation program. Body composition parameters for muscle, muscle adiposity, subcutaneous fat (SF) and abdominal visceral fat (AVF) were assessed using 2D images at the third lumbar (L3) level and 3D images of the abdominal waist (L1-L5). The cut-off points for each parameter were determined using X-tile software. A Cox proportional hazards regression model was used to identify the association between the parameters and the treatment outcomes, and the relative influence of each parameter was compared using a gradient boosting model.
resultsOverall, 499 patients were included in the study. At a median follow-up of 59 months, higher 3D parameters of the abdominal muscles and SF from the abdominal waist were found to be associated with longer overall survival (OS) and disease-free survival (all P < 0.001). Although the 3D parameters of AVF were not related to survival outcomes, patients with a high AVF volume and mass experienced higher rate of postoperative complications than those with low AVF volume (27.4% vs. 18.7%, P = 0.021, for mass; 27.1% vs. 19.0%, P = 0.028, for volume). Low muscle mass and volume (hazard ratio [HR] 1.959, P = 0.016; HR 2.093, P = 0.036, respectively) and low SF mass and volume (HR 1.968, P = 0.008; HR 2.561, P = 0.003, respectively), both in the abdominal waist, were identified as independent prognostic factors for worse OS. Along with muscle mass and volume, SF mass and volume in the abdominal waist were negatively correlated with mortality (all P < 0.001). Both AVF mass and volume in the abdominal waist were positively correlated with postoperative complications (P < 0.05); 3D muscle volume and SF at the abdominal waist were the most influential factors for OS.
conclusions3D volumetric parameters generated using an automatic segmentation program showed higher correlations with the short- and long-term outcomes of patients with CRC than conventional 2D parameters.
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