Predictive value of multimodal models combining CT body composition and clinical variables for short-term postoperative complications in colorectal cancer: a systematic review and meta-analysis.
- 1Department of Surgical Oncology, The First Affiliated Hospital of China Medical University, Shenyang, China.
Abstract
Background
Malnutrition and sarcopenic obesity are prevalent in colorectal cancer (CRC) and linked to adverse surgical outcomes. Conventional screening often overlooks this occult malnutrition. Preoperative computed tomography (CT)-based body composition analysis (BCA) provides objective biomarkers for muscle depletion and fat distribution. This systematic review evaluates multimodal machine learning (ML) models integrating BCA and clinical variables to predict CRC postoperative complications and inform precision nutrition strategies.Methods
We systematically searched PubMed, Embase, Web of Science, and Cochrane Library (inception to April 13, 2026) for ML models predicting postoperative complications in CRC using BCA. Quality was assessed using PROBAST. Pooled area under the curve (AUC) was estimated via a random-effects model, alongside subgroup analyses.Results
Eight studies (n = 7,835) were included. The pooled AUC was 0.84 (95% CI: 0.76-0.89), indicating good discriminative performance, although substantial heterogeneity was present (I ² = 79.7%). Nomogram-based models showed a pooled AUC of 0.87 (95% CI: 0.78-0.92). For anastomotic leakage, the pooled AUC was 0.83 (95% CI: 0.35-0.98), although this estimate was imprecise because only two studies contributed to this subgroup.Conclusion
Multimodal ML models integrating BCA biomarkers offer a robust, data-driven framework for preoperative risk stratification in CRC. Identifying high-risk body composition phenotypes may facilitate tailored prehabilitation and targeted immunonutrition strategies. Future efforts must prioritize standardized BCA and multi-center external validation.Systematic review registration
CRD420261364363.
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BibTeX
@article{li2026predictive,
title = {{Predictive value of multimodal models combining CT body composition and clinical variables for short-term postoperative complications in colorectal cancer: a systematic review and meta-analysis.}},
author = {Yiheng Li and Chi Xue and Xinwei Zhang and Zhi Zhu},
journal = {Frontiers in nutrition},
year = {2026},
month = oct,
doi = {10.3389/fnut.2026.1880518},
url = {https://europepmc.org/article/MED/42903050},
note = {CC BY},
}
RIS
TY - JOUR
TI - Predictive value of multimodal models combining CT body composition and clinical variables for short-term postoperative complications in colorectal cancer: a systematic review and meta-analysis.
AU - Yiheng Li
AU - Chi Xue
AU - Xinwei Zhang
AU - Zhi Zhu
PY - 2026
DA - 2026/10/06
JO - Frontiers in nutrition
DO - 10.3389/fnut.2026.1880518
UR - https://europepmc.org/article/MED/42903050
AB - Background Malnutrition and sarcopenic obesity are prevalent in colorectal cancer (CRC) and linked to adverse surgical outcomes. Conventional screening often overlooks this occult malnutrition. Preoperative computed tomography (CT)-based body composition analysis (BCA) provides objective biomarkers for muscle depletion and fat distribution. This systematic review evaluates multimodal machine learning (ML) models integrating BCA and clinical variables to predict CRC postoperative complications and inform precision nutrition strategies.Methods We systematically searched PubMed, Embase, Web of Science, and Cochrane Library (inception to April 13, 2026) for ML models predicting postoperative complications in CRC using BCA. Quality was assessed using PROBAST. Pooled area under the curve (AUC) was estimated via a random-effects model, alongside subgroup analyses.Results Eight studies (n = 7,835) were included. The pooled AUC was 0.84 (95% CI: 0.76-0.89), indicating good discriminative performance, although substantial heterogeneity was present (I ² = 79.7%). Nomogram-based models showed a pooled AUC of 0.87 (95% CI: 0.78-0.92). For anastomotic leakage, the pooled AUC was 0.83 (95% CI: 0.35-0.98), although this estimate was imprecise because only two studies contributed to this subgroup.Conclusion Multimodal ML models integrating BCA biomarkers offer a robust, data-driven framework for preoperative risk stratification in CRC. Identifying high-risk body composition phenotypes may facilitate tailored prehabilitation and targeted immunonutrition strategies. Future efforts must prioritize standardized BCA and multi-center external validation.Systematic review registration CRD420261364363.
SN - 2296-861X
ER -