ArticleFrontiers in nutrition2026
Local fat, local risk: mesorectal fat volume outperforms BMI in predicting rectal cancer surgery outcomes.
Article in Frontiers in nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Accurate pre-operative assessment of surgical difficulty and post-operative risk in rectal cancer remains challenging. Conventional indices such as body mass index (BMI) fail to adequately reflect the pelvic surgical anatomy due to their systemic nature. Mesorectal fat volume-an imaging-derived, site-specific parameter-may better represent the operative field and associated risks, while also serving as a marker of regional adiposity relevant to nutritional and metabolic status. Methods: This single-center retrospective cohort study (2018-2026) analyzed 450 consecutive patients undergoing elective rectal cancer surgery with total mesorectal excision. Mesorectal fat volume was quantified using pre-operative computed tomography or magnetic resonance imaging with semi-automated segmentation. Patients were categorized into quartiles based on mesorectal fat volume. Operative outcomes and 30-day post-operative complications were compared across quartiles. Multivariable linear and logistic regression analyses were performed to identify independent predictors. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminative performance of mesorectal fat volume, BMI, and pelvic index. Results: A total of 450 patients were stratified into quartiles according to mesorectal fat volume (MFV). Patients in the highest MFV quartile had significantly longer operative times (182.26 ± 9.28 vs. 163.03 ± 9.67 min, Conclusion: CT-derived mesorectal fat volume is independently associated with increased operative difficulty and a higher risk of post-operative complications following total mesorectal excision. Compared with BMI and pelvic index, MFV provides better, although moderate, discrimination for predicting post-operative complications and may serve as a useful imaging biomarker for pre-operative risk stratification. Incorporation of MFV into multivariable prediction models may improve individualized surgical planning and perioperative risk assessment.
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