{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T17:49:24Z","timestamp":1790704164654,"version":"4.1.0"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:00:00Z","timestamp":1774483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Complete mesocolic excision (CME) is a technically complex and highly invasive surgical approach, and patients undergoing CME are consequently at elevated risk of postoperative acute kidney injury (AKI). Despite this vulnerability, reliable tools for individualized AKI risk prediction in this population remain unavailable. Moreover, the contributions of perioperative inflammatory responses and blood transfusion to AKI pathogenesis have not been comprehensively examined. Here, we sought to develop and validate a multicenter, machine learning\u2013based model to predict AKI following CME and to delineate the relative impact of inflammation- and transfusion-related determinants.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively enrolled patients with colon cancer who underwent CME between 2010 and 2020 at five tertiary referral centers. Patients were allocated to an internal cohort or an external validation cohort according to hospital of origin. The internal cohort was randomly divided into training and validation subsets in a 7:3 ratio. Feature selection and model construction were performed using multivariable analyses and five machine learning algorithms: Extreme Gradient Boosting (XGBoost), Random Forest, Support Vector Machine, k-Nearest Neighbors (KNN), and Multilayer Perceptron (MLP). Model performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and k-fold cross-validation, followed by independent external validation. Model interpretability and the quantitative contribution of inflammatory and transfusion variables were evaluated using SHapley Additive exPlanations (SHAP).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among the evaluated models, XGBoost achieved the most favorable performance, exhibiting superior discrimination, calibration, clinical utility, and generalizability, with area under the curve (AUC) values of 0.92 in the training set, 0.88 in the validation set, and 0.922 in the external cohort. SHAP analysis highlighted tumor size, operative duration, preoperative anemia, postoperative neutrophil-to-lymphocyte ratio (NLR), intraoperative blood loss, C-reactive protein (CRP), intraoperative hypoxemia, and perioperative blood transfusion as the dominant predictors of AKI. Notably, inflammation-related markers (NLR and CRP) and transfusion-related factors exerted a substantial influence on AKI risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We established an interpretable, multicenter machine learning\u2013based model with high predictive accuracy, robustness, and clinical relevance for AKI following CME. Our findings identify perioperative inflammation and blood transfusion as key drivers of postoperative AKI, offering mechanistic insight and a foundation for early risk stratification and targeted preventive strategies in high-risk patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1782613","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T06:35:27Z","timestamp":1774506927000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Multicenter machine learning study for long-term prediction of acute kidney injury after complete mesocolic excision: integrating inflammatory biomarkers and transfusion-related risk factors"],"prefix":"10.3389","volume":"16","author":[{"given":"Shunpeng","family":"He","sequence":"first","affiliation":[{"name":"Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine","place":["Hangzhou, 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