ArticleFrontiers in oncology2026
Machine learning to develop and validate a model for predicting the risk of lymph node metastasis in colorectal cancer patients.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
9 authors.
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Abstract
Background: The presence of lymph node metastasis (LNM) serves as a critical determinant of prognosis in colorectal cancer (CRC), often correlating with markedly unfavorable clinical outcomes. Consequently, this investigation sought to construct and rigorously validate a machine learning (ML) algorithm capable of estimating LNM probability within the CRC population. Methods: Through the application of logistic regression analysis, six independent predictors were identified: extramural vascular invasion (EMVI), T stage, fibrinogen (FIB), systolic blood pressure (SBP), thrombin time (TT), and alpha-fucosidase (AFU). Subsequently, diverse ML architectures were generated and assessed utilizing receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Results: Among the evaluated algorithms, the stochastic gradient boosting (gbm) model exhibited superior discriminatory power, yielding area under the curve (AUC) metrics of 0.815 in the training cohort and 0.733 in the external validation set. Calibration assessments revealed a robust concordance between projected probabilities and actual observed events. Notably, the xgbTree algorithm also demonstrated commendable predictive efficacy. Conclusion: We successfully established and verified an ML-based framework designed to forecast LNM risk in individuals with CRC. These computational tools offer clinicians a refined mechanism for the precise identification of nodal involvement, thereby laying the groundwork for formulating personalized therapeutic regimens.
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