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<p>Liver metastasis is a frequent complication in colorectal cancer (CRC), significantly impacting patient prognosis. This study aims to develop a machine learning-based prediction model for metachronous liver metastasis (MLM) in CRC patients, facilitating early diagnosis and intervention to potentially improve treatment outcomes and survival rates. A retrospective analysis was conducted on 620 consecutive patients who underwent radical colorectal cancer resection at the First People’s Hospital of Changzhou during the study period and met the predefined inclusion and exclusion criteria. MLM status was determined according to postoperative follow-up outcomes rather than used as a sampling criterion. Among the final eligible primary cohort, 373 patients had no observed liver metastasis during follow-up, whereas 247 patients were diagnosed with metachronous liver metastasis more than 6&#xa0;months after radical CRC resection. Patients were split into training (non-MLM = 258, MLM = 176) and internal validation (non-MLM = 258, MLM = 71) cohorts, with an external cohort of 52 non-MLM and 30 MLM patients from the Seventh People’s Hospital of Changzhou. Missing values were imputed using KNN. Based on the features selected by Logistic Regression (LR) and LASSO, five machine learning models (LR, RF, LightGBM, XGBoost, and SVM) were developed. Model performance was comprehensively evaluated using AUROC, DCA, accuracy, sensitivity, specificity, and F1 score, with SHAP illustrating feature influence. The best model was validated internally and externally. Eight independent risk factors (Age, Tumor Embolus, Size, T stage, N stage, Tumor Differentiation, RDW, AST) were included as features in the model. The LR model demonstrated the best performance, with SHAP indicating T stage as the most influential factor. A dynamic online nomogram was constructed to visualize the LR model. ROC analysis showed good discriminative performance of the final LR model, and calibration analysis suggested acceptable agreement between predicted and observed MLM risk. DCA and CIC analyses suggested potential clinical net benefit and clinical impact within the prespecified clinically relevant threshold probability range. In this study, LR model was selected as the final model for predicting the risk of metachronous liver metastasis after radical CRC resection. The dynamic nomogram provides an interpretable and accessible tool for visualizing individualized risk estimates. Given the retrospective design and current validation limitations, the model should be regarded as a supplementary tool to support postoperative risk stratification and follow-up planning rather than as definitive evidence for clinical decision-making. Further prospective and multicenter validation is warranted before routine clinical implementation.</p>

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Interpretable machine learning models for predicting the risk of metachronous colorectal liver metastases

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月27日

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  • Kejia Wu,
  • Honghu Xie,
  • Changbing Shen,
  • Bo Yuan,
  • Ziqing Zhu,
  • Weigang Zhang,
  • Lei Qin,
  • Wei Zhang,
  • Sen Ge,
  • Zhengwei Zhang,
  • Yunjie Lu,
  • Yiwen Xu

摘要

Liver metastasis is a frequent complication in colorectal cancer (CRC), significantly impacting patient prognosis. This study aims to develop a machine learning-based prediction model for metachronous liver metastasis (MLM) in CRC patients, facilitating early diagnosis and intervention to potentially improve treatment outcomes and survival rates. A retrospective analysis was conducted on 620 consecutive patients who underwent radical colorectal cancer resection at the First People’s Hospital of Changzhou during the study period and met the predefined inclusion and exclusion criteria. MLM status was determined according to postoperative follow-up outcomes rather than used as a sampling criterion. Among the final eligible primary cohort, 373 patients had no observed liver metastasis during follow-up, whereas 247 patients were diagnosed with metachronous liver metastasis more than 6 months after radical CRC resection. Patients were split into training (non-MLM = 258, MLM = 176) and internal validation (non-MLM = 258, MLM = 71) cohorts, with an external cohort of 52 non-MLM and 30 MLM patients from the Seventh People’s Hospital of Changzhou. Missing values were imputed using KNN. Based on the features selected by Logistic Regression (LR) and LASSO, five machine learning models (LR, RF, LightGBM, XGBoost, and SVM) were developed. Model performance was comprehensively evaluated using AUROC, DCA, accuracy, sensitivity, specificity, and F1 score, with SHAP illustrating feature influence. The best model was validated internally and externally. Eight independent risk factors (Age, Tumor Embolus, Size, T stage, N stage, Tumor Differentiation, RDW, AST) were included as features in the model. The LR model demonstrated the best performance, with SHAP indicating T stage as the most influential factor. A dynamic online nomogram was constructed to visualize the LR model. ROC analysis showed good discriminative performance of the final LR model, and calibration analysis suggested acceptable agreement between predicted and observed MLM risk. DCA and CIC analyses suggested potential clinical net benefit and clinical impact within the prespecified clinically relevant threshold probability range. In this study, LR model was selected as the final model for predicting the risk of metachronous liver metastasis after radical CRC resection. The dynamic nomogram provides an interpretable and accessible tool for visualizing individualized risk estimates. Given the retrospective design and current validation limitations, the model should be regarded as a supplementary tool to support postoperative risk stratification and follow-up planning rather than as definitive evidence for clinical decision-making. Further prospective and multicenter validation is warranted before routine clinical implementation.