Objective:

To develop, validate, and deploy an interpretable machine learning model to predict long-term survival in patients with colorectal cancer liver metastasis (CRLM), specifically examining the impact of Traditional Chinese Medicine (TCM) intervention on survival outcomes.

Approach:

Key Findings:

• The XGBoost algorithm showed superior predictive performance with an AUC of 0.891 for 36-month survival in the training cohort and 0.833 in the testing cohort.
• Key prognostic factors included TNM stage, liver metastasis burden, and TCM intervention intensity, with TCM showing a protective association in a dose-dependent manner.
• The web-based tool allows clinicians to input patient parameters for dynamic risk estimates.

Interpretation:

The study successfully developed and validated a machine learning-based prognostic model for CRLM, integrating TCM intervention into predictive modeling.

Limitations:

• The study is retrospective and may be subject to selection bias, potentially affecting the reliability of the findings.
• Generalizability may be limited to similar patient populations.

Conclusion:

An interpretable ML-based prognostic model for CRLM was developed and validated, offering a practical tool for personalized prognostic assessment.

Attribution Notice

This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.