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Related Experiment Video

Updated: Jun 29, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Dynamic Treatment Strategy of Chinese Medicine for Metastatic Colorectal Cancer Based on Machine Learning Algorithm.

Yu-Ying Xu¹, Qiu-Yan Li¹, Dan-Hui Yi²

¹Department of Oncology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, China.

Chinese Journal of Integrative Medicine

|March 27, 2024

Summary

This summary is machine-generated.

A machine learning model established a dynamic Chinese medicine (CM) strategy for metastatic colorectal cancer (mCRC), improving median survival from 26 to 35 months. This approach offers clinical guidance for personalized CM treatment in mCRC patients.

Keywords:

Chinese medicine cost sensitive classification learning for survival dynamic treatment strategy metastatic colorectal cancer

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Area of Science:

Oncology
Computational Medicine
Integrative Medicine

Background:

Metastatic colorectal cancer (mCRC) presents complex treatment challenges.
Chinese medicine (CM) offers potential therapeutic strategies for mCRC.
Personalized treatment approaches are crucial for improving patient outcomes.

Purpose of the Study:

To develop a dynamic treatment strategy for mCRC using Chinese medicine (CM) guided by machine learning.
To provide a reference for selecting optimal CM treatment strategies for mCRC patients.
To leverage machine learning for personalized cancer therapy.

Main Methods:

A cohort of 197 mCRC patients was analyzed.
Patients were categorized into three groups based on CM intervention: CM alone, CM combined with local/oral treatments, and CM assisting Western medicine.
A cost-sensitive classification learning algorithm for survival (CSCLSurv) was employed to establish the dynamic strategy.
Kaplan-Meier analysis compared survival between model-recommended and actual treatment plans.

Main Results:

A dynamic CM intervention strategy for mCRC was successfully established using CSCLSurv.
Treatment recommendations varied based on patient-specific factors like age, ECOG score, tumor site, and treatment stage.
The median survival time for patients receiving the model-recommended plan was 35 months, compared to 26.0 months for the actual treatment plan (P=0.06).

Conclusions:

The CSCLSurv-based dynamic CM treatment strategy shows promise for mCRC.
This approach provides valuable clinical insights for personalized CM interventions.
Further validation with larger prospective studies is recommended to refine the strategy.