Dynamic Treatment Strategy of Chinese Medicine for Metastatic Colorectal Cancer Based on Machine Learning Algorithm[J]. 中国结合医学杂志(英文版), 2024,30(11):993-1000.
XU Yu-ying, LI Qiu-yan, YI Dan-hui, et al. Dynamic Treatment Strategy of Chinese Medicine for Metastatic Colorectal Cancer Based on Machine Learning Algorithm[J]. Chinese Journal of Integrative Medicine, 2024,30(11):993-1000.
Dynamic Treatment Strategy of Chinese Medicine for Metastatic Colorectal Cancer Based on Machine Learning Algorithm[J]. 中国结合医学杂志(英文版), 2024,30(11):993-1000. DOI: 10.1007/s11655-024-3718-4.
XU Yu-ying, LI Qiu-yan, YI Dan-hui, et al. Dynamic Treatment Strategy of Chinese Medicine for Metastatic Colorectal Cancer Based on Machine Learning Algorithm[J]. Chinese Journal of Integrative Medicine, 2024,30(11):993-1000. DOI: 10.1007/s11655-024-3718-4.
Dynamic Treatment Strategy of Chinese Medicine for Metastatic Colorectal Cancer Based on Machine Learning Algorithm
摘要
Abstract
Objective:
2
To establish the dynamic treatment strategy of Chinese medicine (CM) for metastatic colorectal cancer (mCRC) by machine learning algorithm
in order to provide a reference for the selection of CM treatment strategies for mCRC.
Methods:
2
From the outpatient cases of mCRC in the Department of Oncology at Xiyuan Hospital
China Academy of Chinese Medical Sciences
197 cases that met the inclusion criteria were screened. According to different CM intervention strategies
the patients were divided into 3 groups: CM treatment alone
equal emphasis on Chinese and Western medicine treatment (CM combined with local treatment of tumors
oral chemotherapy
or targeted drugs)
and CM assisted Western medicine treatment (CM combined with intravenous regimen of Western medicine). The survival time of patients undergoing CM intervention was taken as the final evaluation index. Factors affecting the choice of CM intervention scheme were screened as decision variables. The dynamic CM intervention and treatment strategy for mCRC was explored based on the cost-sensitive classification learning algorithm for survival (CSCLSurv). Patients' survival was estimated using the Kaplan-Meier method
and the survival time of patients who received the model-recommended treatment plan were compared with those who received actual treatment plan.
Results:
2
Using the survival time of patients undergoing CM intervention as the evaluation index
a dynamic CM intervention therapy strategy for mCRC was established based on CSCLSurv. Different CM intervention strategies for mCRC can be selected according to dynamic decision variables
such as gender
age
Eastern Cooperative Oncology Group score
tumor site
metastatic site
genotyping
and the stage of Western medicine treatment at the patient's first visit. The median survival time of patients who received the model-recommended treatment plan was 35 months
while those who receive the actual treatment plan was 26.0 months (
P
=0.06).
Conclusions:
2
The dynamic treatment strategy of CM
based on CSCLSurv for mCRC
plays a certain role in providing clinical hints in CM. It can be further improved in future prospective studies with larger sample sizes.