Dynamic Treatment Strategy of Chinese Medicine for Metastatic Colorectal Cancer Based on Machine Learning Algorithm.
10.1007/s11655-024-3718-4
- Author:
Yu-Ying XU
1
;
Qiu-Yan LI
1
;
Dan-Hui YI
2
;
Yue CHEN
3
;
Jia-Wei ZHAI
3
;
Tong ZHANG
1
;
Ling-Yun SUN
1
;
Yu-Fei YANG
4
Author Information
1. Department of Oncology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, China.
2. School of Statistics, Renmin University of China, Beijing, 100872, China.
3. Graduate School, Beijing University of Chinese Medicine, Beijing, 100029, China.
4. Department of Oncology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, China. yyf93@vip.sina.com.
- Publication Type:Journal Article
- Keywords:
Chinese medicine;
cost sensitive classification learning for survival;
dynamic treatment strategy;
metastatic colorectal cancer
- MeSH:
Humans;
Colorectal Neoplasms/therapy*;
Machine Learning;
Female;
Male;
Middle Aged;
Neoplasm Metastasis;
Algorithms;
Medicine, Chinese Traditional/methods*;
Aged;
Adult;
Kaplan-Meier Estimate
- From:
Chinese journal of integrative medicine
2024;30(11):993-1000
- CountryChina
- Language:English
-
Abstract:
OBJECTIVE: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: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: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: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.
