Xu Yu-Ying, Li Qiu-Yan, Yi Dan-Hui, Chen Yue, Zhai Jia-Wei, Zhang Tong, Sun Ling-Yun, Yang Yu-Fei
Department of Oncology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, China.
School of Statistics, Renmin University of China, Beijing, 100872, China.
Chin J Integr Med. 2024 Nov;30(11):993-1000. doi: 10.1007/s11655-024-3718-4. Epub 2024 Mar 27.
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.
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.
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).
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.
通过机器学习算法建立转移性结直肠癌(mCRC)的中医动态治疗策略,为mCRC中医治疗策略的选择提供参考。
从中国中医科学院西苑医院肿瘤科mCRC门诊病例中筛选出197例符合纳入标准的病例。根据不同的中医干预策略,将患者分为3组:单纯中医治疗、中西医并重治疗(中医联合肿瘤局部治疗、口服化疗或靶向药物)、中医辅助西医治疗(中医联合西医静脉用药方案)。将接受中医干预患者的生存时间作为最终评价指标。筛选影响中医干预方案选择的因素作为决策变量。基于生存成本敏感分类学习算法(CSCLSurv)探索mCRC的中医动态干预和治疗策略。采用Kaplan-Meier法估计患者生存情况,并将接受模型推荐治疗方案患者的生存时间与接受实际治疗方案患者的生存时间进行比较。
以接受中医干预患者的生存时间为评价指标,基于CSCLSurv建立了mCRC的中医动态干预治疗策略。可根据性别、年龄、东部肿瘤协作组评分、肿瘤部位、转移部位、基因分型及患者初诊时西医治疗阶段等动态决策变量选择mCRC不同的中医干预策略。接受模型推荐治疗方案患者的中位生存时间为35个月,而接受实际治疗方案患者的中位生存时间为26.0个月(P = 0.06)。
基于CSCLSurv的mCRC中医动态治疗策略在为中医临床提供提示方面发挥了一定作用。在未来更大样本量的前瞻性研究中可进一步完善。