Evidence map›Paper›PMID 38532153›Full record

ArticleChinese journal of integrative medicine2024

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

Yu-Ying Xu, Qiu-Yan Li, Dan-Hui Yi, Yue Chen, Jia-Wei Zhai, Tong Zhang, Ling-Yun Sun, Yu-Fei Yang

Abstract read
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In one paragraph

Article in Chinese journal of integrative medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 40% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 4 institutions in 1 country.

Yu-Ying XuDepartment of Oncology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, China.
Qiu-Yan LiDepartment of Oncology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, China.
Dan-Hui YiSchool of Statistics, Renmin University of China, Beijing, 100872, China.
Yue ChenGraduate School, Beijing University of Chinese Medicine, Beijing, 100029, China.
Jia-Wei ZhaiGraduate School, Beijing University of Chinese Medicine, Beijing, 100029, China.
Tong ZhangDepartment of Oncology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, China.
Ling-Yun SunDepartment of Oncology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, China.
Yu-Fei YangDepartment of Oncology, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, China. yyf93@vip.sina.com.
Xiyuan Hospital · CNBeijing University of Chinese Medicine · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNRenmin University of China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo 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.

methodsFrom 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.

resultsUsing 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).

conclusionsThe 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.

Indexed as

AlgorithmsColorectal NeoplasmsMachine LearningNeoplasm MetastasisAdultAgedFemaleHumansKaplan-Meier EstimateMaleMedicine, Chinese TraditionalMiddle AgedChinese medicinecost sensitive classification learning for survivaldynamic treatment strategymetastatic colorectal cancer

Identifiers

PMID38532153
OpenAlexW4394748994

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.