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Journal of Integrative Medicine ›› 2022, Vol. 20 ›› Issue (5): 402-415.doi: 10.1016/j.joim.2022.06.003

• Systematic Review • Previous Articles     Next Articles

Examining patterns of traditional chinese medicine use in pediatric oncology: A systematic review, meta-analysis and data-mining study

Chun Sing Lam a, Li Wen Peng a, Lok Sum Yang a, Ho Wing Janessa Chou a, Chi-Kong Li b,c,d, Zhong Zuo a, Ho-Kee Koon e, Yin Ting Cheung a   

  1. a School of Pharmacy, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong, China
    b Department of Paediatrics, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong, China
    c Hong Kong Hub of Paediatric Excellence, the Chinese University of Hong Kong, Hong Kong, China
    d Department of Paediatrics & Adolescent Medicine, Hong Kong Children’s Hospital, Hong Kong, China
    e School of Chinese Medicine, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong, China
  • Received:2021-07-09 Accepted:2022-05-05 Online:2022-09-15 Published:2022-06-10
  • Contact: Ho-Kee Koon,E-mail:; Yin Ting Cheung, E-mail:

Traditional Chinese medicine (TCM) is becoming a popular complementary approach in pediatric oncology. However, few or no meta-analyses have focused on clinical studies of the use of TCM in pediatric oncology.

We explored the patterns of TCM use and its efficacy in children with cancer, using a systematic review, meta-analysis and data mining study.

Search strategy
We conducted a search of five English (Allied and Complementary Medicine Database, Embase, PubMed, Cochrane Central Register of Controlled Trials, and and four Chinese databases (Wanfang Data, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, and VIP Chinese Science and Technology Periodicals Database) for clinical studies published before October 2021, using keywords related to “pediatric,” “cancer,” and “TCM.”

Inclusion criteria
We included studies which were randomized controlled trials (RCTs) or observational clinical studies, focused on patients aged < 19 years old who had been diagnosed with cancer, and included at least one group of subjects receiving TCM treatment.

Data extraction and analysis
The methodological quality of RCTs and observational studies was assessed using the six-item Jadad scale and the Effective Public Healthcare Panacea Project Quality Assessment Tool, respectively. Meta-analysis was used to evaluate the efficacy of combining TCM with chemotherapy. Study outcomes included the treatment response rate and occurrence of cancer-related symptoms. Association rule mining (ARM) was used to investigate the associations among medicinal herbs and patient symptoms.

The fifty-four studies included in this analysis were comprised of RCTs (63.0%) and observational studies (37.0%). Most RCTs focused on hematological malignancies (41.2%). The study outcomes included chemotherapy-induced toxicities (76.5%), infection rate (35.3%), and response, survival or relapse rate (23.5%). The methodological quality of most of the RCTs (82.4%) and observational studies (80.0%) was rated as “moderate.” In studies of leukemia patients, adding TCM to conventional treatment significantly improved the clinical response rate (odds ratio [OR] = 2.55; 95% confidence interval [CI] = 1.49–4.36), lowered infection rate (OR = 0.23; 95% CI = 0.13–0.40), and reduced nausea and vomiting (OR = 0.13; 95% CI = 0.08–0.23). ARM showed that Radix Astragali, the most commonly used medicinal herb (58.0%), was associated with treating myelosuppression, gastrointestinal complications, and infection.

There is growing evidence that TCM is an effective adjuvant therapy for children with cancer. We proposed a checklist to improve the quality of TCM trials in pediatric oncology. Future work will examine the use of ARM techniques on real-world data to evaluate the efficacy of medicinal herbs and drug-herb interactions in children receiving TCM as a part of integrated cancer therapy.

Key words: Traditional Chinese Medicine, Herbal medicine, Pediatric oncology, Data mining, Associate rule mining, Chemotherapy

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