Mingxiao Yang MD (CMD), PhD, Linda Zhong MD (CMD), PhD, Rose W. Y. Fok MBBS, GDMH, GDFM, MMed (Family Medicine), FCFP(S), FAMS (Family Medicine), Yan Yin Tjioe PhD, Bo Siang Teo BSc, BCM, Furong Zhang PhD, Ting Bao MD, MS
Cancer-related symptoms are detrimental to the quality of life of people with cancer. This review systematically evaluates phase 3 randomized clinical trials (RCTs) assessing the effectiveness and safety of traditional Chinese medicine (TCM) interventions in managing cancer-related symptoms throughout the cancer care trajectory. A comprehensive literature search was conducted of PubMed, Embase, and the Cochrane Library until April 27, 2025, to further identify eligible RCTs involving patients with cancer or survivors and assessing TCM interventions against valid control arms. Data extraction and quality assessments were conducted in accordance with Cochrane standards and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Nineteen phase 3 RCTs involving 5387 participants (female, 3321; primarily breast, gastrointestinal, and lung cancers) from six countries or regions were included. Nonpharmacological interventions, namely acupuncture and Tai Chi, significantly reduced pain, fatigue, insomnia, radiation-induced xerostomia, and hormonal therapy–related hot flashes compared to usual care (UC). Their effects on preventing chemotherapy- and radiotherapy-induced nausea and vomiting were mixed, which depended on control arms and outcome measures. Conversely, evidence for pharmacological interventions was limited, with inconclusive results regarding chemotherapy-induced peripheral neuropathy and hematologic toxicities, although promising outcomes were noted for preventing chemoradiotherapy-induced mucositis, reducing colorectal adenoma recurrence, and enhancing chemotherapy completion rates compared to placebo or UC. Safety data suggested similar adverse event profiles across groups. These findings show strong evidence for the inclusion of nonpharmacological interventions in oncology practice. However, pharmacological interventions require more high-quality, multicenter research to fully understand their effectiveness and safety. Implementing rigorous safety assessments and standardized adverse event reporting protocols is crucial to enhance clinical confidence in TCM modalities.
{"title":"Effects and safety of traditional Chinese medicine approaches in cancer symptom care: A systematic review of phase 3 randomized clinical trials","authors":"Mingxiao Yang MD (CMD), PhD, Linda Zhong MD (CMD), PhD, Rose W. Y. Fok MBBS, GDMH, GDFM, MMed (Family Medicine), FCFP(S), FAMS (Family Medicine), Yan Yin Tjioe PhD, Bo Siang Teo BSc, BCM, Furong Zhang PhD, Ting Bao MD, MS","doi":"10.1002/cncr.70170","DOIUrl":"10.1002/cncr.70170","url":null,"abstract":"<p>Cancer-related symptoms are detrimental to the quality of life of people with cancer. This review systematically evaluates phase 3 randomized clinical trials (RCTs) assessing the effectiveness and safety of traditional Chinese medicine (TCM) interventions in managing cancer-related symptoms throughout the cancer care trajectory. A comprehensive literature search was conducted of PubMed, Embase, and the Cochrane Library until April 27, 2025, to further identify eligible RCTs involving patients with cancer or survivors and assessing TCM interventions against valid control arms. Data extraction and quality assessments were conducted in accordance with Cochrane standards and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Nineteen phase 3 RCTs involving 5387 participants (female, 3321; primarily breast, gastrointestinal, and lung cancers) from six countries or regions were included. Nonpharmacological interventions, namely acupuncture and Tai Chi, significantly reduced pain, fatigue, insomnia, radiation-induced xerostomia, and hormonal therapy–related hot flashes compared to usual care (UC). Their effects on preventing chemotherapy- and radiotherapy-induced nausea and vomiting were mixed, which depended on control arms and outcome measures. Conversely, evidence for pharmacological interventions was limited, with inconclusive results regarding chemotherapy-induced peripheral neuropathy and hematologic toxicities, although promising outcomes were noted for preventing chemoradiotherapy-induced mucositis, reducing colorectal adenoma recurrence, and enhancing chemotherapy completion rates compared to placebo or UC. Safety data suggested similar adverse event profiles across groups. These findings show strong evidence for the inclusion of nonpharmacological interventions in oncology practice. However, pharmacological interventions require more high-quality, multicenter research to fully understand their effectiveness and safety. Implementing rigorous safety assessments and standardized adverse event reporting protocols is crucial to enhance clinical confidence in TCM modalities.</p>","PeriodicalId":138,"journal":{"name":"Cancer","volume":"131 22","pages":""},"PeriodicalIF":5.1,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145487129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multicancer early detection (MCED) tests are more than a new class of blood-based tests; they are complex medical innovations representing an integrated diagnostic platform combining molecular and computational technologies. They embody a paradigm shift in how to conceptualize, detect, and manage cancer—carrying the potential to improve outcomes and reduce disparities, yet also the risk of exacerbating them. Real-world evidence remains limited, and existing evidence point to substantial heterogeneity even in standard-of-care screening practices—reflecting patterns of overuse and underuse, fluctuations, and practice variation—despite notable advances in cancer treatment and technology over time. Integrating complex medical innovations into equally complex health systems poses significant challenges, underscoring the urgent need for model-based policy guidance to support their incorporation as a complement to population-based screening within standard-of-care pathways. In this editorial, existing policy-oriented dynamic simulation models on MCED tests are summarized, and insights on how modeling frameworks should evolve in parallel with the growing complexity of medical technologies are offered. Traditional approaches often rest on the implicit assumption that evidence reviews lead linearly to interpretation, policy, and adoption—without accounting for feedback between these stages. Evidence-based guideline formation as a feedback process is revisited as is how modelers develop a suite of flexible models tailored to distinct policy questions. Models that coexist and evolve iteratively as new evidence emerges, thereby capturing the adaptive and evolving nature of the problem itself. Such an approach must transcend disciplinary silos, enabling the integration of diverse data sources and supporting innovative portfolio approaches with methodological flexibility.
{"title":"At the brink of a paradigm shift in early cancer detection: Insights and directions for the modeling community","authors":"Özge Karanfil PhD, Zeynep Akşin PhD, Raheelah Ahmad PhD, Rifat Atun MBBS, MBA, DIC, FRCGP, FFPH, FRCP, Maarten Ijzerman PhD, Dian Kusuma ScD, Sandra Sülz PhD, Nina Zhu PhD","doi":"10.1002/cncr.70160","DOIUrl":"10.1002/cncr.70160","url":null,"abstract":"<p>Multicancer early detection (MCED) tests are more than a new class of blood-based tests; they are complex medical innovations representing an integrated diagnostic platform combining molecular and computational technologies. They embody a paradigm shift in how to conceptualize, detect, and manage cancer—carrying the potential to improve outcomes and reduce disparities, yet also the risk of exacerbating them. Real-world evidence remains limited, and existing evidence point to substantial heterogeneity even in standard-of-care screening practices—reflecting patterns of overuse and underuse, fluctuations, and practice variation—despite notable advances in cancer treatment and technology over time. Integrating complex medical innovations into equally complex health systems poses significant challenges, underscoring the urgent need for model-based policy guidance to support their incorporation as a complement to population-based screening within standard-of-care pathways. In this editorial, existing policy-oriented dynamic simulation models on MCED tests are summarized, and insights on how modeling frameworks should evolve in parallel with the growing complexity of medical technologies are offered. Traditional approaches often rest on the implicit assumption that evidence reviews lead linearly to interpretation, policy, and adoption—without accounting for feedback between these stages. Evidence-based guideline formation as a feedback process is revisited as is how modelers develop a suite of flexible models tailored to distinct policy questions. Models that coexist and evolve iteratively as new evidence emerges, thereby capturing the adaptive and evolving nature of the problem itself. Such an approach must transcend disciplinary silos, enabling the integration of diverse data sources and supporting innovative portfolio approaches with methodological flexibility.</p>","PeriodicalId":138,"journal":{"name":"Cancer","volume":"131 22","pages":""},"PeriodicalIF":5.1,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145480348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jagpreet Chhatwal PhD, Jade Xiao PhD, Andrew K. ElHabr PhD, Christopher Tyson PhD, Xiting Cao PhD, Sana Raoof MD, PhD, A. Mark Fendrick MD, A. Burak Ozbay PhD, Paul Limburg MD, Tomasz M. Beer MD, Andrew Briggs DPhil, Ashish A. Deshmukh PhD, MPH