Chinese Herbal Medicine Is Associated With Improved Survival in Patients With Advanced Lung Cancer Receiving Standard Treatment: A Nationwide Population-Based Propensity Score Matching Study - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. 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Learn more: PMC Disclaimer | PMC Copyright Notice Integr Cancer Ther . 2026 Apr 13;25:15347354261439131. doi: 10.1177/15347354261439131 Search in PMC Search in PubMed View in NLM Catalog Add to search Chinese Herbal Medicine Is Associated With Improved Survival in Patients With Advanced Lung Cancer Receiving Standard Treatment: A Nationwide Population-Based Propensity Score Matching Study Kai-Yuan Hsiao Kai-Yuan Hsiao , PhD 1 Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City, Taiwan 2 Department of Finance and Banking, Shih Chien University, Taipei City, Taiwan Find articles by Kai-Yuan Hsiao 1, 2 , Wei-Shan Chang Wei-Shan Chang , PhD 2 Department of Finance and Banking, Shih Chien University, Taipei City, Taiwan 3 Department of Computer Science & Information Management, College of Computing and Informatics, Providence University, Taichung City, Taiwan Find articles by Wei-Shan Chang 2, 3 , Shi-Chen Ou Shi-Chen Ou , MD, PhD 4 Department of Chinese Medicine, China Medical University Hospital, Taichung city, Taiwan 5 School of Post Baccalaureate Chinese Medicine, China Medical University, Taichung city, Taiwan. China Find articles by Shi-Chen Ou 4, 5 , Hao-Ren Yao Hao-Ren Yao , PhD 6 National Institute of Health, Bethesda, MD, USA Find articles by Hao-Ren Yao 6 , Der-Chen Chang Der-Chen Chang , PhD 7 Department of Mathematics and Statistics, Georgetown University, Washington DC, USA Find articles by Der-Chen Chang 7 , Ben-Chang Shia Ben-Chang Shia , PhD 1 Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City, Taiwan 8 Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City, Taiwan Find articles by Ben-Chang Shia 1, 8 , Ming-Chih Chen Ming-Chih Chen , PhD 1 Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City, Taiwan 8 Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City, Taiwan Find articles by Ming-Chih Chen 1, 8 , Meng-Che Hsieh Meng-Che Hsieh , MD, PhD 9 College of Medicine, I-Shou University, Kaohsiung city, Taiwan 10 Department of Hematology-Oncology, E-Da Cancer Hospital, Kaohsiung city, Taiwan Find articles by Meng-Che Hsieh 9, 10, ✉ Author information Article notes Copyright and License information 1 Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City, Taiwan 2 Department of Finance and Banking, Shih Chien University, Taipei City, Taiwan 3 Department of Computer Science & Information Management, College of Computing and Informatics, Providence University, Taichung City, Taiwan 4 Department of Chinese Medicine, China Medical University Hospital, Taichung city, Taiwan 5 School of Post Baccalaureate Chinese Medicine, China Medical University, Taichung city, Taiwan. China 6 National Institute of Health, Bethesda, MD, USA 7 Department of Mathematics and Statistics, Georgetown University, Washington DC, USA 8 Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City, Taiwan 9 College of Medicine, I-Shou University, Kaohsiung city, Taiwan 10 Department of Hematology-Oncology, E-Da Cancer Hospital, Kaohsiung city, Taiwan ✉ Meng-Che Hsieh, Medical Oncologist, Department of Hematology-Oncology, E-Da Cancer Hospital, No.21, E-Da Rd., Yan-Chao District, Kaohsiung City 82445, Taiwan. Email: [email protected] Received 2025 Jun 15; Accepted 2026 Mar 19; Collection date 2026. © The Author(s) 2026 This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/ ) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage ). PMC Copyright notice PMCID: PMC13080189 PMID: 41974648 Abstract Purpose: There is a lack of large-scale evidence regarding the prognostic impact of Chinese herbal medicine (CHM) on patients with lung cancer. This paper aimed to conduct a nationwide population-based study to investigate the role of CHM for patients with lung cancer with standard treatment. Methods: Patients diagnosed with lung cancer and treated with standard therapy between 2013 and 2015 were retrospectively identified from National Health Insurance Research Database and Taiwan Cancer Registry. Patients were classified into a CHM group and a non-CHM group based on prior medical records. Kaplan-Meier analysis was performed to estimate overall survival (OS) between each group. Results: A total of 7351 matched patients were included in the outcome analysis and comparison, with 3677 patients in the CHM cohort and 3674 patients in the non-CHM cohort. The mean age of our patients was 60.68 ± 11.26 years, and 56% of our patients were female. Our results demonstrates that patients who received CHM had significantly longer OS compared to those who did not receive CHM (HR: 0.59, 95% CI: 0.556-0.625, P < .0001). In subgroup analysis, survival differences were not significant for stage I-II disease, whereas OS was significantly better in the CHM group among patients with stage III-IV disease. Multivariable analysis showed CHM usage was found to significantly influence OS. The most commonly used single CHM was Hedyotis diffusa Willd and compound CHM was San-Zhong-Kui-Jian-Tang. Conclusion: Our nationwide population-based study suggested that CHM in conjugation with standard treatment was associated with improved survival in patients with advanced lung cancer. Integrating cancer treatment with CHM could be adopted in the treatment of lung cancer to improve prognosis. Keywords: lung cancer, Chinese herbal medicine, advanced stage, survival, prognosis Introduction Lung cancer is one of the most prevalent and lethal forms of cancer globally. Characterized by uncontrolled cell growth in lung tissues, it is a significant public health concern that devastates millions of lives each year. According to the World Health Organization, lung cancer accounts for approximately 18% of all cancer deaths, making it the leading cause of cancer-related mortality worldwide. 1 Lung cancer incidence varies significantly across different geographical regions, with the highest rates observed in developed countries. 2 According to Global Cancer Observatory 2020 data, there were approximately 2.2 million new cases of lung cancer diagnosed worldwide. 3 Men have a higher incidence rate compared to women, largely due to historical smoking patterns and genetic predisposition. 4 Surgery is the standard treatment for early stage lung cancer, while chemoradiotherapy will be considered in patients with locally advanced lung cancer. 5 , 6 Recently, several novel agents including targeted therapy and immune checkpoints inhibitors were demonstrated to prolong survival in metastatic non-small cell lung cancer (NSCLC) 7 , 8 or extensive stage small cell lung cancer (SCLC). 9 Despite advances in treatment, the 5-year survival rate for lung cancer is approximately 19%, reflecting the challenges associated with early detection and treatment. 10 Furthermore, treatment-related toxicities remains a major concern during the treatment of lung cancer. Some patients experience severe adverse events leading to treatment discontinuation and poor prognosis. 11 Hence, the management of side effects becomes an important issue for patients with lung cancer. In recent years, the integration of complementary and alternative medicine practices, particularly traditional Chinese medicine (TCM), has gained popularity among cancer patients. 12 TCM emphasizes a holistic approach to health by restoring balance within the body through herbs, acupuncture, dietary therapy, and lifestyle changes. Among the various components of TCM, Chinese herbal medicine (CHM) is prominently used, boasting centuries of empirical knowledge and a growing body of scientific research supporting its efficacy. 13 Several clinical studies have explored the effects of CHM on cancer patients, often focusing on its use as an adjunct to conventional treatments such as chemotherapy and radiotherapy. 14 Although several nationwide population-based studies using Taiwan’s National Health Insurance Research Database (NHIRD) have investigated the association between Chinese herbal medicine (CHM) and survival outcomes in lung cancer, important gaps remain regarding stage-specific effects, integration with validated cancer registry data, and contemporary treatment contexts. The present study aimed to conduct a nationwide, population-based propensity score–matched analysis integrating data from the National Health Insurance Research Database and the Taiwan Cancer Registry to evaluate the prognostic impact of CHM in patients with lung cancer receiving standard oncologic treatment. By incorporating validated AJCC staging information, performing rigorous 1:1 propensity score matching, and conducting stage-stratified survival analyses, we sought to clarify the real-world survival benefits of CHM, particularly in advanced-stage disease, and to characterize commonly prescribed herbal formulations in contemporary integrative lung cancer care. Method Data Sources The data utilized in the present study were obtained from the National Health Insurance Research Database (NHIRD), which covers over 99% of the Taiwanese population. In addition, the Taiwan Cancer Registry (TCR) has comprehensive coverage, capturing more than 98% of potential cancer cases in Taiwan. Cancer diagnoses within the TCR are validated through histological or cytological examinations. This retrospective, nationwide, population-based study integrates data from both the NHIRD and TCR, which contain detailed information regarding cancer diagnosis, staging, and treatment modalities. The TNM staging in this study was based on the 7th edition of the American Joint Committee on Cancer (AJCC) staging system. All diagnoses recorded in the NHIRD and TCR are coded according to the International Classification of Diseases (ICD), 9th Revision, Clinical Modification and 10th Revision. To ensure patient privacy, all identification numbers are encrypted prior to database release. This study was approved by the Institutional Review Board of Fu Jen Catholic University (IRB No. C110199), and the requirement for individual written informed consent was waived because this was a retrospective analysis of NHIRD. Study Population In this population-based study, patients diagnosed with lung cancer (ICD-O-3 codes C33, C34) and treated with standard therapy between 2013 and 2015 were retrospectively identified from the TCR. Standard treatment was defined to include conventional lung cancer therapies reimbursed by the National Health Insurance system, including surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, as applicable to disease stage and clinical practice during the study period. Lung cancer patients who received only palliative hospice care without any active treatment were excluded from our study. The index date was defined as the date of the first recorded lung cancer diagnosis for each patient. The follow-up period extended from the index date until the date of death or the last available date in the NHIRD, which was December 31, 2021. Patients who received CHM, excluding acupuncture or traumatology therapies, in conjunction with standard treatment for more than 14 days were classified as CHM users. Patients without any records of CHM nor less than 14 days of CHM usage were classified as non-CHM users. The 14-day cutoff was selected based on conventions used in prior studies utilizing the National Health Insurance Research Database (NHIRD), 15 , 16 where short-term CHM exposure is considered insufficient to reflect therapeutic intent or sustained use. Figure 1 provides a flow diagram illustrating the enrollment process for this study. Figure 1. Open in a new tab Flow chart of patients’ collection. Covariates Demographic characteristics evaluated in this study included gender, age, initial cancer stage, treatment status, and comorbidities. Comorbidities were assessed using the Charlson Comorbidity Index (CCI), as well as specific conditions such as hypertension (ICD-9 codes 401-405), chronic kidney disease (ICD-9 codes 585-586), and chronic hepatitis (ICD-9 codes 571-573). Comorbidities were included if they appeared in outpatient records at least twice or in inpatient records at least once within 1 year prior to the lung cancer diagnosis. A higher CCI score indicates a greater risk of mortality, suggesting the presence of multiple comorbidities, which can adversely impact the prognosis of lung cancer and related conditions. Matching To minimize selection bias between the 2 groups, we employed 1:1 propensity score matching. The propensity scores were calculated using logistic regression analysis, incorporating variables such as gender, age, CCI score, and clinical cancer stage. Patients with incomplete stage data were categorized as “other”. Statistical Analysis Between-group differences for continuous variables were assessed using the Student’s t-test, while categorical variables were analyzed using the Chi-square test. Kaplan-Meier analysis was performed to estimate overall survival (OS) between the matched study groups, and survival curves were plotted accordingly. OS was calculated from the date of initial diagnosis of lung cancer to the date of death or last follow-up. Mortality risks between the 2 cohorts were evaluated using hazard ratios (HR) with 95% confidence intervals (CIs). Multivariable Cox proportional hazards models were employed to assess lung cancer patient survival, adjusting for covariates such as age at diagnosis, pathology, treatment, comorbidities, and CHM use. All statistical analyses were conducted using SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA) and R software (version 4.4.1). A 2-sided P -value of less than .05 was considered statistically significant. Results Patients Characteristics We identified patients diagnosed with lung cancer between 2013 and 2015 using data from the NHIRD and TCR ( Figure 1 ). Among the 47 751 eligible patients, 3930 received CHM treatment, while 33 477 did not receive CHM. Following propensity score matching (PSM) using nearest neighbor matching, a total of 7351 matched patients were included in the outcome analysis and comparison, with 3677 patients in the CHM cohort and 3674 patients in the non-CHM cohort, ensuring balanced baseline characteristics for subsequent outcome comparisons. The distribution of patient characteristics between the CHM and non-CHM groups is summarized in Table 1 . The average age in both groups was approximately 60 years, and the majority of patients were female. Additionally, the average age of patients in the non-CHM group was higher compared to that of the CHM group. Regarding pathological type, non-small cell lung cancer was the predominant category in both groups. No significant differences were observed between the 2 groups in terms of gender, age, CCI score, and cancer stage. Table 1. Baseline Characteristics of the Patients with All Stage Lung Cancer. Characteristics Before matching After matching Variable Level CHM No CHM CHM No CHM N 3930 (%) 33 477 (%) P 3677 (%) 3674 (%) P Gender Male 1716 (43.7) 20 299 (60.6) < .001 1616 (43.9) 1620 (44.1) .919 Female 2214 (56.3) 13 178 (39.4) 2061 (56.1) 2054 (55.9) Age, mean (SD) 60.63 (11.26) 67.91 (12.75) < .001 60.75 (11.21) 60.60 (11.31) .558 Age group <60 1834 (46.7) 8799 (26.3) < .001 1702 (46.3) 1712 (46.6) .808 ≥60 2096 (53.3) 24 678 (73.7) 1975 (53.7) 1962 (53.4) Pathology NSCLC 3794 (96.5) 30 996 (92.6) < .001 3542 (96.3) 3479 (94.7) .001 SCLC 136 (3.5) 2481 (7.4) 135 (3.7) 195 (5.3) T stage T1-T2 1849 (47.0) 12 991 (38.8) < .001 1824 (49.6) 1738 (47.3) .055 T3-T4 2081 (52.9) 20 486 (61.2) 1853 (50.4) 1936 (52.7) N stage N0-N1 1752 (44.6) 12 204 (36.5) < .001 1720 (46.8) 1651 (44.9) .050 N2-N3 2178 (55.4) 21 273 (53.1) 1957 (53.2) 2023 (55.1) M stage M0 1867 (47.5) 13 038 (38.9) < .001 1781 (48.4) 1714 (46.7) .214 M1 2063 (52.5) 20 439 (61.1) 1896 (51.6) 1960 (53.3) Stage I-II 1168 (29.7) 7288 (21.8) < .001 1168 (31.8) 1132 (30.8) .422 III-IV 2762 (70.3) 26 189 (78.2) 2509 (68.2) 2542 (69.2) Viral hepatitis No 3846 (97.9) 32 977 (98.5) .003 3598 (97.9) 3597 (97.9) .940 Yes 84 (2.1) 500 (1.5) 79 (2.1) 77 (2.1) CKD No 3844 (97.8) 32 273 (96.4) < .001 3598 (97.9) 3597 (97.9) .940 Yes 86 (2.2) 1204 (3.6) 79 (2.1) 77 (2.1) Hypertension No 3603 (91.7) 30 670 (91.6) .915 3364 (91.5) 3352 (91.2) .732 Yes 327 (8.3) 2807 (8.4) 313 (8.5) 322 (8.8) Diabetes No 3783 (96.3) 31 749 (94.8) < .001 3535 (96.1) 3470 (94.4) .001 Yes 147 (3.7) 1728 (5.2) 142 (3.9) 204 (5.6) CCI, mean (SD) 1.23 (1.97) 1.23 (2.08) .936 1.19 (1.96) 1.14 (1.90) .223 Open in a new tab Abbreviations: CHM, Chinese herbal medicine; SD, standard deviation; CKD, chronic kidney disease; CCI, Charlson comorbidity index. Overall Survival The Kaplan-Meier survival curve for overall mortality across all cancer stages is presented in Figure 2 . The figure demonstrates that patients who received CHM had significantly longer OS compared to those who did not receive CHM, accounting for 3.6 versus 2.4 years (HR: 0.59, 95% CI: 0.556-0.625, P < .0001). Figure 2. Open in a new tab Overall survival of all stage of lung cancer. Subgroup Analysis With Clinical Stage Our patients were then stratified according to their stage. Table 2 summarizes the characteristics of patients across cancer stages I to IV. The Kaplan-Meier survival curves for mortality in each cancer stage are presented in Figure 3 . The results indicate that for stage I ( P = .9) and stage II ( P = .67) patients, those who received CHM had longer OS compared to those who did not receive CHM, although the differences were not statistically significant. In contrast, for stage III ( P < .0001) and stage IV ( P < .0001) patients, CHM users demonstrated significantly better OS compared to non-CHM users. Table 2. Basic Characteristics of Stage I-IV Lung Cancer After PSM. Characteristics Stage I Stage II Stage III Stage IV Level CHM Non-CHM CHM Non-CHM CHM Non-CHM CHM Non-CHM 978 (%) 960 (%) P 201 (%) 179 (%) P 627 (%) 600 (%) P 1871 (%) 1935 (%) P Gender Male 326 (33.3) 318 (33.1) .961 99 (49.3) 84 (46.9) .726 365 (58.2) 364 (60.7) .414 826 (44.1) 854 (44.1) .999 Female 652 (66.7) 642 (66.9) 102 (50.7) 95 (53.1) 262 (41.8) 236 (39.3) 1045 (55.9) 1081 (55.9) Age (mean[SD]) 59.08 (10.76) 59.30 (10.83) .655 62.41 (11.38) 61.97 (11.48) .707 62.71 (11.24) 63.30 (10.79) .350 60.79 (11.28) 60.27 (11.54) .165 <60 528 (54.0) 505 (52.6) .572 85 (42.3) 80 (44.7) .713 237 (37.8) 219 (36.5) .681 852 (45.5) 908 (46.9) .409 ≥60 450 (46.0) 455 (47.4) 116 (57.7) 99 (55.3) 390 (62.2) 381 (63.5) 1019 (54.5) 1027 (53.1) Pathology NSCLC 974 (99.6) 952 (99.2) .368 196 (97.5) 170 (95.0) .358 578 (92.2) 545 (90.8) .455 1794 (95.8) 1812 (93.6) .002 SCLC 4 (0.4) 8 (0.8) 5 (2.5) 9 (5.0) 49 (7.8) 55 (9.2) 77 (4.1) 123 (6.4) T stage T1-T2 978 (100) 960 (100) .999 109 (54.2) 112 (62.6) .258 238 (38.0) 207 (34.5) .386 532 (28.4) 491 (25.4) .023 T3-T4 0 (0) 0 (0) 92 (44.8) 67 (37.4) 389 (62.0) 393 (65.5) 1339 (71.6) 1444 (74.6) N stage N0-N1 978 (100) 960 (100) .999 177 (88.1) 150 (83.8) .867 89 (14.2) 85 (14.2) .948 476 (25.4) 456 (23.6) .073 N2-N3 0 (0) 0 (0) 24 (11.9) 29 (16.2) 528 (85.8) 515 (85.8) 1321 (70.6) 1376 (71.1) M stage M0 978 (100) 960 (100) .999 201 (100) 179( 100) .999 627 (100) 560 (100) .999 0 (0) 0 (0) .552 M1 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 1896 (51.6) 1960 (53.3) Viral hepatitis No 949 (97.0) 937 (97.6) .525 194 (96.5) 177 (98.9) .240 611 (97.4) 585 (97.5) .999 1844 (98.6) 1898 (98.1) .318 Yes 29 (3.0) 23 (2.4) 7 (3.5) 2 (1.1) 16 (2.6) 15 (2.5) 27 (1.4) 37 (1.9) CKD No 963 (98.5) 938 (97.7) .292 194 (96.5) 177 (98.9) .240 612 (97.6) 588 (98.0) .784 1826 (97.6) 1894 (97.9) .628 Yes 15 (1.5) 22 (2.3) 7 (3.5) 2 (1.1) 15 (2.4) 12 (2.0) 45 (2.4) 41 (2.1) Hypertension No 908 (92.8) 884 (92.1) .584 182 (90.5) 165 (92.2) .703 575 (91.7) 550 (91.7) .999 1699 (90.8) 1753 (90.6) .865 Yes 70 (7.2) 76 (7.9) 19 (9.5) 14 (7.8) 52 (8.3) 50 (8.3) 172 (9.2) 182 (9.4) Diabetes No 958 (98.0) 910 (94.8) < .001 191 (95.0) 172 (96.1) .801 594 (94.7) 566 (94.3) .853 1792 (95.8) 1822 (94.2) .027 Yes 20 (2.0) 50 (5.2) 10 (5.0) 7 (3.9) 33 (5.3) 34 (5.7) 79 (4.2) 113 (5.8) CCI, mean (SD) 0.92 (1.50) 0.80 (1.28) .046 1.27 (2.02) 0.69 (1.11) .001 1.15 (1.74) 1.07 (1.66) .450 1.34 (2.21) 1.37 (2.22) .695 Open in a new tab Abbreviations: CHM, Chinese herbal medicine; SD, standard deviation; NSCLC, non-small cell lung cancer; SCLC, small cell lung cancer; CKD, chronic kidney disease; CCI, Charlson comorbidity index. Figure 3. Open in a new tab Overall survival of lung cancer patients, stratified by stage: (A) stage I, (B) stage II, (C) stage III, and (D) stage IV. Univariate and Multivariate Analysis Table 3 presents the results of Cox regression analysis for mortality, expressed as HR with 95% confidence intervals. In the univariate analysis, age group, hypertension, and chronic hepatitis were not found to have significant effects on mortality outcomes in lung cancer patients. In the multivariable analysis, after adjusting for potential confounding factors, variables including CHM usage, gender, age group, pathology type and cancer stage were found to significantly influence 5-year mortality. Table 3. Cox Regression Analysis of OS in Patients with All Stage Lung Cancer. Univariate Multivariate Variable HRs P HRs P Treatment, CHM vs non-CHM 0.590(0.556-0.625) <.0001 0.648(0.610-0.689) <.0001 Age, <60 vs ≥60 0.944(0.889-1.002) .0561 1.076(1.011-1.145) .0211 Gender, female vs male 0.767(0.724-0.814) <.0001 0.803(0.755-0.855) <.0001 Pathology, NSCLC vs SCLC 0.533(0.473-0.600) <.0001 1.143(1.007-1.298) .0388 T stage, T1-T2 vs T3-T4 0.666(0.625-0.709) <.0001 0.889(0.829-0.954) .001 N stage, N0-N1 vs N2-N3 0.598(0.560-0.639) <.0001 0.748(0.691-0.810) <.0001 M stage, M0 vs M1 0.610(0.572-0.652) <.0001 0.715(0.656-0.779) <.0001 Stage, I-II vs III-IV 0.434(0.394-0.478) <.0001 0.807(0.694-0.938) .0053 Diabetic mellitus, no vs yes 0.812(0.712-0.927) .0021 0.988(0.864-1.130) .8603 Hypertension, no vs yes 0.955(0.866-1.053) .3538 1.029(0.932-1.136) .569 Chronic kidney disease, no vs yes 0.807(0.669-0.972) .0241 0.863(0.714-1.043) .1283 Chronic hepatitis, no vs yes 0.969(0.781-1.200) .7704 0.909(0.731-1.130) .388 Open in a new tab Abbreviations: HR, hazard ratio; CHM, Chinese herbal medicine; NSCLC, non-small cell lung cancer; SCLC, small cell lung cancer. Commonly Used CHM in Lung Cancer Table 4 displays the 10 most commonly used CHM among patients with stage III and stage IV lung cancer, categorizing these herbal medicines into single and compound formulations. For patients with stage III-IV lung cancer, the most commonly used single CHM was Hedyotis diffusa Willd and most commonly used compound CHM was San-Zhong-Kui-Jian-Tang. Table 4. Top 10 Most Commonly Used Chinese Herbal Medicine in Stage III and Stage IV Lung Cancer Patients. Level Single herb (Chinese name) Frequency Number of person-days Average duration per prescription (days) Herbal formula (Chinese name) Frequency Number of person-days Average duration per prescription (days) Stage III Hedyotis diffusa Willd (Baihuasheshecao) 3468 16 079 034 13.10 San-Zhong-Kui-Jian-Tang 1391 3 428 009 14.76 Scutellaria barbata (Banzhilian) 1399 3 639 675 13.34 Xiao-Chai-Hu-Tang 1040 2 389 673 12.29 Platycodon grandiflorum (Jiegeng) 1332 4 732 922 11.32 Wen-Dan-Tang 831 2 083 242 12.99 Astragalus membranaceus (Huangqi) 1192 3 843 530 14.02 Tuo-Li-Xiao-Du-Yin 724 486 928 12.93 Houttuynia cordata Thunb (Yuxingcao) 1160 2 919 800 12.59 Liu-Jun-Zi-Tang 716 1 812 220 13.32 Prunus armeniaca (Xingren) 1112 3 274 740 11.69 Gan-Lu-Yin 712 1 389 265 12.59 Taraxacum mongolicum (Pugongying) 1092 2 535 810 12.83 Zhi-Gan-Cao-Tang 647 876 300 10.66 Rheum officinale (Dahuang) 982 1 466 964 10.59 Qing-Zao-Jiu-Fei-Tang 623 1 021 119 13.55 Atractylodes macrocephala (Baizhu) 978 3 227 466 13.92 Ban-Xia-Xie-Xin-Tang 612 1 203 536 13.29 Scutellaria baicalensis (Huangqin) 943 2 631 328 12.46 Liu-Wei-Di-Huang-Wan 607 842 379 12.50 Stage IV Hedyotis diffusa Willd (Baihuasheshecao) 10 157 129 375 584 12.51 San-Zhong-Kui-Jian-Tang 4034 30 048 300 13.35 Scutellaria barbata (Banzhilian) 4559 34 459 350 13.26 Liu-Jun-Zi-Tang 2929 23 951 235 11.70 Rheum officinale (Dahuang) 3576 18 078 576 10.97 Xiao-Chai-Hu-Tang 2493 18 960 589 12.29 Houttuynia cordata Thunb (Yuxingcao) 3473 25 030 364 11.93 Jia-Wei-Xiao-Yao-San 2060 11 527 311 12.09 Scutellaria baicalensis (Huangqin) 3226 26 453 610 12.24 Bu-Zhong-Yi-Qi-Tang 1918 9 148 716 11.87 Platycodon grandiflorum (Jiegeng) 3003 25 052 274 9.90 Qing-Zao-Jiu-Fei-Tang 1907 9 161 339 12.35 Taraxacum mongolicum (Pugongying) 2948 21 492 468 13.45 Ban-Xia-Xie-Xin-Tang 1898 10 156 825 11.71 Prunus armeniaca (Xingren) 2922 24 269 824 12.00 Gan-Lu-Yin 1780 9 516 960 12.38 Poria cocos (Fuling) 2707 22 395 776 12.03 Ma-Zi-Ren-Wan 1744 5 313 880 11.72 Atractylodes macrocephala Koidz. (Baizhu) 2700 21 929 375 13.00 Sha-Shen-Mai-Dong-Tang 1689 7 965 965 11.94 Open in a new tab Discussion To the best of our knowledge, this is one of the largest nationwide population-based propensity score–matched analyses integrating NHIRD and TCR data to evaluate the stage-specific prognostic impact of CHM in lung cancer. Our study included the largest cohort and provided a real-world evidence for physicians who treated patients with lung cancer. Our result showed that lung cancer patients treated with standard care and CHM concomitantly had longer survival than those who received standard care alone without CHM. After subgroup analysis, the survival benefits were robust in patients with stage III-IV lung cancer. Multivariate analysis also demonstrated the superior prognosis of CHM in patients with lung cancer. Our conclusion highlighted again the importance of supportive treatment like CHM. Under CHM support, lung cancer patients continued the standard of care and prolonged longest survival. Interestingly, our study also disclosed that patients with advanced lung cancer benefited from CHM support the most, while patients with early-stage lung cancer derived modest benefit from CHM support. The reasons were unclear. One possible explanation might be advanced-stage patients may derive greater benefit from symptom control, immune modulation, or treatment tolerance enhancement provided by CHM, whereas early-stage patients already have favorable prognoses with standard curative therapies, limiting the observable incremental benefit. Several prior nationwide studies based on the National Health Insurance Research Database have examined the survival impact of CHM in lung cancer. Our study further supported a positive association between integrative Chinese and Western medicine and improved prognosis. However, several methodological and contextual distinctions differentiate the present study from prior investigations. First, many earlier NHIRD-based analyses relied primarily on administrative claims data, which may lack granular and validated staging information. 17 - 19 Our study integrates NHIRD claims with data from the Taiwan Cancer Registry, which captures over 98% of cancer cases in Taiwan and includes histologically confirmed diagnoses and AJCC TNM staging. This integration enhances staging accuracy and strengthens internal validity. Second, while some previous studies adjusted for confounders using regression models, not all employed rigorous 1:1 propensity score matching incorporating age, gender, comorbidity burden (CCI), and clinical stage. 20 In our study, 7351 patients were matched to ensure balanced baseline characteristics, thereby reducing selection bias and improving comparability between CHM and non-CHM cohorts. Finally, unlike several prior survival-focused analyses, 21 our study also characterizes commonly prescribed single herbs and compound formulas among stage III-IV patients. The most frequently used single herb was Hedyotis diffusa Willd (Baihuasheshecao), and the most commonly used formula was San-Zhong-Kui-Jian-Tang. This prescription-level analysis provides additional clinical insight into real-world integrative practice patterns. Few prior NHIRD studies reported detailed prescription-level frequency data. Taken together, while previous NHIRD-based studies have demonstrated an association between CHM use and improved survival in lung cancer, our study contributes additional value through registry-validated staging, rigorous propensity score matching, stage-stratified survival analysis, detailed prescription pattern evaluation, and extended follow-up within a contemporary treatment era. CHM has been explored for its potential benefits in various types of cancer, both as a complementary therapy and as part of integrative cancer treatment approaches. Recently, Liu et al published a systemic review and demonstrated CHM contains numerous flavonoids, alkaloids, terpenoids, polyphenols, and other active compounds that are effective against lung cancer. 22 However, overall methodological quality of these publications were poor, and the level of evidence required improvement. It is necessary to carry out large-scale, standardized, and higher-quality research in the superior and weak areas of CHM treatment of lung cancer. Our study is a nationwide population-based study with a largest cohort to suggest the positive role of CHM in the treatment of lung cancer. CHM functions through various mechanisms that might influence cancer progression and patient survival. First, certain CHM are believed to enhance immune function, promoting the activity of natural killer cells and lymphocytes, which are crucial for targeting cancer cells. 23 A trial indicated that patients treated with a combination of herbs, including Astragalus membranaceus and Ganoderma lucidum , showed increased levels of CD4+ T cells and NK cell activity, correlating with better clinical outcomes. 24 These findings underscore the potential of herbal medicine to bolster the body’s immune response, which is particularly important in cancer management. Second, many CHM are rich in antioxidants, which can help counteract oxidative stress that contributes to cancer progression. 25 Moreover, some CHM exhibit cytotoxic properties against cancer cells, inhibit their growth and promote apoptosis leading to extend survival. 26 A systematic review of randomized controlled trials indicated that cancer patients receiving CHM alongside conventional treatment had improved overall survival rates compared to those who did not. 27 One notable study found that patients given a specific herbal formula, Shenqi Fuzheng Injection, showed a significant increase in median survival time. 28 This formula was synthesized from a combination of herbs traditionally used to invigorate Qi and nourish the body. Last, CHM may increase the efficacy of conventional treatments by reducing side effects, alleviating symptoms, and improving overall quality of life. 29 In a study involving patients with advanced lung cancer undergoing chemotherapy, adjunct treatment with specific Chinese herbal formulas was found to significantly improve quality of life and reduce chemotherapy-related side effects, such as nausea, fatigue, and neuropathic pain. 30 Improved quality of life can lead to better treatment adherence and potentially improve survival outcomes. Limitations This study has some limitations. Our study is a nationwide population based retrospective study and has several inevitable limitations. First, patients with CHM were treated with different herbal medicine. This might be the major bias in this study. Due to the nature of NHIRD data, granular information on dosage, duration, and specific herbal combinations is not consistently available. Therefore, our study could not make a conclusion that which CHM had best benefits for patients with lung cancer. Furthermore, the immortal time bias arising from the interval between lung cancer diagnosis and initiation of CHM might also be a potential limitation. Due to database limitations, CHM exposure was treated as a fixed covariate, and a time-dependent Cox model could not be fully implemented. Second, although every patient received standard therapy, the treatments were variable. Chemotherapy drug, targeted therapy, immune checkpoint inhibitors and radiotherapy course were divergent between each other. Third, the associations between annual accumulation dosage and response were not clear. It would be better to perform such a subgroup analysis to answer this question. However, due to restrictions in the period of database access and licensing limitations, we were unable to reconstruct year-by-year CHM usage data for this cohort. These limitations should be acknowledged as inherent to retrospective studies. Lastly, different durations of CHM treatment, different dosage of CHM, and differences in follow-up duration also limit the strength of the findings of our study. Nonetheless, detailed information, including dosage, duration, combination regimens and adverse events, could not be systematically assessed using the NHIRD, as laboratory data and toxicity grading are not captured in the database. Thus, propensity score matching was used to diminish selection bias. Although propensity score matching was performed, unmeasured confounders such as performance status, treatment adherence, and socioeconomic factors may still influence outcomes. Given these inherent limitations, our study was a nationwide population-based study with largest cohort to investigate the role of CHM in the treatment of lung cancer. To date, there are no prospective randomized controlled trials focusing on the impact of CHM for patients with lung cancer. In spite of a retrospective study with selection bias inherent to any retrospective studies, our study remains clinically valuable and provides clinical implication for physicians who treat patients with lung cancer. Conclusion The concept of integrative oncology emphasizes the combination of complementary therapies with conventional cancer treatments. The prognostic impact and benefits of Chinese herbal medicine in improving survival outcomes for lung cancer patients are gaining recognition in the medical community. Our nationwide population-based study suggested that CHM was associated with better survival benefits of standard treatment in patients with lung cancer, especially stage III-IV lung cancer. However, further rigorous clinical trials and studies are crucial for understanding their efficacy, safety, and mechanisms of action in cancer treatment. Healthcare providers should consider adopting an integrative approach, where patients can receive personalized plans that incorporate both CHM and standard oncological treatments. This model not only respects the patient’s cultural beliefs and preferences but can also provide comprehensive care addressing physical, emotional, and psychological aspects of cancer treatment. Acknowledgments This work was supported by grants from E-Da Cancer Hospital (Grant no. EDCHP112016). The authors would like to express their sincere appreciation to the Taiwan Association of Cancer Research (TACR) for providing support in project coordination and research administration throughout the study (TACR No. 24A07). Footnotes ORCID iD: Meng-Che Hsieh https://orcid.org/0000-0002-1626-3396 Ethical Considerations: This study was approved by the Institutional Review Board of Fu Jen Catholic University (IRB No. C110199), and was conducted in accordance with the Declaration of Helsinki. Consent to Participate: The requirement for individual written informed consent was waived because this was a retrospective analysis of National Health Insurance Registry Database. Funding: The authors received no financial support for the research, authorship, and/or publication of this article. The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.* References 1. Ganti AK, Klein AB, Cotarla I, Seal B, Chou E. Update of incidence, prevalence, survival, and initial treatment in patients with non-small cell lung cancer in the US. JAMA Oncol. 2021;7(12):1824-1832. doi: 10.1001/jamaoncol.2021.4932 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. Henderson LM, Su IH, Rivera MP, et al. Prevalence of lung cancer screening in the US, 2022. 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Biosci Trends. 2021;15(5):283-298. doi: 10.5582/bst.2021.01318 [ DOI ] [ PubMed ] [ Google Scholar ] Articles from Integrative Cancer Therapies are provided here courtesy of SAGE Publications ACTIONS View on publisher site PDF (983.2 KB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top