Identification of the beneficial population for Shenlingcao oral liquid in non-small cell lung cancer using the Rboost method_Chinese Journal of Evidence-Based Medicine

Authors：

LIANG Qigao ^1,2,3,4 , LIU Yanmei ^2,3,4 , DENG Kelin ^2,3,4 , NIE Liqin ^2,3,4 , MA Yuhu ^2,3,4 , YAO Minghong ^2,3,4 ,  LI Ling ^2,3,4 ,  SUN Xin ^1,2,3,4

1. West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. Clinical Epidemiology and Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
3. NHC Key Laboratory of Clinical Epidemiology and Evidence-based Medicine, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
4. Sichuan Provincial Center for Evidence-based Chinese Herbal Medicine Research, Chengdu 610041, P. R. China;

Corresponding?author：

LI Ling, Email: liling@wchscu.cn; SUN Xin, Email: sunxin@wchscu.cn

Keywords：

Shenlingcao oral liquid; Non-small cell lung cancer; Individual treatment effect; Rboost; Machine learning

DOI：

10.7507/1672-2531.202601203

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To estimate the individual treatment effect (ITE) of Shenlingcao oral liquid (SOL) combined with adjuvant chemotherapy in patients with stage Ⅱ-ⅢA non-small cell lung cancer (NSCLC) after complete resection using the Rboost method, and to identify characteristics of the potential high-benefit population, thereby providing a decision-making basis for clinical precision medicine. Methods Based on data from a previously conducted nationwide, multicenter, pragmatic, open-label randomized controlled trial (n=516), the change in the overall quality of life score from baseline to the 4th chemotherapy cycle, as measured by the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30), was used as the primary outcome. The Rboost method was employed to establish an ITE estimation model. Internal validation was performed using 500 bootstrap resampling iterations. The model's ability to discriminate between beneficiaries was evaluated using the C-for-benefit. Patients were divided into high- and low-benefit groups based on the median ITE. A linear regression model incorporating an interaction term between treatment allocation and benefit group was constructed to verify treatment effect heterogeneity. Differences in baseline characteristics between the two groups were compared. Results The established Rboost model yielded a C-for-benefit of 0.52 (95%CI 0.38 to 0.66). The median individual treatment effect was 5.84 (range: –0.86 to 12.50). The estimated individual treatment effect was 3.79 (95%CI –0.25 to 5.74) in the low-benefit group and 7.89 (95%CI 5.98 to 11.35) in the high-benefit group. After adjusting for covariates including sex, age, and BMI, the heterogeneity of treatment effect was statistically significant (P=0.01). Comparisons between benefit groups revealed that patients who were male, had a smoking history, had higher BMI, had squamous cell carcinoma, were at stage ⅢA, or had higher levels of white blood cells, neutrophils, hemoglobin, total bilirubin, aspartate aminotransferase, or alanine aminotransferase derived greater benefit from treatment with SOL. Conclusion The Rboost model can effectively identify the advantageous population for SOL. The findings provide evidence-based support for the precise adjuvant use of traditional Chinese medicine during chemotherapy.

Citation： LIANG Qigao, LIU Yanmei, DENG Kelin, NIE Liqin, MA Yuhu, YAO Minghong, LI Ling, SUN Xin. Identification of the beneficial population for Shenlingcao oral liquid in non-small cell lung cancer using the Rboost method. Chinese Journal of Evidence-Based Medicine, 2026, 26(8): 918-925. doi: 10.7507/1672-2531.202601203 Copy

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1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2024, 74(3): 229-263.
2. Riely GJ, Wood DE, Aisner DL, et al. NCCN guidelines? insights: non-small cell lung cancer, version 7. 2025[J]. J Natl Compr Canc Netw, 2025, 23(9): 354-362.
3. Postmus PE, Kerr KM, Oudkerk M, et al. Early and locally advanced non-small-cell lung cancer (NSCLC): ESMO clinical practice guidelines for diagnosis, treatment and follow-up[J]. Ann Oncol, 2017, 28(suppl_4): iv1-iv21.
4. Winton T, Livingston R, Johnson D, et al. Vinorelbine plus cisplatin vs. observation in resected non-small-cell lung cancer[J]. N Engl J Med, 2005, 352(25): 2589-2597.
5. Arriagada R, Bergman B, Dunant A, et al. Cisplatin-based adjuvant chemotherapy in patients with completely resected non-small-cell lung cancer[J]. N Engl J Med, 2004, 350(4): 351-360.
6. Xi Z, Dai R, Ze Y, et al. Traditional Chinese medicine in lung cancer treatment[J]. Mol Cancer, 2025, 24(1): 57.
7. Su XL, Wang JW, Che H, et al. Clinical application and mechanism of traditional Chinese medicine in treatment of lung cancer[J]. Chin Med J (Engl), 2020, 133(24): 2987-2997.
8. 堯梅香, 丁晟. 參靈草牌蟲草口服液毒理實驗研究[J]. 江西中醫藥, 2017, 48(5): 65-68.
9. 許錦珍, 劉文君, 付玉梅, 等. 參靈草口服液提高缺氧耐受力的臨床研究[J]. 江西中醫藥大學學報, 2017, 29(4): 81-84.
10. 許錦珍, 劉文君, 付玉梅, 等. 參靈草口服液對小鼠耐缺氧實驗研究[J]. 江西中醫藥, 2017, 48(4): 59-60.
11. Shan Y, Li T, Chen X, et al. Exploration of the immunomodulatory material basis of Shenlingcao oral liquid: comprehensive insights into its chemical composition, metabolic pathways, and mechanistic actions[J]. J Ethnopharmacol, 2025, 353(Pt A): 120286.
12. Liu Y, Luo X, Liu J, et al. Shenlingcao oral liquid for patients with non-small cell lung cancer receiving adjuvant chemotherapy after radical resection: a multicenter randomized controlled trial[J]. Phytomedicine, 2023, 113: 154723.
13. Inoue K, Adomi M, Efthimiou O, et al. Machine learning approaches to evaluate heterogeneous treatment effects in randomized controlled trials: a scoping review[J]. J Clin Epidemiol, 2024, 176: 111538.
14. Angus DC, Chang CH. Heterogeneity of treatment effect: estimating how the effects of interventions vary across individuals[J]. JAMA, 2021, 326(22): 2312-2313.
15. Kent DM, Paulus JK, van Klaveren D, et al. The predictive approaches to treatment effect heterogeneity (PATH) statement[J]. Ann Intern Med, 2020, 172(1): 35-45.
16. Aaronson NK, Ahmedzai S, Bergman B, et al. The European organization for research and treatment of cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology[J]. J Natl Cancer Inst, 1993, 85(5): 365-376.
17. Chie WC, Yang CH, Hsu C, et al. Quality of life of lung cancer patients: validation of the Taiwan Chinese version of the EORTC QLQ-C30 and QLQ-LC13[J]. Qual Life Res, 2004, 13(1): 257-262.
18. Liu P, Li S, Zheng T, et al. Subphenotyping heterogeneous patients with chronic critical illness to guide individualised fluid balance treatment using machine learning: a retrospective cohort study[J]. EClinicalMedicine, 2023, 59: 101970.
19. Azur MJ, Stuart EA, Frangakis C, et al. Multiple imputation by chained equations: what is it and how does it work[J]. Int J Methods Psychiatr Res, 2011, 20(1): 40-49.
20. 石清陽, 李玲, 任燕, 等. 實效性隨機對照試驗中的統計方法(Ⅱ): 解決結局缺失問題[J]. 中國循證醫學雜志, 2021, 21(6): 729-736.
21. Nie X, Wager S. Quasi-oracle estimation of heterogeneous treatment effects[J]. Biometrika, 2020, 108(2): 299-319.
22. Buell KG, Spicer AB, Casey JD, et al. Individualized treatment effects of oxygen targets in mechanically ventilated critically ill adults[J]. JAMA, 2024, 331(14): 1195-1204.
23. Chen T, Guestrin C. XGBoost: a scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: Association for Computing Machinery, 2016: 785.
24. Liang JW, Tian L, Tamura MK, et al. A framework for the rigorous assessment of heterogeneous treatment effects from a single randomized controlled trial[J]. Am J Epidemiol, 2025.
25. Desai RJ, Glynn RJ, Solomon SD, et al. Individualized treatment effect prediction with machine learning - salient considerations[J]. NEJM evidence, 2024, 3(4): EVIDoa2300041.
26. 閆明海, 朱映璇, 林小瑩, 等. 基于隨機對照試驗的個體處理效應預測模型的系統評價[J]. 中國循證醫學雜志, 2024, 24(11): 1299-1304.
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28. Barlesi F, Chouaid C, Crequit J, et al. A randomized trial comparing adjuvant chemotherapy with gemcitabine plus cisplatin with docetaxel plus cisplatin in patients with completely resected non-small-cell lung cancer with quality of life as the primary objective[J]. Interact Cardiovasc Thorac Surg, 2015, 20(6): 783-790.
29. 羅佳欣, 詹揚, 孫登龍, 等. 基于網絡藥理學、分子對接與實驗驗證探討參靈草口服液抑制非小細胞肺癌的作用機制[J]. 中國實驗動物學報, 2025, 33(1): 54-69.
30. Coussens LM, Werb Z. Inflammation and cancer[J]. Nature, 2002, 420(6917): 860-867.
31. Lee H, Yu SR, Lim D, et al. Galla Chinensis attenuates cigarette smoke-associated lung injury by inhibiting recruitment of inflammatory cells into the lung[J]. Basic Clin Pharmacol Toxicol, 2015, 116(3): 222-228.
32. Lu J, He R, Sun P, et al. Molecular mechanisms of bioactive polysaccharides from Ganoderma lucidum (Lingzhi), a review[J]. Int J Biol Macromol, 2020, 150: 765-774.
33. Ouyang MZ, Lin LZ, Lv WJ, et al. Effects of the polysaccharides extracted from Ganoderma lucidum on chemotherapy-related fatigue in mice[J]. Int J Biol Macromol, 2016, 91: 905-910.
34. 宋丹君, 李品, 冷嘉鵬, 等. 基于煙草煙霧致病研究進展芻議"煙毒"之外內因機[J]. 世界科學技術-中醫藥現代化, 2023, 25(10): 3193-3200.
35. Zhou R, He D, Xie J, et al. The synergistic effects of polysaccharides and ginsenosides from American Ginseng (Panax quinquefolius L. ) ameliorating cyclophosphamide-induced intestinal immune disorders and gut barrier dysfunctions based on microbiome-metabolomics analysis[J]. Front Immunol, 2021, 12: 665901.
36. Sabbula BR, Gasalberti DP, Mukkamalla SKR, et al. Squamous cell lung cancer[Z]. StatPearls Publishing LLC, 2025.
37. Lau SCM, Pan Y, Velcheti V, et al. Squamous cell lung cancer: current landscape and future therapeutic options[J]. Cancer Cell, 2022, 40(11): 1279-1293.

Chinese Journal of Evidence-Based Medicine

Identification of the beneficial population for Shenlingcao oral liquid in non-small cell lung cancer using the Rboost method

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