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Article Open Access

Real‑world performance of the machine learning‑based prediction of chemotherapy‑associated adverse effects in lung cancer

  • Authors:
    • Sha Huang
    • Zhengwei Huang
    • Zhoujian Sun
    • Tianao Xie
    • Xingyu Zhu
    • Sheng Lu
    • Zhengxing Huang
    • Jian Hu
    • Zhengfu He
  • View Affiliations / Copyright

    Affiliations: Department of Thoracic Surgery, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou, Zhejiang 310016, P.R. China, Research Center for Life Sciences Computing, Zhejiang Lab, Hangzhou, Zhejiang 311121, P.R. China, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, P.R. China, College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang 310027, P.R. China
    Copyright: © Huang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 24
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    Published online on: November 6, 2025
       https://doi.org/10.3892/ol.2025.15377
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Abstract

Systemic chemotherapy is the cornerstone for treating patients with locally advanced non‑small‑cell lung cancer (NSCLC). Various adverse effects (AEs) are caused by anticancer therapy, limiting the efficacy of chemotherapy. The precise prediction and early detection of AEs could result in improved efficacy of chemotherapy and quality of life. In the present study, machine learning (ML) algorithms, including random forest (RF), multilayer perceptron and AdaBoost, were employed to develop prediction models for common AEs using dynamic treatment information. A total of 1,659 chemotherapeutic information data points for 403 patients with NSCLC who underwent chemotherapy were extracted from an electronic health record system. A five‑fold cross‑validation was performed, and the received operating characteristic (ROC) curve and calibration curve were used to evaluate the model performance. Patients with multi‑AEs had worse therapeutic efficacy of neoadjuvant chemotherapy (P<0.001; Fisher's exact test) and worse prognosis (P<0.05; log‑rank test) compared with patients without multi‑AEs. The area under ROC curve values of the RF model were 0.75, 0.74 and 0.76 for predicting myelosuppression, low albumin and hepatic impairment, respectively, and its calibration curve was found linear in the calibration range with regression factor r2≥0.99. The RF model outperformed the other models. A marked performance improvement was observed when <10 selected features were used and feature importance was ranked by Shapley Additive Explanation values. In conclusion, the occurrence of multi‑AEs limits the efficacy of chemotherapy and negatively affects the outcomes of patients with lung cancer. ML‑based prediction models of chemotherapy‑associated AEs may be a breakthrough for improving the prognosis of patients receiving lung cancer chemotherapy. 
View Figures

Figure 1

Effects of multi-AE on
chemotherapy-efficacy and prognosis of patients with lung cancer.
(A) Multi-AE were associated with the overall survival in patients
with neoadjuvant NSCLC. (B) Multi-AE were associated with the ORR
in patients with neoadjuvant NSCLC. (C) The association between
single AE of low-ALB and ORR. (D) The association between single AE
of bone marrow suppression and ORR. (E) The association between
single AE of hepatic impairment and ORR. ORR, objective response
rate; AE, adverse effect; ALB, albumin; NSCLC, non-small cell lung
cancer; HR, hazard ratio; SD, stable disease; PR, partial
response.

Figure 2

Comparison of receiver operating
characteristic curves for the machine learning-based prediction
model of chemotherapy-associated adverse effects. A total of four
independent prediction models were constructed based on various
algorithms, namely RF, MLP, AdaBoost and LR, and the performance of
all models was evaluated and compared. RF, random forest; MLP,
multi-layer perceptron; LR, logistic regression.

Figure 3

Effect of different training set
numbers on model performance. Different proportions of the training
set were adjusted and the impact on model performance was
evaluated. The line plots represent average AUC trends
corresponding to different proportions of training data. The dot
plot on the line plots represents the corresponding average AUC for
this training set number. RF, random forest; MLP, multi-layer
perceptron; LR, logistic regression; AUC, area under the curve.

Figure 4

Effect of the number of incorporated
features on the performance of the model. The impact on the
predictive performance of the model was evaluated according to the
number of important features. The line plots represent average AUC
trends corresponding to the number of important features and the
dot plot represents the corresponding average AUC for this number
of features. AUC, area under the curve.

Figure 5

Calibration curves of the proposed
prediction models. The x-axis represents the predicted probability
of chemotherapy-associated AEs from the model. The y-axis
represents the actual probabilities of occurring this AEs. The
45-degree black dashed line indicates perfect calibration. AEs,
adverse effects; RF, random forest; MLP, multi-layer perceptron;
LR, logistic regression.

Figure 6

SHAP values and feature interaction
scores in machine learning-based prediction. (A) The most important
features for the prediction of chemotherapy-associated adverse
effects (ranked from most to least important). (B) The distribution
of the impacts of each of the most important features on model
output. The horizontal location shows whether the effect of that
value is associated with a higher or lower prediction. The colors
represent the feature values: Red for larger values and blue for
smaller values. SHAP, Shapley Additive Explanation; WBC, white
blood cell; PLT, platelet; ToP, total protein; Hb, hemoglobin; HTN,
hypertension; rhG-CSF, recombinant-human granulocyte colony
stimulating factor; EP, etoposide/cisplatin; Tumor Hx, tumor
history; DL, docetaxel/lobaplatin; non-comp, non-complete; EL,
etoposide/lobaplatin; ALB, albumin; BMI, body mass index; LYM,
lymphocytes; ALT, alanine aminotransferase; DP,
docetaxel/cisplatin; POX, pemetrexed/oxaliplatin; PL,
pemetrexed/lobaplatin; TP, paclitaxel/ cisplatin.
View References

1 

Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I and Jemal A: Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 74:229–263. 2024.PubMed/NCBI

2 

Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ and He J: Cancer statistics in China, 2015. CA Cancer J Clin. 66:115–132. 2016.PubMed/NCBI

3 

Cao M and Chen W: Epidemiology of lung cancer in China. Thorac Cancer. 10:3–7. 2019. View Article : Google Scholar : PubMed/NCBI

4 

Asamura H, Nishimura KK, Giroux DJ, Chansky K, Hoering A, Rusch V and Rami-Porta R; Members of the IASLC Staging, Prognostic Factors Committee of the Advisory Boards, Participating Institutions, : IASLC Lung cancer staging project: The new database to inform revisions in the ninth edition of the TNM classification of lung cancer. J Thorac Oncol. 18:564–575. 2023. View Article : Google Scholar : PubMed/NCBI

5 

Pirker R: Chemotherapy remains a cornerstone in the treatment of nonsmall cell lung cancer. Curr Opin Oncol. 32:63–67. 2020. View Article : Google Scholar : PubMed/NCBI

6 

Powell SF, Rodríguez-Abreu D, Langer CJ, Tafreshi A, Paz-Ares L, Kopp HG, Rodríguez-Cid J, Kowalski DM, Cheng Y, Kurata T, et al: Outcomes with pembrolizumab plus platinum-based chemotherapy for patients with NSCLC, sTable brain metastases: Pooled analysis of KEYNOTE-021, −189, and −407. J Thorac Oncol. 16:1883–1892. 2021. View Article : Google Scholar : PubMed/NCBI

7 

Forde PM, Spicer J, Lu S, Provencio M, Mitsudomi T, Awad MM, Felip E, Broderick SR, Brahmer JR, Swanson SJ, et al: Neoadjuvant nivolumab plus chemotherapy in resecTable lung cancer. N Engl J Med. 386:1973–1985. 2022. View Article : Google Scholar : PubMed/NCBI

8 

Wang C, Qiao W, Jiang Y, Zhu M, Shao J, Wang T, Liu D and Li W: The landscape of immune checkpoint inhibitor plus chemotherapy versus immunotherapy for advanced non-small-cell lung cancer: A systematic review and meta-analysis. J Cell Physiol. 235:4913–4927. 2020. View Article : Google Scholar : PubMed/NCBI

9 

Jiang J, Wang Y, Gao Y, Sugimura H, Minervini F, Uchino J, Halmos B, Yendamuri S, Velotta JB and Li M: Neoadjuvant immunotherapy or chemoimmunotherapy in non-small cell lung cancer: A systematic review and meta-analysis. Transl Lung Cancer Res. 11:277–294. 2022. View Article : Google Scholar : PubMed/NCBI

10 

NSCLC Meta-analysis Collaborative Group, . Preoperative chemotherapy for non-small-cell lung cancer: A systematic review and meta-analysis of individual participant data. Lancet. 383:1561–1571. 2014. View Article : Google Scholar : PubMed/NCBI

11 

Lavan AH, O'Mahony D, Buckley M, O'Mahony D and Gallagher P: Adverse drug reactions in an oncological population: Prevalence, predictability, and preventability. Oncologist. 24:e968–e977. 2019. View Article : Google Scholar : PubMed/NCBI

12 

Liu M, Wu Y, Chen Y, Sun J, Zhao Z, Chen XW, Matheny ME and Xu H: Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs. J Am Med Inform Assoc. 19:e28–e35. 2012. View Article : Google Scholar : PubMed/NCBI

13 

Wang Z, Clark NR and Ma'ayan A: Drug-induced adverse events prediction with the LINCS L1000 data. Bioinformatics. 32:2338–2345. 2016. View Article : Google Scholar : PubMed/NCBI

14 

Luo H, Fokoue-Nkoutche A, Singh N, Yang L, Hu J and Zhang P: Molecular docking for prediction and interpretation of adverse drug reactions. Comb Chem High Throughput Screen. 21:314–322. 2018. View Article : Google Scholar : PubMed/NCBI

15 

Blonde L, Khunti K, Harris SB, Meizinger C and Skolnik NS: Interpretation and impact of real-world clinical data for the practicing clinician. Adv Ther. 35:1763–1774. 2018. View Article : Google Scholar : PubMed/NCBI

16 

Shickel B, Tighe PJ, Bihorac A and Rashidi P: Deep EHR: A survey of recent advances in deep learning techniques for Electronic Health Record (EHR) analysis. IEEE J Biomed Health Inform. 22:1589–1604. 2018. View Article : Google Scholar : PubMed/NCBI

17 

Rajkomar A, Oren E, Chen K, Dai AM, Hajaj N, Hardt M, Liu PJ, Liu X, Marcus J, Sun M, et al: Scalable and accurate deep learning with electronic health records. NPJ Digit Med. 1:182018. View Article : Google Scholar : PubMed/NCBI

18 

Choi E, Schuetz A, Stewart WF and Sun J: Using recurrent neural network models for early detection of heart failure onset. J Am Med Inform Assoc. 24:361–370. 2017. View Article : Google Scholar : PubMed/NCBI

19 

Bernardini M, Romeo L, Misericordia P and Frontoni E: Discovering the type 2 diabetes in electronic health records using the sparse balanced support vector machine. IEEE J Biomed Health Inform. 24:235–246. 2020. View Article : Google Scholar : PubMed/NCBI

20 

Tseng PY, Chen YT, Wang CH, Chiu KM, Peng YS, Hsu SP, Chen KL, Yang CY and Lee OK: Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit Care. 24:4782020. View Article : Google Scholar : PubMed/NCBI

21 

Liu X, Zheng D, Zhong Y, Xia Z, Luo H and Weng Z: Machine-learning prediction of oral drug-induced liver injury (DILI) via multiple features and endpoints. Biomed Res Int. 2020:47951402020. View Article : Google Scholar : PubMed/NCBI

22 

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, et al: Scikit-learn: Machine learning in python. J Mach Learn Res. 12:2825–2830. 2011.

23 

Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, et al: Author correction: SciPy 1.0: Fundamental algorithms for scientific computing in python. Nat Methods. 17:3522020. View Article : Google Scholar : PubMed/NCBI

24 

Huang S, He T, Yang S, Sheng H, Tang X, Bao F, Wang Y, Lin X, Yu W, Cheng F, et al: Metformin reverses chemoresistance in non-small cell lung cancer via accelerating ubiquitination-mediated degradation of Nrf2. Transl Lung Cancer Res. 9:2337–2355. 2020. View Article : Google Scholar : PubMed/NCBI

25 

Murphy KP: Machine learning: A probabilistic perspective. The MIT Press; Cambridge, Massachusetts: 2012

26 

Carr DF and Pirmohamed M: Biomarkers of adverse drug reactions. Exp Biol Med (Maywood). 243:291–299. 2018. View Article : Google Scholar : PubMed/NCBI

27 

Kheifetz Y and Scholz M: Individual prediction of thrombocytopenia at next chemotherapy cycle: Evaluation of dynamic model performances. Br J Clin Pharmacol. 87:3127–3138. 2020. View Article : Google Scholar : PubMed/NCBI

28 

Wang Y, Zhang R, Shen Y, Su L, Dong B and Hao Q: Prediction of chemotherapy adverse reactions and mortality in older patients with primary lung cancer through frailty index based on routine laboratory data. Clin Interv Aging. 14:1187–1197. 2019. View Article : Google Scholar : PubMed/NCBI

29 

Dranitsaris G, Molassiotis A, Clemons M, Roeland E, Schwartzberg L, Dielenseger P, Jordan K, Young A and Aapro M: The development of a prediction tool to identify cancer patients at high risk for chemotherapy-induced nausea and vomiting. Ann Oncol. 28:1260–1267. 2017. View Article : Google Scholar : PubMed/NCBI

30 

Boudali I and Messaoud IB: Machine learning models for toxicity prediction in chemotherapy. Intelligent Systems Design and Applications. Springer Nature Switzerland Cham; pp. 350–364. 2023, View Article : Google Scholar

31 

Wu Y, Zhao W, Zhang L, Wang Y, Wen Y and Liu L: Machine learning models for predicting chemotherapy-induced adverse drug reactions in colorectal cancer patients. Dig Liver Dis. 57:1845–1852. 2025. View Article : Google Scholar : PubMed/NCBI

32 

Polevikov S: Advancing AI in healthcare: A comprehensive review of best practices. Clin Chim Acta. 548:1175192023. View Article : Google Scholar : PubMed/NCBI

33 

Adlung L, Cohen Y, Mor U and Elinav E: Machine learning in clinical decision making. Med. 2:642–665. 2021. View Article : Google Scholar : PubMed/NCBI

34 

Kline A, Wang H, Li Y, Dennis S, Hutch M, Xu Z, Wang F, Cheng F and Luo Y: Multimodal machine learning in precision health: A scoping review. NPJ Digit Med. 5:1712022. View Article : Google Scholar : PubMed/NCBI

35 

Zheng S, Zhu Z, Liu Z, Guo Z, Liu Y, Yang Y and Zhao Y: Multi-modal graph learning for disease prediction. IEEE Trans Med Imaging. 41:2207–2216. 2022. View Article : Google Scholar : PubMed/NCBI

36 

Luscher TF, Wenzl FA, D'Ascenzo F, Friedman PA and Antoniades C: Artificial intelligence in cardiovascular medicine: Clinical applications. Eur Heart J. 45:4291–4304. 2024. View Article : Google Scholar : PubMed/NCBI

37 

Petzl AM, Jabbour G, Cadrin-Tourigny J, Pürerfellner H, Macle L, Khairy P, Avram R and Tadros R: Innovative approaches to atrial fibrillation prediction: Should polygenic scores and machine learning be implemented in clinical practice? Europace. 26:euae2012024. View Article : Google Scholar : PubMed/NCBI

38 

Couckuyt A, Seurinck R, Emmaneel A, Quintelier K, Novak D, Van Gassen S and Saeys Y: Challenges in translational machine learning. Human Genetics. 141:1451–1466. 2022. View Article : Google Scholar : PubMed/NCBI

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Copy and paste a formatted citation
Spandidos Publications style
Huang S, Huang Z, Sun Z, Xie T, Zhu X, Lu S, Huang Z, Hu J and He Z: Real‑world performance of the machine learning‑based prediction of chemotherapy‑associated adverse effects in lung cancer. Oncol Lett 31: 24, 2026.
APA
Huang, S., Huang, Z., Sun, Z., Xie, T., Zhu, X., Lu, S. ... He, Z. (2026). Real‑world performance of the machine learning‑based prediction of chemotherapy‑associated adverse effects in lung cancer. Oncology Letters, 31, 24. https://doi.org/10.3892/ol.2025.15377
MLA
Huang, S., Huang, Z., Sun, Z., Xie, T., Zhu, X., Lu, S., Huang, Z., Hu, J., He, Z."Real‑world performance of the machine learning‑based prediction of chemotherapy‑associated adverse effects in lung cancer". Oncology Letters 31.1 (2026): 24.
Chicago
Huang, S., Huang, Z., Sun, Z., Xie, T., Zhu, X., Lu, S., Huang, Z., Hu, J., He, Z."Real‑world performance of the machine learning‑based prediction of chemotherapy‑associated adverse effects in lung cancer". Oncology Letters 31, no. 1 (2026): 24. https://doi.org/10.3892/ol.2025.15377
Copy and paste a formatted citation
x
Spandidos Publications style
Huang S, Huang Z, Sun Z, Xie T, Zhu X, Lu S, Huang Z, Hu J and He Z: Real‑world performance of the machine learning‑based prediction of chemotherapy‑associated adverse effects in lung cancer. Oncol Lett 31: 24, 2026.
APA
Huang, S., Huang, Z., Sun, Z., Xie, T., Zhu, X., Lu, S. ... He, Z. (2026). Real‑world performance of the machine learning‑based prediction of chemotherapy‑associated adverse effects in lung cancer. Oncology Letters, 31, 24. https://doi.org/10.3892/ol.2025.15377
MLA
Huang, S., Huang, Z., Sun, Z., Xie, T., Zhu, X., Lu, S., Huang, Z., Hu, J., He, Z."Real‑world performance of the machine learning‑based prediction of chemotherapy‑associated adverse effects in lung cancer". Oncology Letters 31.1 (2026): 24.
Chicago
Huang, S., Huang, Z., Sun, Z., Xie, T., Zhu, X., Lu, S., Huang, Z., Hu, J., He, Z."Real‑world performance of the machine learning‑based prediction of chemotherapy‑associated adverse effects in lung cancer". Oncology Letters 31, no. 1 (2026): 24. https://doi.org/10.3892/ol.2025.15377
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