Machine learning-derived model for predicting poor post-treatment quality of life in Korean cancer survivors (original) (raw)

Abstract

Purpose

A substantial number of cancer survivors have poor quality of life (QOL) even after completing cancer treatment. Thus, in this study, we used machine learning (ML) to develop predictive models for poor QOL in post-treatment cancer survivors in South Korea.

Methods

This cross-sectional study used online survey data from 1,005 post-treatment cancer survivors in South Korea. The outcome variable was QOL, which was measured using the global QOL subscale of the European Organization of Cancer and Treatment for Cancer Quality of Life Questionnaire, where a global QOL score < 60.4 was defined as poor QOL. Three ML models (random forest (RF), support vector machine, and extreme gradient boosting) and three deep learning models were used to develop predictive models for poor QOL. Model performance regarding accuracy, area under the receiver operating characteristic curve, F1 score, precision, and recall was evaluated. The SHapely Additive exPlanation (SHAP) method was used to identify important features.

Results

Of the 1,005 participants, 65.1% had poor QOL. Among the six models, the RF model had the best performance (accuracy = 0.85, F1 = 0.90). The SHAP method revealed that survivorship concerns (e.g., distress, pain, and fatigue) were the most important factors that affected poor QOL.

Conclusions

The ML-based prediction model developed to predict poor QOL in Korean post-treatment cancer survivors showed good accuracy. The ML model proposed in this study can be used to support clinical decision-making in identifying survivors at risk of poor QOL.

Access this article

Log in via an institution

Subscribe and save

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Data availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical constraints.

References

  1. Sung H, Ferlay J, Siegel RL et al (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71:209–249. https://doi.org/10.3322/caac.21660
    Article PubMed Google Scholar
  2. Jung KW, Kang MJ, Park EH et al (2023) Prediction of cancer incidence and mortality in Korea, 2023. Cancer Res Treat 55:400–407. https://doi.org/10.4143/crt.2023.448
    Article PubMed PubMed Central Google Scholar
  3. Kang MJ, Jung KW, Bang SH et al (2023) Cancer statistics in Korea: incidence, mortality, survival, and prevalence in 2020. Cancer Res Treat 55:385–399. https://doi.org/10.4143/crt.2023.447
    Article PubMed PubMed Central Google Scholar
  4. Sanft T, Tevaarwerk A, Denlinger CS et al (2021) NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) Survivorship. 2021. https://www.nccn.org/guidelines/guidelines-detail?category=3&id=1466. Accessed 2 Sep 2022
  5. Valdivieso M, Kujawa AM, Jones T, Baker LH (2012) Cancer survivors in the United States: a review of the literature and a call to action. Int J Med Sci 9:163–173. https://doi.org/10.7150/ijms.3827
    Article PubMed PubMed Central Google Scholar
  6. Harrington CB, Hansen JA, Moskowitz M, Todd BL, Feuerstein M (2010) It’s not over when it’s over: long-term symptoms in cancer survivors–a systematic review. Int J Psychiatry Med 40:163–181. https://doi.org/10.2190/PM.40.2.c
    Article PubMed Google Scholar
  7. Jung JY, Lee JM, Kim MS, Shim YM, Zo JI, Yun YH (2018) Comparison of fatigue, depression, and anxiety as factors affecting post-treatment health-related quality of life in lung cancer survivors. Psychooncology 27:465–470. https://doi.org/10.1002/pon.4513
    Article PubMed Google Scholar
  8. Kim SH, Son BH, Hwang SY et al (2008) Fatigue and depression in disease-free breast cancer survivors: prevalence, correlates, and association with quality of life. J Pain Symptom Manag 35:644–655. https://doi.org/10.1016/j.jpainsymman.2007.08.012
    Article Google Scholar
  9. Park JH, Park EC, Park JH, Kim SG, Lee SY (2008) Job loss and re-employment of cancer patients in Korean employees: a nationwide retrospective cohort study. J Clin Oncol 26:1302–1309. https://doi.org/10.1200/JCO.2007.14.2984
    Article PubMed Google Scholar
  10. Ahn SH, Park BW, Noh DY et al (2007) Health-related quality of life in disease-free survivors of breast cancer with the general population. Ann Oncol 18:173–182. https://doi.org/10.1093/annonc/mdl333
    Article CAS PubMed Google Scholar
  11. Pinto M, Marotta N, Caraco C, Simeone E, Ammendolia A, de Sire A (2022) Quality of life predictors in patients with melanoma: a machine learning approach. Front Oncol 12:843611. https://doi.org/10.3389/fonc.2022.843611
    Article PubMed PubMed Central Google Scholar
  12. Di Meglio A, Havas J, Gbenou AS et al (2022) Dynamics of long-term patient-reported quality of life and health behaviors after adjuvant breast cancer chemotherapy. J Clin Oncol 40:3190–3204. https://doi.org/10.1200/JCO.21.00277
    Article CAS PubMed PubMed Central Google Scholar
  13. Goyal NG, Levine BJ, Van Zee KJ, Naftalis E, Avis NE (2018) Trajectories of quality of life following breast cancer diagnosis. Breast Cancer Res Treat 169:163–173. https://doi.org/10.1007/s10549-018-4677-2
    Article PubMed PubMed Central Google Scholar
  14. Seow H, Tanuseputro P, Barbera L et al (2021) Development and validation of a prediction model of poor performance status and severe symptoms over time in cancer patients (PROVIEW+). Palliat Med 35:1713–1723. https://doi.org/10.1177/02692163211019302
    Article PubMed PubMed Central Google Scholar
  15. Nakagami G, Yokota S, Kitamura A et al (2021) Supervised machine learning-based prediction for in-hospital pressure injury development using electronic health records: a retrospective observational cohort study in a university hospital in Japan. Int J Nurs Stud 119:103932. https://doi.org/10.1016/j.ijnurstu.2021.103932
    Article PubMed Google Scholar
  16. Abbas H, Garberson F, Glover E, Wall DP (2018) Machine learning approach for early detection of autism by combining questionnaire and home video screening. J Am Med Inform Assoc 25:1000–1007. https://doi.org/10.1093/jamia/ocy039
    Article PubMed PubMed Central Google Scholar
  17. Savić M, Kurbalija V, Ilić M et al (2023) The application of machine learning techniques in prediction of quality of life features for cancer patients. Comput Sci Inf Syst 20:381–404. https://doi.org/10.2298/CSIS220227061S
    Article Google Scholar
  18. Liu YH, Jin J, Liu YJ (2022) Machine learning-based random forest for predicting decreased quality of life in thyroid cancer patients after thyroidectomy. Support Care Cancer 30:2507–2513. https://doi.org/10.1007/s00520-021-06657-0
    Article PubMed Google Scholar
  19. Karri R, Chen YP, Drummond KJ (2022) Using machine learning to predict health-related quality of life outcomes in patients with low grade glioma, meningioma, and acoustic neuroma. PLoS ONE 17:e0267931. https://doi.org/10.1371/journal.pone.0267931
    Article CAS PubMed PubMed Central Google Scholar
  20. Kumar S, Rana ML, Verma K et al (2014) PrediQt-Cx: post treatment health related quality of life prediction model for cervical cancer patients. PLoS ONE 9:e89851. https://doi.org/10.1371/journal.pone.0089851
    Article ADS CAS PubMed PubMed Central Google Scholar
  21. Quality of Life Group (2022) Definition of quality of life. https://qol.eortc.org. Accessed 6 Sep 2022
  22. Yun YH, Bae SH, Kang IO et al (2004) Cross-cultural application of the Korean version of the European Organization for Research and Treatment of Cancer (EORTC) Breast-Cancer-Specific Quality of Life Questionnaire (EORTC QLQ-BR23). Support Care Cancer 12:441–445. https://doi.org/10.1007/s00520-004-0632-3
    Article PubMed Google Scholar
  23. Fayers P, Aaronson NK, Bjordal K, Groenvold M, Curran D, Bottomley A (2001) The EORTC QLQ-C30 scoring manual, 3rd edn. European Organization for Research and Treatment of Cancer, Brussels
    Google Scholar
  24. Yun YH, Kim SH, Lee KM, Park SM, Kim YM (2007) Age, sex, and comorbidities were considered in comparing reference data for health-related quality of life in the general and cancer populations. J Clin Epidemiol 60:1164–1175. https://doi.org/10.1016/j.jclinepi.2006.12.014
    Article PubMed Google Scholar
  25. King MT (1996) The interpretation of scores from the EORTC quality of life questionnaire QLQ-C30. Qual Life Res 5:555–567. https://doi.org/10.1007/BF00439229
    Article CAS PubMed Google Scholar
  26. Osoba D, Rodrigues G, Myles J, Zee B, Pater J (1996) Interpreting the significance of changes in health-related quality-of-life scores. J Clin Oncol 16:139–144. https://doi.org/10.1200/JCO.1998.16.1.139
    Article Google Scholar
  27. Guyon I, Elisseeff A, Kaelbling LP (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182
    Google Scholar
  28. Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97:273–324. https://doi.org/10.1016/S0004-3702(97)00043-X
    Article Google Scholar
  29. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
    Article Google Scholar
  30. Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intell Syst App 13:18–28. https://doi.org/10.1109/5254.708428
    Article Google Scholar
  31. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. Association for Computing Machinery, San Francisco. https://doi.org/10.1145/2939672.2939785
  32. Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. Proceedings of the 31st international conference on neural information processing systems, Long Beach
  33. Biau G, Scornet E (2016) A random forest guided tour. TEST 25:197–227. https://doi.org/10.1007/s11749-016-0481-7
    Article MathSciNet Google Scholar
  34. Kim JH (2019) Update on distress management for cancer patients. J Korean Med Assoc 62:167–173. https://doi.org/10.5124/jkma.2019.62.3.167
    Article Google Scholar
  35. Kim JH, Kang JI, Kim JH et al (2009) Development of recommendations for distress management toward improvement of quality of life in cancer patients. Ministry of Health and Welfare, Seoul

Download references

Funding

This work was supported by a National Research Foundation of Korea grant funded by the Korean government Ministry of Science and ICT (MSIT; Grant No. 2022R1A2C1092797).

Author information

Author notes

  1. Yu Hyeon Choe and Sujee Lee contributed equally to this work.

Authors and Affiliations

  1. Department of Nursing, Inha University, Incheon, Republic of Korea
    Yu Hyeon Choe & Soo Hyun Kim
  2. Department of Industrial and Information Systems Engineering, Soongsil University, Seoul, Republic of Korea
    Sujee Lee & Yooseok Lim

Authors

  1. Yu Hyeon Choe
    You can also search for this author inPubMed Google Scholar
  2. Sujee Lee
    You can also search for this author inPubMed Google Scholar
  3. Yooseok Lim
    You can also search for this author inPubMed Google Scholar
  4. Soo Hyun Kim
    You can also search for this author inPubMed Google Scholar

Contributions

Study conception and design: SHK, YHC, and SL; Data collection: YHC; Data analysis and interpretation: SL and YL; Drafting of the article: SHK and SL; Critical revision of the article: SHK, YHC, and SL. All authors have read and approved the manuscript for submission.

Corresponding author

Correspondence toSoo Hyun Kim.

Ethics declarations

Ethical approval

This study was performed in line with the principles of the Declaration of Helsinki. The study was approved by the Institutional Review Board of the INHA University (No. 230220-1A).

Informed consent was obtained from all individual participants included in the study.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article

Choe, Y.H., Lee, S., Lim, Y. et al. Machine learning-derived model for predicting poor post-treatment quality of life in Korean cancer survivors.Support Care Cancer 32, 143 (2024). https://doi.org/10.1007/s00520-024-08347-z

Download citation

Keywords