Skip to main content
SpringerLink
Log in

Comparison of Models for Predicting Quality of Life After Surgical Resection of Hepatocellular Carcinoma: a Prospective Study

  • Original Article
  • Published:
Journal of Gastrointestinal Surgery

Abstract

Background

The essential issue of internal validity has not been adequately addressed in prediction models such as artificial neural network (ANN), support vector machine (SVM), Gaussian process regression (GPR), and multiple linear regression (MLR) models.

Methods

This prospective study compared the accuracy of these four models in predicting quality of life (QOL) after hepatic resection received by 332 patients with hepatocellular carcinoma (HCC) during 2012–2015. An estimation subset was used to train the models, and a validation subset was used to evaluate their performance. Sensitivity score approach was also used to assess the relative significance of input parameters in the system models.

Results

The ANN model had significantly higher performance indicators compared to the SVM, GPR, and MLR models (P < 0.05). Additionally, the ANN prediction of QOL at 6 months after hepatic resection significantly correlated with age, gender, marital status, Charlson comorbidity index (CCI) score, chemotherapy, radiotherapy, hospital volume, surgeon volume, and preoperational functional status (P < 0.05). Preoperational functional status was the most influential (sensitive) variable affecting sixth-month QOL followed by surgeon volume, hospital volume, age, and CCI score.

Conclusions

The comparisons showed that, in preoperative and postoperative healthcare consultations with HCC surgery candidates, QOL at 6 months post-surgery should be estimated with an ANN model rather than with SVM, GPR, or MLR models. The best QOL predictors identified in this study can also be used to educate candidates for HCC surgery in the expected course of recovery and other surgical outcomes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Beal EW, Tumin D, Kabir A, et al. Trends in the Mortality of Hepatocellular Carcinoma in the United States. J Gastrointest Surg 2017; 21:2033–8.

    Article  PubMed  Google Scholar 

  2. Llovet JM, Burroughs A, Bruix J. Hepatocellular carcinoma. Lancet 2003; 362:1907–17.

    Article  PubMed  Google Scholar 

  3. Wang J, Mao Y, Liu Y, et al. Hepatocellular Carcinoma in Children and Adolescents: Clinical Characteristics and Treatment. J Gastrointest Surg 2017; 21:1128–35.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Fernandez B, Dore L, Velanovich V. Patient-Centered Outcomes in Surgical Research and Practice. J Gastrointest Surg 2017; 21:892–5.

    Article  PubMed  Google Scholar 

  5. Chie WC, Yu F, Li M, et al. Quality of life changes in patients undergoing treatment for hepatocellular carcinoma. Qual Life Res 2015; 24:2499–506.

    Article  PubMed  Google Scholar 

  6. Lei JY, Yan LN, Wang WT, Zhu JQ, Li DJ. Health-Related Quality of Life and Psychological Distress in Patients With Early-Stage Hepatocellular Carcinoma After Hepatic Resection or Transplantation. Transplant Proc 2016; 48:2107–11.

    Article  CAS  PubMed  Google Scholar 

  7. Lagoudakis MG, Kumar A. Artificial neural networks in predicting outcome of gastrointestinal haemorrhage. Lancet 2004; 363:170.

    Article  PubMed  Google Scholar 

  8. Das A, Ben-Menachem T, Cooper GS, et al. Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: internal and external validation of a predictive model. Lancet 2003; 362:1261–6.

    Article  PubMed  Google Scholar 

  9. Ware Jr JE, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care 1992; 30:473–83.

    Article  Google Scholar 

  10. Tseng HM, Lu JF, Gandek B. Cultural issues in using the SF-36 Health Survey in Asia: results from Taiwan. Health Qual Life Outcomes 2003; 1:72.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Heffernan N, Cella D, Webster K, et al. Measuring health-related quality of life in patients with hepatobiliary cancers: the functional assessment of cancer therapy-hepatobiliary questionnaire. J Clin Oncol 2002; 20:2229–39.

    Article  PubMed  Google Scholar 

  12. Buyse M, Piedbois P. Artificial neural networks. Lancet 1997; 350:1175–6.

    Article  CAS  PubMed  Google Scholar 

  13. Wang GL, Li YF, Bi DX. Support-vector networks in adaptive friction compensation. IEEE Trans Neural Netw 2007; 18:1209–19.

    Article  CAS  PubMed  Google Scholar 

  14. Li L, Wang P, Chao KH, Zhou Y, Xie Y. Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Gaussian Processes Mixture. PLoS One 2016; 11:e0163004.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. He Y, Pan A, Yang Y, et al. Prevalence of Underweight, Overweight, and Obesity Among Reproductive-Age Women and Adolescent Girls in Rural China. Am J. Public Health 2016; 106:2103–10.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Chen CH, George SL. The bootstrap and identification of prognostic factors via Cox’s proportional hazards regression model. Statist Med 1985; 4:39.

    Article  CAS  Google Scholar 

  17. Rossi F, Conan-Guez B. Functional multi-layer perceptron: a non-linear tool for functional data analysis. Neural Netw 2005; 18:45–60.

    Article  PubMed  Google Scholar 

  18. Lu TC, Yu GR, Juang JC. Quantum-based algorithm for optimizing artificial neural networks. IEEE Trans Neural Netw Learn Syst 2013; 24:1266–78.

    Article  Google Scholar 

  19. Westreich D, Lessler J, Funk MJ. Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. J Clin Epidemiol 2010; 63:826–33.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Shen Y, Zhou C, Zhu G, et al. Liver Stiffness Assessed by Shear Wave Elastography Predicts Postoperative Liver Failure in Patients with Hepatocellular Carcinoma. J Gastrointest Surg 2017; 21:1471–9.

    Article  PubMed  Google Scholar 

  21. Muir J, Aronson M, Esplen MJ, Pollett A, Swallow CJ. Prophylactic Total Gastrectomy: a Prospective Cohort Study of Long-Term Impact on Quality of Life. J Gastrointest Surg 2016; 20:1950–8.

    Article  PubMed  Google Scholar 

  22. Shi HY, Tsai JT, Chen YM, Culbertson R, Chang HT, Hou MF. Predicting two-year quality of life after breast cancer surgery using artificial neural network and linear regression models. Breast Cancer Res Treat 2012; 135:221–9.

    Article  PubMed  Google Scholar 

  23. Chellali MR, Abderrahim H, Hamou A, Nebatti A, Janovec J. Artificial neural network models for prediction of daily fine particulate matter concentrations in Algiers. Environ Sci Pollut Res Int 2016; 23:14008–17.

    Article  CAS  PubMed  Google Scholar 

  24. Ivanović D, Kupusinac A, Stokić E, Doroslovački R, Ivetić D. ANN Prediction of Metabolic Syndrome: a Complex Puzzle that will be Completed. J Med Syst 2016; 40:264.

    Article  PubMed  Google Scholar 

  25. Tsai JT, Hou MF, Chen YM, Wan TT, Kao HY, Shi HY. Predicting quality of life after breast cancer surgery using ANN-based models: performance comparison with MR. Support Care Cancer 2013; 21:1341–50.

    Article  PubMed  Google Scholar 

  26. Chang HT, Shi HY, Wang BW, Yeh SJ. Breast Cancer Incidence and Predictors of Surgical Outcome: a Nationwide Longitudinal Study in Taiwan. Clin Oncol 2017; 29:362–9.

    Article  Google Scholar 

  27. Chiu CC, Lee KT, Wang JJ, Sun DP, Lee HH, Shi HY. Health-Related Quality of Life before and after Surgical Resection of Hepatocellular Carcinoma: A Prospective Study. Asian Pac J Cancer Prev 2018; 19:65–72.

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This study was supported by grants from Chi Mei Medical Center, Liouying (CLFHR 10409), and from the Chi-Mei Medical Center and the Kaohsiung Medical University Research Foundation (106CM-KMU-09).

Author information

Authors and Affiliations

  1. Department of General Surgery, Chi Mei Medical Center, Liouying, Taiwan

    Chong-Chi Chiu & Hao-Hsien Lee

  2. Department of General Surgery, Chi Mei Medical Center, Tainan, Taiwan

    Chong-Chi Chiu & Ding-Ping Sun

  3. Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan, Taiwan

    Chong-Chi Chiu

  4. Division of Hepatobiliary Surgery, Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan

    King-Teh Lee

  5. Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, 100, Zihyou 1st Road, Kaohsiung, 807, Taiwan

    King-Teh Lee & Hon-Yi Shi

  6. Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan

    Jhi-Joung Wang

  7. Department of Emergency Medicine, Chi Mei Medical Center, Tainan, Taiwan

    Chien-Cheng Huang

  8. Bachelor Program of Senior Service, Southern Taiwan University of Science and Technology, Tainan, Taiwan

    Chien-Cheng Huang

  9. Department of Business Management, National Sun Yat-sen University, Kaohsiung, Taiwan

    Hon-Yi Shi

  10. Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan

    Hon-Yi Shi

Authors
  1. Chong-Chi Chiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. King-Teh Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hao-Hsien Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jhi-Joung Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ding-Ping Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chien-Cheng Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hon-Yi Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Chong-Chi Chiu: conception of study, analysis and interpretation of data, writing and preparation of manuscript, and final approval of manuscript; King-Teh Lee: acquisition of data; Hao-Hsien Lee: acquisition of data; Jhi-Joung Wang: acquisition of data; Ding-Ping Sun: acquisition of data; Chien-Cheng Huang: acquisition of data; Hon-Yi Shi: conception of study, analysis and interpretation of data, writing and preparation of manuscript, and final approval of manuscript.

Corresponding author

Correspondence to Hon-Yi Shi.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflicts of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chiu, CC., Lee, KT., Lee, HH. et al. Comparison of Models for Predicting Quality of Life After Surgical Resection of Hepatocellular Carcinoma: a Prospective Study. J Gastrointest Surg 22, 1724–1731 (2018). https://doi.org/10.1007/s11605-018-3833-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11605-018-3833-7

Keywords

Search

Navigation