Personalized Survival Prediction for Hepatocellular Carcinoma Using Artificial Intelligence

Authors

  • Steven Lin Kaohsiung American School
  • Justin Pilon Kaohsiung American School
  • Chen-Wen Yen
  • Chih-Wen Lin

DOI:

https://doi.org/10.47611/jsrhs.v13i3.7177

Keywords:

Hepatocellular carcinoma; Artificial intelligence; Survival; Predictors;

Abstract

Hepatocellular carcinoma (HCC) presents a significant global health challenge. Researchers have explored machine artificial intelligence-based systems for treatment recommendation. However, these models only consider a limited range of treatment options and primarily replicate historical treatment decisions, potentially leading to suboptimal outcomes. The XGBoost-based Accelerated Failure Time (XGBAFT) model, a novel machine learning-based system, predicts survival times for nine HCC treatment options. Developed using data from 8,511 patients at two medical centers in Taiwan, the model integrates the XGBoost algorithm with the accelerated failure time framework. Its key strength lies in comparing predicted survival times and curves across treatment options, facilitating personalized decision-making. The model can also predict survival beyond a specified target survival time, with accuracy and reliability established using binary classification measures. The model achieved a concordance index of 0.831, outperforming the Cox proportional hazards model. Notably, treatment was identified as the most influential covariate, underscoring the importance of selecting appropriate and effective treatments tailored to patient characteristics for improving survival outcomes. In conclusion, the XGBAFT model demonstrates significant potential in predicting HCC outcomes and personalized survival predictions. Future research should focus on integrating advanced machine learning algorithms and improving model interpretability to support clinical decision-making and personalized care.

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References or Bibliography

Barnwal, A., Cho, H., & Hocking, T. (2022). Survival regression with accelerated failure time model in XGBoost. Journal of Computational and Graphical Statistics, 31(4), 1292-1302.

Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).

Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC genomics, 21, 1-13.

Choi, G. H., Yun, J., Choi, J., Lee, D., Shim, J. H., Lee, H. C., ... & Kim, K. M. (2020). Development of machine learning-based clinical decision support system for hepatocellular carcinoma. Scientific reports, 10(1), 14855.

Cox, D. R. (1972). Regression analysis of length of life. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187-220.

Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008). Random survival forests.

Lee, K. H., Choi, G. H., Yun, J., Choi, J., Goh, M. J., Sinn, D. H., ... & Kim, K. M. (2024). Machine learning-based clinical decision support system for treatment recommendation and overall survival prediction of hepatocellular carcinoma: a multi-center study. NPJ Digital Medicine, 7(1), 2.

Galle, P. R., Forner, A., Llovet, J. M., Mazzaferro, V., Piscaglia, F., Raoul, J. L., ... & Vilgrain, V. (2018). EASL clinical practice guidelines: management of hepatocellular carcinoma. Journal of hepatology, 69(1), 182-236.

Harrell Jr, F. E., Califf, R. M., Pryor, D. B., Lee, K. L., & Rosati, R. A. (1982). Evaluating the yield of medical tests. JAMA, 247(18), 2543-2546.

Heimbach, J. K., Kulik, L. M., Finn, R. S., Sirlin, C. B., Abecassis, M. M., Roberts, L. R., ... & Marrero, J. A. (2018). AASLD guidelines for the treatment of hepatocellular carcinoma. Hepatology, 67(1), 358-380.

Leoni, S., Piscaglia, F., Serio, I., Terzi, E., Pettinari, I., Croci, L., ... & Bolondi, L. (2014). Adherence to AASLD guidelines for the treatment of hepatocellular carcinoma in clinical practice: experience of the Bologna Liver Oncology Group. Digestive and Liver Disease, 46(6), 549-555.

Mittal, S., Kanwal, F., Ying, J., Chung, R., Sada, Y. H., Temple, S., ... & El-Serag, H. B. (2016). Effectiveness of surveillance for hepatocellular carcinoma in clinical practice: a United States cohort. Journal of hepatology, 65(6), 1148-1154.

Nagpal, C., Potosnak, W., & Dubrawski, A. (2022, December). Auton-survival: An open-source package for regression, counterfactual estimation, evaluation and phenotyping with censored time-to-event data. In Machine Learning for Healthcare Conference (pp. 585-608). PMLR.

Omata, M., Cheng, A. L., Kokudo, N., Kudo, M., Lee, J. M., Jia, J., ... & Sarin, S. K. (2017). Asia–Pacific clinical practice guidelines on the management of hepatocellular carcinoma: a 2017 update. Hepatology international, 11, 317-370.

Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical Bayesian optimization of machine learning algorithms. Advances in neural information processing systems, 25.

World Health Organization. (2021). Cancer. https://www.who.int/news-room/fact-sheets/detail/cancer.

Published

08-31-2024

How to Cite

Lin, S., Pilon, J., Yen, C.-W., & Lin, C.-W. (2024). Personalized Survival Prediction for Hepatocellular Carcinoma Using Artificial Intelligence. Journal of Student Research, 13(3). https://doi.org/10.47611/jsrhs.v13i3.7177

Issue

Section

HS Research Articles