Development of an intelligent support system for hepatocellular carcinoma treatment selection

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.347800

Keywords:

hepatocellular carcinoma, treatment selection, intelligent decision support system, knowledge base

Abstract

The object of the study is the clinical decision-making process for selecting of hepatocellular carcinoma (HCC) treatment method based on the patient's medical data. The process of the HCC treatment method selection remains poorly formalized and is characterized by multi-criteria and the presence of numerous clinical situations, for each of which it is necessary to promptly identify the most accurate therapeutic solution.

This study develops an intelligent medical decision support system for HCC treatment method selection based on knowledge applicable in clinical practice.

It offers architectural and functional principles of the intelligent decision support system, classifying clinical situations by HCC treatment method, consisting of multiple possible combinations of 44 informative parameters. Based on the current values of these parameters, expert knowledge is transformed into production rules identifying HCC treatment methods.

A heuristic procedure treatment selection is developed based on production rule analysis in accordance with current parameter values, reproducing the reasoning patterns of participants in a multidisciplinary council during their consensus decision-making process regarding HCC treatment appointment. A software implementation of a decision-making model for HCC treatment selection, implemented in C# using the Visual Study 2019 platform, enabled the integration of an intelligent system with web technologies.

The intelligent medical decision support system automates the unique experience of professionals and helps physicians in a multidisciplinary consultation make prompt and informed decisions online regarding the appointment of personalized therapy. The system was piloted with expert physicians in several iterations until complete match between the consensus decision of the multidisciplinary council and the decision made by the developed system in accordance with clinical recommendations was achieved

Author Biographies

Masuma Mammadova, Institute of Information Technology

Doctor of Technical Sciences, Professor, Head of Department

Department Number 11

Nuru Bayramov, Azerbaijan Medical University

Doctor of Medical Sciences, Professor, Head of Department

Department of Surgical Diseases 1

Zarifa Jabrayilova, Institute of Information Technology

Doctor of Technical Sciences, Assistant Professor, Chief Researcher

Department Number 11

Tetyana Baydyk, Universidad Nacional Autónoma de México

Doctor of Technical Sciences, Professor, Investigator Titular C

Department of Micro and Nanotechnology

Instituto de Ciencias Aplicadas y Tecnología

Mehriban Huseynova, Azerbaijan Medical University

Assistant Doctor

Department of Surgical Diseases 1

References

  1. Reynolds, A. R., Furlan, A., Fetzer, D. T., Sasatomi, E., Borhani, A. A., Heller, M. T., Tublin, M. E. (2015). Infiltrative Hepatocellular Carcinoma: What Radiologists Need to Know. RadioGraphics, 35 (2), 371–386. https://doi.org/10.1148/rg.352140114
  2. Bayramov, N., Mammadova, Sh. (2022). A review of the current ERAS guidelines for liver resection, liver transplantation and pancreatoduodenectomy. Annals of Medicine & Surgery, 82. https://doi.org/10.1016/j.amsu.2022.104596
  3. Huang, D. Q., Tran, A., Tan, E. X., Nerurkar, S. N., Teh, R., Teng, M. L. P. et al. (2022). Characteristics and outcomes of hepatocellular carcinoma patients with macrovascular invasion following surgical resection: a meta-analysis of 40 studies and 8,218 patients. Hepatobiliary Surgery and Nutrition, 11 (6), 848–860. https://doi.org/10.21037/hbsn-21-419
  4. Ray, S., Mehta, N. N., Golhar, A., Nundy, S. (2018). Post hepatectomy liver failure – A comprehensive review of current concepts and controversies. Annals of Medicine and Surgery, 34, 4–10. https://doi.org/10.1016/j.amsu.2018.08.012
  5. Calderon, F., Masino, E., Caram, L., Ardiles, V. (2021). Short-and-Long-Term Outcomes of Hepatic Resection for Hepatocellular Carcinoma in Cirrhotic and Non-Cirrhotic Liver Parenchyma., 1 (1), 12–19. Journal of Surgery and Clinical Reports. Available at: https://www.researchgate.net/publication/351706093_Journal_of_Surgery_and_Clinical_Reports_Short-and-Long-Term_Outcomes_of_Hepatic_Resection_for_Hepatocellular_Carcinoma_in_Cirrhotic_and_Non-Cirrhotic_Liver_Parenchyma
  6. Citterio, D., Facciorusso, A., Sposito, C., Rota, R., Bhoori, S., Mazzaferro, V. (2016). Hierarchic Interaction of Factors Associated With Liver Decompensation After Resection for Hepatocellular Carcinoma. JAMA Surgery, 151 (9), 846. https://doi.org/10.1001/jamasurg.2016.1121
  7. Park, S., Davis, A. M., Pillai, A. A. (2024). Prevention, Diagnosis, and Treatment of Hepatocellular Carcinoma. JAMA, 332 (12), 1013. https://doi.org/10.1001/jama.2024.14101
  8. Brown, Z. J., Tsilimigras, D. I., Ruff, S. M., Mohseni, A., Kamel, I. R., Cloyd, J. M., Pawlik, T. M. (2023). Management of Hepatocellular Carcinoma. JAMA Surgery, 158 (4), 410. https://doi.org/10.1001/jamasurg.2022.7989
  9. Singh, A., Pandey, B. (2016). An Efficient Diagnosis System for Detection of Liver Disease Using a Novel Integrated Method Based on Principal Component Analysis and K-Nearest Neighbor (PCA-KNN). International Journal of Healthcare Information Systems and Informatics, 11 (4), 56–69. https://doi.org/10.4018/ijhisi.2016100103
  10. Sartakhti, J. S., Zangooei, M. H., Mozafari, K. (2012). Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA). Computer Methods and Programs in Biomedicine, 108 (2), 570–579. https://doi.org/10.1016/j.cmpb.2011.08.003
  11. Gorunescu, F., Belciug, S., Gorunescu, M., Badea, R. (2012). Intelligent decision-making for liver fibrosis stadialization based on tandem feature selection and evolutionary-driven neural network. Expert Systems with Applications, 39 (17), 12824–12832. https://doi.org/10.1016/j.eswa.2012.05.011
  12. Li, B. N., Chui, C. K., Chang, S., Ong, S. H. (2012). A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images. Expert Systems with Applications, 39 (10), 9661–9668. https://doi.org/10.1016/j.eswa.2012.02.095
  13. Farahi, R., Derakhshanfard, N., Ghaffari, A. (2025). Intelligent Decision Support System for Liver Disease Diagnosis with MLP Network Optimized by Genetic Algorithm. International Journal of Computational Intelligence Systems, 18 (1). https://doi.org/10.1007/s44196-025-01013-0
  14. Curtidor, A., Kussul, E., Baydyk, T., Mammadova, M. (2023). Analysis and automated classification of images of blood cells to diagnose acute lymphoblastic leukemia. EUREKA: Physics and Engineering, 5, 177–190. https://doi.org/10.21303/2461-4262.2023.003070
  15. Curtidor, A., Mammadova, M., Velasco Herrera, G., Baydyk, T., Kussul, E. (2023). Recognition of images of blood cells using texture and neural networks to diagnose leukemia. Expert Assessments in Decision Making: Risks and Safety, 36–65. https://doi.org/10.21303/978-9916-9850-2-1.ch2
  16. Curtidor, A., Baydyk, T., Kussul, E. (2025). Classification based on Neural Networks to Detect Acute Lymphoblastic Leukemia. Wseas Transactions On Signal Processing, 21, 25–30. https://doi.org/10.37394/232014.2025.21.4
  17. Schuler, K., Jung, I.-C., Zerlik, M., Hahn, W., Sedlmayr, M., Sedlmayr, B. (2025). Context factors in clinical decision-making: a scoping review. BMC Medical Informatics and Decision Making, 25 (1). https://doi.org/10.1186/s12911-025-02965-1
  18. Mirmozaffari, M. (2019). Developing an Expert System for Diagnosing Liver Diseases. European Journal of Engineering Research and Science, 4 (3), 1–5. https://doi.org/10.24018/ejers.2019.4.3.1168
  19. Mammadova, M., Bayramov, N., Jabrayilova, Z. (2021). Development of the principles of fuzzy rule-based system for hepatocelular carcinoma staging. EUREKA: Physics and Engineering, 3, 3–13. https://doi.org/10.21303/2461-4262.2021.001829
  20. Məmmədova, M. H., Bayramov, N. Y., Cəbrayılova, Z. G., Manaflı, M. İ., Hüseynova, M. R. (2024). Hepatosellülyar karsinomanin diaqnostikasi üçün həkim qərarlarinin dəstəklənməsi sistemi. Azerbaijan Medical Journal, 4, 163–169. https://doi.org/10.34921/amj.2024.4.026
Development of an intelligent support system for hepatocellular carcinoma treatment selection

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Published

2025-12-30

How to Cite

Mammadova, M., Bayramov, N., Jabrayilova, Z., Baydyk, T., & Huseynova, M. (2025). Development of an intelligent support system for hepatocellular carcinoma treatment selection. Eastern-European Journal of Enterprise Technologies, 6(9 (138), 28–36. https://doi.org/10.15587/1729-4061.2025.347800

Issue

Vol. 6 No. 9 (138) (2025): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2025 Masuma Mammadova, Nuru Bayramov, Zarifa Jabrayilova, Tetyana Baydyk, Mehriban Huseynova

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