Intelligent support system for hepatocellular carcinoma treatment method selection
Keywords:
Digital medicine, HCC treatment method, AI support tools, consensus decision-making, multidisciplinary council, clinical validationSynopsis
Digital transformation has opened up new opportunities for the application of artificial intelligence in all areas of the economy and business. One of the most stable, rapidly growing, and promising business sectors is the medical industry, which focuses on providing diagnostic, treatment, and preventative services, boasting high social significance and commercial effectiveness. The primary application of artificial intelligence is forecasting and decision making. One of the key objectives of digital medicine is the development of tools to improve the quality, accuracy, and timeliness of medical decisions. Such tools, implemented using AI and computer systems that simulate medical decision-making processes for various purposes, are known as decision support systems. Selecting a treatment method for hepatocellular carcinoma (HCC) based on the diagnosis is a critical and relatively understudied decision-making problem. This study develops an intelligent medical decision support system for treatment method selection. 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 council 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.
References
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
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
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
Calderon, F., Masino, E., Caram, L., Ardiles, V., Vicens, J., Aliper, V. et al. (2021). Short-and-Long-Term Outcomes of Hepatic Resection for Hepatocellular Carcinoma in Cirrhotic and Non-Cirrhotic Liver Parenchyma. Journal of Surgery and Clinical Reports, 1 (1), 12–19. 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
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), 846–853. https://doi.org/10.1001/jamasurg.2016.1121
Park, S., Davis, A. M., Pillai, A. A. (2024). Prevention, Diagnosis, and Treatment of Hepatocellular Carcinoma. JAMA, 332 (12), 1013–1014. https://doi.org/10.1001/jama.2024.14101
Brustia, R., Monsel, A., Skurzak, S., Schiffer, E., Carrier, F. M., Patrono, D. et al. (2021). Guidelines for Perioperative Care for Liver Transplantation: Enhanced Recovery After Surgery (ERAS) Recommendations. Transplantation, 106 (3), 552–561. https://doi.org/10.1097/tp.0000000000003808
Carrier, F. M., Chassé, M., Wang, H. T., Aslanian, P., Iorio, S., Bilodeau, M., Turgeon, A. F. (2019). Stratégies de prise en charge liquidienne restrictive et pronostics en transplantation hépatique : une revue systématique. Canadian Journal of Anesthesia/Journal Canadien d’Anesthésie, 67 (1), 109–127. https://doi.org/10.1007/s12630-019-01480-y
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), 410–420. https://doi.org/10.1001/jamasurg.2022.7989
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
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
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
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
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
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
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
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
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
Mammadova, M., Bayramov, N., Jabrayilova, Z., Manafli, M., Huseynova, M. (2024). Decision support system for the hepatocellular carcinoma diagnosis. Azerbaijan Medical Journal, 4, 163–169. https://doi.org/10.34921/amj.2024.4.026
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
Text Identifier of this Chapter
Chapter 3
Published
August 26, 2025
Copyright (c) 2026 Masuma Mammadova, Nuru Bayramov, Zarifa Jabrayilova
How to Cite
Mammadova, M., Bayramov, N., & Jabrayilova, Z. (2025). Intelligent support system for hepatocellular carcinoma treatment method selection. In S. Volosovych, L. Morozova, N. Husarevych, M. Mammadova, N. Bayramov, Z. Jabrayilova, T. Alibeyli, & H. Gasimov, In Press. Digital intelligence: AI in industry and business. Scientific Route OÜ®. https://monograph.route.ee/rout/catalog/book/digital_intelligence_ai_in_industry_and_business/chapter/211
