ORIGINAL RESEARCH article
Front. Bioinform.
Sec. Network Bioinformatics
Volume 5 - 2025 | doi: 10.3389/fbinf.2025.1574797
This article is part of the Research TopicThe Future of Oncology: Digital Twins and Precision Cancer CareView all 4 articles
Bridging the gap between HCC management guidelines and personalised medicine: a Bayesian network study
Provisionally accepted
- 1University of Oxford, Oxford, United Kingdom
- 2Perspectum Diagnostics, Oxford, England, United Kingdom
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Introduction: There are numerous treatment options available for patients with confirmed hepatocellular carcinoma (HCC). Guidelines such as Barcelona Clinic Liver Cancer (BCLC) support treatment decisions by way of a flow diagram that is organized around groups of patients. Though such guidelines continue to make a major contribution to standardization of treatment, in clinical reality, cases are often more nuanced than is captured in any flow diagram, even one as comprehensive as BCLC. A fundamental challenge for a clinician is to combine such a population-wide guideline with specific information about the individual patient. Bayesian networks (BNs) offer a way to "bridge this gap" and combine standardized care and precision medicine. They do this by enabling answers to detailed "what-if" questions from the clinician. Methods: We use real-world data of HCC patients who received treatments between 2019 and 2020 to construct a BN to assess the potential treatment effect for cases that were not treated in compliance with BCLC. Results: We report detailed scenarios for ten randomly selected cases and summarise the difference in survival time for each scenario. For each case, the counterfactual treatment scenarios are made based on whether or not the case is in compliance with BCLC guidelines, the type of treatment received and the waiting time to receive treatment. Discussion: We consider two cases with similar clinical characteristics (but received different treatments) and discuss whether or not they are treated in compliance to the guidelines resulting in better outcomes than the actual clinical decision. We include a detailed discussion about the assumptions made in constructing the BN and we highlight why such a BN can serve as an AI-based clinical decision support system particularly when there is need for further patient stratification.
Keywords: Bayesian network, BCLC, causal inference, Counterfactual reasoning, HCC, individualised treatment effect, liver cancer
Received: 11 Feb 2025; Accepted: 05 May 2025.
Copyright: © 2025 Wang, Bulte and Brady. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yi-Chun Wang, University of Oxford, Oxford, United Kingdom
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