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Lookup NU author(s): Sam SeeryORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© The Author(s) 2025.Hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) is a major contributor to mortality. We developed a recurrence prediction system for HCC patients before and after LT. Data from patients with HCC who underwent LT were retrospectively collected from three specialist centres in China. Pre- and post-operative variables were selected using support vector machine, random forest, and logistic regression (LR). Then, pre- and post-operative models were developed using three machine learning methods: LR, stacking, and two survival-based approaches. Models were evaluated using seven assessment indices, and patients were classified as either high- or low-risk based on recurrence risk. 466 patients were included and followed for a median of 51.0 months (95% CI 47.8–54.2). The pre-DeepSurv model (pre-DSM) had a C-index of 0.790 ± 0.003 during training, 0.775 ± 0.037 during testing, and 0.765 ± 0.001 and 0.819 ± 0.002 during external validation. After incorporating clinicopathologic variables, the post-DeepSurv model (post-DSM) had a 0.835 ± 0.008 C-index during training, 0.812 ± 0.082 during testing, and 0.839 ± 0.001 and 0.831 ± 0.002 during external validation. The post-DSM outperformed the Milan criteria by more accurately identifying patients at high risk of recurrence. Tumour recurrence predictions also improved significantly with DeepSurv. Both pre- and post-DSMs have the potential to guide personalised surveillance strategies for LT patients with HCC.
Author(s): Cao S, Yu S, Huang L, Seery S, Xia Y, Zhao Y, Si Z, Zhang X, Zhu J, Lang R, Kou J, Zhang H, Wei L, Zhou G, Sun L, Wang L, Li T, He Q, Zhu Z
Publication type: Article
Publication status: Published
Journal: Scientific Reports
Year: 2025
Volume: 15
Issue: 1
Online publication date: 05/03/2025
Acceptance date: 24/02/2025
Date deposited: 24/03/2025
ISSN (electronic): 2045-2322
Publisher: Nature Publishing Group
URL: https://doi.org/10.1038/s41598-025-91728-z
DOI: 10.1038/s41598-025-91728-z
Data Access Statement: The datasets generated and/or analysed during the current study are not publicly available due to other studies using this data are still being written and published but are available from the corresponding author on reasonable request.
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