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March 31, 2026SHILAP Revista de lepidopterologíaOpen Access

Multiphasic CT-based multimodal deep learning model for predicting early hepatocellular carcinoma recurrence following liver transplantation

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Authors

ZWZheng WangShihezi UniversityXLXi-Ran LiTianjin First Center HospitalZHZhong-Yi HuangHebei University

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Implication

Retrospective analysis develops a predictive model for early HCC recurrence, suggesting improved diagnostic accuracy.

Key Points

  • The study aims to create a multimodal deep learning model to predict early recurrence of hepatocellular carcinoma following liver transplantation.
  • Conducted a retrospective analysis on 147 patients with HCC after liver transplantation.
  • Categorized patients into recurrence and non-recurrence groups.
  • Developed deep learning models using single-phase and multiphasic CT images combined with clinical parameters.
  • Evaluated model performance using receiver operating characteristic curves and SHAP analysis.
  • Identified independent risk factors for recurrence: platelet count, alpha-fetoprotein levels, ascites, arterial enhancement, and portal vein tumor thrombus.
  • The MD DL model achieved area under the curve values of 0.972, 0.885, and 0.985 in training, validation, and test sets, respectively, exceeding conventional model performance.

Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/69cb63c9e6a8c024954b8733https://doi.org/10.3389/fonc.2026.1767885
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