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A Transformer-Based Deep Learning Model for predicting Early Recurrence in Hepatocellular Carcinoma After Hepatectomy

Hongxiang Li1, Zehong Qiu2, Jing Zhang1

  • 1Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China.

Journal of Hepatocellular Carcinoma
|March 26, 2026
PubMed
Summary

A new deep learning model using intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) shows promise for predicting early recurrence in hepatocellular carcinoma (HCC). The combined model demonstrated superior accuracy, potentially guiding personalized postoperative monitoring for HCC patients.

Keywords:
deep learningearly recurrencehepatocellular carcinomavision transformer

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Hepatocellular carcinoma (HCC) recurrence after treatment poses a significant clinical challenge.
  • Accurate prediction of early HCC recurrence is crucial for effective patient management and treatment planning.

Purpose of the Study:

  • To develop and validate a deep learning (DL) network utilizing intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for predicting early recurrence in HCC.
  • To assess the performance of a transformer framework-based DL model compared to traditional clinical models.

Main Methods:

  • A retrospective study included 122 HCC patients who underwent IVIM-DWI MRI before resection.
  • A vision transformer (ViT) framework-based DL model (ViT-fDL) was developed, extracting deep features from IVIM-DWI images and parametric maps.
  • A combined model integrated ViT-fDL features with clinical data for enhanced prediction.

Main Results:

  • The combined model achieved the highest predictive performance with an area under the curve (AUC) of 0.991 (training) and 0.821 (test).
  • The ViT-fDL model also outperformed the clinical model, demonstrating AUCs of 0.968 (training) and 0.815 (test).
  • The clinical model showed AUCs of 0.755 (training) and 0.764 (test).

Conclusions:

  • The IVIM-based ViT-fDL model is effective for preoperative prediction of early HCC recurrence.
  • The combined model offers a more precise and effective prediction tool, potentially guiding individualized postoperative monitoring strategies for HCC patients.