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Hepatocellular carcinoma (HCC) is marked by high morbidity and is often diagnosed in middle or late stages. Transarterial chemoembolization (TACE) stands as the current standard of care for intermediate-stage HCC patients. Nevertheless, the tumor’s heterogeneity significantly impacts patient prognosis. In this paper, a new dynamic multi-model graph network fusion multi-sequence magnetic resonance imaging is proposed to predict the prognosis of HCC patients after TACE treatment. The model proposes a spatial graph convolution module focusing on active regions within the tumor, a multi-module dynamic fusion module capturing the potential relationship between the tumor and the liver, and a cross-model topology fusion module using topological information to guide the multi-sequence MRI fusion. Our method achieved the best results compared to the state-of-the-art method, with an ACC of 75.27%, AUC of 76.69%, F1 of 73.84%, C-index of 0.6978, HR of 3.1988.

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Multi-Modal Learning for Predicting the Progression of Transarterial Chemoembolization Therapy in Hepatocellular Carcinoma

  • 会议论文

  • 发表日期：

    2025年

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  • Lingzhi Tang,
  • Haibo Shao,
  • Jinzhu Yang,
  • Jiachen Xu,
  • Jiao Li,
  • Yong Feng,
  • Jiayuan Liu,
  • Song Sun,
  • Qisen Wang

摘要

Hepatocellular carcinoma (HCC) is marked by high morbidity and is often diagnosed in middle or late stages. Transarterial chemoembolization (TACE) stands as the current standard of care for intermediate-stage HCC patients. Nevertheless, the tumor’s heterogeneity significantly impacts patient prognosis. In this paper, a new dynamic multi-model graph network fusion multi-sequence magnetic resonance imaging is proposed to predict the prognosis of HCC patients after TACE treatment. The model proposes a spatial graph convolution module focusing on active regions within the tumor, a multi-module dynamic fusion module capturing the potential relationship between the tumor and the liver, and a cross-model topology fusion module using topological information to guide the multi-sequence MRI fusion. Our method achieved the best results compared to the state-of-the-art method, with an ACC of 75.27%, AUC of 76.69%, F1 of 73.84%, C-index of 0.6978, HR of 3.1988.