Scholarlink

<p>To address the clinical challenge of preoperative microvascular invasion (MVI) grading in hepatocellular carcinoma (HCC), this paper proposes a cross-channel attention fine-grained Network (CRAF-Net) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Current methods often suffer from fragmented representations and suboptimal channel weighting, obscuring key features and reducing accuracy. CRAF-Net addresses these challenges through three innovations: (1) a Dense-NeXt encoder that enhances 3D hierarchical feature reuse; (2) a cross-channel attention (CCA) Module that adaptively weights multi-phase MRI sequences to emphasize tumor microenvironment (TME) biomarkers; and (3) a dual-branch disentangled network with a multi-classifier fusion Module that separates morphological MVI risk assessment from severity grading, alleviating inter-class ambiguities. Evaluated on a primary cohort of 472 HCC cases, CRAF-Net achieves an 84.8% three-category accuracy and a macro-average AUC of 0.950. Crucially, on an independent external cohort (<i>n</i> = 118), it demonstrates robust cross-center generalizability with 81.4% accuracy and a 0.923 AUC. Decision Curve analyses validate its optimal net benefit for clinical decision-making. Operating efficiently (0.6&#xa0;s/case), the model satisfies real-time clinical requirements. Finally, survival analysis demonstrates strong concordance between predicted grades and pathological outcomes (5-year overall survival: M0 = 81.4%, M1 = 69.5%, M2 = 43.5%). CRAF-Net serves as a robust auxiliary framework for personalized HCC management.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

CRAF-Net: A Fine-Grained Cross-Channel Attention Network for Preoperative Microvascular Invasion Grading in Hepatocellular Carcinoma via DCE-MRI

  • 期刊论文

  • 发表日期：

    2026年6月29日

查看全文
  • Zebang Zhong,
  • Xiao Luo,
  • Daoying Geng,
  • Zhiji Zheng,
  • Kun Zhou,
  • Bin Hu,
  • Xiao Liu,
  • Yan Geng

摘要

To address the clinical challenge of preoperative microvascular invasion (MVI) grading in hepatocellular carcinoma (HCC), this paper proposes a cross-channel attention fine-grained Network (CRAF-Net) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Current methods often suffer from fragmented representations and suboptimal channel weighting, obscuring key features and reducing accuracy. CRAF-Net addresses these challenges through three innovations: (1) a Dense-NeXt encoder that enhances 3D hierarchical feature reuse; (2) a cross-channel attention (CCA) Module that adaptively weights multi-phase MRI sequences to emphasize tumor microenvironment (TME) biomarkers; and (3) a dual-branch disentangled network with a multi-classifier fusion Module that separates morphological MVI risk assessment from severity grading, alleviating inter-class ambiguities. Evaluated on a primary cohort of 472 HCC cases, CRAF-Net achieves an 84.8% three-category accuracy and a macro-average AUC of 0.950. Crucially, on an independent external cohort (n = 118), it demonstrates robust cross-center generalizability with 81.4% accuracy and a 0.923 AUC. Decision Curve analyses validate its optimal net benefit for clinical decision-making. Operating efficiently (0.6 s/case), the model satisfies real-time clinical requirements. Finally, survival analysis demonstrates strong concordance between predicted grades and pathological outcomes (5-year overall survival: M0 = 81.4%, M1 = 69.5%, M2 = 43.5%). CRAF-Net serves as a robust auxiliary framework for personalized HCC management.