最近的研究已确定微血管侵犯（MVI）是与早期肿瘤复发相关的最重要的独立生物标志物。随着医疗技术的进步，已经开发出多种计算方法来使用不同的医学图像来预测术前 MVI。这些现有方法依赖于人类经验、属性选择或临床试验测试，这通常是耗时且费力的。利用深度学习的优势，本研究提出了一种新颖的端到端算法，用于在手术前预测 MVI。我们设计了一系列数据预处理策略，从数据中充分提取多视图特征，同时保留瘤周信息。值得注意的是，引入了一种基于ResNet（DFFResNet）的新的多分支深度融合特征算法，该算法结合了来自不同序列的磁共振图像（MRI）以增强信息互补和集成。我们对兰州大学第一医院放射科的数据集进行了预测实验，该数据集包含 117 个人和 7 个 MRI 序列。该模型使用 10 倍交叉验证对 80% 的数据进行训练，其余 20% 用于测试。该评估在两种情况下进行：CROI，包含具有完整感兴趣区域 (ROI) 的样本；PROI，包含具有部分 ROI 区域的样本。在图像和患者水平上重复实验的稳健性结果表明，与替代模型相比，所提出的方法具有优越的性能和改进的泛化能力。即使 ROI 区域轮廓不完整，我们的方法也能产生极具竞争力的预测结果，为预测术前 MVI 提供新颖且有效的多序列融合策略。

Recent studies have identified microvascular invasion (MVI) as the most vital independent biomarker associated with early tumor recurrence. With advancements in medical technology, several computational methods have been developed to predict preoperative MVI using diverse medical images. These existing methods rely on human experience, attribute selection or clinical trial testing, which is often time-consuming and labor-intensive. Leveraging the advantages of deep learning, this study presents a novel end-to-end algorithm for predicting MVI prior to surgery. We devised a series of data preprocessing strategies to fully extract multi-view features from the data while preserving peritumoral information. Notably, a new multi-branch deep fused feature algorithm based on ResNet (DFFResNet) is introduced, which combines Magnetic Resonance Images (MRI) from different sequences to enhance information complementarity and integration. We conducted prediction experiments on a dataset from the Radiology Department of the First Hospital of Lanzhou University, comprising 117 individuals and seven MRI sequences. The model was trained on 80% of the data using 10-fold cross-validation, and the remaining 20% were used for testing. This evaluation was processed in two cases: CROI, containing samples with a complete region of interest (ROI), and PROI, containing samples with a partial ROI region. The robustness results from repeated experiments at both image and patient levels demonstrate the superior performance and improved generalization of the proposed method compared to alternative models. Our approach yields highly competitive prediction results even when the ROI region outline is incomplete, offering a novel and effective multi-sequence fused strategy for predicting preoperative MVI.