Hepatocellular carcinoma (HCC) is prevalent worldwide. Among the various therapeutic options, transarterial chemoembolization (TACE) can be applied to the tumor vascular network by restricting the nutrients and oxygen supply to the tumor. Unique morphologic properties of this network may provide information predictive of future therapeutic responses, which would be significant for decision making during treatment planning. The extraction of morphologic features from the tumor vascular network depicted in abdominal contrast-enhanced ultrasound (CEUS) images faces several challenges, such as organ motion, limited resolution caused by clutter signal and segmentation of the vascular structures at multiple scales. In this study, we present an image processing and analysis approach for the prediction of HCC response to TACE treatment using clinical CEUS images and known pathologic responses. This method focuses on addressing the challenges of CEUS by incorporating a two-stage motion correction strategy, clutter signal removal, vessel enhancement at multiple scales and machine learning for predictive modeling. The morphologic features, namely, number of vessels (NV), number of bifurcations (NB), vessel to tissue ratio (VR), mean vessel length, tortuosity and diameter, from tumor architecture were quantified from CEUS images of 36 HCC patients before TACE treatment. Our analysis revealed that NV, NB and VR are the dominant features for the prediction of long-term TACE response. The model had an accuracy of 86% with a sensitivity and specificity of 89% and 82%, respectively. Reliable prediction of the TACE therapy response using CEUS-derived image features may help to provide personalized therapy planning, which will ultimately improve patient outcomes.

肝细胞癌（HCC）在全世界都很流行。在各种治疗选择中，经动脉化疗栓塞（TACE）可以通过限制肿瘤的营养和氧气供应来应用于肿瘤血管网络。该网络独特的形态学特性可以提供预测未来治疗反应的信息，这对于治疗计划期间的决策具有重要意义。从腹部超声造影（CEUS）图像中描绘的肿瘤血管网络中提取形态特征面临着一些挑战，例如器官运动、杂波信号引起的有限分辨率以及多尺度血管结构的分割。在这项研究中，我们提出了一种图像处理和分析方法，使用临床 CEUS 图像和已知的病理反应来预测 HCC 对 TACE 治疗的反应。该方法侧重于通过结合两阶段运动校正策略、杂波信号去除、多尺度血管增强和用于预测建模的机器学习来解决 CEUS 的挑战。肿瘤结构的形态学特征，即血管数 (NV)、分叉数 (NB)、血管与组织比 (VR)、平均血管长度、迂曲度和直径，通过 TACE 前 36 名 HCC 患者的 CEUS 图像进行量化治疗。我们的分析表明，NV、NB 和 VR 是预测长期 TACE 反应的主要特征。该模型的准确度为 86%，敏感性和特异性分别为 89% 和 82%。使用 CEUS 衍生的图像特征对 TACE 治疗反应进行可靠预测可能有助于提供个性化治疗计划，最终改善患者的治疗效果。