南方医科大学学报

南方医科大学学报 ›› 2024, Vol. 44 ›› Issue (2): 260-269.doi: 10.12122/j.issn.1673-4254.2024.02.08

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基于多期动态增强CT影像组学特征和多分类器分层融合模型预测肝细胞癌的微血管侵犯

钟伟雄，梁芳蓉，杨蕊梦，甄 鑫   

  1. 南方医科大学生物医学工程学院，广东 广州 510515；华南理工大学附属第二医院（广州市第一人民医院）放射科，广东 广州 510180；华南理工大学医学院，广东 广州 510006
  • 发布日期:2024-03-14

Prediction of microvascular invasion in hepatocellular carcinoma based on multi-phase dynamic enhanced CT radiomics feature and multi-classifier hierarchical fusion model

ZHONG Weixiong, LIANG Fangrong, YANG Ruimeng, ZHEN Xin   

  1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Department of Radiology, Second Affiliated Hospital of South China University of Technology (Guangzhou First People's Hospital), Guangzhou 510180, China; School of Medicine, South China University of Technology, Guangzhou 510006, China
  • Published:2024-03-14

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摘要： 目的 探讨预测肝细胞癌（HCC）患者是否发生微血管侵犯（MVI）而提出了一种基于多期动态增强CT（DCE-CT）影像组学特征和多分类器分层融合的预测模型。方法 回顾性收集2016年1月~2020年4月广州市第一人民医院111例经病理证实的HCC患者的术前DCE-CT图像。分别在早期动脉期（EAP）、晚期动脉期（LAP）、门静脉期（PVP）和平衡期（EP）进行了感兴趣容积（VOI）的勾画，并从中提取出这4个期相的影像组学特征。利用经过筛选后的特征子集分别训练7种基于不同算法的分类器，得到不同期相下的多个基分类器。然后采用一种新型的基于多准则决策的权重分配算法，按照分层融合的策略依次对同一期相下多个基分类器以及提取了不同期相信息后的模型进行融合，最终得到基于多期DCE-CT影像组学特征和多分类器分层融合预测模型。采用五折交叉验证的方法和ROC曲线下面积（AUC）、准确率（ACC）、灵敏度（SEN）和特异度（SPE）4种评价指标来定量评价所提出的预测模型的性能。提出的模型与使用单一期相或多个不同期相的融合模型、基于单期相单分类器的模型、不同基分类器多样性的模型以及八种基于其他集成方法的分类器模型进行定量比较。结果 提出的模型预测HCCMVI的性能在融合4个期相及7种分类器后达到最优，AUC、ACC、SEN和SPE分别为：0.828、0.766、0.877、0.648。对比实验显示，所提出的模型性能优于基于单期相单分类器的模型以及其他集成模型。结论 基于多期DCE-CT影像组学特征和多分类器分层融合模型能够很好地预测HCC的MVI情况，相比于其他模型具有较大的性能优势。

关键词: 肝细胞癌；微血管侵犯；动态增强计算机断层扫描；多分类器；多准则决策

Abstract: Objective To predict microvascular invasion (MVI) in hepatocellular carcinoma (HCC) using a model based on multi-phase dynamic-enhanced CT (DCE-CT) radiomics feature and hierarchical fusion of multiple classifiers. Methods We retrospectively collected preoperative DCE- CT images from 111 patients with pathologically confirmed HCC in Guangzhou First People's Hospital between January, 2016 and April, 2020. The volume of interest was outlined in the early arterial phase, late arterial phase, portal venous phase and equilibrium phase, and radiomics features of these 4 phases were extracted. Seven classifiers based on different algorithms were trained using the filtered feature subsets to obtain multiple base classifiers under each phase. According to the hierarchical fusion strategy, a multi-criteria decision-making-based weight assignment algorithm was used for fusing each base classifier under the same phase with the model after extracting the phase information to obtain the prediction model. The proposed model was evaluated using a 5-fold cross-validation and assessed for area under the ROC curve (AUC), accuracy, sensitivity, and specificity. The prediction model was also compared with the fusion models using a single phase or multiple phases, models based on a single phase with a single classifier, models with different base classifier diversities, and 8 classifier models based on other ensemble methods. Results The experimental results showed that the performance of the proposed model for predicting HCCMVI was optimal after incorporating the 4 phases and 7 classifiers, with AUC, accuracy, sensitivity, and specificity of 0.828, 0.766, 0.877, and 0.648, respectively. Comparative experiments showed that this prediction model outperformed the models based on a single phase with a single classifier and other ensemble models. Conclusion The proposed prediction model is effective for predicting MVI in HCC with superior performance to other models.

Key words: hepatocellular carcinoma; microvascular invasion; dynamic enhanced computed tomography; multi classifier; multi-criteria decision making