整合多模态超声与临床数据术前预测肝细胞癌微血管侵犯：一项多中心研究
Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma by Integrating Multimodal Ultrasound and Clinical Data: A Multicenter Study
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中文总结 AI 辅助
本研究提出一种整合多模态超声与临床数据的多模态融合网络，用于术前预测肝细胞癌微血管侵犯，外部验证AUC达0.8953，DCE-US为主要预测信息源，支持术前风险分层。
中文摘要 AI 辅助
背景：微血管侵犯（MVI）可预测肝细胞癌（HCC）的复发与生存，但需术后组织病理学检查方可确诊。我们开发并验证了一种整合多模态超声与临床数据的模型，用于术前预测MVI。方法：这项多中心研究纳入了来自八个中心的489例HCC患者。所有患者均接受了B型超声（BUS）、彩色多普勒血流成像（CDFI）、动态对比增强超声（DCE-US）检查，并收集了临床信息。来自七个中心（n=421）的数据用于模型开发，并采用五折交叉验证；其余一个中心（n=68）的数据构成独立外部验证队列。所提出的多模态信息融合网络采用模态特定编码器、用于双向DCE-US灌注变化（即动态对比增强超声灌注变化）的血流动力学时间变化模块，以及用于在基于Transformer的融合之前对齐异质超声表征的表征一致性学习模块。结果：在外部验证中，DCE-US取得了最高的单模态受试者工作特征曲线下面积（AUC；0.8545±0.0198），而临床信息为0.6715±0.0156，CDFI为0.6435±0.0344，BUS为0.6087±0.0417。像素差分采样和所提出的时间模块优于替代采样和视频表征方法。完整模型取得了最佳性能，AUC为0.8953±0.0180，准确率为81.18%±2.83%，灵敏度为86.40%±6.69%，特异度为78.14%±6.28%。结论：整合多模态超声与临床信息可实现有前景的HCC术前MVI预测。DCE-US是预测信息的主要来源，而BUS、CDFI和临床信息提供了互补价值。所提出的框架可能支持术前风险分层和个体化临床决策。
英文摘要
Background: Microvascular invasion (MVI) predicts recurrence and survival in hepatocellular carcinoma (HCC) but requires postoperative histopathology for diagnosis. We developed and validated a model integrating multimodal ultrasound and clinical data for preoperative MVI prediction. Methods: This multicenter study included 489 patients with HCC from eight centers. All patients had B-mode ultrasound (BUS), color Doppler flow imaging (CDFI), dynamic contrast-enhanced ultrasound (DCE-US), and clinical information. Data from seven centers (n = 421) were used for model development with five-fold cross-validation; data from the remaining center (n = 68) formed an independent external validation cohort. The proposed multimodal information fusion network used modality-specific encoders, a hemodynamic temporal change module for bidirectional DCE-US perfusion changes, and a representation consistency learning module to align heterogeneous ultrasound representations before Transformer-based fusion. Results: In external validation, DCE-US achieved the highest single-modality area under the receiver operating characteristic curve (AUC; 0.8545+/-0.0198), versus clinical information (0.6715+/-0.0156), CDFI (0.6435+/-0.0344), and BUS (0.6087+/-0.0417). Pixel-difference sampling and the proposed temporal module outperformed alternative sampling and video representation methods. The full model achieved the best performance, with an AUC of 0.8953+/-0.0180, accuracy of 81.18%+/-2.83%, sensitivity of 86.40%+/-6.69%, and specificity of 78.14%+/-6.28. Conclusions: Integrating multimodal ultrasound and clinical information enabled promising preoperative MVI prediction in HCC. DCE-US was the main source of predictive information, while BUS, CDFI, and clinical information provided complementary value. The proposed framework may support preoperative risk stratification and individualized clinical decision-making.
发表机构
- Shenzhen University（深圳大学）
- Peking University Shenzhen Hospital（北京大学深圳医院）
- Chinese PLA General Hospital（中国人民解放军总医院）
机构由 AI 辅助整理，请以论文原文为准。
