利用超声造影和深度学习预测肝细胞癌微血管侵犯
Prediction of microvascular invasion in hepatocellular carcinoma using contrast-enhanced ultrasound and deep learning.
摘要(中文译文)
微血管侵犯（MVI）是肝细胞癌的关键预后因素，但目前仅在术后才能检出。在此，我们开发了MAPUSE，一种利用超声造影（CEUS）无创预测MVI的深度学习模型。我们在来自多个中心的1716例患者的5148段CEUS视频上对该模型进行训练和测试。结果显示，MAPUSE在不同肿瘤大小、造影剂及前瞻性验证中均能实现准确的MVI预测（AUC 0.835-0.978）。转录组分析将模型的预测与CD8+ T细胞免疫浸润相关联，并通过模型的注意力图得到证实。在一个临床队列中，被预测为MVI阳性的患者可从消融后免疫治疗中获益。因此，MAPUSE能够实现术前无创的MVI评估，并为肿瘤免疫微环境提供见解，为临床决策提供有价值的工具。
Abstract
Microvascular invasion (MVI) is a key prognostic factor in hepatocellular carcinoma but is currently only detectable after surgery. Here, we develop MAPUSE, a deep learning model using contrast-enhanced ultrasound (CEUS) to predict MVI non-invasively. We train and test the model on 5148 CEUS videos from 1716 patients across multiple centers. Results show that MAPUSE achieves accurate MVI prediction (AUCs 0.835-0.978) across different tumor sizes, contrast agents, and prospective validations. Transcriptomic analysis links the model's predictions to CD8 + T cell immune infiltration, confirmed via the model's attention maps. In a clinical cohort, patients predicted as MVI-positive can benefit from post-ablation immunotherapy. MAPUSE thus enables preoperative, non-invasive MVI assessment and provides insights into the tumor immune microenvironment, offering a valuable tool for clinical decision-making.
如何引用
AMA
Chuan Pang, Jinyu Ru, Yue Liu, Wenzhen Ding, Suwan Chai, Jundong Yao, et al. Prediction of microvascular invasion in hepatocellular carcinoma using contrast-enhanced ultrasound and deep learning.. Nature communications. 2026; doi:10.1038/s41467-026-74985-y.
APA
Chuan Pang, Jinyu Ru, Yue Liu, Wenzhen Ding, Suwan Chai, Jundong Yao, et al (2026). Prediction of microvascular invasion in hepatocellular carcinoma using contrast-enhanced ultrasound and deep learning.. Nature communications. https://doi.org/10.1038/s41467-026-74985-y
GB/T 7714
Chuan Pang, Jinyu Ru, Yue Liu, Wenzhen Ding, Suwan Chai, Jundong Yao, et al. Prediction of microvascular invasion in hepatocellular carcinoma using contrast-enhanced ultrasound and deep learning.[J]. Nature communications, 2026 doi:10.1038/s41467-026-74985-y.
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