ArticleNature communications2026
Prediction of microvascular invasion in hepatocellular carcinoma using contrast-enhanced ultrasound and deep learning.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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