Prediction of microvascular invasion in hepatocellular carcinoma using contrast-enhanced ultrasound and deep learning.
prospective_cohort · Level II
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- Record sourced from PubMed, PMID 42431906.
- Also identified by DOI 10.1038/s41467-026-74985-y.
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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.