This study aims to address the issue of a lack of individualized basis for selecting liver resection (LH) or microwave ablation (MWA) in early-stage hepatocellular carcinoma (HCC) patients to reduce the early recurrence rate (≤2 years). Given that existing machine learning-based recurrence prediction studies have failed to guide the optimal treatment plan selection, and that multidisciplinary consultations rely on guidelines (universality) and experience (subjectivity) which have their limitations, we propose to utilize artificial intelligence (AI), specifically the advantages of multimodal deep learning technology (which outperforms traditional machine learning by integrating complementary information to provide more accurate predictions), to establish a hybrid deep learning model that integrates contrast-enhanced ultrasound (CEUS) and enhanced magnetic resonance imaging (MRI) features. This model will predict the probability of early recurrence (ER≤2 years) in patients and, based on this, recommend LH or MWA as the optimal first treatment option for newly diagnosed early HCC patients to optimize individualized treatment decisions.
Age range
18 Years – 85 Years
Sex
ALL
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The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.
A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
Early recurrence status (binary: recurrence vs. non-recurrence within 2 years)
Timeframe: 2 years