Hybrid Deep Learning Integrating Multimodal CEUS and Enhanced MRI to Optimize Early-Stage HCC Treatment Decisions

Hepatocellular Carcinoma (HCC)

Multimodal, CEUS, Enhanced MRI, Treatment Decisions, hepatocellular carcinoma

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.

Core ObjectiveTo develop a hybrid deep learning model based on contrast-enhanced ultrasound (CEUS) clinical data for predicting the early recurrence (ER) probability within 2 years after liver resection (LH) or microwave ablation (MWA) in patients with early-stage liver cancer (CNLC I/IIa), providing a basis for individualized treatment decisions.Key MethodsStudy DesignType: Multicenter retrospective cohort study (2009.10-2026.12, 30 hospitals)Population:Inclusion: First diagnosed HCC (single lesion ≤5cm or 2-3 lesions with the largest ≤3cm), Child-Pugh A/B grade, CEUS/MRI examination within one month before surgery.Exclusion: Extrahepatic metastasis/other malignancies, previous treatment history, missing imaging/follow-up data.Sample size: 1441 cases (LH group 609 cases, MWA group 831 cases).Data CollectionClinical Data: Age, hepatitis type, cirrhosis, tumor characteristics (size/location/number), laboratory indicators (AFP, etc.).Imaging Data:CEUS: Dynamic acquisition of arterial phase (AP), portal venous phase (PVP), and delayed phase (LP) videos after SonoVue injection.Enhanced MRI: Acquisition of AP, PVP, transitional phase (TP), and hepatobiliary-specific phase (HBP) images after Gd-EOB-DTPA enhancement.Follow-Up: Imaging re-examination (CEUS/CT/MRI) every 3-6 months within 2 years after surgery, with endpoints being recurrence/death/last follow-up (≥24 months).Model ConstructionInput Data:Imaging ROI: Manual drawing of the tumor and a 5mm peritumoral area, extracting 64×64 pixel frames (CEUS videos downsampled to 32 frames).Fusion Methods: Clinical CEUS, clinical MRI, and clinical CEUS MRI.Deep Learning Architecture:Feature Extraction: 2D-CNN (convolutional layers → spatial features) → Bi-LSTM (fusion of spatiotemporal features) → Attention module.Data Augmentation: Translation and flipping to reduce overfitting.Training/Validation:5-fold cross-validation, dividing the training/validation set in a 4:1 ratio (training LH and MWA groups separately).Optimal Model Selection: The one with the highest AUC.Decision ApplicationHybrid model: To predict the ER probabilities for LH or MWA treatment for the same patient separately.Treatment Recommendations:If the predicted ER probability: MWA \> LH → recommend LH;If the predicted ER probability: LH ≥ MWA → recommend MWA.

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