Advances in artificial intelligence for imaging-based diagnosis of hepatocellular carcinoma

Table 1 Comparison of major imaging modalities for hepatocellular carcinoma

Modality	Advantages	Limitations
CT	(1) Rapid, whole-liver coverage; (2) High spatial resolution for vascular and calcified structures; and (3) Widely available with standardized interpretation	(1) Ionizing radiation and contrast-related risks; (2) Limited soft-tissue contrast; (3) Lower sensitivity for sub-centimeter lesions; and (4) Susceptible to respiratory artifacts
MRI	(1) Excellent soft-tissue contrast and multi-parametric capability; (2) Specific contrast agents improve HCC detection accuracy; (3) Functional imaging (e.g., DWI, elastography); and (4) No ionizing radiation	(1) Long acquisition time and high cost; (2) Contraindicated for patients with metallic implants; (3) Limited availability; and (4) Less effective in showing calcifications
US	(1) Real-time and dynamic imaging of hepatic blood flow; (2) Useful for biopsy guidance and follow-up; (3) Contrast-enhanced US improves detection of small lesions; and (4) Low cost and radiation-free	(1) Highly operator-dependent; (2) Reduced accuracy in obese patients or with bowel gas; (3) Low sensitivity for sub-centimeter HCC; and (4) Limited for definitive qualitative diagnosis

CT: Computed tomography; MRI: Magnetic resonance imaging; US: Ultrasound; HCC: Hepatocellular carcinoma; DWI: Diffusion-weighted imaging.

Table 2 Comparison of artificial intelligence model architectures for hepatocellular carcinoma imaging analysis

Model architecture type	Core principles/technical features	Core advantages	Main limitations	Clinical application scenarios (HCC imaging analysis)
Traditional CNNs	Extract features through local receptive fields to achieve translation invariance; available in 2D/3D forms, relying on stacked sequential convolutional layers	(1) 2D CNNs offer high computational efficiency and adapt to conventional 2D images; (2) 3D CNNs can capture partial spatial structures; and (3) Mature technology with strong generalization	(1) 2D CNNs fail to fully utilize 3D spatial information; (2) 3D CNNs have high computational cost and large GPU memory consumption; and (3) Difficulty in modeling complex spatiotemporal dependencies and global semantic associations	Liver lesion recognition, tumor classification/localization, semi-automatic segmentation of CT/MRI images
Transformer models	Capture global semantic associations based on self-attention mechanism, emphasizing “global contextual awareness”, often used in fusion with CNNs	(1) Break through the limitation of local features to accurately capture global associations; (2) Clearly distinguish tumors from surrounding tissues under weak contrast; and (3) Complement CNNs to balance global context and detailed features	(1) Prone to losing fine structural details when used alone; and (2) High demand for annotated data volume	Detection of small/low-contrast HCC lesions, accurate delineation of tumor boundaries, global imaging feature association analysis
2D/3D hybrid networks (e.g., H-DenseUNet)	Integrate 2D CNNs (extract intra-slice features) and 3D CNNs (aggregate volumetric context), optimizing performance through hybrid feature fusion layers	(1) Balance spatial information utilization and computational efficiency; (2) Enhance the ability to model cross-slice structural consistency and temporal consistency; and (3) Superior segmentation performance	Relatively complex technical architecture, requiring targeted optimization of feature fusion logic	Accurate segmentation of liver tumors, cross-slice feature association analysis of multi-phase images
Multi-phase spatiotemporal modeling	Align arterial phase/portal venous phase/delayed phase images and extract temporal change features of dynamic lesion enhancement	(1) Fully utilize the diagnostic value of multi-phase images; (2) Facilitate tumor staging and characterization; and (3) Distinguish early HCC from benign nodules	Rely on complete multi-phase imaging data with high requirements for data quality	Recognition of dynamic lesion evolution, differentiation between early HCC and benign nodules, treatment plan optimization
Few-shot learning	Achieve efficient learning from extremely small datasets based on data augmentation, pre-trained models, and cross-modal feature alignment	(1) Adapt to scenarios with limited annotated data; (2) Improve model robustness under low-resource conditions; and (3) Suitable for analyzing rare HCC subtypes	Performance still needs improvement in complex scenarios (e.g., multi-center heterogeneous data)	Diagnosis of rare HCC subtypes, lesion detection/classification in centers with limited imaging resources

HCC: Hepatocellular carcinoma; CNNs: Convolutional neural networks; 2D: Two-dimensional; 3D: Three-dimensional; CT: Computed tomography; MRI: Magnetic resonance imaging.