Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation

Table 2 Comprehensive summary of deep learning architectures for hepatocellular carcinoma studies

Ref.	Year	DL architecture	Application area	Modality/efficiency techniques	Key findings	Challenges addressed
Aatresh et al[96]	2021	LiverNet (CNN-like model)	Histopathology	H&E stained liver histopathology images	Accuracy of 90.93% on the proposed KMC liver dataset for distinguishing HCC subtypes	Diagnosing low sub-type liver HCC tumors correctly
Hamm et al[71]	2019	CNN	Lesion classification	Multi-phasic MRI/CNN	AUC 0.992 for lesion classification	Ungrouped lesions make classification harder in clinical use
Ioannou et al[75]	2020	DL RNN	Risk prediction	EHR time-series/sequential data modeling	AUC 0.76 for 3-year HCC risk, RNN models performed better in virologic responders (AUC 0.806)	External validation, computational cost, etc.
Ma et al[77]	2024	Transformer dense CenterNet (TDCenterNet)	Liver tumor detection	CT images/attention mechanism	Improved capturing lesions with different sizes	Feature deficiency, ignoring lesions with varies sizes, etc.
Deshpande et al[83]	2024	Hybrid DL (like CNN, ResNet50, EfficientNetb3, etc.)	Grade classification	Histopathology/slide images	> 97% accuracy for HCC grade classification	Interpretable histopathological images, high parameter count of the models
Qu et al[95]	2025	DL model	Subtype identification	Dynamic contrast-enhanced MRI	Identified proliferative HCC subtype (AUC 0.94 for external test set)	Limited labeled data utilization
Li et al[97]	2025	HTRecNet (hybrid)	Differential diagnosis	Histopathological data (including images)	Early and accurate differentiation of HCC and CCA from normal liver tissues (accuracy of 0.97 in external testing)	Computational resource requirements, integrating AI models into clinical practice, etc.
Wang et al[79]	2025	Gabor attention + transformer	Segmentation	CT/hierarchical monitoring	Enhanced tumor segmentation accuracy	Multimodal data fusion
He et al[98]	2025	AI/ML	Molecular subtype discovery	Multi-omics integration/dimensionality reduction	Novel molecular subtype identification	Heterogeneity of HCC, dimensionality, integration of data
He et al[106]	2024	DL (like UNet, swim transformer)	Microvascular invasion and HCC prediction	CT/clinical parameters/attention modules	Predicted microvascular invasion/HCC (AUC > 0.94)	Clinical outcome stratification
Lei et al[121]	2024	Multimodal DL	Microvascular invasion prediction	CT/MRI/feature fusion	Improved microvascular invasion prediction (AUC 0.844-0.871)	Data heterogeneity
Paproki et al[111]	2024	GANs	Data augmentation	Synthetic data/data augmentation	Reduced bias in training datasets	Data diversity and fairness
Xia et al[76]	2024	CRNN	Survival prediction	Multi modal (CT scans + clinical variables)	Improved survival prediction (AUC of 0.777 and 0.704 in the validation and test sets)	Temporal-spatial analysis
Mitrea et al[92]	2023	CNN + traditional ML	HCC detection	Ultrasound/data augmentation	> 98% accuracy for HCC detection	Noise reduction in low-quality images
Yasaka et al[72]	2018	CNN	Lesion classification	Dynamic CT/standard CNN	High accuracy distinguishing liver masses on CT (median AUC 0.92)	Generalizability
Cao et al[73]	2022	CNN	Lesion differentiation	Non-contrast CT	High accuracy distinguishing HCC vs hemangioma on non-contrast CT	Handling low-contrast images
Wang et al[74]	2020	SCCNN	Differential diagnosis	CT/multi-branch fusion	Improved HCC vs cholangiocarcinoma classification (AUC > 0.95)	Multi-class lesion discrimination

HCC: Hepatocellular carcinoma; DL: Deep learning; H&E: Hematoxylin and eosin; MRI: Magnetic resonance imaging; CT: Computed tomography; AUC: Area under curve; CNN: Convolutional neural network; RNN: Recurrent neural network; CRNN: Convolutional recurrent neural network hybrid; ML: Machine learning; SCCNN: Siamese cross convolutional neural network; GANs: Generative adversarial neural networks; EHR: Electronic health record; AI: Artificial intelligence.