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

Table 1 Summary of quality assessment based on Scale for the Assessment of Narrative Review Articles criteria

SANRA criterion	Summary of findings
Justification of article importance	High: Most studies clearly stated the clinical problem of HCC detection and the potential of DL
Statement of concrete aims	Medium/high: Modeling aims (e.g., classification accuracy) were usually clear; efficiency aims were sometimes less explicitly stated
Description of literature search	Low: A significant weakness across almost all primary studies; search strategies were rarely reported
Scientific accuracy and rigor	Medium: Methods were mostly sound technically, but clinical validation rigor (prospective/multicenter) was often low
Discussion of limitations	Medium: Common limitations like small sample size were often acknowledged; discussion of bias or generalizability was less frequent
Quality of illustrations/reporting	High: Most studies included high-quality figures of models, results, and attention maps. Efficiency metrics (e.g., model size, inference time) were not consistently reported

SANRA: Scale for the Assessment of Narrative Review Articles; HCC: Hepatocellular carcinoma; DL: Deep learning.

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.

Table 3 Taxonomy of deep learning architectures for hepatocellular carcinoma tasks: Mapping to data types and efficiency profiles

Primary task	Data modality	Suitable DL architectures	Efficiency profile & trade-offs
Lesion detection & diagnosis	Static US/CT/MRI slice	Lightweight CNNs (MobileNet, EfficientNet), standard CNNs	Pareto-efficient: Lightweight CNNs are optimized for high speed and low cost with minimal accuracy loss. Standard CNNs offer a balance
	Volumetric CT/MRI	3D CNNs, 2.5D CNNs (processing slices sequentially)	Trades speed for accuracy: 3D CNNs are accurate but computationally heavy. 2.5D/pseudo-3D approaches are a more efficient compromise
	Multi-phase CT/MRI	Multi-input/weight-sharing CNNs, transformer-CNN hybrids	Pareto-efficient: Multi-input CNNs efficiently fuse phase data. Hybrids use transformers to capture long-range dependencies between phases without a full transformer's cost
Segmentation	CT/MRI volumes	U-Net variants, transformer-based (e.g., UNETR)	Trades speed for accuracy: U-Nets are relatively efficient. Pure transformers (e.g., UNETR) offer superior accuracy for complex shapes but are computationally intensive
Longitudinal risk prediction	Tabular EHR time-series	RNNs (LSTM/GRU), transformers, lightweight ML	Context-dependent: RNNs/transformers model time well but can be heavy. For simpler tasks, logistic regression/gradient boosting (non-DL) are often more efficient and perform similarly
Histopathology classification	WSI	MIL + CNN, ViT	Pareto-efficient: MIL frameworks are inherently efficient, processing bags of image patches. New efficient ViT variants are emerging for WSI analysis
Multimodal fusion	Fused imaging + clinical	Hybrid architectures (e.g., CNN branch for images, DNN for tabular data)	Pareto-efficient: Hybrid designs effectively integrate data types. The efficiency is determined by the choice of the image backbone (e.g., using a lightweight CNN)
High-precision detection	CT/MRI (small lesions)	Dense detectors, ViT	Trades speed for accuracy: Models like ViT and complex detectors excel at finding small lesions due to global attention but have high computational demands

US: Ultrasonography; MRI: Magnetic resonance imaging; CT: Computed tomography; CNN: Convolutional neural network; LSTM: Long short-term memory; ML: Machine learning; WSI: Whole slide image; ViT: Vision transformers; MIL: Multiple instance learning; UNETR: UNEt transformers; EHR: Electronic health record; GRU: Gated recurrent units; DNN: Deep neural network; DL: Deep learning; RNNs: Recurrent neural networks; CNNs: Convolutional neural networks.