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

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Abstract

Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality worldwide and is often diagnosed at advanced stages, reducing opportunities for curative treatment. Current screening tools, including ultrasonography with or without alpha-fetoprotein, lack sufficient sensitivity for early detection. Deep learning (DL) has emerged as a transformative approach, capable of detecting subtle, high-dimensional patterns in ultrasonography, computed tomography, magnetic resonance imaging, histopathological whole-slide images, and electronic health records. Convolutional neural networks, recurrent neural networks, and Transformer-based models have achieved strong performance in classification, segmentation, and risk prediction tasks, with sensitivities and specificities frequently above 89% and 90%. In some applications, DL has matched or even exceeded expert interpretation. However, the high computational cost and limited feasibility in real-time, resource-constrained settings remain major barriers to adoption. To overcome these challenges, recent studies emphasize efficiency-oriented strategies. Lightweight architectures such as MobileNet and EfficientNet, model compression through pruning and quantization, and data-efficient methods like self-supervised pretraining and targeted augmentation enable smaller and faster models without major loss of accuracy. Hybrid or pseudo-3D approaches that summarize volumetric information from sequential slices further reduce computational load, while multimodal fusion of imaging, clinical, and omics data extends applications beyond detection toward personalized prognostication and treatment guidance. These developments highlight that efficiency is essential for real-world deployment, not merely a technical refinement. Nonetheless, significant gaps remain. Most studies are retrospective, single-center, and limited in sample size, underscoring the need for rigorous external validation across multicenter cohorts and prospective trials assessing patient-relevant outcomes. Bias and fairness audits are critical to ensure equitable performance across demographic and etiological groups, while privacy-preserving strategies such as federated learning are required to harness diverse datasets securely. Seamless integration into hospital workflows, including Picture Archiving and Communication Systems and Electronic Medical Records using standards such as substitutable medical applications reusable technologies on fast healthcare interoperability resources, together with clear regulatory frameworks and post-market monitoring, will be essential for safe and scalable clinical translation. In conclusion, efficient and explainable DL offers a promising path to earlier detection and more personalized therapy in HCC. Achieving this potential will require not only technical innovation but also disciplined validation, thoughtful design for resource-limited contexts, and strong collaboration between clinicians, engineers, and regulators. This review synthesizes current advances, identifies persistent challenges, and provides guidance for developing efficient DL systems that are both clinically relevant and broadly deployable.

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