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Advances in Liver Tumour Diagnosis and Treatment: Etiology, Classification, and the Emerging Role of Machine Learning

Year: 2025

Type: article

Abstract: Liver Tumours represent a significant global health challenge due to their diverse etiology, complex classification, and high mortality rates. This literature offers a comprehensive review of advancements in the diagnosis and treatment of liver tumours, encompassing their causes, classifications, symptoms, staging, and current treatment modalities. It begins with an exploration of the etiology and risk factors contributing to liver tumours, including genetic, environmental, and lifestyle influences, and a detailed classification into benign and malignant tumours. Staging systems, particularly the Barcelona Clinic Liver Cancer (BCLC) system, are discussed to elucidate diagnostic pathways and therapeutic implications. The paper delves into various treatment options, ranging from surgical approaches and locoregional therapies to systemic treatments such as targeted therapies and immunotherapies. Emerging treatment strategies, including the combination of Stereotactic Body Radiotherapy (SBRT) and immunotherapy, are highlighted for their potential to enhance survival outcomes. Additionally, significant attention is given to the transformative role of Artificial Intelligence (AI) in liver tumour diagnosis and treatment. This includes AI applications in imaging modalities like Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) for segmentation, histopathological analysis, and predictive modeling for tumour growth and survival. The review also explores architectures such as Convolutional Neural Networks (CNNs), U-Net and its variants, and Transformer-based models, which have been pivotal in segmentation, classification and prognosis. Furthermore, it discusses annotation tools that streamline dataset preparation, ensuring accurate and efficient training of AI models. By providing a comprehensive analysis of existing literature and advancements, this review aims to support researchers and practitioners in advancing the field of liver tumour diagnosis and treatment.

Source: IEEE Access

DOI: 10.1109/access.2025.3591622

Authors: Yana Gaur, Abhay Chaudhary, Ajith Abraham

Institutions: Bennett University, Sai University

Language: English

Topic: Hepatocellular Carcinoma Treatment and Prognosis

Subfield: Hepatology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,075