Clinic-radiomics model using liver magnetic resonance imaging helps predict chronicity of drug-induced liver injury

Mapping the knowledge domains of literature on hepatocellular carcinoma and liver failure: a bibliometric approach

Background

Hepatocellular carcinoma (HCC) accounts for 75-85% of primary liver cancers, with its incidence continually rising, posing a threat to socio-economic development. Currently, liver resection is the standard treatment for HCC. However, post-hepatectomy liver failure (PHLF) is a severe and formidable postoperative complication that increases patients' medical expenses and mortality risk. Additionally, liver failure can occur at any stage of HCC development, severely affecting patients' quality of life and prognosis.

Method

Using the Web of Science Core Collection, this bibliometric study analyzed English articles and reviews on HCC and liver failure from 2003 to 2023. Bibliometric tools like CiteSpace, VOSviewer, and R-studio were employed for data visualization and analysis, focusing on publication trends, citation metrics, explosive intensity, and collaborative networks. Use the Comparative Toxicogenomics and Genecards databases to screen for genes related to liver failure, and perform enrichment analyses using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and PubMed on the identified differentially expressed genes.

Results

The study identified a significant increase in publications on HCC and liver failure, with key contributions from journals such as the World Journal of Gastroenterology and the Journal of Hepatology. The United States, China, and Japan were the leading countries in research output. Prominent authors and institutions, including Kudo Masatoshi and Sun Yat-sen University, were identified. Enrichment analysis showed drug metabolism, oxidative stress, lipid metabolism, and other pathways are closely related to this field. Research hotspots included risk prediction models and novel therapies.

Conclusion

This bibliometric analysis highlights the growing research interest and advancements in HCC and liver failure. Future research should focus on improving risk prediction, developing new therapies, and enhancing international collaboration to address these critical health issues.

Artificial Intelligence: An Emerging Tool for Studying Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a complex and potentially severe adverse reaction to drugs, herbal products or dietary supplements. DILI can mimic other liver diseases clinical presentation, and currently lacks specific diagnostic biomarkers, which hinders its diagnosis. In some cases, DILI may progress to acute liver failure. Given its public health risk, novel methodologies to enhance the understanding of DILI are crucial. Recently, the increasing availability of larger datasets has highlighted artificial intelligence (AI) as a powerful tool to construct complex models. In this review, we summarise the evidence about the use of AI in DILI research, explaining fundamental AI concepts and its subfields. We present findings from AI-based approaches in DILI investigations for risk stratification, prognostic evaluation and causality assessment and discuss the adoption of natural language processing (NLP) and large language models (LLM) in the clinical setting. Finally, we explore future perspectives and challenges in utilising AI for DILI research.

Artificial Intelligence in Liver Diseases: Recent Advances

Liver diseases cause a significant burden on public health worldwide. In spite of great advances during recent years, there are still many challenges in the diagnosis and treatment of liver diseases. During recent years, artificial intelligence (AI) has been widely used for the diagnosis, risk stratification, and prognostic prediction of various diseases based on clinical datasets and medical images. Accumulative studies have shown its performance for diagnosing patients with nonalcoholic fatty liver disease and liver fibrosis and assessing their severity, and for predicting treatment response and recurrence of hepatocellular carcinoma, outcomes of liver transplantation recipients, and risk of drug-induced liver injury. Herein, we aim to comprehensively summarize the current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases.