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Liver cancer, or hepatic cancer, is a malignant tumour that grows on the surface or inside the liver. The leading cause is a viral infection with hepatitis B or C virus. The most frequent liver cancers are hepatocellular carcinoma and hepatoblastoma (formed by immature liver cells).
A recent study reported the development and validation of the Liver Artificial Intelligence Diagnosis System (LiAIDS), a fully automated system that integrates deep learning for the diagnosis of liver lesions on the basis of contrast-enhanced CT scans and clinical information. This tool improved diagnostic precision, surpassed the accuracy of junior radiologists (and equalled that of senior radiologists) and streamlined patient triage. These advances underscore the potential of artificial intelligence to enhance hepatology care, although challenges to widespread clinical implementation remain.
Hepatocellular carcinomas (HCC) present spatial intratumour heterogeneity (sITH). Here, the authors perform a genomic and phylogenetic analysis of spatially-sampled HCC tumour sections, observe block-shaped sITH, and find ongoing natural selection where ancestral and derived clones spatially compete in the same tumor.
Hepatoblastoma (HB) is the most frequent paediatric liver tumour with heterogeneous cellular phenotypes that influence clinical outcomes. Here, the authors integrate bulk, single-cell, and spatial multi-omics to characterise HB cells, and find that clonal evolution and epigenetic plasticity shape response to therapy.
Treatment of patients with advanced hepatocellular carcinoma with a personalized DNA vaccine in combination with anti-PD-1 therapy was safe and led to encouraging clinical efficacy, with immunological analyses confirming the induction of tumor antigen-specific T cell responses.
A recent study reported the development and validation of the Liver Artificial Intelligence Diagnosis System (LiAIDS), a fully automated system that integrates deep learning for the diagnosis of liver lesions on the basis of contrast-enhanced CT scans and clinical information. This tool improved diagnostic precision, surpassed the accuracy of junior radiologists (and equalled that of senior radiologists) and streamlined patient triage. These advances underscore the potential of artificial intelligence to enhance hepatology care, although challenges to widespread clinical implementation remain.
Recently published in Nature, Fan et al. demonstrate that accumulation of advanced glycation end-products in the extracellular matrix of the liver increases viscoelasticity to promote hepatocellular carcinoma growth, independent of stiffness.
