Researchers at Beth Israel Deaconess Medical Center and Harvard Medical School explored whether artificial intelligence could distinguish two major forms of liver cancer by analysing routine microscope-slide images.

Hepatocellular carcinoma and intrahepatic cholangiocarcinoma arise from different types of liver cells and have different treatment approaches, making accurate classification important.

Using an AI model capable of analysing pathology images without additional disease-specific training, the team examined 50 digitised tissue slides from The Cancer Genome Atlas public dataset.

The model detected visual patterns linked to cancer-type-specific genes, especially those characteristics of liver cells (hepatocytes), and RNA sequencing data from the same tumours supported these findings.

The results suggest that AI may be able to extract molecular information from tissue images that are already collected during routine diagnosis, potentially providing another tool to support cancer classification.

This work was led by Avash Das, MBBS, Ph.D., of Beth Israel Deaconess Medical Center/Harvard Medical School, who presented the work at the Association for Molecular Pathology 2026 Annual Meeting & Expo.

The conference explores the emerging role of AI in molecular diagnostics, including identifying difficult-to-classify cancers, improving the accuracy of AI-generated reports and helping laboratories use genetic testing more efficiently.

Source: Association for Molecular Pathology