Scientists at Stanford University investigated whether artificial intelligence could predict cancer-associated genetic mutations using standard pathology images, potentially providing another tool to complement molecular testing.

Although genomic sequencing provides valuable information for cancer diagnosis and treatment, it can be expensive and may not be readily available in every clinical setting.

The researchers analysed digitised tissue slides from 367 patients with gastric cancer, using an AI foundation model to identify image features associated with mutations in four cancer-related genes.

The strongest results were for mutations in the PIK3CA and CDH1 genes, with predictive performance scores of 0.80 and 0.77, respectively, on a scale where 0.50 represents chance-level discrimination and 1.0 represents perfect discrimination.

While the findings require validation in additional patient populations, the study suggests that AI analysis of existing pathology slides could eventually help laboratories prioritise molecular testing.

This work was overseen by Kiran Nijjer of Stanford University, who presented the research 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