Researchers at Memorial Sloan Kettering Cancer Center and the Icahn School of Medicine at Mount Sinai investigated whether artificial intelligence could help pathologists determine which lung cancer specimens need rapid genetic testing.

Testing for mutations in the EGFR gene can help identify patients who may benefit from targeted treatments, but performing multiple tests can consume valuable tumor tissue and laboratory resources.

This work was led by Nicholas Dcunha, MBBS, of Memorial Sloan Kettering Cancer Center, 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.

The team incorporated AI analysis of digital pathology slides into a workflow in which six molecular pathology fellows reviewed 80 consecutive lung adenocarcinoma cases.

The project used pathology-related AI models, namely TAPFM and EAGLE.

Based on the AI findings, reviewers indicated that rapid EGFR testing could have been deferred in 26 cases, or 33%, without overlooking any of the confirmed EGFR-positive cases in the study.

These preliminary findings suggest that AI could help laboratories prioritise rapid testing while preserving tissue for comprehensive genomic analysis.

However, the approach was less reliable for identifying cases that could forgo rapid testing for certain gene fusions, highlighting the need for further refinement and human-in-loop oversight.

Overall, the authors said, this approach provided a safe and cost-effective way to reduce rapid molecular testing, preserve tissue for more comprehensive molecular testing and make better treatment decisions.

Source: Association for Molecular Pathology