Clinical laboratories and pathology groups may someday be able to obtain molecular information about cancer directly from routine pathology slides using artificial intelligence (AI), potentially expanding the role of digital pathology in cancer diagnosis.
Researchers have developed an AI model that can analyze pathology images to detect key gene mutations and predict biomarkers across 32 different solid cancers. The findings highlight the potential for computational pathology to bridge routine diagnostic imaging and molecular oncology, according to the study published in The American Journal of Pathology.
The AI model was designed to analyze hematoxylin and eosin (H&E)-stained whole-slide images. Researchers evaluated its ability to perform multiple tasks, including identifying cancer types and predicting genetic and molecular characteristics.
More information from routine pathology slides
The findings are notable for pathologists and clinical laboratory professionals because molecular testing has become increasingly important in oncology. Genetic mutations and other biomarkers can help physicians classify cancers, determine prognosis, and select targeted treatments.
Co-lead investigator Abadh K. Chaurasia, PhD, Menzies Institute for Medical Research, University of Tasmania, and Pandani Solutions Pty Ltd, notes, “This approach could help identify patients who may benefit from confirmatory molecular testing, support triage in settings with limited genomic testing, and provide additional decision support to clinicians. Importantly, this method should be viewed as complementary to molecular testing, not a replacement. Its potential impact is strongest as a screening, prioritization, or decision-support tool within broader diagnostic pathways.”
AI systems capable of identifying some of those characteristics from routine pathology images could eventually provide pathologists with another tool for determining which cases warrant additional molecular testing.
Such technology could be particularly useful as laboratories adopt digital pathology systems and generate increasing numbers of digitized whole-slide images.
The research does not mean AI can replace molecular or genomic testing. Rather, the findings demonstrate the growing amount of clinically relevant information researchers may be able to extract from pathology images using computational methods.
For clinical laboratories, the study is another example of how AI and digital pathology could increasingly intersect with molecular diagnostics. As the technology develops and undergoes further validation, pathologists may eventually be able to use routine H&E slides not only to examine tissue morphology but also to gain additional insight into the molecular characteristics of a patient's cancer.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
