QIMR Berghofer researchers have developed an artificial intelligence tool that can predict hidden genetic markers of cancer from standard pathology slides, potentially helping pathologists make faster and more accurate diagnoses.
The research published in Nature Communications shows how the machine learning tool, STimage, accurately predicted breast, skin and kidney cancers and a liver immune disease. It found the tool was reliable, low cost and rapidly generated results that were easy for pathologists to interpret.
The technology could support a new generation of digital pathology by providing molecular information from standard tissue samples, reducing reliance on specialised testing and improving access to advanced diagnostics for regional and rural patients.
Lead researcher Associate Professor Quan Nguyen said STimage was designed to assist, rather than replace, pathologists by providing information about cell types and genetic activity that cannot be seen through conventional microscopy.
“It’s like giving pathologists the super resolution vision of Superman or Superwoman to scan millions of invisible biomarkers in a tiny tissue sample to find the two or three that are showing signs of cancer. This capability is critical for earlier detection, more precise diagnosis, and better-informed treatment decisions,” said Associate Professor Quan Nguyen, who led development of the tool with QIMR Berghofer’s National Centre for Spatial Tissue and AI Research (NCSTAR).
The team hopes this tool could help pathologists across rural, regional and metropolitan areas manage high demand and workload, enhance diagnostic precision and reduce the time involved in screening and analysing samples by providing a way for them to access crucial molecular information currently limited to specialist research centres.
“The STimage tool does not replace the experience and expertise of pathologists. Rather, it assists them in their important and technically challenging work, by providing extra information about cell types and genetic activity that they can’t see with their own eyes,” A/Prof Nguyen said.
The next stage of the project is clinical evaluation in pathology laboratories, with researchers hoping the technology could enter clinical practice within two years.
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