New Delhi: Researchers have developed a new artificial intelligence (AI) tool that interprets medical images with unprecedented clarity and may help clinicians diagnose and better treat cancers that might otherwise go undetected. The tool, called iStar (Inferring Super-Resolution Tissue Architecture), and developed by researchers at the University of Pennsylvania, US, provides both highly detailed views of individual cells and a broader look at the full spectrum of how people’s genes operate.
The imaging technique, described in the journal Nature Biotechnology, would allow doctors to see cancer cells that might otherwise have been virtually invisible, the researchers said.
This tool can be used to determine whether safe margins were achieved through cancer surgeries and automatically provide annotation for microscopic images, paving the way for molecular disease diagnosis at that level, they said.
The researchers said iStar has the ability to automatically detect critical anti-tumor immune formations called “tertiary lymphoid structures,” whose presence correlates with a patient’s likely survival and favourable response to immunotherapy, which is often given for cancer and requires high precision in patient selection.
This means that iStar could be a powerful tool for determining which patients would benefit most from immunotherapy, they said.
“The power of iStar stems from its advanced techniques, which mirror, in reverse, how a pathologist would study a tissue sample,” said Mingyao Li, a professor at the University of Pennsylvania.
“Just as a pathologist identifies broader regions and then zooms in on detailed cellular structures, iStar can capture the overarching tissue structures and also focus on the minutiae in a tissue image,” Li explained.
To test the efficacy of the tool, the researchers evaluated iStar on many different types of cancer tissue, including breast, prostate, kidney, and colorectal cancers, mixed with healthy tissues.
Within these tests, iStar was able to automatically detect tumour and cancer cells that were hard to identify just by eye, according to the researchers.
Clinicians in the future may be able to pick up and diagnose more hard-to-see or hard-to-identify cancers with iStar acting as a layer of support, they said.
In addition to the clinical possibilities presented by the iStar technique, the tool moves extremely quickly compared to other, similar AI tools.
For example, when set up with the breast cancer dataset the team used, iStar finished its analysis in just nine minutes.
By contrast, the best competitor AI tool took more than 32 hours to come up with a similar analysis, making iStar 213 times faster.
“The implication is that iStar can be applied to a large number of samples, which is critical in large-scale biomedical studies,” Li added.
