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To determine the type and severity of a cancer, pathologists typically analyze thin slices of a tumor biopsy under a microscope. But to figure out what genomic changes are driving the tumor's growth—information that can guide how it is treated—scientists must perform genetic sequencing of the RNA isolated from the tumor, a process that can take weeks and costs thousands of dollars. This AI breakthrough is transforming cancer biopsy images into genetic insights.
Now, Stanford Medicine researchers have developed an artificial intelligence-powered computational program that can predict the activity of thousands of genes within tumor cells based only on standard microscopy images of the biopsy.
The tool, described online in Nature Communications on Nov. 14, was created using data from more than 7,000 diverse tumor samples. The team showed that this AI breakthrough will transform cancer biopsy images into genetic insights. It could use routinely collected biopsy images to predict genetic variations in breast cancers and patient outcomes.
"This kind of software could be used to quickly identify gene signatures in patients' tumors, speeding up clinical decision-making and saving the health care system thousands of dollars," said Olivier Gevaert, Ph.D., a professor of biomedical data science and the senior author of the paper.
This work to create an AI breakthrough to transform cancer biopsy images into genetic insights was also led by Stanford graduate student Marija Pizuria and postdoctoral fellows Yuanning Zheng, Ph.D., and Francisco Perez, Ph.D.
Clinicians have increasingly guided the selection of which cancer treatments—including chemotherapies, immunotherapies and hormone-based therapies—to recommend to their patients based on not only which organ a patient's cancer affects, but which genes a tumor is using to fuel its growth and spread. Turning on or off certain genes could make a tumor more aggressive, more likely to metastasize, or more or less likely to respond to certain drugs.
However, accessing this information often requires costly and time-consuming genomic sequencing.
Gevaert and his colleagues knew that the gene activity within individual cells can alter the appearance of those cells in ways that are often imperceptible to a human eye. They turned to artificial intelligence to find these patterns, resulting in an AI breakthrough to transform cancer biopsy images into genetic insights.
Read the full article about the AI tool predicting cancer gene activity by Sarah C. P. Williams at Medical Xpress.