An artificial intelligence method to detect biomarkers in tumor biopsies promises to cut weeks and thousands of dollars from cancer detection, extending the benefits of precision oncology to underserved and under-resourced patients.
A new generation of artificial intelligence tools designed to allow rapid, low-cost detection of clinically actionable genomic alterations directly from tumor biopsy slides has been developed by a team led by engineers and medical researchers at University of California San Diego.Senior author Ludmil Alexandrov, Ph.D.
At UC San Diego, this work represents a collaboration spanning all across campus, including the Department of Cellular and Molecular Medicine in the UC San Diego School of Medicine, the Shu Chien-Gene Lay Department of Bioengineering at the UC San Diego Jacobs School of Engineering, Institute of Engineering in Medicine, Department of Medicine, and the UC San Diego Moores Cancer Center.
Bergstrom said the collaborators focused on leveraging the minimum amount of patient information that is available early in the diagnostic process. He explained that virtually every cancer patient undergoes a tumor biopsy, a tissue sample that is routinely processed and examined through a light microscope. The process was developed in the late 19th century and is still the standard backbone of early clinical oncology workflows today.
"This AI approach saves the patient critical time," Alexandrov added."Oncologists can prescribe treatment immediately after initial tissue diagnosis. Remarkably, the AI test has a negligible failure rate, while current genomic tests have a failure rate of 20 to 30 percent, necessitating re-testing, or even invasive re-biopsy."
The AI technology behind DeepHRD is protected by provisional patents through UC San Diego, which have been licensed to io9, a company with strong involvement by Alexandrov, Bergstrom and Lippman, and the goal to move this AI platform into the clinical arena as quickly as possible to make precision therapy real for patients with cancer by getting them onto the precise treatments they need faster.
Erik N. Bergstrom, Ammal Abbasi, Marcos Díaz-Gay, Loïck Galland, Sylvain Ladoire, Scott M. Lippman, Ludmil B. Alexandrov.A discovery promises to improve care for patients with endometrial cancer, the most common gynecologic malignancy. Using artificial intelligence to spot patterns across thousands of cancer cell ...
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