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Researchers Use Artificial Intelligence in Oral Cancers With $3.3 Million NIH Grant


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Researchers at Case Western Reserve University and partners in the United States and India are applying the investigative and predictive capabilities of artificial intelligence (AI) to help physicians customize treatments for patients with oral squamous cell carcinomas. Research shows that oral squamous cell carcinoma is already the eighth most common type worldwide, and numbers are steadily increasing in the United States, India, and other parts of Asia.

Anant Madabhushi, PhD

Anant Madabhushi, PhD

James Lewis, Jr, MD

James Lewis, Jr, MD

The National Cancer Institute (NCI) awarded a 5-year, $3.3 million grant to a group led by Anant Madabhushi, PhD, Donnell Institute Professor of Biomedical Engineering at Case Western Reserve and Head of the Center for Computational Imaging and Personalized Diagnostics (CCIPD), and James Lewis, Jr, MD, Professor of Pathology, Microbiology, and Immunology at Vanderbilt University Medical Center.

Using AI to Customize Treatment

The CCIPD has become a global leader in AI-driven precision medicine research. Dr. Madabhushi and his research team at the CCIPD hold more than 60 patents, many tied to their work in various cancers. In this work, researchers will use advanced computer vision and machine learning techniques to identify cancer and immune cells on digitized images of oral squamous cell carcinoma tissue slides and then recognize spatial patterns among those cells.

This technology allows computerized vision to recognize patterns and quantify features that simply are beyond the human visual system but are powerful indicators of tumor biology. These algorithms will help oncologists and pathologists then to better determine which cancers are more vs less aggressive. This, in turn, will then enable them to identify which patients with early-stage disease could safely receive surgery alone, vs those who might need postoperative radiation therapy. In addition, it could help identify which patients with advanced-stage disease might need chemotherapy with radiation therapy after initial treatment vs those who may be adequately treated with radiation therapy alone.

“We have known for a long time that pathologic features of oral cavity squamous cell carcinomas correlate with tumor behavior and prognosis, but human visual systems cannot extract these features consistently or quantitatively,” Dr. Lewis said. “AI now allows us to do just that, and we are hopeful that the extracted information can be turned into clinically available algorithms that drive better patient care decisions.”

Dr. Madabhushi and Dr. Lewis will work with a number of partners in a national and global endeavor to improve the care of patients with oral cavity squamous cell carcinoma with advanced technology and data sharing. The clinical partners will provide glass slides to be digitized or will directly provide digitally scanned whole slide images, which will be used to train the AI algorithms for predicting outcomes as well as treatment benefit. 


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