Case Western Reserve University works with NYU and pharmaceutical companies Bristol Myers Squibb, AstraZeneca to validate imaging-based solutions for predicting response to therapy for lung cancer patients.
For Artificial Intelligence (AI) tools being developed at Case Western Reserve University to have impact in the fight against cancer, they are going to have to be validated in rigorous human clinical trials. That validation may be a step closer following two recent agreements among bioengineering pioneer Anant Madabhushi; a longtime collaborator at New York University; and select large pharmaceutical companies.
Recent research by CCIPD scientists has demonstrated that AI and machine learning can be employed with potential to predict which lung cancer patients will benefit from immunotherapy.
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In April, Madabhushi entered into a contract with AstraZeneca (LSE/STO/NYSE: AZN), a global, science-led biopharmaceutical company that focuses on the discovery, development and commercialization of prescription medicines, primarily for the treatment of diseases in three therapy areas - Oncology; Cardiovascular, Renal & Metabolism; and Respiratory & Immunology.
Earlier this year, Madabhushi inked a similar deal with United States-based Bristol-Myers Squibb Company (NYSE: BMY), a global biopharmaceutical company whose mission is to discover, develop and deliver innovative medicines that help patients prevail over serious diseases.
"This is an important step in not only validating our research, but in further advancing efforts to get the right treatment to the patients who will benefit the most," said Madabhushi, the F. Alex Nason Professor II of Biomedical Engineering at Case Western Reserve and director of the Center for Computational Imaging and Personalized Diagnostics (CCIPD). "We have shown that our AI, our computational-imaging tools, can have the potential to predict an individual cancer patient's response to immunotherapy."
The researchers essentially teach computers to seek and identify changes in patterns in CT scans taken when lung cancer is first diagnosed, compared to scans taken during immunotherapy treatment.
The team has also been training AI algorithms to look at patterns from tissue biopsy images of cancer patients to identify the likelihood of a favorable response to treatment and is also looking beyond lung cancer. Researchers showcased these computational approaches for predicting immunotherapy response to gynecologic cancers at the 2020 American Society of Clinical Oncology (ASCO) meeting in May.
"One of the goals in any clinical trial is to choose patients who will actually benefit from the immunotherapy, and there is much more to learn by investigating how those biomarkers inform that selection," Madabhushi said. "But the question has always been: 'How do you actually identify a subset that will benefit most?' We can help answer that question with the image-based biomarkers we are developing."
MEDICA-tradefair.com; Source: Case Western Reserve University