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Machine learning is being used by scientists and medical researchers at Case Western Reserve University, NYU Langone Health, and University Hospitals to predict immunotherapy response.
Fremont, CA: A five-year, $3 million National Cancer Institute grant has been awarded to medical researchers from Case Western Reserve University, New York University (NYU), and University Hospitals to develop and apply artificial intelligence (AI) tools for predicting which lung cancer patients will respond to immunotherapy. The Case Western Reserve-led project will be unusual in that it will evaluate their specialized AI technologies for the first time within an active clinical trial.
While these real-time treatment predictions will only be utilized for research and not for clinical diagnosis or treatment, the study is the first step toward clinical trials in which clinicians will be able to use the tools with real patients.
AI for Immunotherapy
The CCIPD digital imaging lab has established itself as a world leader in applying machine learning to detect patterns in digital images of tissue scans that aren't visible to the naked eye, such as for cancer detection. The group was one of the first to apply artificial intelligence to forecast which patients might benefit from chemotherapy. AI and machine learning can also assist in predicting which lung cancer patients will benefit from immunotherapy, according to recent research by CCIPD experts.
According to the National Cancer Institute, immunotherapy employs medications to help the immune system fight cancer, whereas chemotherapy uses drugs to kill cancer cells directly. The researchers can reliably identify who will or will not benefit from immunotherapy by training a computer to look for microscopic changes in patterns in CT scans taken when lung cancer is initially detected, just as they can with chemotherapy. After then, the scans are compared to scans done after the first two to three rounds of immunotherapy.
Immunotherapy is beneficial to many cancer patients, but it is costly. Researchers seek to develop a better method for determining which cancer patients are likely to benefit and those who don't want to pay for treatment that turns out to be useless and expensive.