Researchers vie to employ AI for Cancer Immunotherapy

Healthcare

In the bid to develop tools for the fight against cancer, artificial intelligence (AI) tools being developed at Case Western Reserve University have to be established in strict human clinical trials. The validation of the tool may be a step close following two recent agreements a bioengineering pioneer at New York University and select large pharmaceutical companies.

β€œThe step is an important one for validating the research, and for further advancing efforts to get the right treatment for patients who will benefit most,” stated the research associate. The potential of AI, computational imaging tools to predict an individual response of cancer patient to immunotherapy is shown, added the research associate.

Meanwhile, recent research at the Center for Computational Imaging and personalized Diagnostics demonstrates that AI and ML can be employed in the fight against cancer. These technologies can predict patients who will benefit from immunotherapy.

To use AI and ML, researchers intrinsically teach computers to seek and detect changes in patterns of CT scans. It involves detecting changes in patterns of CT scans taken at the time when lung cancer diagnosed, and the scans taken during immunotherapy treatment.

Besides Immunotherapy, AI Algorithms to find use for Tissue biopsy

Besides this, the researchers have been training AI algorithms for tissue biopsy images of cancer patients. This involves use of the algorithms to study patterns of tissue biopsy images of these patients and to identify the possibility of favorable response to treatment. The algorithms are being trained for use beyond lung cancer. Meanwhile, computational approaches for predicting immunotherapy response of gynecologic cancers showcased at the American Society of Clinical Oncology meeting in May, 2020.

While the benefit of immunotherapy for cancer patients established, the quest of researchers now is beyond this. They are seeking an improved way to identify patients who are most likely to respond to this treatment.

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