, /PRNewswire/ -- In a proof-of-concept study, researchers at the National Institutes of Health (NIH) have developed an artificial intelligence (AI) tool that uses routine clinical data, such as that from a simple blood test, to predict whether someone's cancer will respond to immune checkpoint inhibitors, a type of immunotherapy drug that helps immune cells kill cancer cells. The machine-learning model may help doctors determine if immunotherapy drugs are effective for treating a patient's cancer. The study, published , in , was led by researchers at the and Memorial Sloan Kettering Cancer Center in .
NCI is part of the National Institutes of Health. Currently, two predictive biomarkers are approved by the Food and Drug Administration for use in identifying patients who may be candidates for treatment with immune checkpoint inhibitors. The first is tumor mutational burden, which is the number of mutations in the DNA of cancer cells.
The second is PD-L1, a tumor cell protein that limits the immune response and is a target of some immune checkpoint inhibitors. However, these biomarkers do not always accurately predict response to immune checkpoint inhibitors. Recent machine-leaning models that use molecular sequencing data have shown value in predicting response, but this kind of data is expensive to obtain and not routinely collected.
The new study details a different kind of machine-learning model that makes predictions based on five clinical features that are routinely coll.
