U.S. researchers 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, according to a study published on Monday.

The machine-learning model may help doctors determine if immunotherapy drugs are effective for treating a patient's cancer, according to the study published in Nature Cancer.

The study details a distinct kind of machine-learning model that makes predictions based on five clinical features routinely collected from patients: a patient's age, cancer type, history of systemic therapy, blood albumin level, and blood neutrophil-to-lymphocyte ratio, a marker of inflammation.

The model takes into account the tumor mutational burden, assessed through sequencing panels. It was constructed and evaluated using data from multiple independent datasets that included 2,881 patients treated with immune checkpoint inhibitors across 18 solid tumor types.

The researchers at the U.S. National Institutes of Health have noted that larger prospective studies are needed to further evaluate the AI model in clinical settings.