Predicting Childhood Leukemia Relapse Using Machine Learning

Parker Institute researchers at Stanford create data-driven method to more accurately predict relapse at time of diagnosis

Predicting Childhood Leukemia (parkerici.org) – Parker Institute for Cancer Immunotherapy researchers at Stanford University School of Medicine have developed a better way to test early on which childhood leukemia patients will relapse in the future.

The method, used at the time of diagnosis, predicts which patients will relapse with 85 percent accuracy – a significant improvement over the traditional method. The results were published in Nature Medicine today.

Researchers analyzed samples from 60 children with pediatric acute lymphoblastic leukemia and compared them to samples from healthy patients. Using single-cell mass cytometry (also called CyTOF), the team looked at 35 proteins involved in B-cell development. Applying machine learning to that data, researchers identified six features of leukemia cells that could help predict relapse after treatment.

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