Researchers have developed a machine-learning computer algorithm that predicts the health outcome of patients with acute myeloid leukemia — identifying who is likely to relapse or go into remission after treatment.
Acute myeloid leukemia (AML) is a cancer characterized by the rapid growth of abnormal white blood cells that build up in the bone marrow and interfere with the production of normal blood cells.
A standard tool used for AML diagnosis and treatment monitoring is flow cytometry, which measures the physical and chemical characteristics of cells in a blood or bone marrow sample to identify malignant leukemic cells. The tool can even detect residual levels of the disease after treatment.
Unfortunately, scientists typically analyze this flow cytometry data using a time-consuming manual process. Now, researchers from Purdue University and Roswell Park Cancer Institute believe they have developed a machine-learning computer algorithm that can extract information from the data better than humans.
“Machine learning is not about modeling data. It’s about extracting knowledge from the data you have so you can build a powerful, intuitive tool that can make predictions about future data that the computer has not previously seen — the machine is learning, not memorizing — and that’s what we did,” said Murat Dundar, PhD, associate processor at Indiana University-Purdue University, in a recent news release.
The research team trained their computer algorithm using bone marrow data and medical histories of AML patients along with blood data from healthy individuals. They then tested the algorithm using data collected from 36 additional AML patients.
In addition to being able to differentiate between normal and abnormal samples, they were able to use the flow cytometry bone marrow data to predict patient outcome — with between 90 and 100 percent accuracy — as recently reported in IEEE Transactions on Biomedical Engineering.
Although more work is needed, the researchers hope their algorithm will improve monitoring of treatment response and enable early detection of disease progression.
Dudar explained in the release:
“It’s pretty straightforward to teach a computer to recognize AML. … What was challenging was to go beyond that work and teach the computer to accurately predict the direction of change in disease progression in AML patients, interpreting new data to predict the unknown: which new AML patients will go into remission and which will relapse.”
This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.