Machine-learning model predicts remission, relapse in cancer patients
Researchers have developed an algorithm to accurately predict which patients diagnosed with acute myelogenous leukemia (AML), a cancer of the blood and bone marrow, will go into remission following treatment and which ones will relapse.
Using bone marrow data and medical histories of AML patients, as well as blood data from healthy individuals, researchers were able to teach a standard 64-bit computer workstation running Windows to predict remission with 100 percent accuracy, while relapse was correctly predicted in 90 percent of relevant cases.
“It’s pretty straightforward to teach a computer to recognize AML, once you develop a robust algorithm, and in previous work we did it with almost 100 percent accuracy,” said Murat Dundar, associate professor of computer science in the School of Science at Indiana University-Purdue University Indianapolis.
“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,” adds Dundar.
Ultimately, Bartek Rajwa, research assistant professor of computational biology in the Bindley Bioscience Center at Purdue University who collaborated with Dundar, contends that the machine-learning algorithm was better at extracting knowledge from complex data than humans performing manual analysis of cytometry data.
“The computer was more adept at automatically discovering patterns which humans would not notice,” says Rajwa. “But, we don’t want to replace physicians. We want to give them an additional tool to enhance their ability to correctly diagnose their patients and accurately predict the direction of change in their disease progression.”
Also See: Machine learning seen speeding diagnoses
Dunbar and Rajwa believe that in addition to AML the machine-learning algorithm has the potential to be applied to other hematological neoplasms.
Nonetheless, Rajwa emphasizes that the research with Dundar and Roswell Park Cancer Institute’s Paul Wallace, a flow cytometry expert, and Elizabeth Griffiths, a physician who treats patients with AML, was only a proof-of-concept. Before these kinds of machine-learning models can be implemented in clinical settings, additional research needs to be conducted with expanded datasets, according to Dundar.