Computer algorithm predicts outcome for leukemia patients

Image by PeteLinforth
Image by PeteLinforth

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.

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New blood test could detect early-stage pancreatic cancer

Photo by PublicDomainPictures
Photo by PublicDomainPictures

Pancreatic cancer is one of the leading causes of cancer death, because it is seldom detected before the disease has spread to other organs. Only 8 percent of people with pancreatic cancer survive five or more years after diagnosis.

Now, researchers hope to change this bleak scenario with an improved blood test that can detect early-stage pancreatic cancer. A multi-institutional team led by Tony Hu, PhD, an associate professor at Arizona State University, recently reported on their results in Nature Biomedical Engineering.

The researchers first identified the presence of a protein in the blood, called ephrin type-A receptor (EphA2), which is overexpressed by pancreatic tumors. Next, they developed a biosensor using gold nanoparticles that selectively bind to EphA2, changing their light emitting properties. This allowed the team to quantify the amount of EphA2 in a blood sample to see if it is overexpressed.

They validated their biosensor in a pilot study involving 48 healthy people, 59 patients with stage I-III pancreatic cancer and 48 patients with chronic pancreas inflammation. The later condition is often confused with pancreatic cancer using existing diagnostic tests like ultrasound.

The biosensor was able to accurately identify the patients with pancreatic cancer — even those with early stage disease — as well as the patients with chronic pancreas inflammation. If these results are validated with a larger clinical trial, the blood test could screen for pancreatic cancer and could be adapted for other diseases.

“We are now working on lung cancer and lymphoma and have very positive results,” Hu said in a recent news story. “In addition to cancer, we are conducting a project on tuberculosis diagnosis. Theoretically this test could be applied to any type of disease.”

This is a reposting of my Scope blog story, courtesy of Stanford School of Medicine.