Russian Scientists Develop New Method to Rapidly Detect Blood Cancer

A new method for detecting multiple myeloma – a type of blood cancer – has been developed by researchers at Samara University as part of an international team.

Unlike current diagnostic techniques used in clinical practice, this new method is faster and more cost-effective. The results have been published in Light: Advanced Manufacturing.

Multiple myeloma is a tumor of plasma cells in the blood, diagnosed annually in 7 out of every 100,000 people, according to the American Cancer Society.

Currently, diagnosing myeloma involves expensive and invasive procedures, Samara University specialists explain. Together with colleagues from local medical institutions and Northwestern University in Xi’an, China, the team has created a more accessible and faster diagnostic approach.

One of the developers, Lyudmila Bratchenko, associate professor at the Department of Laser and Biotechnical Systems at Samara University, explained:

“Multiple myeloma is a cancer arising from abnormal plasma cells in the bone marrow, and early diagnosis is critical for successful treatment. The new method is designed to simplify and accelerate the diagnostic process. It works using accessible equipment without complex preparation procedures, making it a promising solution for widespread use.”

The diagnostic test requires a laser and silver nanoparticle-coated substrates onto which a drop of blood serum is applied, Bratchenko explained.

“Imagine having a special ‘super microscope’ that allows you to see the ‘chemical fingerprint’ of blood. Silver nanoparticles amplify this fingerprint many times over, enabling the detection of changes in blood composition that are characteristic of multiple myeloma—even in very small quantities. AI then analyzes these signals to determine whether a person has the disease,” she added.

The research showed that the method can detect multiple myeloma with over 96% accuracy, which could significantly reduce mortality from the disease.

Researchers plan to expand the study by including more samples and diverse patient groups to increase the method’s robustness and applicability. They also aim to automate the analysis process and develop compact devices for rapid diagnosis in clinical settings.