Biodynamic imaging illuminates ovarian cancer treatments

Scientists estimate that nearly 60% of all cancer patients do not respond effectively to chemotherapy treatments. Even worse - many of those same patients experience toxic and sometimes deadly side effects.

Now, a Purdue University scientist and entrepreneur is working to use simple LED light to help determine if certain chemotherapy options will work for specific patients. The work is published in Scientific Reports.

We are using a technique very similar to doppler radar used in weather to advance personalized medicine. We take the LED light and shine it on biopsies. We then apply chemotherapy to the biopsies and analyze how the light scatters off the tissues."

David Nolte, Edward M. Purcell Distinguished Professor, Physics and Astronomy, College of Science, Purdue University

Nolte, who also is a member of the Purdue University Center for Cancer Research, said the light scattering dynamics give scientists and doctors detailed information about the likelihood of a chemotherapy drug being effective for a patient.

Nolte said they have results within 24 hours. This first trial looked at biodynamic imaging on human patients with ovarian cancer.

"We look for signs of apoptosis, or what we call the controlled death of cells," Nolte said. "Apoptosis is the signal that indicates the effectiveness of the chemotherapy for this patient's tissues and tumors.

For some cancers, there are so many treatment options available that it's like a doctor is trying to fit square pegs in circular holes until a desired outcome is found. We want to make this process better for patients."

Source:
Journal reference:

Li, Z., et al. (2020) Intracellular optical doppler phenotypes of chemosensitivity in human epithelial ovarian cancer. Scientific Reports. doi.org/10.1038/s41598-020-74336-x.

Comments

The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of News Medical.
You might also like... ×
Novel targeted therapy blocks metabolic pathways in cancer cells with specific genetic defects