U. Michigan accelerates, automates image analysis for brain tumor removal surgery

University of Michigan medical technician Karen Eddy prepares a sample for imaging using the stimulated Raman scattering microscopy technique. (Credit: Joe Hollisy, University of Michigan)

University of Michigan scientists have adapted a microscopy technique to accelerate and automate the analysis of brain tissue during tumor removal surgery, making it more accurate and efficient.

Surgeons aim to remove as much cancerous brain tissue as possible while preserving healthy tissue. During a procedure, a pathologist may examine tissue under a microscope to help the surgeon make decisions. But this can be a lengthy process as it requires the tissue to be processed, increasing surgery time, raising costs and raising risks for the patient.

The Michigan team, led by neurosurgeon Daniel Orringer, M.D., adapted stimulated Raman scattering (SRS) for use in the operating room, according to a statement. This type of microscopy does not require tissue to be processed, sliced or stained. They successfully tested their system in more than 100 patients, according to a statement.

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The team used a fiber-laser microscope, which uses fiber optics to enable the use of SRS in a clinical setting, according to the statement. They also used simulated Raman histology to accelerate the processing of images—this technique does not require staining because it leverages chemical properties of tissue, making proteins and DNA look purple and lipids appear pink.

“This technology reduces tissue processing time and could significantly increase the accuracy of brain tumor surgery in operating room,” said Behrouz Shabestari, director of the Optical Imaging and Spectroscopy program at the National Institute of Biomedical Imaging and Bioengineering. The NIBIB funded the research, which was published in Nature Biomedical Engineering. “It basically optimizes the surgical result and has the potential to improve patient outcomes by increasing safety and survival rates.”

Additionally, the researchers also used an algorithm to interpret the images, reporting that it was able to accurately predict the subtype of brain tumor 90% of the time. Orringer hopes that the machine will be able to make a preliminary diagnosis which can be confirmed by a remote pathologist. This would be a boon for hospitals without easy access to a neuropathologist.

“In this current era where we’re increasingly connected, this system might be the linchpin that brings the expertise to the center where the surgery is being performed,” Orringer said.

The SRS method may also be applicable in other cancers, such as breast and neck cancer, where tumor margins are unclear.

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