Lasers allow better Identification of Cancer Infiltrated Cells during Neurosurgery

When I was an undergraduate doing physical chemistry research, we often used a Raman Spectrometer to characterize the glass samples we had made. Little did I know, this infrared laser would soon be researched for application in neuropathology and surgery. When resecting a glioblastoma or other cancerous tumors from the brain, balancing the amount of tissue removed is key; however, this is often easier said than done. Cells recently infiltrated by cancer look normal to the naked eye, but if they get left behind, there is a high risk of cancer recurrence. If the surgeon takes too much tissue in order to ensure that all of the infiltrated cells are removed, there can be poor outcomes for the patient in regards to their everyday function. To allow for better outcomes and prevent surgeons from having to take their best guess on where infiltrated cancer tissue ends and healthy tissue begins there is a need for real time imaging of malignant tumors, and using Raman spectrometry, this may soon be a reality.

            Scattered Raman Spectroscopy, or SRS, has been used to detect and characterize glioma cells in animal models as well as in unprocessed human surgical samples. Raman spectroscopy uses an infrared laser to detect differences in vibration of molecules in normal versus cancerous tissue. By measuring normal tissue as a baseline, dense and even invasive cancer cells can be identified by the differences in their spectra. This may replace certain biopsy staining methods, as the SRS technique properly identified a higher percentage of invasive cancer cells than standardized staining techniques.

So when used during neurosurgery, wouldn’t this require specific personnel in the operating room to read the spectra properly? Actually, no! The researchers developing this technology realize the need for the surgeon him or herself to identify the tissue, and not waste crucial time identifying spectral differences, so they additionally invented a handheld objective classifier that uses image qualities of glioblastomas and translates them into certain colors. These qualities include: density of axons, protein to fat ratio in the tissue, and the amount of cellularity, which together can be utilized to identify tumor cells with 99% accuracy. This technology will allow for better functional outcomes, as well as, less recurrence for cancer patients. As the future of neuropathology and surgery, SRS is paving the way for earlier tumor identification and lower risk tumor resections.    

 Use the DOIs in the citations below for more information: 

Jermyn, M., et all. (2015). Intraoperative brain cancer detection with Raman spectroscopy in humans. Science Translational Medicine. DOI: 10.1126/scitranslmed.aaa2384

Ji, M., Lewis, S.,  et all. (2015). Detection of human brain tumor infiltration with quantitative stimulated Raman scattering microscopy. Science Translational Medicine. DOI: 10.1126/scitranslmed.aab0195