Accurate intraoperative tissue diagnosis is central to medical decision making during brain tumor surgery. Existing intraoperative histologic techniques deplete scant tissue biopsies, introduce freezing artifacts, and rely on highly- skilled technicians and neuropathologists working in dedicated surgical pathology laboratories to produce and interpret slides. In addition, the number of centers where brain tumor surgery is performed far exceeds the number of board-certified neuropathologists, eliminating the possibility for expert consultation in many cases. Even in the most advanced, well-staffed hospitals, turnaround time for intraoperative pathology poses a major barrier for the efficient delivery of surgical care, highlighting the need for an improved system for rapid diagnosis. Stimulated Raman histology (SRH) creates high-resolution digital microscopic images of unprocessed tissue specimens in a fraction of the time of conventional techniques and eliminates reliance on a frozen section laboratory for sectioning, staining, mounting and reviewing slides. While SRH has been shown to reveal key diagnostic histologic features in brain tumor specimens, major technical hurdles related to laser safety and performance have hindered its clinical translation. The existing academic-industrial partnership between established collaborators has resulted in the development and initial validation of a clinically-compatible SRH microscope in a patient care setting (Nature Biomedical Engineering 1:0027, 2017). We have demonstrated that SRH has diagnostic value comparable to conventional histologic techniques and that SRH images are well-suited for interpretation via an automated machine learning algorithm. The proposed research program represents a multi-disciplinary (neurosurgery, neuropathology, biostatistics, computer science and medical device manufacturing) academic-industry partnership to accelerate the development of an SRH imager for use during brain tumor surgery. The overall goal of the partnership is to create pathways for online collaboration between surgeons and pathologists using the SRH imager, provide strong evidence for the utility of SRH in the setting of a prospective randomized controlled trial and to enhance our capabilities for automated intraoperative diagnosis employing state-of the-art image classification methods including convolutional neural networks. Once completed, this research program will improve the care of brain tumor patients by streamlining the process for intraoperative diagnosis. It will also create a pathway for remote and automated diagnosis, extending expertise in neuropathology to more centers caring for brain tumor patients. SRH technology combined with algorithms for automated intraoperative diagnosis create the possibility of advancing the larger field of surgical oncology where histologic information is essential for making surgical decisions.
Existing techniques for creating histologic images of tissue during surgery are time-consuming, prone to error, may complicate diagnosis and surgical-decision making. We have developed an optical imaging technology which streamlines tissue diagnosis during surgery, enabling a uniform workflow that will improve the care of cancer patients. Through this academic-industrial partnership of successful collaborators, we aim to accumulate data which will propel this emerging technology towards becoming the new standard of care for intraoperative histology.
