New York University Develops New Imaging System And Artificial Intelligence Algorithm That Accurately Identifies Brain Tumors
Source: Thailand Medical News Jan 07, 2020 4 years, 10 months, 2 weeks, 1 day, 22 hours, 8 minutes ago
A new method of combining advanced optical
imaging with an
artificial intelligence algorithm produces accurate, real-time intraoperative diagnosis of
brain tumors, a new study finds.
The study examined the
diagnostic accuracy of
brain tumor image classification through
AI and
machine learning, compared with the accuracy of pathologist interpretation of conventional histologic images. The results for both methods were comparable: the
AI-based
diagnosis was 94.6% accurate, compared with 93.9% for the pathologist-based interpretation.
The new
imaging technique, stimulated Raman histology (SRH), reveals tumor infiltration in human tissue by collecting scattered laser light, illuminating essential features not typically seen in standard histologic images.
The detailed microscopic images are then processed and analyzed with
artificial intelligence, and in under two and a half minutes, surgeons are able to see a predicted
brain tumor diagnosis. Using the same technology, after the resection, they are able to accurately detect and remove otherwise undetectable
tumor.
Senior author Dr Daniel A. Orringer, MD, Associate Professor of Neurosurgery at
New York University Grossman School of Medicine, who helped develop SRH and co-led the study with colleagues at the University of Michigan told
Thailand Medical News, "As surgeons, we're limited to acting on what we can see; this technology allows us to see what would otherwise be invisible, to improve speed and accuracy in the OR, and reduce the risk of misdiagnosis. With this
imaging technology,
cancer operations are safer and more effective than ever before."
To construct the
artificial intelligence tool used in the study, researchers trained a deep convolutional neural network (CNN) with more than 2.5 million samples from 415 patients to classify tissue into 13 histologic categories that represent the most common
brain tumors, including malignant glioma, lymphoma, metastatic
tumors, and meningioma.
To validate the CNN, researchers enrolled 278 patients undergoing
brain tumor resection or epilepsy surgery at three university medical centers in the prospective clinical trial. Brain tumor specimens were biopsied from patients, split intraoperatively into sister specimens, and randomly assigned to the control or experimental arm.
Research specimens routed through the control arm, the current standard practice were transported to a pathology laboratory and went through specimen processing, slide preparation by technicians, and interpretation by pathologists, a process which takes 20-30 minutes. The experimental arm was performed intraoperatively, from image acquisition and processing to
t; diagnostic prediction via CNN.
Significantly, the diagnostic errors in the experimental group were unique from the errors in the control group, suggesting that a pathologist using the novel technique could achieve close to 100% accuracy. The system's precise diagnostic capacity could also be beneficial to centers that lack access to expert neuropathologists.
Dr Matija Snuderl, MD, associate professor in the Department of Pathology at New York University Grossman School of Medicine and a co-author of the study commented, "SRH will revolutionize the field of neuropathology by improving decision-making during surgery and providing expert-level assessment in the hospitals where trained neuropathologists are not available,"
Professor Orringer joined New York University Langone in August 2019, bringing with him the SRH technology he helped to develop. New York University Langone's Brain and Spine Tumor Center is the first to offer this technique, using Invenio's NIO Laser Imaging System, in the Northeast.
The latest addition to the center's comprehensive suite of neurosurgical imaging technologies, SRH works in concert with intraoperative MRI and fluorescence-guided surgery to provide high-resolution precision guidance for New York University Langone's world-class neurosurgeons.
Dr John G. Golfinos, MD, Joseph P. Ransohoff Professor of Neurology and Chair of the Department of Neurosurgery added, "New York University Langone's Department of Neurosurgery has long been a leader in bringing the most advanced treatment options to our patients. With the addition of Dr. Orringer's expertise and this game-changing technology, we're now even better equipped to provide safe surgeries and quality outcomes for the most complex brain tumor cases."
The introduction and implementation of this new system is the most recent of New York University Langone's efforts to integrate artificial intelligence in clinical practice to improve diagnostics of cancer. Researchers and clinicians at NYU Langone's Perlmutter Cancer Center have made recent strides in lung cancer, breast cancer, and brain tumor.
Reference: Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks, Nature Medicine (2020). DOI: 10.1038/s41591-019-0715-9 ,
