PURPOSE: Raman spectroscopy can quickly and accurately diagnose tissue in near real-time. This study evaluated the capacity of Raman spectroscopy to diagnose pediatric brain tumors. EXPERIMENTAL DESIGN: Samples of untreated pediatric medulloblastoma (4 samples and 4 patients), glioma (i.e. astrocytoma, oligodendroglioma, ependymoma, ganglioglioma and other gliomas; 27 samples and 19 patients), and normal brain samples (33 samples and 5 patients) were collected fresh from the operating room or from our frozen tumor bank. Samples were divided and tested using routine pathology and Raman spectroscopy. Twelve Raman spectra were collected per sample. Support vector machine analysis was used to classify spectra using the pathology diagnosis as the gold standard. RESULTS: Normal brain (321 spectra), glioma (246 spectra) and medulloblastoma (82 spectra) were identified with 96.9, 96.7 and 93.9% accuracy, respectively, when compared with each other. High-grade ependymomas (41 spectra) were differentiated from low-grade ependymomas (25 spectra) with 100% sensitivity and 96.0% specificity. Normal brain tissue was distinguished from low-grade glioma (118 spectra) with 91.5% sensitivity and 97.8% specificity. For these analyses, the tissue-level classification was determined to be 100% accurate. CONCLUSION: These results suggest Raman spectroscopy can accurately distinguish pediatric brain neoplasms from normal brain tissue, similar tumor types from each other and high-grade from low-grade tumors.
PURPOSE: Raman spectroscopy can quickly and accurately diagnose tissue in near real-time. This study evaluated the capacity of Raman spectroscopy to diagnose pediatric brain tumors. EXPERIMENTAL DESIGN: Samples of untreated pediatric medulloblastoma (4 samples and 4 patients), glioma (i.e. astrocytoma, oligodendroglioma, ependymoma, ganglioglioma and other gliomas; 27 samples and 19 patients), and normal brain samples (33 samples and 5 patients) were collected fresh from the operating room or from our frozen tumor bank. Samples were divided and tested using routine pathology and Raman spectroscopy. Twelve Raman spectra were collected per sample. Support vector machine analysis was used to classify spectra using the pathology diagnosis as the gold standard. RESULTS: Normal brain (321 spectra), glioma (246 spectra) and medulloblastoma (82 spectra) were identified with 96.9, 96.7 and 93.9% accuracy, respectively, when compared with each other. High-grade ependymomas (41 spectra) were differentiated from low-grade ependymomas (25 spectra) with 100% sensitivity and 96.0% specificity. Normal brain tissue was distinguished from low-grade glioma (118 spectra) with 91.5% sensitivity and 97.8% specificity. For these analyses, the tissue-level classification was determined to be 100% accurate. CONCLUSION: These results suggest Raman spectroscopy can accurately distinguish pediatric brain neoplasms from normal brain tissue, similar tumor types from each other and high-grade from low-grade tumors.
Authors: Steven N Kalkanis; Rachel E Kast; Mark L Rosenblum; Tom Mikkelsen; Sally M Yurgelevic; Katrina M Nelson; Aditya Raghunathan; Laila M Poisson; Gregory W Auner Journal: J Neurooncol Date: 2014-01-04 Impact factor: 4.130
Authors: Todd Hollon; Spencer Lewis; Christian W Freudiger; X Sunney Xie; Daniel A Orringer Journal: Neurosurg Focus Date: 2016-03 Impact factor: 4.047
Authors: Damon DePaoli; Émile Lemoine; Katherine Ember; Martin Parent; Michel Prud'homme; Léo Cantin; Kevin Petrecca; Frédéric Leblond; Daniel C Côté Journal: J Biomed Opt Date: 2020-05 Impact factor: 3.170
Authors: Christopher D Bennett; Sarah E Kohe; Simrandip K Gill; Nigel P Davies; Martin Wilson; Lisa C D Storer; Timothy Ritzmann; Simon M L Paine; Ian S Scott; Ina Nicklaus-Wollenteit; Daniel A Tennant; Richard G Grundy; Andrew C Peet Journal: Sci Rep Date: 2018-08-10 Impact factor: 4.379