Intraoperative Detection of Pediatric Brain Tumor Margins Using Raman Spectroscopy. (1st September 2019)

• Record Type: Journal Article
• Authors: Jabarkheel, Rashad; Parker, Jonathon J; Ho, Chi-Sing; Shaffer, Travis; Gambhir, Sanjiv; Grant, Gerald A; Yecies, Derek W
• Is Part Of: Neurosurgery. Volume 66(2010)Supplement 1
• Publication Date: 2019-09-01
• Subjects: Nervous system -- Surgery -- Periodicals; 617.48005
• DOI: 10.1093/neuros/nyz310_132
• Languages: English
• ISSNs: 0148-396X

Abstract

Abstract: INTRODUCTION: Surgical resection is a mainstay of treatment in patients with brain tumors both for tissue diagnosis and for tumor debulking. While maximal resection of tumors is desired, neurosurgeons can be limited by the challenge of differentiating normal brain from tumor using only microscopic visualization and tactile feedback. Additionally, intraoperative decision-making regarding how aggressively to pursue a gross total resection frequently relies on pathologic preliminary diagnosis using frozen sections which are both time consuming and fallible. Here, we investigate the potential for Raman spectroscopy (RS) to rapidly detect pediatric brain tumor margins and classify brain tissue samples equivalent to histopathology. METHODS: Using a first-of-its-kind rapid acquisition RS device we intraoperatively imaged fresh ex vivo pediatric brain tissue samples (2-3 mm × 2-3 mm × 2-3 mm) at the Lucille Packard Children's Hospital. All imaged samples received standard final histopathological analysis, as RS is a nondestructive imaging technique. We curated a labeled dataset of 575 + unique Raman spectra gathered from 160 + brain samples resulting from 23 pediatric patients who underwent brain tissue resection as part of tumor debulking or epilepsy surgery (normal controls). RESULTS: To our knowledge we have created the largest labeled Raman spectra dataset of pediatric brain tumors. We are developing an end-to-end machine learning model that can predict final histopathology diagnosis within minutes from Raman spectral data. Our preliminary principle component analyses suggest that RS can be used to classify various brain tumors similar to "frozen" histopathology and can differentiate normal from malignant brain tissue in the context of low-grade glioma resections. CONCLUSION: Our work suggests that machine learning approaches can be used to harness the material identification properties of RS for classifying brain tumors and detecting their margins.