Evaluation of a Raman Spectroscopy Probe in the Diagnosis of Brain Tumors
ISEF · 2021 Biomedical and Health Sciences
Overview
As the percentage of brain tumor that is resected increases, a patient’s life expectancy drastically increases. Thus, neurosurgeons need to be able to differentiate between healthy brain tissue and tumors in real-time during surgery, and more easily make an intraoperative decision as to which part of the brain to resect. Raman Spectroscopy is a unique type of measurement of inelastic light spectra that can be used to determine the spectral fingerprint of molecules, and identify and differentiate molecules of any substance. In this project, a wand utilizing Raman spectroscopy was used to take 1066 spectra from 225 brain tissue samples from brain surgeries. In the operating room, I used the wand to assess the spectra of the brain tissues. I then compared these spectra against the pathology reports of the same tissues, to judge the efficacy of the Raman wand in identifying normal brain tissue vs. the various brain tumors. Finally, I split the data into two groups: a training subset to teach an algorithm to differentiate between the groups, and a testing subset to judge the efficacy of the machine learning program on actual specimens. The result is that the Spectroscopic wand was 94% accurate in differentiating between healthy and tumor tissues, with 100% specificity and 100% sensitivity between the various types of tissues. Ultimately, with this new Raman technology that has only recently been tested on living human tissue, we can hopefully help patients with the dreaded disease of malignant brain tumors live longer.
Competition history
- ISEF 2021
Resources
Related projects
ISEF · 2024
Intraoperative Brain Tumor Detection Using Raman Spectroscopy Data and Machine Learning
ISEF · 2023
NueROX: A Visual Diagnosing System Using a Fully Optimized Convolutional Neural Network Architecture for Rapid and Efficient Classification of Tumorous and Non-Tumorous Brain MRI's and a Model for the Segmentation, Dimension, and Severity Estimation of Brain Tumors
ISEF · 2020
Raman Spectroscopy as a Novel Autonomous Machine Learning and Chemical Imaging System Used to Distinguish Quantitative Abnormalities in MCF-7, MDA-MB-231, and MCF10-A Cells for the Early Detection of Basal and Epithelial Breast Cancer through Inelastic Scattering and Spectroscopic Analysis in an in vitro Model
ISEF · 2024
CereVis: A Novel Multimodal Comprehensive Surgical Assistance System for Brain Tumors Utilizing Machine Learning for Pre-Operative Planning and Intra-Operative Surgery
Closest projects by meaning, across every fair and year in the corpus.
Source: Regeneron International Science and Engineering Fair