Complete resection of malignant gliomas is hampered by the difficulty in distinguishing tumor cells at the infiltration zone. Fluorescence guidance with 5-ALA assists in reaching this goal. Using hyperspectral imaging, previous work characterized five fluorophores' emission spectra in most human brain tumors. In this paper, the effectiveness of these five spectra was explored for different tumor and tissue classification tasks in 184 patients (891 hyperspectral measurements) harboring low- (n=30) and high-grade gliomas (n=115), non-glial primary brain tumors (n=19), radiation necrosis (n=2), miscellaneous (n=10) and metastases (n=8). Four machine learning models were trained to classify tumor type, grade, glioma margins and IDH mutation. Using random forests and multi-layer perceptrons, the classifiers achieved average test accuracies of 74-82%, 79%, 81%, and 93% respectively. All five fluorophore abundances varied between tumor margin types and tumor grades (p < 0.01). For tissue type, at least four of the five fluorophore abundances were found to be significantly different (p < 0.01) between all classes. These results demonstrate the fluorophores' differing abundances in different tissue classes, as well as the value of the five fluorophores as potential optical biomarkers, opening new opportunities for intraoperative classification systems in fluorescence-guided neurosurgery.

使用5-ALA辅助荧光引导，通过高光谱成像对184名患有低级别和高级别胶质瘤以及其他脑肿瘤的患者的891个高光谱测量进行了评估，训练了四个机器学习模型以进行肿瘤分类和辨别，结果表明五个发光物质在不同组织类型中的丰度存在差异，并展示了作为光学生物标志物具有潜在价值，为荧光引导神经外科手术中的围手术期分类系统提供了新机遇。

基于机器学习的定量高光谱图像引导脑肿瘤切除