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.

翻译：恶性胶质瘤的完全切除因难以区分浸润区的肿瘤细胞而受阻。使用5-ALA的荧光成像有助于实现这一目标。通过高光谱成像，先前研究表征了大多数人类脑肿瘤中五种荧光团的发射光谱。本文在184例患者（891次高光谱测量）中探讨了这五种光谱对不同肿瘤和组织分类任务的有效性，患者包括低级别胶质瘤（30例）、高级别胶质瘤（115例）、非胶质源性原发性脑肿瘤（19例）、放射性坏死（2例）、其他类型（10例）及转移瘤（8例）。训练了四种机器学习模型以分类肿瘤类型、级别、胶质瘤边界和IDH突变。采用随机森林和多层感知器，分类器在测试集上的平均准确率分别达到74-82%、79%、81%和93%。五种荧光团的丰度在肿瘤边界类型和肿瘤级别间均存在显著差异（p < 0.01）。对于组织类型，所有类别间至少四种荧光团的丰度存在显著差异（p < 0.01）。这些结果揭示了不同组织类别中荧光团的差异化丰度，以及五种荧光团作为潜在光学生物标志物的价值，为荧光引导神经外科手术中的术中分类系统开辟了新机遇。