分子分类已通过使预后更准确和个性化治疗而转变了脑肿瘤的管理。 脑肿瘤患者获得及时的分子诊断测试有限，这使得手术和辅助治疗复杂化，并阻碍了临床试验的招募。 通过结合刺激拉曼组织学（SRH），一种快速，无标记，非消耗性的光学成像方法和基于深度学习的图像分类，我们能够预测由世界卫生组织（WHO）用于定义成人型弥漫性胶质瘤分类法的分子遗传特征，包括IDH-1/2，1p19q-缺失和ATRX缺失。我们开发了一种多模式深度神经网络训练策略，使用SRH图像和大规模公共弥漫性胶质瘤基因组数据（即TCGA，CGGA等），以实现最佳分子分类性能。 模型训练使用了一所机构（密歇根大学），而4个机构（纽约大学，加利福尼亚大学旧金山分校，维也纳医科大学和科隆大学医院）被纳入前瞻性测试队列的患者招募。使用我们的系统-DeepGlioma，在手术室内2分钟内，我们实现了93.2％的平均分子遗传分类准确性，并以91.5％的准确性确定了正确的弥漫性胶质瘤分子亚组。 DeepGlioma表现优于传统的IDH1-R132H免疫组织化学（94.2％对91.4％准确性），作为弥漫性胶质瘤的一线分子诊断筛查方法，并可检测规范和非规范IDH突变。我们的研究结果展示了人工智能和光学组织学如何在手术期间为脑肿瘤患者提供快速可扩展的分子诊断方法的替代方案。版权所有©神经外科医师学会2023年。保留所有权利。

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. Access to timely molecular diagnostic testing for brain tumor patients is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment.By combining stimulated Raman histology (SRH), a rapid, label-free, non-consumptive, optical imaging method, and deep learning-based image classification, we are able to predict the molecular genetic features used by the World Health Organization (WHO) to define the adult-type diffuse glioma taxonomy, including IDH-1/2, 1p19q-codeletion, and ATRX loss. We developed a multimodal deep neural network training strategy that uses both SRH images and large-scale, public diffuse glioma genomic data (i.e. TCGA, CGGA, etc.) in order to achieve optimal molecular classification performance.One institution was used for model training (University of Michigan) and four institutions (NYU, UCSF, Medical University of Vienna, and University Hospital Cologne) were included for patient enrollment in the prospective testing cohort. Using our system, called DeepGlioma, we achieved an average molecular genetic classification accuracy of 93.2% and identified the correct diffuse glioma molecular subgroup with 91.5% accuracy within 2 minutes in the operating room. DeepGlioma outperformed conventional IDH1-R132H immunohistochemistry (94.2% versus 91.4% accuracy) as a first-line molecular diagnostic screening method for diffuse gliomas and can detect canonical and non-canonical IDH mutations.Our results demonstrate how artificial intelligence and optical histology can be used to provide a rapid and scalable alternative to wet lab methods for the molecular diagnosis of brain tumor patients during surgery.Copyright © Congress of Neurological Surgeons 2023. All rights reserved.