Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. In this study, we developed DeepGlioma, a rapid ($< 90$ seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas. DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH); a rapid, label-free, non-consumptive, optical imaging method; and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of patients with diffuse glioma ($n=153$) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular classification accuracy of $93.3\pm 1.6\%$. Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular screening of patients with diffuse glioma.

翻译：分子分型通过实现更精准的预后评估和个性化治疗，彻底改变了脑肿瘤的诊疗模式。然而，脑肿瘤患者的及时分子诊断检测仍存在诸多限制，这不仅使手术及辅助治疗方案复杂化，也阻碍了临床试验的入组。在本研究中，我们开发了DeepGlioma——一种基于人工智能的快速（<90秒）诊断筛查系统，旨在简化弥漫性胶质瘤的分子诊断流程。DeepGlioma采用多模态数据集进行训练，该数据集包含受激拉曼组织学成像（SRH）——一种快速、无标记、无耗损的光学成像方法——以及大规模公共基因组数据。在一项针对接受实时SRH成像的弥漫性胶质瘤患者（n=153）的前瞻性、多中心国际测试队列中，我们证明了DeepGlioma能够预测世界卫生组织定义的成人型弥漫性胶质瘤分类标准所需的分子改变（IDH突变、1p19q共缺失和ATRX突变），平均分子分型准确率达到93.3±1.6%。我们的研究结果表明，人工智能与光学组织学技术可作为湿实验方法的快速可扩展辅助手段，用于弥漫性胶质瘤患者的分子筛查。