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新技术实现近实时术中脑肿瘤诊断
作者:小柯机器人 发布时间:2020/1/9 14:37:06

2020年1月6日,《自然—医学》在线发表了美国科学家的一项最新研究成果。来自密歇根大学的Daniel A. Orringer团队利用受激拉曼组织学和深度神经网络实现了近实时术中脑肿瘤诊断。

研究人员报告了一个并行工作流程,其结合了受激拉曼组织学(SRH)无标签光学成像方法和深度卷积神经网络(CNN),以自动化方式几乎实时地在床边预测诊断。具体而言,这一CNN经过250万幅SRH图像训练后,可以在150微秒内预测手术室中的脑肿瘤诊断,比传统技术要快一个数量级(20–30微分钟)。在一项多中心、前瞻性临床试验(n = 278)中,研究人员证明了基于CNN的SRH图像诊断不逊于基于病理学家对常规组织学图像的解读(总体准确性分别为94.6%和93.9%)。这一CNN学习了可识别组织学特征的层次结构,从而能够对脑肿瘤的主要组织病理学类别进行分类。此外,研究人员实施了语义分割方法以识别SRH图像中的肿瘤浸润诊断区域。这些结果证明了如何简化术中癌症诊断,为组织诊断创造了一条独立于传统病理实验室的互补途径。

据悉,术中诊断对于在癌症手术期间提供安全有效的护理至关重要。现有的基于苏木精和伊红染色对被处理组织进行术中诊断的工作流程非常耗时、耗费资源和劳动力。此外,术中组织学图像的解读取决于人手不够的且分布不均的病理诊断人员。

附:英文原文

Title: Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks

Author: Todd C. Hollon, Balaji Pandian, Arjun R. Adapa, Esteban Urias, Akshay V. Save, Siri Sahib S. Khalsa, Daniel G. Eichberg, Randy S. DAmico, Zia U. Farooq, Spencer Lewis, Petros D. Petridis, Tamara Marie, Ashish H. Shah, Hugh J. L. Garton, Cormac O. Maher, Jason A. Heth, Erin L. McKean, Stephen E. Sullivan, Shawn L. Hervey-Jumper, Parag G. Patil, B. Gregory Thompson, Oren Sagher, Guy M. McKhann, Ricardo J. Komotar, Michael E. Ivan, Matija Snuderl, Marc L. Otten, Timothy D. Johnson, Michael B. Sisti, Jeffrey N. Bruce, Karin M. Muraszko, Jay Trautman, Christian W. Freudiger, Peter Canoll, Honglak Lee, Sandra Camelo-Piragua, Daniel A. Orringer

Issue&Volume: 2020-01-06

Abstract: Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce4. In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH)5,6,7, a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the operating room in under 150s, an order of magnitude faster than conventional techniques (for example, 20–30min)2. In a multicenter, prospective clinical trial (n=278), we demonstrated that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% versus 93.9%). Our CNNs learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. In addition, we implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory.

DOI: 10.1038/s41591-019-0715-9

Source: https://www.nature.com/articles/s41591-019-0715-9

期刊信息

Nature Medicine:《自然—医学》,创刊于1995年。隶属于施普林格·自然出版集团,最新IF:30.641
官方网址:https://www.nature.com/nm/
投稿链接:https://mts-nmed.nature.com/cgi-bin/main.plex