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Clinical Translation of Stimulated Raman Histology

受激拉曼组织学的临床转化

基本信息

批准号：
10654632
负责人：
Daniel Orringer
金额：
$ 44.89万
依托单位：
NEW YORK UNIVERSITY SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-01 至 2027-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10654632
关键词：
ATRX gene Adjuvant Therapy Algorithms Artificial Intelligence Artificial Intelligence platform Biopsy Brain Neoplasms Classification Clinical Clinical Data Clinical Trials Clinical Trials Design Complex Computer software Data Development Diagnosis Diagnostic Diffuse Epigenetic Process Excision Genetic Glioma Goals Guidelines Histologic Histology Human Image Infrastructure Institution Label Link Malignant Neoplasms Medicine Methods Microscopic Molecular Molecular Diagnosis Morphology Multi-Institutional Clinical Trial Mutation Natural Language Processing Nature Operating Rooms Operative Surgical Procedures Optics Pathology Pathology Report Patient Care Patients Radiation Tolerance Specimen Study Subject System Time Training Training and Infrastructure Work algorithm training artificial intelligence algorithm automated algorithm central database clinical translation convolutional neural network data exchange data pipeline deep neural network design diagnostic screening digital pathology feature extraction graph neural network histological image imaging system improved industry partner learning strategy long short term memory molecular diagnostics molecular marker prediction algorithm prospective repository supervised learning targeted treatment tumor tumor diagnosis

项目摘要

Molecular classification has transformed the diagnosis and treatment of brain tumors and created promising avenues for targeted therapies. However, the clinical impact of molecular classification for brain tumor patients has been blunted by long turnaround times (days-weeks), as well as the complex infrastructure and workflow required to access genetic and epigenetic data from clinical specimens. A system for rapid (<2 minute) molecular diagnostic screening would redefine the surgical and non-surgical treatment of diffuse gliomas. Rapid intraoperative molecular classification would identify the patients who would benefit most from radical resection from those with chemo- and/or radio-sensitive tumors where a more conservative surgical approach, relying more heavily on adjuvant treatment, might be best. Immediate molecular classification would also facilitate the use of targeted therapeutics and transform the way clinical trials in the glioma field are designed by enabling rapid identification of potential study subjects based on molecular criteria during or shortly after biopsy. Through our academic-industrial partnership with Invenio Imaging Inc, we developed, implemented and validated an accurate bedside system for rapid morphologic diagnosis through label-free stimulated Raman histologic (SRH) imaging paired with an artificial intelligence-based algorithm (Hollon et al. Nature Medicine 2020). Here, we intend to leverage and enhance our unique AI-based platform for intraoperative diagnosis to predict the molecular alterations that define diffuse gliomas within minutes of biopsy in the operating room without the need for a pathology lab or specialized analytic facility. This proposal is expected to yield an autonomous diagnostic workflow for human brain tumor molecular diagnostics. The work proposed here represents the development of a platform approach by which molecular diagnosis is unlocked through SRH and artificial intelligence, thus creating a new standard of accessibility for molecular diagnosis in human cancer.

分子分类已经改变了脑肿瘤的诊断和治疗，靶向治疗的途径。然而，分子分类对于脑肿瘤患者的临床影响由于周转时间长（数天-数周）以及复杂的基础设施和工作流程，获取临床标本的遗传和表观遗传数据。快速（<2分钟）分子诊断筛查系统将重新定义手术和非手术弥漫性神经胶质瘤的治疗。快速术中分子分类将确定患者，将受益最大的根治性切除那些与化疗和/或放射敏感的肿瘤，其中更多的最好的方法是保守手术，更多地依靠辅助治疗。立即分子分类也将促进靶向治疗的使用，并改变临床治疗的方式。神经胶质瘤领域的试验设计为能够快速识别潜在的研究对象，活检期间或活检后不久的分子标准。通过我们与Invenio Imaging Inc.的学术-工业合作伙伴关系，我们开发，实施和通过无标记受激拉曼验证了用于快速形态学诊断的精确床边系统组织学（SRH）成像与基于人工智能的算法配对（Hollon等人，Nature Medicine 2020年）。在这里，我们打算利用和增强我们独特的基于人工智能的术中诊断平台，预测在活检后几分钟内定义弥漫性胶质瘤的分子改变，手术室，而不需要病理实验室或专门的分析设施。这一建议有望产生一个自主的诊断工作流程，为人类脑肿瘤分子诊断这里提出的工作代表了一个平台方法的发展，通过该平台方法，通过SRH和人工智能解锁诊断，从而为人类癌症的分子诊断。