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.

分子分类改变了脑肿瘤的诊断和治疗，并创造了有希望的有针对性疗法的途径。但是，分子分类对脑肿瘤患者的临床影响漫长的周转时间（日至周年）以及复杂的基础设施和工作流程使从临床标本中访问遗传和表观遗传数据所需。快速（<2分钟）分子诊断筛查的系统将重新定义手术和非手术弥漫性神经胶质瘤的处理。快速术中分子分类将确定从化学敏感性肿瘤和/或无线电敏感的肿瘤的根治切除术中最大的受益保守的外科手术方法更依赖辅助治疗可能是最好的。即时分子分类还将促进靶向治疗剂的使用并改变临床方式神经胶质瘤领域的试验是通过基于基于潜在研究对象的快速鉴定而设计的活检后或不久后的分子标准。通过与Invenio Imaging Inc.的学术工业合作伙伴关系，我们开发，实施并通过无标签的刺激拉曼验证了准确的床头系统，用于快速形态学诊断组织学（SRH）成像与基于人工智能的算法配对（Hollon等人自然医学 2020）。在这里，我们打算利用和增强我们独特的基于AI的术中诊断平台预测在活检分钟内定义弥漫性神经胶质瘤的分子改变手术室无需病理实验室或专门的分析设施。预计该建议将为人脑肿瘤分子产生自主诊断工作流程诊断。此处提出的工作代表了平台方法的发展通过SRH和人工智能解锁诊断，从而为人类癌的分子诊断。