Real time colon histopathology by infrared spectroscopic imaging

通过红外光谱成像进行实时结肠组织病理学

• 批准号: 10318008
• 负责人: Rohit Bhargava
• 金额: $ 46.61万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10318008
• 关键词: Analysis of Variance; Archives; Artificial Intelligence; Benchmarking; Cancerous; Caring; Cause of Death; Chemicals; Clinical; Code; Collaborations; Colon; Color; Colorectal Cancer; Complement; Computer software; Confusion; Custom; Data; Detection; Disease; Dyes; Early Intervention; Electronics; Epithelial Cells; Excision; Foundations; Fourier Transform; Fresh Tissue; Future; Goals; Gold; Histocompatibility Testing; Histologic; Histology; Histopathology; Image; Image Analysis; Imaging Device; Imaging technology; Immersion; Laboratories; Laboratory Chemicals; Laboratory Technicians; Lead; Logistic Regressions; Lymph Node Involvement; Machine Learning; Malignant Neoplasms; Measures; Methods; Microscope; Microscopy; Microtomy; Modeling; Modernization; Molecular; Molecular Analysis; Morphology; Operative Surgical Procedures; Optics; Outcome; Pathology; Patient Care; Patient-Focused Outcomes; Patients; Pattern; Performance; Persons; Polyps; Process; Protocols documentation; ROC Curve; Reagent; Regression Analysis; Reporting; Research; Resources; Risk; Route; Sampling; Severity of illness; Solid; Spectroscopy, Fourier Transform Infrared; Staging; Stains; Statistical Methods; Surface; System; Techniques; Technology; Testing; Time; Tissue Microarray; Tissues; Training; Translating; Validation; Work; analytical method; base; cancer diagnosis; cancer imaging; cell type; clinical translation; colorectal cancer treatment; coronavirus disease; deep learning; design; design and construction; disorder risk; experience; follow-up; high risk; human data; imaging system; improved outcome; instrument; intelligent algorithm; learning strategy; lens; novel; pathology imaging; pre-clinical; prevent; prognostic; real-time images; rural setting; sample fixation; screening; seal; spectroscopic imaging; success; technology validation; tissue archive; tool; tumor microenvironment

Abstract Colorectal cancer (CRC) is one of the leading causes of death in the US. Active screening and early intervention in risky cancers can lead to good outcomes; however, a bottleneck in rapidly delivering appropriate patient care is the long time period for histologic assessment and lack of precision in predicting disease severity. Morphological assessments prevalent in histology are useful but resource intensive and not predictive enough. Molecular techniques to complement traditional pathology are emerging but often require much more effort and time, without being especially compatible with histologic assessments. Here, we seek to develop a technology that measures the chemical content of tissues, does not require reagents, is entirely compatible with clinical workflows and leverages modern artificial intelligence (AI) techniques to provide real-time histologic assessment. The foundation of our approach is a new design for an infrared spectroscopic imaging system that is faster than any reported, offers a higher spatial and spectral quality and uses a solid immersion lens with a fixed focus at the sealed surface of the lens to enable use by a minimally trained person. In conjunction with the instrument, we develop AI algorithms that measure the chemical content of tissue and use it to provide (a) conventional pathology images without the use of dyes (“stainless staining”), and (b) histologic assessment based on molecular data, which can provide complementary composition, disease and risk of lethal cancer images akin to conventional pathology. The instrument will be usable by laboratory technicians, without the need to prepare thin sections from excised tissue and will provide information in minutes. Using preliminary data from human patients on over 850 tissue microarray (TMA) samples from 8 TMAs and 30 surgical resections, we validate the use of technology in providing complete histologic and disease grade assessment. Statistical methods will be used to assess the results rigorously and quantitative milestones guide the entire approach. We then translate the results to fresh tissue chunks, providing histology minutes after tissue is extracted from the body. Finally, we use the detailed tumor and microenvironment information available from the tissue to segment patients into a “high risk” and “low risk” group. The availability of rapid histologic assessment can help prevent delays in providing care, provide intraoperative assessment, and add more information to morphologic assessments following screening, enabling a wide use in CRC and other cancer pathologies.

摘要 结直肠癌（CRC）是美国的主要死亡原因之一。积极筛查和早期干预 在危险的癌症可以导致良好的结果;然而，在迅速提供适当的病人护理的瓶颈 组织学评估的时间较长，并且在预测疾病严重程度方面缺乏准确性。 组织学中普遍存在的形态学评估是有用的，但资源密集且预测性不足。 补充传统病理学的分子技术正在出现，但往往需要更多的努力， 时间，而不是特别符合组织学评估。在这里，我们寻求开发一种技术， 它测量组织的化学成分，不需要试剂，完全符合临床要求。 我们的工作流程，并利用现代人工智能（AI）技术提供实时组织学评估。 我们的方法的基础是一种新的红外光谱成像系统的设计， 任何报道，提供了更高的空间和光谱质量，并使用固体浸没透镜与固定焦点， 该透镜的密封表面能够由最低限度训练的人使用。结合该仪器， 我们开发人工智能算法，测量组织的化学成分，并使用它来提供（一个）传统的 不使用染料的病理学图像（“不锈钢染色”），和（B）基于 分子数据，它可以提供互补的组成，疾病和致命癌症的风险图像类似， 传统病理学该仪器将由实验室技术人员使用，而无需准备薄 从切除的组织切片，并将在几分钟内提供信息。利用人类患者的初步数据 在来自8个TMA和30个手术切除的850多个组织微阵列（TMA）样本上，我们验证了 技术提供完整的组织学和疾病分级评估。统计方法将用于 严格评估结果，量化里程碑指导整个方法。然后我们将结果 到新鲜的组织块，在组织从身体中提取后几分钟提供组织学。最后，我们使用 从组织中获得详细的肿瘤和微环境信息，以将患者分为“高风险” “低风险”人群。快速组织学评估的可用性有助于防止提供护理的延误， 提供术中评估，并在筛选后的形态学评估中添加更多信息， 使得能够广泛用于CRC和其它癌症病理学。