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: 10426352
• 负责人: Rohit Bhargava
• 金额: $ 41.4万
• 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/10426352
• 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; artificial intelligence algorithm; base; cancer diagnosis; cancer imaging; cell type; clinical translation; colorectal cancer treatment; coronavirus disease; deep learning; design; design and construction; disease prognostic; disorder risk; experience; follow-up; high risk; human data; imaging system; improved outcome; instrument; learning strategy; lens; novel; pathology imaging; pre-clinical; prevent; prognostic; prognostication; 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.

抽象的 结直肠癌（CRC）是美国的主要原因之一。主动筛查和早期干预 在高危癌症中可以带来良好的结果；然而，快速提供适当的患者护理存在瓶颈 组织学评估的时间较长，并且预测疾病严重程度缺乏精确性。 组织学中流行的形态学评估很有用，但资源密集且预测性不够。 补充传统病理学的分子技术正在兴起，但通常需要更多的努力和 时间，但与组织学评估并不特别兼容。在这里，我们寻求开发一种技术 测量组织的化学含量，不需要试剂，完全符合临床 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红外光谱成像系统的新设计，该系统的速度比 据报道，它提供了更高的空间和光谱质量，并使用固定焦点的固体浸没式镜头 镜片的密封表面可供经过最低限度培训的人员使用。与仪器配合使用， 我们开发人工智能算法来测量组织的化学含量，并用它来提供（a）传统的 不使用染料（“不锈钢染色”）的病理学图像，以及（b）基于 分子数据，可以提供补充成分、疾病和致命癌症图像的风险，类似于 常规病理学。该仪器可供实验室技术人员使用，无需准备薄层 切除组织的切片，将在几分钟内提供信息。使用人类患者的初步数据 在来自 8 个 TMA 和 30 个手术切除的超过 850 个组织微阵列 (TMA) 样本中，我们验证了 提供完整的组织学和疾病等级评估的技术。统计方法将用于 严格评估结果，量化里程碑指导整个方法。然后我们翻译结果 新鲜组织块，在从体内提取组织几分钟后提供组织学分析。最后，我们使用 从组织中获得详细的肿瘤和微环境信息，将患者分为“高风险” 和“低风险”组。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，并在筛查后为形态学评估添加更多信息， 使其能够广泛应用于结直肠癌和其他癌症病理学。