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.

抽象的 结直肠癌（CRC）是美国死亡的主要原因之一。主动筛查和早期干预 在危险中，癌症会带来良好的结果；但是，快速提供适当患者护理的瓶颈 是组织学评估的长时间，并且缺乏预测疾病严重程度的精确度。 在组织学中普遍存在的形态评估是有用的，但资源密集且预测不够。 补充传统病理学的分子技术正在出现，但通常需要更多的精力 时间，不与组织学评估特别兼容。在这里，我们试图开发一项技术 测量组织的化学含量（不需要试剂）与临床完全兼容 工作流程和利用现代人工智能（AI）技术提供实时组织学评估。 我们方法的基础是针对红外光谱成像系统的新设计，它比 任何报告，提供更高的空间和光谱质量 镜头的密封表面能够由受过最小训练的人使用。与乐器结合 我们开发了AI算法来测量组织的化学含量并使用它来提供（a）常规 病理图像不使用染料（“不锈钢染色”）和（b）基于组织学评估 分子数据，可以提供类似 常规病理。该工具将由实验室技术人员使用，而无需准备薄薄 优质组织的部分，将在几分钟内提供信息。使用人类患者的初步数据 在850多个组织微阵列（TMA）样品中，我们从8个TMA和30个手术切除术中验证 提供完整的组织学和疾病评估的技术。统计方法将用于 严格评估结果，定量里程碑指导整个方法。然后我们翻译结果 到新鲜的组织块，从人体提取组织后几分钟提供组织学。最后，我们使用 详细的肿瘤和微环境信息从组织到将患者分割为“高风险” 和“低风险”组。快速组织学评估的可用性可以帮助防止延迟提供护理， 提供术中评估，并在筛查后将更多信息添加到形态评估中， 可以广泛用于CRC和其他癌症病理。