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