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: 10661561
• 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/10661561
• 关键词: Analysis of Variance; Artificial Intelligence; Benchmarking; Cancerous; Caring; Cause of Death; Cells; 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; Histocompatibility Testing; Histologic; Histology; Histopathology; Image; Image Analysis; Imaging Device; Imaging technology; Immersion; Laboratories; Laboratory Chemicals; Laboratory Technicians; 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; Property; Protocols documentation; ROC Curve; Reagent; Regression Analysis; Reporting; Research; Resources; Risk; Route; Sampling; Severity of illness; Solid; Spectroscopy, Fourier Transform Infrared; Speed; Staging; Stains; Statistical Methods; Surface; System; Techniques; Technology; Testing; Time; Tissue Microarray; Tissues; Training; Translating; Validation; Work; analytical method; artificial intelligence algorithm; artificial intelligence method; cancer diagnosis; cancer imaging; cell type; chemical addition; colorectal cancer treatment; coronavirus disease; deep learning; design; 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 platform; technology validation; tissue fixing; tool; translational clinical trial; tumor; tumor microenvironment; usability

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）是美国主动筛查的主要原因之一。 在危险中，癌症可以带来良好的结果； 是组织学评估的长时间，并且在预测疾病严重程度方面缺乏精确度。 在组织学中普遍存在的形态评估是我们的，但资源密集型的起诉足够足够。 汇编传统病理学的分子技术正在出现，但是却需要更多更多的努力和 时间，不尤其与组织学评估兼容。 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延展性延展性型光谱成像系统的新设计。 任何报告，提供更高的空间和光谱质量 镜头的密封表面可以与训练有素的人一起使用。 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）基于基于 分子数据，可以提供类似于莱特剂图像的组成，疾病和风险 传统的病理学。 切除组织的切片，并将在几分钟内提供信息。 在超过850个来自8个TMA和30个手术分辨率的组织微阵列（TMA）样品中，我们验证了使用的使用 提供完整的组织学和疾病评估的技术。 评估结果严格和定量里程碑，我们将结果盗窃。 到新鲜的组织块，从人体提取组织后几分钟提供组织学。 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。 提供术中评估，并在筛选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。

项目成果

Colon Cancer Grading Using Infrared Spectroscopic Imaging-Based Deep Learning.

• DOI: 10.1177/00037028221076170
• 发表时间: 2022-04
• 期刊: Applied spectroscopy
• 影响因子: 3.5

Phasor Representation Approach for Rapid Exploratory Analysis of Large Infrared Spectroscopic Imaging Data Sets.

用于快速探索性分析大型红外光谱成像数据集的相量表示方法。

• DOI: 10.1021/acs.analchem.3c01539
• 发表时间: 2023
• 期刊: Analytical chemistry
• 影响因子: 7.4
• 作者: Mukherjee,SudiptaS;Bhargava,Rohit
• 通讯作者: Bhargava,Rohit

A generative adversarial approach to facilitate archival-quality histopathologic diagnoses from frozen tissue sections.

• DOI: 10.1038/s41374-021-00718-y
• 发表时间: 2022-05
• 期刊: LABORATORY INVESTIGATION
• 影响因子: 5
• 作者: Falahkheirkhah, Kianoush;Guo, Tao;Hwang, Michael;Tamboli, Pheroze;Wood, Christopher G.;Karam, Jose A.;Sircar, Kanishka;Bhargava, Rohit
• 通讯作者: Bhargava, Rohit