Development of a label-free and multimodal optical spectroscopy imaging platform for biological tissue characterization and classification based on biophysical and statistical modeling techniques

基于生物物理和统计建模技术，开发用于生物组织表征和分类的无标记和多模态光谱成像平台

• 批准号: RGPIN-2018-06700
• 负责人: Leblond, Frederic
• 金额: $ 3.64万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=689894
• 关键词: Development; label; free; multimodal; optical

Human diseases associated with solid tumors are usually discovered by computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), single positron emission tomography (SPECT) and ultrasound (US). While the resulting medical images can characterize the location(s) and anatomical relationships of the tumors, they are often unable to specifically identify their nature and detect their full spatial extent. Targeted needle biopsy approaches (direct tissue sampling) are often required for histological, molecular, and genomic characterization leading to a precise diagnosis and treatment planning. However, targeting errors, poor quality samples and disease heterogeneity can cause inaccurate sampling often leading to non-diagnostic specimens. After a diagnosis is made, surgery (resection of cancerous tissue) remains the first-line therapy for many cancers, but its effectiveness is reduced when all pathological cells are not detected, often leading to disease recurrences. ***Diseased and normal tissue exhibit different morphological, molecular and biochemical characteristics that can be quantified based on optical properties derived from spectroscopic optical data associated with absorption (vascular information), elastic scattering (tissue microstructure), fluorescence (metabolites, proteins) and inelastic Raman scattering (lipids, proteins, DNA/RNA). This Natural Sciences and Engineering project aims to develop new optical spectroscopy instruments and biophysical/statistical models allowing highly sensitive and accurate molecular tissue characterization with the perspective of improving cancer tissue detection and classification for various organ sites including brain, breast, lung, mouth & throat, ovarian, prostate and skin cancer. Specifically, this project will lead to the development of: 1) a non-contact macroscopic wide-field optical imaging platform and biophysical models for quantitative tissue characterization based on biomarker maps associated with Raman, fluorescence and diffuse reflectance, to be validated using tissue acquired during tumor resection surgeries, 2) integrated multimodal fiber optics needles for real time in situ tissue characterization, and 3) microscopic imaging techniques to discover the underlying cellular and extracellular origin of the signals detected with the macroscopic tools, leading to detailed tissue characterization across all scales. This proposal will set up the stage for a new generation of cancer detection technologies. It should contribute to improving patient survival prospects and health care cost-effectiveness by reducing inefficiencies and downstream costs of the standard procedures through a decrease of the number of repeat procedures, which are expensive and time-consuming.

与实体瘤相关的人类疾病通常通过计算机断层扫描（CT），磁共振成像（MRI），正电子发射断层扫描（PET），单个正电子发射断层扫描（SPECT）和超声（US）发现。尽管所得的医学图像可以表征肿瘤的位置和解剖学关系，但它们通常无法具体识别其性质并检测其全空间范围。靶向针头活检方法（直接组织采样）通常需要进行组织学，分子和基因组表征，从而导致精确的诊断和治疗计划。但是，靶向错误，质量差的样本和疾病异质性可能会导致不准确的采样，通常会导致非诊断标本。做出诊断后，手术（癌组织的切除）仍然是许多癌症的一线治疗，但是当未发现所有病理细胞时，其有效性降低了，通常会导致疾病复发。 ***Diseased and normal tissue exhibit different morphological, molecular and biochemical characteristics that can be quantified based on optical properties derived from spectroscopic optical data associated with absorption (vascular information), elastic scattering (tissue microstructure), fluorescence (metabolites, proteins) and inelastic Raman scattering (lipids, proteins, DNA/RNA).这个自然科学和工程项目旨在开发新的光谱仪器和生物物理/统计模型，从而允许高度敏感，准确的分子组织表征，以改善各种器官的癌症组织检测和分类，包括大脑，乳腺癌，肺，肺，肺，嘴和卵巢，卵巢，卵巢，前列腺和皮肤癌。具体而言，该项目将导致：1）基于与拉曼相关的生物标志物图，荧光和弥漫性反射率相关的生物标志物图的定量组织表征的非接触型宏观宽视野光学成像平台和生物物理模型，以在肿瘤切除术中的组织和3个在肿瘤分解过程中获得的组织验证，2）在肿瘤分解过程中进行验证2）微观成像技术可发现用宏观工具检测到的信号的潜在细胞和细胞外来源，从而导致所有尺度上的详细组织表征。该提案将为新一代的癌症检测技术建立舞台。它应该通过减少昂贵且耗时的重复程序数量来降低标准程序的效率低下和下游成本，从而改善患者的生存前景和医疗保健成本效益。