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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714396
• 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）、单正电子发射断层扫描（SPECT）和超声（US）发现。虽然所得到的医学图像可以表征肿瘤的位置和解剖关系，但它们通常无法具体识别其性质并检测其完整的空间范围。靶向穿刺活检方法（直接组织取样）通常需要进行组织学、分子和基因组表征，从而实现精确的诊断和治疗计划。然而，靶向错误、质量差的样本和疾病异质性可能导致不准确的采样，通常导致非诊断性标本。诊断后，手术（切除癌组织）仍然是许多癌症的一线治疗方法，但当所有病理细胞都没有被检测到时，其有效性就会降低，通常会导致疾病复发。 受激组织和正常组织表现出不同的形态学、分子和生物化学特征，这些特征可以基于从与吸收（血管信息）、弹性散射（组织微结构）、荧光（代谢物、蛋白质）和非弹性拉曼散射（脂质、蛋白质、DNA/RNA）相关的光谱光学数据导出的光学性质来量化。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，允许高灵敏度和准确的分子组织表征，以改善各种器官部位的癌症组织检测和分类，包括脑癌，乳腺癌，肺癌，口腔和咽喉癌，卵巢癌，前列腺癌和皮肤癌。具体而言，该项目将导致制定：1）基于与拉曼、荧光和漫反射相关的生物标记图的用于定量组织表征的非接触宏观宽视场光学成像平台和生物物理模型，其将使用在肿瘤切除手术期间获得的组织来验证，2）用于真实的实时原位组织表征的集成多模式光纤针，以及3）显微成像技术，以发现用宏观工具检测的信号的潜在细胞和细胞外起源，从而导致所有尺度上的详细组织表征。该提案将为新一代癌症检测技术奠定基础。它应有助于改善病人的生存前景和医疗保健的成本效益，通过减少重复程序的数量，减少标准程序的低效率和下游成本，这是昂贵和耗时的。