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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=656937
• 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)来发现。虽然生成的医学图像可以表征肿瘤的位置(S)和解剖关系，但它们往往无法具体识别肿瘤的性质和检测其完整的空间范围。组织学、分子和基因组学特征通常需要有针对性的针刺活检方法(直接组织取样)，从而获得准确的诊断和治疗计划。然而，目标错误、样本质量差和疾病异质性可能导致采样不准确，往往导致非诊断性样本。确诊后，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，往往会导致疾病复发。*病变组织和正常组织表现出不同的形态、分子和生化特征，这些特征可以根据与吸收(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得出的光学特性进行量化。这个自然科学和工程项目旨在开发新的光学光谱仪器和生物物理/统计模型，以提高对脑、乳腺癌、肺癌、口腔和喉癌、卵巢癌、前列腺癌和皮肤癌等不同器官部位的癌症组织检测和分类的前景，从而实现高灵敏度和准确的分子组织表征。具体地说，该项目将导致开发：1)非接触式宏观广域光学成像平台和生物物理模型，用于定量组织鉴定，该平台基于与拉曼、荧光和漫反射相关的生物标记图，将使用肿瘤切除手术期间采集的组织进行验证；2)集成多模光纤针，用于实时原位组织鉴定；3)显微成像技术，用于发现宏观工具检测到的信号的潜在细胞和细胞外来源，从而在所有尺度上进行详细的组织鉴定。这项提议将为新一代癌症检测技术奠定基础。它应该有助于通过减少昂贵和耗时的重复程序的次数来减少标准程序的低效和下游成本，从而提高患者的生存前景和医疗保健成本效益。