Depth-resolved Quantitative Multi-Modal Imaging for GI Cancer Detection

用于胃肠道癌症检测的深度分辨定量多模态成像

• 批准号: 8114333
• 负责人: Yu Chen
• 金额: $ 19.94万
• 依托单位: UNIV OF MARYLAND, COLLEGE PARK
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2013-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8114333
• 关键词: Animal Model; Biological Markers; Biometry; Cancer Detection; Cancer Diagnostics; Clinic; Clinical; Collaborations; Data; Detection; Development; Diagnosis; Diagnostic; Diagnostic Imaging; Doctor of Philosophy; Dysplasia; Early Diagnosis; Endoscopy; Enzymes; Epithelial; Epithelium; Excision; Excision biopsy; Fluorescence; Gastroenterology; Gastrointestinal tract structure; Generations; Gold; Histopathology; Image; Imaging technology; Lesion; Light; Literature; Malignant Neoplasms; Malignant neoplasm of gastrointestinal tract; Metaplasia; Modality; Molecular; Morphology; Mucous Membrane; Nuclear; Optical Coherence Tomography; Optical Tomography; Pathology; Patients; Peptide Hydrolases; Principal Investigator; Publishing; Qualifying; Research; Resolution; Sampling Errors; Screening for cancer; Sensitivity and Specificity; Surface; Technology; Testing; Texture; Therapeutic; Time; Tissues; Translating; Up-Regulation; base; cancer diagnosis; cancer risk; cancer therapy; diagnostic accuracy; experience; gastrointestinal; improved; in vivo; molecular imaging; neoplastic; novel; novel diagnostics; optical imaging; outcome forecast; receptor expression; technology development; tool; tumor progression

DESCRIPTION (provided by applicant): Early detection of neoplastic changes remains a critical challenge in clinical cancer diagnosis and treatment. Many cancers arise from epithelial layers such as those of the gastrointestinal (GI) tract. White-light endoscopy guided excisional biopsy and histopathology is currently the gold standard for GI cancer diagnosis. However, it suffers from high false negative rates due to the sampling errors. Furthermore, a significant portion (~10%- 30%) of patients after endoscopic ablative therapeutic treatment showed the presence of metaplasia or dysplasia buried underneath the neo-epithelium, which is associated with the risk of cancer development. Current standard endoscopic technology is unable to detect those subsurface lesions. Therefore, there is a critical need for developing new diagnostic tools which can assess tissue architectural and molecular information across the mucosal depth for improved detection of subsurface cancer, and evaluate the invasion depth of a lesion. Since cancer development is associated with in both morphological and molecular alterations, an imaging technology that can quantitative image tissue's morphological and molecular biomarkers and assess the depth- extent of a lesion in vivo and in real time, without the need for tissue excision, would be a major advance in GI cancer diagnostics and therapy. The objective of this application is to develop a new generation of multi-modal depth-resolved imaging technology for GI cancer detection. This proposal is built upon our novel multi-modal optical imaging platform combining high-resolution optical coherence tomography (OCT) and depth-resolved high-sensitivity fluorescence laminar optical tomography (FLOT) for simultaneous structural and molecular imaging. Our extensive preliminary data on multi-modal imaging of animal models demonstrates the feasibility for simultaneous quantitative imaging of structural and molecular information for more accurate diagnosis and prognosis. To establish the diagnostic ability of OCT/FLOT for GI cancer detection, we propose to pursue an Exploratory/Developmental Research (R21) to pursue three specific aims: 1) Image an animal model of GI cancer using OCT/FLOT and obtain the correlated histopathological diagnosis. 2) Develop quantitative structural and molecular imaging parameters from co-registered OCT/FLOT images for multi-parametric analysis. 3) Establish the sensitivity, specificity, and diagnostic accuracy of OCT/FLOT for GI cancer detection, and test the hypothesis of enhanced cancer detection using combined information compared to single modality alone. If successful, this project will result in a fundamentally new non-invasive multi-modal imaging technology for improved GI cancer detection, which can be readily translated into endoscopic imaging in the GI clinics. It is expected to have a major impact on detection, diagnosis, and characterization of GI cancers, as well as a wide range of epithelial cancers. PUBLIC HEALTH RELEVANCE: Early detection of neoplastic changes especially those buried underneath the surface remains a critical challenge in clinical cancer diagnosis and treatment of the gastrointestinal (GI) tract. The objective of this application is to develop a new generation of multi-modal depth-resolved imaging technology for depth- resolved structural and molecular imaging for enhanced GI cancer detection. If successful, this project will result in a new non-invasive multi-modal imaging technology that can be readily translated into endoscopic imaging of GI cancers, as well as a wide range of epithelial cancers.

描述（由申请人提供）：肿瘤变化的早期检测仍然是临床癌症诊断和治疗中的关键挑战。许多癌症源自上皮层，例如胃肠道 (GI) 的上皮层。白光内窥镜引导切除活检和组织病理学是目前胃肠道癌症诊断的金标准。然而，由于抽样误差，它的假阴性率很高。此外，很大一部分（~10%-30%）的患者在内镜消融治疗后显示存在埋藏在新上皮下方的化生或发育异常，这与癌症发展的风险相关。目前的标准内窥镜技术无法检测到这些皮下病变。因此，迫切需要开发新的诊断工具，该工具可以评估粘膜深度的组织结构和分子信息，以改进对表面下癌症的检测，并评估病变的侵袭深度。 由于癌症的发展与形态和分子的改变相关，因此一种成像技术可以对组织的形态和分子生物标志物进行定量成像，并实时评估体内病变的深度范围，而无需切除组织，这将是胃肠道癌症诊断和治疗的重大进步。该应用的目标是开发用于胃肠道癌症检测的新一代多模态深度分辨成像技术。该提案建立在我们新颖的多模态光学成像平台之上，该平台结合了高分辨率光学相干断层扫描（OCT）和深度分辨高灵敏度荧光层流光学断层扫描（FLOT），可同时进行结构和分子成像。我们关于动物模型多模态成像的大量初步数据证明了结构和分子信息同步定量成像的可行性，以实现更准确的诊断和预后。为了建立 OCT/FLOT 对胃肠道癌症检测的诊断能力，我们建议进行探索性/开发研究 (R21)，以实现三个具体目标：1) 使用 OCT/FLOT 对胃肠道癌症动物模型进行成像，并获得相关的组织病理学诊断。 2) 从联合配准的 OCT/FLOT 图像中开发定量结构和分子成像参数，以进行多参数分析。 3) 建立 OCT/FLOT 用于胃肠道癌症检测的敏感性、特异性和诊断准确性，并测试与单独使用单一模式相比使用组合信息增强癌症检测的假设。如果成功，该项目将产生一种全新的非侵入性多模态成像技术，用于改进胃肠道癌症检测，该技术可以很容易地转化为胃肠道诊所的内窥镜成像。预计它将对胃肠道癌症以及各种上皮癌的检测、诊断和表征产生重大影响。 公共卫生相关性：肿瘤变化（尤其是埋藏在表面以下的肿瘤变化）的早期检测仍然是临床癌症诊断和胃肠道（GI）治疗的关键挑战。该应用的目标是开发新一代多模态深度分辨成像技术，用于深度分辨结构和分子成像，以增强胃肠道癌症检测。如果成功，该项目将产生一种新的非侵入性多模态成像技术，该技术可以轻松转化为胃肠道癌症以及多种上皮癌的内窥镜成像。