A Platform for Cancer Biomarker Validation: Image Fusion Using NIR Fluorescence

癌症生物标志物验证平台：使用近红外荧光的图像融合

• 批准号: 7583121
• 负责人: John V Frangioni
• 金额: $ 74.59万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-02-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7583121
• 关键词: 3-Dimensional; Algorithms; Antibodies; Biological Markers; Blood Vessels; Boston; Cancer Patient; Chronic; Clinical; Clinical Research; Complex; Data Set; Data Storage and Retrieval; Detection; Epithelial; Evaluation; Excision; Fluorescence; Foundations; Frequencies; Gadolinium; Genetic Markers; Genitourinary system; Genotype; Gland; Gleason Grade for Prostate Cancer; Goals; Gold; Hematoxylin and Eosin Staining Method; Histology; Human; Image; Image Enhancement; Individual; Injection of therapeutic agent; Interobserver Variability; Intervention; Israel; Laboratories; Lymphatic; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of prostate; Medical center; Microscope; Microscopic; Morphologic artifacts; Nodule; Pathologist; Pattern Recognition; Phenotype; Positron-Emission Tomography; Postoperative Period; Preparation; Problem Solving; Process; Prostate; Prostatectomy; Radical Prostatectomy; Research; Resected; Resolution; Resources; Sampling; Scanning; Signal Transduction; Slice; Slide; Specimen; Staging; Staining method; Stains; Technology; Three-dimensional analysis; Time; Tissue Banking; Tissue Banks; Tissue Expansion; Tissues; Training; Validation; Work; X-Ray Computed Tomography; base; cancer cell; cancer imaging; cancer type; ergonomics; fluorescence imaging; fluorophore; imaging Segmentation; innovation; interest; men; public health relevance; radiotracer; research study; sample fixation; single photon emission computed tomography; skills; software development; standard of care; tissue processing

DESCRIPTION (provided by applicant): Clinical imaging using magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and/or single-photon emission computed tomography (SPECT) is the standard of care for the detection and staging of human cancer. However, clinical imaging is a macroscopic process, with up to 106 malignant cells filling every 1 mm3 voxel. At present, it is extremely difficult to correlate clinical imaging findings with the cellular genotype and/or phenotype leading to the finding. Hence, "biomarkers," such as dynamic contrast enhancement (DCE) on MRI, or a "hot" voxel on PET seen after injection of a targeted radiotracer, are difficult to validate since the fusion of macroscopic clinical imaging findings with microscopic histological findings is fraught with technical challenges. To solve this problem, our laboratory has developed new near-infrared (NIR) fluorescence technology that permits simultaneous (same slide) immunostaining and hematoxylin/eosin (H&E) staining of any pathological specimen. Hence, the chronic problem of co-registering tissue slices at the cellular level is eliminated, while the "gold-standard" of H&E histology is preserved. This technology lays the foundation for an integrated platform that permits high accuracy co-registration of macroscopic and microscopic data sets from an individual patient's cancer. We have also developed an automated microscope that acquires H&E and NIR fluorescence simultaneously, and which permits up to 28 2"x3" whole-mount slides (or 56 1"x3" slides) to be scanned without human intervention. Using this technology, and several other innovations described in the application, 3-D data sets of clinical pathological specimens, at microscopic resolution, can now be generated. However, to bridge the gap between the microscopic and macroscopic domains, we have formed an academic-industrial partnership with the Imaging Department of Siemens Corporate Research (SCR) in Princeton, NJ. SCR is expert in the co-registration of 3-D volumes that have undergone non-linear deformations, in image segmentation, and in pattern recognition. Using algorithms developed by SCR for this study, we present an automated and integrated platform for cancer biomarker validation. Our study also leverages a unique clinical resource at the Beth Israel Deaconess Medical Center, the Hershey Prostate Cancer Tissue Bank. Through the Hershey Tissue Bank, men undergoing radical prostatectomy for prostate cancer receive a preoperative endorectal coil 3T MRI (pre- and post-Gd DCE), and at prostatectomy, the entire gland is available for whole-mount preparation. The paired data sets of clinical imaging (DCE-MRI) and whole mount histology will provide proof of principle for our biomarker validation platform, and will also be used to determine the mechanism of DCE-MRI at the cellular level. Completion of the specific aims by academic and industrial teams with complementary skill sets will permit virtually any proposed biomarker, for any type of cancer, to be validated rapidly and efficiently. PUBLIC HEALTH RELEVANCE: Biomarkers for cancer imaging are extremely difficult to validate, since clinical imaging is performed on a macroscopic scale and histological evaluation of resected tissue is performed on a microscopic scale. We have formed an academic-industrial partnership between the Frangioni Laboratory at the Beth Israel Deaconess Medical Center in Boston, MA and Siemens Corporate Research in Princeton, NJ aimed at developing an integrated platform for biomarker validation using new near-infrared fluorescence and image fusion technology. This study also leverages a unique prostate cancer tissue bank that provides paired DCE-MRI and histological whole mounts from individual prostate cancer patients.

描述（由申请人提供）：使用磁共振成像（MRI），计算机断层扫描（CT），正电子发射断层扫描（PET）和/或单光子发射计算机断层扫描（SPECT）的临床成像是人类癌症检测和分期的护理标准。然而，临床成像是一个宏观过程，每1mm3体素就有多达106个恶性细胞。目前，将临床影像学发现与导致该发现的细胞基因型和/或表型相关联是极其困难的。因此，“生物标志物”，如MRI上的动态对比增强（DCE），或注射靶向放射性示踪剂后在PET上看到的“热”体素，很难验证，因为宏观临床成像结果与微观组织学结果的融合充满了技术挑战。为了解决这一问题，我们实验室开发了新的近红外（NIR）荧光技术，可以同时（同一载玻片）对任何病理标本进行免疫染色和苏木精/伊红（H&E）染色。因此，消除了在细胞水平上组织切片共登记的慢性问题，同时保留了H&E组织学的“金标准”。这项技术为一个集成平台奠定了基础，该平台允许从单个患者的癌症中高精度地共同注册宏观和微观数据集。我们还开发了一种自动显微镜，可同时获取H&E和近红外荧光，并允许在没有人为干预的情况下扫描多达28个2“x3”整片载玻片（或56个1“x3”载玻片）。使用这项技术，以及应用程序中描述的其他一些创新，现在可以生成显微分辨率的临床病理标本的3-D数据集。然而，为了弥合微观和宏观领域之间的差距，我们与位于新泽西州普林斯顿的西门子公司研究成像部门（SCR）建立了学术-工业合作伙伴关系。SCR在非线性变形的三维体的共配准、图像分割和模式识别方面是专家。利用SCR为本研究开发的算法，我们提出了一个自动化和集成的癌症生物标志物验证平台。我们的研究还利用了贝斯以色列女执事医疗中心的独特临床资源——好时前列腺癌组织库。通过好时组织库，接受前列腺癌根治性前列腺切除术的男性术前接受直肠内线圈3T MRI (gd前和gd后的DCE)，在前列腺切除术时，整个腺体可用于全挂载准备。临床成像（DCE-MRI）和整个mount组织学的配对数据集将为我们的生物标志物验证平台提供原理证明，也将用于确定DCE-MRI在细胞水平上的机制。学术和工业团队通过互补的技能完成特定目标，将允许几乎任何提出的生物标志物，针对任何类型的癌症，快速有效地进行验证。公共卫生相关性：癌症成像的生物标志物极难验证，因为临床成像是在宏观尺度上进行的，而对切除组织的组织学评估是在微观尺度上进行的。我们已经与马萨诸塞州波士顿Beth Israel Deaconess医疗中心的Frangioni实验室和新泽西州普林斯顿的西门子公司研究中心建立了一个学术-工业合作伙伴关系，旨在利用新的近红外荧光和图像融合技术开发生物标志物验证的集成平台。本研究还利用了一个独特的前列腺癌组织库，提供配对的DCE-MRI和来自个体前列腺癌患者的组织学整体标本。