Software to facilitate multimode, multiscale fused data for Pathology and Radiolo

描述（由申请人提供）：从宏观到微观尺度的医学信息学越来越多地用于根据每个患者的病史和当前状况量身定制的一系列检测/诊断/治疗应用。前列腺腺癌（CAP）是男性中第二常见的恶性肿瘤，2008年美国估计有22万例新发病例。随着多参数高分辨率（3特斯拉(T)）前列腺MRI的出现，提供解剖、生化和生理信息，识别这些信息在术前或治疗前CAP筛查中的潜在价值变得越来越重要。然而，体内前列腺MRI缺乏活检核组织病理学检查提供的分辨率和真实诊断准确性。将前列腺核磁共振技术应用于临床的第一步是在细胞水平上验证核磁共振提供的信息。然而，根据组织学基础事实验证MRI目前缺乏将放射成像和病理学提供的信息无缝连接起来的手段。这主要是由于信息学表示和工具之间缺乏互操作性。例如，一个缺失的元素是鲁棒和准确的图像配准工具，以对齐多模态体积数据集。宾夕法尼亚大学、罗格斯大学和西门子公司研究中心之间的这个合作项目的总体目标是在一个软件框架内开发和评估多模态图像分析和机器学习技术，该软件框架将能够对多功能、多分辨率的患者数据进行有效的分析、关联和解释。这些多模式、多尺度分析工具的可用性将使放射学和病理数据的一致性成为可能，这反过来将(a)能够建立和验证有监督的计算机决策支持系统，用于从放射学和病理数据中检测和分级CAP，以及(b)通过整合多模式、多尺度疾病特征来构建CAP的元分类器。这样一套前列腺特异性信息学工具承诺临床益处，包括改善患者预后，更准确的疾病诊断和治疗建议。更一般地说，作为该项目一部分开发的工具也将使其他疾病实体的放射学/病理学研究成为可能。该项目的具体目标是：1)开发跨平台、开源、网格化的注释、图像分析和图像配准工具，使放射学数据（多功能3t前列腺MRI）的交叉模态验证与前列腺切除术标本的专家组织病理学注释，并提供独立的计算机辅助预测放射学和组织病理学上的癌症程度和等级；2)策划一个开源的caGRID连接的前列腺MRI和组织病理学标本数据库，这将使CAP在多个尺度和成像模式上的检测和分级的定量签名的发展成为可能。公共卫生相关性：该项目的目标是开发一个软件工具包（基于西门子公司的XIP应用程序），使科学家能够通过将多种成像模式（放射学和病理学）的数据与分子方法（包括蛋白质、基因、表观遗传学数据）相结合来研究前列腺癌。这些融合和组合的数据集的结果将使科学家能够创建一个基于前列腺癌的更强大的系统视图，从而可以预测诊断，跟踪治疗结果并促进发现新的治疗方案

DESCRIPTION (provided by applicant): Medical informatics from the macro- to micro-scale is increasingly available for a range of detection/diagnosis/theragnostic applications tailored to each patient's history and current condition. Prostatic adenocarcinoma (CAP) is the second most common malignancy among men with an estimated 220,000 new cases in the USA in 2008. With the advent of multi-parametric high resolution (3 Tesla (T)) prostate MRI, providing anatomic, biochemical, and physiologic information, it has become increasingly important to identify the potential value of this information in pre-operative or pre-therapeutic CAP screening. However, in vivo prostate MRI lacks the resolution and ground truth diagnostic accuracy histopathological examination of biopsy cores provides. A first step toward getting prostate MRI for CAP into the clinic would be validating the information provided from MR at the cellular level. However, validating MRI against histological ground truth currently lacks the means to link the information provided by radiological imaging and pathology seamlessly. This is primarily due to a lack of interoperability between informatics representations and tools. One missing element, for instance, is robust and accurate image registration tools to align the multi-modal volumetric data sets. The overarching goal of this collaborative project between the University of Pennsylvania, Rutgers University, and Siemens Corporate Research is to develop and evaluate multi-modal image analysis and machine learning techniques within a software framework that will enable efficient analysis, correlation, and interpretation of multi-functional, multi-resolution patient data. The availability of these multi-modal, multi- scale analysis tools will enable alignment of radiology and pathological data which in turn will (a) enable building and validation of supervised computerized decision support systems for detection and grading of CAP from radiology and pathology data and (b) building meta-classifiers for CAP by integrating multi- modal, multi-scale disease signatures. Such a set of prostate-specific informatics tools promises clinical benefits including improved patient prognoses, more accurate disease diagnoses, and therapeutic recommendations. More generally, the tools developed as part of this project will also enable radiologic/pathologic studies in other disease entities. The specific goals of this project are 1) to develop cross-platform, open source, grid-enabled annotation, image analysis and image registration tools that will enable cross modality validation of radiology data (multi-functional 3 T prostate MRI) with expert histopathological annotation of prostatectomy specimens and provide independent computer-aided predictions of cancer extent and grade on radiology and histopathology, 2) curate an open source caGRID- connected database of prostate MRI and histopathological specimens that will enable development of quantitative signatures for detection and grading of CAP across multiple scales and imaging modalities. PUBLIC HEALTH RELEVANCE: The goal of this project is to develop a software tool kit (based on the XIP application from Siemens corporation) that will allow scientists to study prostate cancer by combining data from multiple imaging modalities (radiology and pathology) with molecular methodologies (including proteins, genes, epigenetic data). The results of this fused and combined data sets will allow scientists to create a more robust system based view of prostate cancer which allow prediction of diagnosis, follow outcomes of therapeutic treatment and facilitate discovery of new treatment options