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

用于促进病理学和放射学多模式、多尺度融合数据的软件

基本信息

批准号：
8512667
负责人：
MICHAEL D FELDMAN
金额：
$ 56.93万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-07-17 至 2015-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8512667
关键词：
Active Learning Algorithms American Cancer Society Anatomy Architecture Biochemical Clinic Clinical Communities Computer Assisted Computer software Computer-Assisted Diagnosis Core Biopsy Data Data Set Databases Decision Support Systems Detection Development Diagnosis Diagnostic Diffusion Disease Elements Epigenetic Process Evaluation Explosion Gene Proteins Gleason Grade for Prostate Cancer Goals Health Healthcare Histology Histopathology Hospitals Image Image Analysis Imagery Informatics Label Lead Learning Lesion Link Location MRI Scans Machine Learning Magnetic Resonance Imaging Malignant Neoplasms Malignant neoplasm of prostate Medical Medical Informatics Methodology Methods Modality Molecular Nuclear Outcome Pathologic Pathologist Pathology Patients Pennsylvania Physiological Population Premalignant Procedures Prostate Prostate Adenocarcinoma Prostatectomy Radical Prostatectomy Radiology Specialty Reader Recommendation Recording of previous events Research Research Infrastructure Resolution Schedule Scientist Sensitivity and Specificity Software Tools Source Specimen Stream System Techniques Therapeutic Tissues Training United States Universities Validation Weight Work anticancer research base cancer Biomedical Informatics Grid cancer imaging computerized diagnostic accuracy disease diagnosis image registration imaging modality improved in vivo interoperability malignant breast neoplasm men novel open source outcome forecast pre-clinical radiologist repository screening spectroscopic imaging tool

项目摘要

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.

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可用于针对每个患者的病史和当前状况定制的一系列检测/诊断/治疗诊断应用。前列腺腺癌（CAP）是男性中第二常见的恶性肿瘤，2008年在美国估计有220，000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，提供解剖学，生化和生理信息，它已变得越来越重要，以确定这些信息在术前或治疗前CAP筛查的潜在价值。然而，体内前列腺MRI缺乏活检组织病理学检查所提供的分辨率和真实诊断准确性。将CAP的前列腺MRI应用于临床的第一步是在细胞水平上验证MR提供的信息。然而，针对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链接的手段。这主要是由于信息表示和工具之间缺乏互操作性。例如，一个缺失的元素是用于对齐多模态体积数据集的鲁棒且准确的图像配准工具。宾夕法尼亚大学，罗格斯大学和西门子公司研究之间的这个合作项目的总体目标是在软件框架内开发和评估多模态图像分析和机器学习技术，从而实现对多功能，多分辨率患者数据的有效分析，关联和解释。这些多模态、多尺度分析工具的可用性将使得能够对准放射学和病理学数据，这又将（a）使得能够构建和验证用于根据放射学和病理学数据对CAP进行检测和分级的监督计算机化决策支持系统，以及（B）通过集成多模态、多尺度疾病特征来构建CAP的元分类器。这样一组前列腺特异性信息学工具有望带来临床益处，包括改善患者病情、更准确的疾病诊断和治疗建议。更一般地说，作为该项目的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、支持网格的注释、图像分析和图像配准工具，以实现放射学数据的跨模态验证（多功能3 T前列腺MRI），具有前列腺切除术标本的专家组织病理学注释，并提供放射学和组织病理学上癌症范围和等级的独立计算机辅助预测，2）策划一个开源的前列腺MRI和组织病理学标本的caGRID连接数据库，该数据库将能够开发用于跨多个尺度和成像模式的CAP检测和分级的定量特征。

项目成果

期刊论文数量（73）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 Tesla endorectal, in vivo T2-weighted MR imagery.

DOI：
10.1002/jmri.23618
发表时间：
2012-07
期刊：
JOURNAL OF MAGNETIC RESONANCE IMAGING
影响因子：
4.4
作者：
Viswanath, Satish E.;Bloch, Nicholas B.;Chappelow, Jonathan C.;Toth, Robert;Rofsky, Neil M.;Genega, Elizabeth M.;Lenkinski, Robert E.;Madabhushi, Anant
通讯作者：
Madabhushi, Anant

Fully Automated Prostate Magnetic Resonance Imaging and Transrectal Ultrasound Fusion via a Probabilistic Registration Metric.