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）：越来越多地用于从宏到微型尺度的医学信息学，可用于针对每个患者的病史和当前状况量身定制的一系列检测/诊断/theragnostic应用。前列腺腺癌（CAP）是男性中第二大最常见的恶性肿瘤，估计在2008年美国有22万例新病例。随着多参数高分辨率（3 Tesla（t））前列腺MRI的出现，提供了解剖学，生物化学和物理学信息，它变得越来越重要，它变得越来越重要，它变得越来越重要，它变得越来越重要，或者是潜在的潜在信息，并且有价值的有价值的信息 - 筛选。然而，体内前列腺MRI缺乏对活检核心提供的分辨率和地面真理诊断准确性的组织病理学检查。将CAP进入诊所的前列腺MRI迈出的第一步将验证MR在细胞水平上提供的信息。然而，目前，验证MRI针对组织学基础真理缺乏手段，无法无缝地链接放射学成像和病理学提供的信息。这主要是由于信息学表示和工具之间缺乏互操作性。例如，一个缺失的元素是可靠，准确的图像注册工具，可以对齐多模式的体积数据集。宾夕法尼亚大学，罗格斯大学和西门子公司研究之间的合作项目的总体目标是在软件框架内开发和评估多模式的图像分析和机器学习技术，以实现有效的分析，相关性，相关性，并解释多函数，多功能，多分辨率，多分辨率的患者数据。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对齐，而这些数据又（a）将（a）启用监督计算机化的决策支持系统，以通过放射学和病理学数据的检测和（b）通过集成多模态，多种模量疾病的cap building Meta-classifiers来检测和分级CAP，并验证CAP。这样的一组前列腺特异性信息学工具有望临床益处，包括改善患者预后，更准确的疾病诊断和治疗建议。更普遍地，作为该项目的一部分开发的工具还将在其他疾病实体中实现放射/病理学研究。 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-连接的前列腺MRI和组织病理学标本的数据库，这些数据库将使定量特征开发用于跨多个尺度和成像方式的CAP的检测和分级。

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）

Novel morphometric based classification via diffeomorphic based shape representation using manifold learning.

使用流形学习通过基于微分同胚的形状表示进行新颖的基于形态测量的分类。

DOI：
10.1007/978-3-642-15711-0_82
发表时间：
2010
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
0
作者：
Sparks,Rachel;Madabhushi,Anant
通讯作者：
Madabhushi,Anant

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

DOI：
10.1117/12.2007610
发表时间：
2013-03-08
期刊：
Proceedings of SPIE--the International Society for Optical Engineering
影响因子：
0
作者：
Sparks R;Bloch BN;Feleppa E;Barratt D;Madabhushi A
通讯作者：
Madabhushi A

Nuclear Shape and Architecture in Benign Fields Predict Biochemical Recurrence in Prostate Cancer Patients Following Radical Prostatectomy: Preliminary Findings.

DOI：
10.1016/j.euf.2016.05.009
发表时间：
2017-10
期刊：
European urology focus
影响因子：
5.4
作者：
Lee G;Veltri RW;Zhu G;Ali S;Epstein JI;Madabhushi A
通讯作者：
Madabhushi A

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

Identifying in vivo DCE MRI markers associated with microvessel architecture and gleason grades of prostate cancer.

DOI：
10.1002/jmri.24975
发表时间：
2016-01
期刊：
Journal of magnetic resonance imaging : JMRI
影响因子：
0
作者：
Singanamalli A;Rusu M;Sparks RE;Shih NN;Ziober A;Wang LP;Tomaszewski J;Rosen M;Feldman M;Madabhushi A
通讯作者：
Madabhushi A