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量身定制的一系列检测/诊断/治疗诊断应用。前列腺癌 (CAP) 是男性中第二常见的恶性肿瘤，2008 年美国估计有 220,000 例新发病例。随着多参数高分辨率 (3 Tesla (T)) 前列腺 MRI 的出现，提供了解剖、生化和生理信息，确定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 缺乏活检核心组织病理学检查所提供的分辨率和真实诊断准确性。将用于 CAP 的前列腺 MRI 纳入临床的第一步是在细胞水平上验证 MR 提供的信息。然而，根据组织学基本事实验证 MRI 目前缺乏将放射成像和病理学提供的信息无缝链接的方法。这主要是由于信息学表示和工具之间缺乏互操作性。例如，一个缺失的元素是强大而准确的图像配准工具，用于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研究中心之间的这一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，从而实现多模态图像分析和机器学习技术的高效分析、关联和解释。 -功能性、多分辨率患者数据。这些多模式、多尺度分析工具的可用性将能够协调放射学和病理学数据，这反过来又将（a）能够构建和验证受监督的计算机化决策支持系统，用于根据放射学和病理学数据检测和分级 CAP； (b) 通过整合多模式、多尺度疾病特征来构建 CAP 元分类器。这样一套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，作为该项目的一部分开发的工具还将支持其他疾病实体的放射学/病理学研究。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（多功能 3 T 前列腺 MRI）与组织病理学专家的跨模态验证前列腺切除标本的注释，并提供癌症范围和放射学和组织病理学分级的独立计算机辅助预测，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，这将使得开发用于跨多种尺度和成像方式检测和分级 CAP 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）

Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging.

计算机提取的特征可以通过多参数 MR 成像区分非癌性混杂疾病和前列腺腺癌。

DOI：
10.1148/radiol.2015142856
发表时间：
2024-09-14
期刊：
Radiology
影响因子：
19.7
作者：
G. Litjens;R. Elliott;N. Shih;M. Feldman;T. Kobus;C. Hulsbergen–van de Kaa;J. Barentsz;H. Huisman;A. Madabhushi
通讯作者：
A. Madabhushi

Texture Descriptors to distinguish Radiation Necrosis from Recurrent Brain Tumors on multi-parametric MRI.

在多参数 MRI 上区分放射性坏死和复发性脑肿瘤的纹理描述符。

DOI：
发表时间：
2014
期刊：
影响因子：
0
作者：
Pallavi, Tiwari;Prateek, Prasanna;Lisa, Rogers;Leo, Wolansky;Chaitra, Badve;Andrew, Sloan;Mark, Cohen;Anant, Madabhushi
通讯作者：
Anant, Madabhushi

Selective invocation of shape priors for deformable segmentation and morphologic classification of prostate cancer tissue microarrays.

选择性调用形状先验以进行前列腺癌组织微阵列的可变形分割和形态学分类。

DOI：
发表时间：
2015-04
期刊：
影响因子：
0
作者：
Ali, Sahirzeeshan;Veltri, Robert;Epstein, Jonathan I;Christudass, Christhunesa;Madabhushi, Anant
通讯作者：
Madabhushi, Anant

Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology.

非线性嵌入 (FINE) 中的特征重要性：数字病理学中的应用。