PathMiner: Web Telemicroscopy & Intelligent Databases

描述（由申请人提供）： 大规模、多地点的协作对于广泛的研究和临床活动（包括组织库、蛋白质组学和结果研究）来说是必不可少的。未来几个关键领域的进展将取决于个人动态获取、共享和评估图像和相关数据的能力。该提案的主要目标是开发，评估和维护PathMiner，这是一个基于网络的模型，用于交互式远程医疗，智能存档和病理学自动决策支持。PathMiner的主要组成部分是一个分布式显微镜子系统（DT），一个智能存档（IA）子系统和一个图像引导决策支持（IGDS）子系统。DT子系统使用户能够远程控制机器人显微镜，同时将成像的标本广播给参与者。它具有基于熵的，自动聚焦，共享图形和消息传递功能。IA子系统执行数据库的多元索引和远程管理。IGDS子系统支持跨网络提交查询，以便从分布式“地面实况”数据库中自动定位和检索这些病例的数字化病理学标本和相关分子研究，这些数据库显示与查询图像一致的光谱和空间分布。在初步研究中，使用1，900个测试细胞的混合数据库，原型在超过83%的病例中正确区分了三种常见混淆的血液恶性肿瘤和正常细胞。新泽西州医学和牙科大学、罗格斯大学和宾夕法尼亚大学医学院将合作：1）开发在多用户环境中用新病例填充地面实况数据库的方法; 2）用更广泛的血液疾病扩展地面实况数据库; 3）发展无监督标本分析方法，确定形态学与临床之间的关系（蛋白质/分子）谱; 4）确定恶性肿瘤谱特征空间的置信区间; 5）为PazhMiner开发网络接口以协调子系统之间的数据和对象流; 6）开展多中心前瞻性性能研究，并将PathMiner和地面实况数据库作为可共享资源，用于协作研究、教育和临床决策支持。

DESCRIPTION (provided by applicant): Large-scale, multi-site collaboration is becoming indispensable for a wide range of research and clinical activities including tissue banking, proteomics, and outcome studies. Future progress in several key areas will rely on the capacity of individuals to dynamically acquire, share and assess images and correlated data. The main objective of this proposal is to develop, evaluate, and maintain PathMiner, a web-based model for interactive telemedicine, intelligent archiving, and automated decision support in pathology. The major components of PathMiner are a distributed telemicroscopy subsystem (DT), an intelligent archival (IA) subsystem and an Image Guided Decision Support (IGDS) subsystem. The DT subsystem enables users to control robotic microscopes, remotely, while the imaged specimen is broadcast to participants. It features entropy-based, auto-focusing, shared graphics, and messaging. The IA subsystem performs multivariate indexing and remote management of databases. The IGDS subsystem supports submission of queries across networks in order to automatically locate and retrieve digitized pathology specimens and correlated molecular studies of those cases from within distributed "ground-truth" databases which exhibit spectral and spatial profiles which are consistent with the query image. During preliminary studies, a prototype correctly discriminated among three commonly confused hematologic malignancies and normal cells in more than 83% of the cases using a mixed database of 1,900 test cells. The University of Medicine and Dentistry of NJ, Rutgers University, and the University of Pennsylvania School of Medicine will collaborate to 1) develop the means for populating ground-truth databases with new cases in multi-user environments; 2) expand ground-truth databases with a broader spectrum of hematologic disorders; 3) develop the means for unsupervised specimen analysis and determine the relationship between morphologic and clinical (protein/molecular) profiles; 4) determine confidence intervals in feature space for the spectrum of malignancies; 5) develop aweb interface for PazhMiner to coordinate flow of data and objects among subsystems; 6) conduct multi-site prospective performance studies and make PathMiner and the ground truth databases available as shareable resources for collaborative research, education and clinical decision support.