Enormous data sets and information are involved in TMA based studies; well structured data management system are mandatory. The incremental need to share data among different institutions and multicentric studies suggests the implementation of web based systems and the definition of a standard to be applied in exchanging data (see http://www.pathinfo.com/jjb/tmadtd2.htm). Data concerning with TMA based studies are biological/genetic, clinical and morphologic. Digital images of entire array slides and of single core sections are necessary for eventually automated scoring and for exhaustive storage. Relational database addresses the requirements to organize in an efficient manner all the data. In our system insertion, updating and retrieval of data are provided through web pages (Active Server Pages) constructed to satisfy different data entry types and peculiar work phases. Different views are designed to map the workflows of different workphases. The intrinsic difficulty of TMA samples analysis by conventional microscope (hundreds of tumours spots on the same slide) suggests the storage of digital images of single core section, uniquely related to the tumour identifier, and of overview images of entire slides. Robotic microscopy is well suitable for this kind of issues. Digital images of core sections enable the pathologists to easily compare the immunostainings tumour samples on computer screen through a microscope simulator. Having a TMA management system the problem shifts from data generation to data-mining and deriving information from such material (Journal of Pathology, 2001 195:1-2). Patterns in TMA biological data of gene expression have to be evaluated. Correlations of expression with clinical and follow up data are of main interest for prognosis and therapeutic investigation. To assess the screening approach to the analysis of potential markers, classical statistics as uni-multivariate analysis together with supervised and unsupervised cluster analysis will be utilized. This work has been granted by the Ministry of Public Health of Italian Government
Web based Tissue Microarray Management System
Dell'Anna, Rossana;Demichelis, Francesca
2002-01-01
Abstract
Enormous data sets and information are involved in TMA based studies; well structured data management system are mandatory. The incremental need to share data among different institutions and multicentric studies suggests the implementation of web based systems and the definition of a standard to be applied in exchanging data (see http://www.pathinfo.com/jjb/tmadtd2.htm). Data concerning with TMA based studies are biological/genetic, clinical and morphologic. Digital images of entire array slides and of single core sections are necessary for eventually automated scoring and for exhaustive storage. Relational database addresses the requirements to organize in an efficient manner all the data. In our system insertion, updating and retrieval of data are provided through web pages (Active Server Pages) constructed to satisfy different data entry types and peculiar work phases. Different views are designed to map the workflows of different workphases. The intrinsic difficulty of TMA samples analysis by conventional microscope (hundreds of tumours spots on the same slide) suggests the storage of digital images of single core section, uniquely related to the tumour identifier, and of overview images of entire slides. Robotic microscopy is well suitable for this kind of issues. Digital images of core sections enable the pathologists to easily compare the immunostainings tumour samples on computer screen through a microscope simulator. Having a TMA management system the problem shifts from data generation to data-mining and deriving information from such material (Journal of Pathology, 2001 195:1-2). Patterns in TMA biological data of gene expression have to be evaluated. Correlations of expression with clinical and follow up data are of main interest for prognosis and therapeutic investigation. To assess the screening approach to the analysis of potential markers, classical statistics as uni-multivariate analysis together with supervised and unsupervised cluster analysis will be utilized. This work has been granted by the Ministry of Public Health of Italian GovernmentI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
