Definiens Releases TissueMap 2.0 for Advanced Oncology Research
Product News Dec 07, 2007
Definiens has unveiled the release of Definiens TissueMap 2.0, highlighting the company’s focus on oncology research. The image analysis software application is especially designed for the detailed, automated morphometric quantification of biomarkers of nuclei or cell bodies in epithelial tumors or xenografts.
The application features the detection of viable and necrotic tissue in xenografts, as well as regions of IHC (DAB) positively stained nuclei or cells in the viable tissue. This enables pathologists to automate the process of complex or highly apoptotic xenograft analysis in oncology research.
The discovery and application of oncology biomarkers have enhanced opportunities for individual disease treatment by identifying new drug targets. They therefore have a significant impact on the entire process of drug development including the emergence and growth of imaging technologies utilized to acquire accurate measurements of disease parameters. The use of imaging technologies, such as Definiens TissueMap 2.0, improves the selection and prioritization of quality candidates early in the drug development pipeline.
Definiens TissueMap 2.0 identifies areas and structures of interest in image data allowing researchers to distinguish between viable and necrotic tumor areas and to quantify markers. It offers:
• Full tissue slide analysis
• Reproducible results
• Platform independence and connectivity
• Easy customization
• Measurement of morphological parameters
Definiens TissueMap 2.0 encompasses the assessment of any biomarkers targeting an antigen located in the nucleus such as proliferation markers (Ki67, PCNA, BrdU, etc.), apoptosis markers such as Cap3 as well as estrogen or progesterone receptors. Antibodies or markers co-/located in the cell body, such as cytokeratines (CD31, AE1/3, etc.) and other proteins (CD45, CD23 or similar) can also be evaluated.
Definiens TissueMap 2.0 enables the quantification of biomarker expression patterns in image data regardless of staining methodology. The software application effectively analyzes image data in oncology research, including cases involving poor staining technique and data obtained from heterogeneous equipment. It also enables the automated detection of relevant morphological structure in tissue and tumor sections.