Journal of Pathology Informatics Journal of Pathology Informatics
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ORIGINAL ARTICLE
Year : 2012  |  Volume : 3  |  Issue : 1  |  Page : 42

Tissue microarray design and construction for scientific, industrial and diagnostic use


1 Department of Pathology, San Gerardo Hospital, Via Pergolesi 33, 20900, Monza, Italy
2 Department of Surgical Sciences, Milano-Bicocca State University, via Cadore 48, 20900, Monza, Italy
3 NoemaLife S.p.A., Via Piero Gobetti 52, 40129, Bologna, Italy
4 Nikon Instruments S.P.A, Via Meucci 59, 50041 Calenzano, Firenze, Italy
5 Integrated System Engineering srl, Via Fantoli 16/15, 20138 Milano, Italy
6 Institute for Genetic and Biomedical Research, National Research Center, Via Fantoli 16/15, 20138 Milan, Italy
7 Department of Mathematical Sciences (Matematica e Applicazioni), Milano-Bicocca State University, Via Cozzi 53, 20125, Milano, Italy
8 Department of Pathology, San Gerardo Hospital, Via Pergolesi 33; Department of Surgical Sciences, Milano-Bicocca State University, via Cadore 48, 20900, Monza, Italy

Correspondence Address:
Giorgio Cattoretti
Department of Pathology, San Gerardo Hospital, Via Pergolesi 33; Department of Surgical Sciences, Milano-Bicocca State University, via Cadore 48, 20900, Monza
Italy
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2153-3539.104904

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Context: In 2013 the high throughput technology known as Tissue Micro Array (TMA) will be fifteen years old. Its elements (design, construction and analysis) are intuitive and the core histopathology technique is unsophisticated, which may be a reason why has eluded a rigorous scientific scrutiny. The source of errors, particularly in specimen identification and how to control for it is unreported. Formal validation of the accuracy of segmenting (also known as de-arraying) hundreds of samples, pairing with the sample data is lacking. Aims: We wanted to address these issues in order to bring the technique to recognized standards of quality in TMA use for research, diagnostics and industrial purposes. Results: We systematically addressed the sources of error and used barcode-driven data input throughout the whole process including matching the design with a TMA virtual image and segmenting that image back to individual cases, together with the associated data. In addition we demonstrate on mathematical grounds that a TMA design, when superimposed onto the corresponding whole slide image, validates on each and every sample the correspondence between the image and patient's data. Conclusions: High throughput use of the TMA technology is a safe and efficient method for research, diagnosis and industrial use if all sources of errors are identified and addressed.


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