Pilla Daniela, Bosisio Francesca M, Marotta Roberto, Faggi Stefano, Forlani Paolo, Falavigna Maurizio, Biunno Ida, Martella Emanuele, De Blasio Pasquale, Borghesi Simone, Cattoretti Giorgio
Department of Pathology, San Gerardo Hospital, Via Pergolesi 33, 20900, Monza, Italy.
J Pathol Inform. 2012;3:42. doi: 10.4103/2153-3539.104904. Epub 2012 Dec 20.
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.
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.
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.
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.
2013年，被称为组织微阵列（TMA）的高通量技术将迎来问世的第15个年头。其要素（设计、构建和分析）直观易懂，核心组织病理学技术也并不复杂，这或许是它一直未受到严格科学审视的原因之一。关于误差来源，尤其是样本识别方面的误差以及如何控制此类误差，尚无相关报道。目前缺乏对数百个样本进行分割（也称为解阵列）准确性的正式验证，以及将分割结果与样本数据进行配对的验证。
我们希望解决这些问题，以便使该技术在用于研究、诊断和工业目的的TMA应用中达到公认的质量标准。
我们系统地解决了误差来源问题，并在整个过程中采用条形码驱动的数据输入方式，包括将设计与TMA虚拟图像进行匹配，再将该图像分割回各个病例，并关联相关数据。此外，我们从数学角度证明，当TMA设计叠加到相应的全切片图像上时，能够对每个样本验证图像与患者数据之间的对应关系。
如果能识别并解决所有误差来源，TMA技术的高通量应用对于研究、诊断和工业用途而言是一种安全有效的方法。
