Semi-automatic identification of punching areas for tissue microarray building: The tubular breast cancer pilot study
Background: Tissue MicroArray technology aims to perform immunohistochemical staining on hundreds of different tissue samples simultaneously. It allows faster analysis, considerably reducing costs incurred in staining. A time consuming phase of the methodology is the selection of tissue areas within paraffin blocks: no utilities have been developed for the identification of areas to be punched from the donor block and assembled in the recipient block.Results: The presented work supports, in the specific case of a primary subtype of breast cancer (tubular breast cancer), the semi-automatic discrimination and localization between normal and pathological regions within the tissues. The diagnosis is performed by analysing specific morphological features of the sample such as the absence of a double layer of cells around the lumen and the decay of a regular glands-and-lobules structure. These features are analysed using an algorithm which performs the extraction of morphological parameters from images and compares them to experimentally validated threshold values. Results are satisfactory since in most of the cases the automatic diagnosis matches the response of the pathologists. In particular, on a total of 1296 sub-images showing normal and pathological areas of breast specimens, algorithm accuracy, sensitivity and specificity are respectively 89%, 84% and 94%.Conclusions: The proposed work is a first attempt to demonstrate that automation in the Tissue MicroArray field is feasible and it can represent an important tool for scientists to cope with this high-throughput technique.
|Persistent URL||dx.doi.org/10.1186/1471-2105-11-566, hdl.handle.net/1765/28487|
|Journal||B M C Bioinformatics|
Viti, F, Merelli, I, Timmermans, A.M, den Bakker, M.A, Beltrame, F, Riegman, P.H.J, & Milanesi, L. (2010). Semi-automatic identification of punching areas for tissue microarray building: The tubular breast cancer pilot study. B M C Bioinformatics, 11. doi:10.1186/1471-2105-11-566