Computational Immunohistochemistry: Recipes for Standardization of Immunostaining

Cancer diagnosis and personalized cancer treatment are heavily based on the visual assessment of immunohistochemically-stained tissue specimens. The precision of this assessment depends critically on the quality of immunostaining, which is governed by a number of parameters used in the staining process. Tuning of the staining-process parameters is mostly based on pathologists' qualitative assessment, which incurs inter- and intra-observer variability. The lack of standardization in staining across pathology labs leads to poor reproducibility and consequently to uncertainty in diagnosis and treatment selection. In this paper, we propose a methodology to address this issue through a quantitative evaluation of the staining quality by using visual computing and machine learning techniques on immunohistochemically-stained tissue images. This enables a statistical analysis of the sensitivity of the staining quality to the process parameters and thereby provides an optimal operating range for obtaining high-quality immunostains. We evaluate the proposed methodology on HER2-stained breast cancer tissues and demonstrate its use to define guidelines to optimize and standardize immunostaining.


Presented at:
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Quebec City, QC, Canada, September 11-13, 2017
Year:
2017
ISBN:
978-3-319-66185-8
Keywords:
Laboratories:




 Record created 2017-09-09, last modified 2018-03-17

Preprint:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)