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.
- 3.Laurinaviciene, A., Dasevicius, D., Ostapenko, V., Jarmalaite, S., Lazutka, J., Laurinavicius, A.: Membrane connectivity estimated by digital image analysis of HER2 immunohistochemistry is concordant with visual scoring and fluorescence in situ hybridization results: algorithm evaluation on breast cancer tissue microarrays. Diagn. Pathol. 6, 87 (2011)CrossRefGoogle Scholar
- 4.Pinard, R., Tedeschi, G.R., Wang, D., Williams, C.: Methods and system for validating sample images for quantitative immunoassays. US Patent 8160348 (2009)Google Scholar
- 5.Grunkin, M., Hansen, J.D.: Assessment of staining quality. International Patent WO2015135550 (2015)Google Scholar
- 6.Brügmann, A., Grunkin, M., Nielsen, S., Jensen, V., Heikkila, P., Gaspar, V., Vyberg, M.: Image analysis of breast cancer HER2 protein expression used in assessment of staining quality. Virchows Arch. 465, S20 (2014)Google Scholar
- 8.Hoda, S.A., Brogi, E., Koerner, F.C., Rosen, P.P.: Rosen’s Breast Pathology. Wolters Kluwer, UK (2014)Google Scholar
- 9.Seguin, B., Saab, H., Gabrani, M., Estellers, V.: Estimating pattern sensitivity to the printing process for varying dose/focus conditions for RET development in the sub-22nm era. In: Proceedings of SPIE, vol. 9050 (2014)Google Scholar