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Computational Immunohistochemistry: Recipes for Standardization of Immunostaining

  • Nuri Murat Arar
  • Pushpak Pati
  • Aditya Kashyap
  • Anna Fomitcheva Khartchenko
  • Orcun Goksel
  • Govind V. Kaigala
  • Maria Gabrani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)

Abstract

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.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nuri Murat Arar
    • 1
    • 3
  • Pushpak Pati
    • 2
    • 3
  • Aditya Kashyap
    • 3
  • Anna Fomitcheva Khartchenko
    • 3
  • Orcun Goksel
    • 2
  • Govind V. Kaigala
    • 3
  • Maria Gabrani
    • 3
  1. 1.Signal Processing Laboratory (LTS5)EPFLLausanneSwitzerland
  2. 2.Computer-Assisted Applications in MedicineETH ZürichZürichSwitzerland
  3. 3.IBM Zürich Research LabZürichSwitzerland

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