Journal Article
. 2011 May; 2(4):187-94.
doi: 10.5306/wjco.v2.i4.187.

Automation of immunohistochemical evaluation in breast cancer using image analysis

Keerthana Prasad 1 Avani Tiwari  Sandhya Ilanthodi  Gopalakrishna Prabhu  Muktha Pai  
Affiliations
  • PMID: 21611095
  •     36 References
  •     3 citations

Abstract

Aim: To automate breast cancer diagnosis and to study the inter-observer and intra-observer variations in the manual evaluations.

Methods: Breast tissue specimens from sixty cases were stained separately for estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor-2 (HER-2/neu). All cases were assessed by manual grading as well as image analysis. The manual grading was performed by an experienced expert pathologist. To study inter-observer and intra-observer variations, we obtained readings from another pathologist as the second observer from a different laboratory who has a little less experience than the first observer. We also took a second reading from the second observer to study intra-observer variations. Image analysis was carried out using in-house developed software (TissueQuant). A comparison of the results from image analysis and manual scoring of ER, PR and HER-2/neu was also carried out.

Results: The performance of the automated analysis in the case of ER, PR and HER-2/neu expressions was compared with the manual evaluations. The performance of the automated system was found to correlate well with the manual evaluations. The inter-observer variations were measured using Spearman correlation coefficient r and 95% confidence interval. In the case of ER expression, Spearman correlation r = 0.53, in the case of PR expression, r = 0.63, and in the case of HER-2/neu expression, r = 0.68. Similarly, intra-observer variations were also measured. In the case of ER, PR and HER-2/neu expressions, r = 0.46, 0.66 and 0.70, respectively.

Conclusion: The automation of breast cancer diagnosis from immunohistochemically stained specimens is very useful for providing objective and repeatable evaluations.

Keywords: Automation; Breast cancer diagnosis; Computer aided diagnosis; Image analysis; Immunohistochemical study.

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