Original Article
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World J Clin Oncol. Apr 10, 2011; 2(4): 187-194
Published online Apr 10, 2011. doi: 10.5306/wjco.v2.i4.187
Automation of immunohistochemical evaluation in breast cancer using image analysis
Keerthana Prasad, Avani Tiwari, Sandhya Ilanthodi, Gopalakrishna Prabhu, Muktha Pai
Keerthana Prasad, Manipal Centre for Information Science, Manipal University, Manipal 576104, Karnataka, India
Avani Tiwari, Muktha Pai, Department of Pathology, Kasturba Medical College, Manipal University, Manipal 576104, Karnataka, India
Sandhya Ilanthodi, Department of Pathology, A.J Institute of Medical Sciences, Manipal 576104, Karnataka, India
Gopalakrishna Prabhu, Department of Biomedical Engineering, Manipal Institute of Technology, Manipal 576104, Karnataka, India
Author contributions: Prasad K designed and carried out this research; Tiwari A carried out the sample collection, tissue processing and acquisition of images; Tiwari A, Pai M and Ilanthodi S provided the readings for the manual evaluations; Prabhu G supervised this research; all authors contributed to the writing of this paper.
Correspondence to: Keerthana Prasad, Assistant Professor, Manipal Centre for Information Science, LG02, Academic Block 5, M.I.T Campus, Manipal 576104, Karnataka, India. keerthana.prasad@manipal.edu
Telephone: +91-984-5308499 Fax: +91-820-2925033
Received: March 18, 2010
Revised: March 31, 2011
Accepted: April 7, 2011
Published online: April 10, 2011
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