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 rec...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.展开更多
文摘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.
