Assessment of automated image analysis of breast cancer tissue microarrays for epidemiologic studies.
Assessment of automated image analysis of breast cancer tissue microarrays for epidemiologic studies.
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DOI：
10.1158/1055-9965.epi-09-1023
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发表时间：
2010-04
期刊：
影响因子：
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通讯作者：
Sherman ME
中科院分区：
文献类型：
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作者：
Bolton KL;Garcia-Closas M;Pfeiffer RM;Duggan MA;Howat WJ;Hewitt SM;Yang XR;Cornelison R;Anzick SL;Meltzer P;Davis S;Lenz P;Figueroa JD;Pharoah PD;Sherman ME
A major challenge in studies of etiologic heterogeneity in breast cancer has been the limited throughput, accuracy and reproducibility of measuring tissue markers. Computerized image analysis systems may help address these concerns but published reports of their use are limited. We assessed agreement between automated and pathologist scores of a diverse set of immunohistochemical (IHC) assays performed on breast cancer TMAs. TMAs of 440 breast cancers previously stained for ER-α, PR, HER-2, ER-β and aromatase were independently scored by two pathologists and three automated systems (TMALabII, TMAx, Ariol). Agreement between automated and pathologist scores of negative/positive was measured using the area under the receiver operator characteristics curve (AUC) and weighted kappa statistics (κ) for categorical scores. We also investigated the correlation between IHC scores and mRNA expression levels. Agreement between pathologist and automated negative/positive and categorical scores was excellent for ER-α and PR (AUC range =0.98-0.99; κ range =0.86-0.91). Lower levels of agreement were seen for ER-β categorical scores (AUC=0.99-1.0; κ=0.80-0.86) and both negative/positive and categorical scores for aromatase (AUC=0.85-0.96; κ=0.41-0.67) and HER2 (AUC=0.94-0.97; κ=0.53-0.72). For ER-α and PR, there was strong correlation between mRNA levels and automated (ρ=0.67-0.74) and pathologist IHC scores (ρ=0.67-0.77). HER2 mRNA levels were more strongly correlated with pathologist (ρ=0.63) than automated IHC scores (ρ=0.41-0.49). Automated analysis of IHC markers is a promising approach for scoring large numbers of breast cancer tissues in epidemiologic investigations. This would facilitate studies of etiologic heterogeneity which ultimately may allow improved risk prediction and better prevention approaches.
