Biologic significance of quantitative estrogen receptor immunohistochemical assay by image analysis in breast cancer. 1994

J M Esteban, and C Ahn, and P Mehta, and H Battifora
Division of Pathology, City of Hope National Medical Center, Duarte, CA 91010.

The authors assayed 209 stage I and II mammary carcinomas for the estrogen receptor (ER) with an immunocytochemical assay (ICA) and quantitated the nuclear stain with the SAMBA 4000 Cell Image Analysis System (Imaging Products International, Inc., Chantilly, VA). The cases had been followed for 54-214 months (average, 64 months). The results were correlated with the patients' overall and disease-free survival times. Three of the parameters obtained from the quantitative analysis were evaluated: labeling index, mean optical density, and quick score. Statistical analysis was performed with the Kaplan-Meier product limit estimator for quantitated values and Cox regression for risk of mortality and disease progression. Mean optical density of the nuclei at a cut-off value of 10 produced the strongest association between quantitative ERICA and overall and disease-free survival times in discriminating high- and low-risk groups (P = .016 and P = .018, respectively). When the mean optical density results were categorized into ranges of values of potential biologic significance according to overall survival times, patients with mean optical density greater than 15 were found to be at lowest risk; patients with values between 5 and 15 had a statistically significant intermediate risk; and the group with values less than 5 had the worst outcome (P = .018). The probability of disease-free survival also was statistically significant (P = .038) among the three groups with cut-off points of less than 10, 10-35, and greater than 35. These results showed that quantified optical density has better discriminating power for risk prediction than that obtained by the biochemical assay for ER at a cut-off value of 10 or 20 fmol/mg. Estrogen receptor ICA has been adequately shown to be as or more accurate than ligand-binding assays. Our results corroborate those studies and support the utility of ICA assays for ER by showing that quantitation performed by image analysis is an objective and reproducible method yielding clinical prognostic information of higher reliability than that given by the dextran-coated charcoal ER assay.

UI MeSH Term Description Entries
D007091 Image Processing, Computer-Assisted A technique of inputting two-dimensional or three-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer. Biomedical Image Processing,Computer-Assisted Image Processing,Digital Image Processing,Image Analysis, Computer-Assisted,Image Reconstruction,Medical Image Processing,Analysis, Computer-Assisted Image,Computer-Assisted Image Analysis,Computer Assisted Image Analysis,Computer Assisted Image Processing,Computer-Assisted Image Analyses,Image Analyses, Computer-Assisted,Image Analysis, Computer Assisted,Image Processing, Biomedical,Image Processing, Computer Assisted,Image Processing, Digital,Image Processing, Medical,Image Processings, Medical,Image Reconstructions,Medical Image Processings,Processing, Biomedical Image,Processing, Digital Image,Processing, Medical Image,Processings, Digital Image,Processings, Medical Image,Reconstruction, Image,Reconstructions, Image
D007150 Immunohistochemistry Histochemical localization of immunoreactive substances using labeled antibodies as reagents. Immunocytochemistry,Immunogold Techniques,Immunogold-Silver Techniques,Immunohistocytochemistry,Immunolabeling Techniques,Immunogold Technics,Immunogold-Silver Technics,Immunolabeling Technics,Immunogold Silver Technics,Immunogold Silver Techniques,Immunogold Technic,Immunogold Technique,Immunogold-Silver Technic,Immunogold-Silver Technique,Immunolabeling Technic,Immunolabeling Technique,Technic, Immunogold,Technic, Immunogold-Silver,Technic, Immunolabeling,Technics, Immunogold,Technics, Immunogold-Silver,Technics, Immunolabeling,Technique, Immunogold,Technique, Immunogold-Silver,Technique, Immunolabeling,Techniques, Immunogold,Techniques, Immunogold-Silver,Techniques, Immunolabeling
D008875 Middle Aged An adult aged 45 - 64 years. Middle Age
D011237 Predictive Value of Tests In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test. Negative Predictive Value,Positive Predictive Value,Predictive Value Of Test,Predictive Values Of Tests,Negative Predictive Values,Positive Predictive Values,Predictive Value, Negative,Predictive Value, Positive
D011379 Prognosis A prediction of the probable outcome of a disease based on a individual's condition and the usual course of the disease as seen in similar situations. Prognostic Factor,Prognostic Factors,Factor, Prognostic,Factors, Prognostic,Prognoses
D011960 Receptors, Estrogen Cytoplasmic proteins that bind estrogens and migrate to the nucleus where they regulate DNA transcription. Evaluation of the state of estrogen receptors in breast cancer patients has become clinically important. Estrogen Receptor,Estrogen Receptors,Estrogen Nuclear Receptor,Estrogen Receptor Type I,Estrogen Receptor Type II,Estrogen Receptors Type I,Estrogen Receptors Type II,Receptor, Estrogen Nuclear,Receptors, Estrogen, Type I,Receptors, Estrogen, Type II,Nuclear Receptor, Estrogen,Receptor, Estrogen
D012044 Regression Analysis Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable. Regression Diagnostics,Statistical Regression,Analysis, Regression,Analyses, Regression,Diagnostics, Regression,Regression Analyses,Regression, Statistical,Regressions, Statistical,Statistical Regressions
D001943 Breast Neoplasms Tumors or cancer of the human BREAST. Breast Cancer,Breast Tumors,Cancer of Breast,Breast Carcinoma,Cancer of the Breast,Human Mammary Carcinoma,Malignant Neoplasm of Breast,Malignant Tumor of Breast,Mammary Cancer,Mammary Carcinoma, Human,Mammary Neoplasm, Human,Mammary Neoplasms, Human,Neoplasms, Breast,Tumors, Breast,Breast Carcinomas,Breast Malignant Neoplasm,Breast Malignant Neoplasms,Breast Malignant Tumor,Breast Malignant Tumors,Breast Neoplasm,Breast Tumor,Cancer, Breast,Cancer, Mammary,Cancers, Mammary,Carcinoma, Breast,Carcinoma, Human Mammary,Carcinomas, Breast,Carcinomas, Human Mammary,Human Mammary Carcinomas,Human Mammary Neoplasm,Human Mammary Neoplasms,Mammary Cancers,Mammary Carcinomas, Human,Neoplasm, Breast,Neoplasm, Human Mammary,Neoplasms, Human Mammary,Tumor, Breast
D005260 Female Females
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man

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