Abstract: Systems and methods are provided for augmenting digital analysis of lesions. An image of tissue having a glandular epithelial component is generated. The image represents a plurality of medium-scale epithelial components. For each of a plurality of cells within the image, a representative point is identified to provide a plurality of representative points for each of the plurality of medium-scale epithelial components. For each of a subset of the plurality of medium-scale epithelial components, a graph connecting the plurality of representative points is constructed. A plurality of classification features is extracted for each of the subset of medium-scale epithelial components from the graph constructed for the medium-scale epithelial component. A clinical parameter is assigned to each medium-scale epithelial component according to the extracted plurality of classification features.

Abstract: Systems and methods are provided for augmenting digital analysis of lesions. An image of tissue having a glandular epithelial component is generated. The image represents a plurality of medium-scale epithelial components. For each of a plurality of cells within the image, a representative point is identified to provide a plurality of representative points for each of the plurality of medium-scale epithelial components. For each of a subset of the plurality of medium-scale epithelial components, a graph connecting the plurality of representative points is constructed. A plurality of classification features is extracted for each of the subset of medium-scale epithelial components from the graph constructed for the medium-scale epithelial component. A clinical parameter is assigned to each medium-scale epithelial component according to the extracted plurality of classification features.

Abstract: The present invention represents a new approach to data analysis for multivariate classification, particularly as used in medical diagnostics. The invention is in part an intuitive decision making tool for rapid classification of “objects” (e.g., cell, tissue or tumor samples) from evaluation of many simultaneous “variables” (e.g., quantitative gene expression profiles). The data analysis methods of the invention provide the end user with a simplified and robust output for diagnostic classification of objects based on identifying and evaluating multiple variables of predetermined diagnostic relevance. The raw data generated by analysis of the variables is transformed by application or appropriate algorithms to scaleless rank differentials between the variables. The rank orders of variables are used to classify tissues based on readily observable user interfaces, such as a graphical (e.g., visual) user interface or an auditory user interface.

Abstract: The invention provided sets of genes that are expressed differentially in normal and malignant endometrium. These sets of genes can be used to discriminate between normal and malignant endometrial tissues. Accordingly, diagnostic assays for classification of tumors, prediction of tumor outcome, selecting and monitoring treatment regimens and monitoring tumor progression/regression also are provided.

Abstract: The invention provides particular sets of genes that are expressed differentially in tumors characterized as high MAI or low MAI tumors. These sets of genes can be used to discriminate between high and low MAI tumors. Diagnostic assays for classification of tumors, prediction of tumor outcome, selecting and monitoring treatment regimens and monitoring tumor progression/regression are also provided.

Abstract: The invention disclosed herein describes novel methods for diagnosing endometrial precancers by measuring PTEN expression and offers an immunohistochemical biomarker for premalignant disease.

Abstract: The present invention represents a new approach to data analysis for multivariate. classification, particularly as used in medical diagnostics. The invention is in part an intuitive decision making tool for rapid classification of “objects” (e.g., cell, tissue or tumor samples) from evaluation of many simultaneous “variables” (e.g., quantitative gene expression profiles). The data analysis methods of the invention provide the end user with a simplified and robust output for diagnostic classification of objects based on identifying and evaluating multiple variables of predetermined diagnostic relevance. The raw data generated by analysis of the variables is transformed by application or appropriate algorithms to scaleless rank differentials between the variables. The rank orders of variables are used to classify tissues based on readily observable user interfaces, such as a graphical (e.g., visual) user interface or an auditory user interface.

Abstract: The invention provided sets of genes that are expressed differentially in normal and malignant endometrium. These sets of genes can be used to discriminate between normal and malignant endometrial tissues. Accordingly, diagnostic assays for classification of tumors, prediction of tumor outcome, selecting and monitoring treatment regimens and monitoring tumor progression/regression also are provided.

Abstract: The invention disclosed herein describes novel methods for diagnosing endometrial precancers by measuring PTEN expression and offers an immunohistochemical biomarker for premalignant disease.