Background: Measurement of estrogen receptor (ER) expression is a routine part of clinical evaluation of individual breast cancers and is used to guide treatment. However not all ER+ breast cancers show equal benefit from hormonal or other treatment. The RT-PCR based Oncotype Dx assay, has been recently shown to robustly stratify early stage ER+ breast cancer and identify those tumors that will have low recurrence rates when treated with adjuvant hormonal therapy alone. Interestingly, standard pathologic grading, based on visual analysis of tumor morphology by trained pathologists, has a strong correlation with Oncotype Dx recurrence scores; low recurrence tumors are mostly low grade, and high recurrence tumors are mostly high grade. However, a major problem with use of pathologist-assigned histologic grade as a prognostic tool is the lack of reproducibility of histological grading between different pathologists. Computer aided image analysis and machine learning techniques offer a way to obtain highly reproducible image-based classification of ER+ breast cancer. These analyses can be performed on digital images of routinely obtained breast cancer histology and be incorporated into a prognostic assay.Materials and Methods: High resolution digital images were obtained for a series of ER+ breast cancers for which associated Oncotype Dx Assay results were available. Regions of invasive breast cancer were identified and then processed by computer-assisted image analysis methods. An automated nuclear detection scheme based on Expectation Maximization algorithm was used to identify cancer cell nuclei, followed by graph-based feature extraction using the Voronoi graph, Delaunay Triangulation and Minimum Spanning Trees, and dimensionality reduction using Graph Embedding with Support Vector Machine based classification. The final manifold generated by GE was unwrapped into a linear space and a Euclidean distance metric was used to generate a single score (Image Based Risk Score- (IbRiS)) for each sample. Correlations between IbRS, clinical features such as grade, and Oncotype Dx Recurrance Score were determined.Results: Unsupervised analysis of image-based features of high resolution digital images of ER+ breast cancer histology leads to natural separation of tumors, with low grade tumors separating from high grade tumors. There is also a robust separation of tumors with high Oncotype Dx Recurrance Scores from tumors with low Recurrance Scores.Discussion: Unsupervised analysis of high resolution image-based features can stratify ER+ breast cancers in a fashion that correlates well with a gene-expression based prognostic assay, Oncotype Dx. These data suggest that tumors with distinct gene-expression profiles also have distinct image-based features that can be measured by computer-aided image analysis and used to build prognostic and predictive assays.
Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 3046.