Manifold learning with graph-based features for identifying extent of lymphocytic infiltration from high grade , HER 2 + breast cancer histology

It has been proposed that molecular changes in breast cancer (BC) may be accompanied by corresponding changes in phenotype. One such phenotype is the presence of lymphocytic infiltration (LI), a form of immune response seen often in high grade BC. The presence of LI in BC histology has been shown to correlate with prognosis and course of treatment. The advent of digitized histopathology has made tissue slides amenable to computer aided diagnosis (CAD). While texture-based features have recently been shown to successfully distinguish between tissue classes in histopathology, the similarity in appearance of BC nuclei and LI suggests that texture features alone may be insufficient. In this paper, we present a methodology that integrates manifold learning with graph-based features to distinguish high grade BC histology specimens based on the presence or absence of LI. Lymphocytes are first automatically detected via a segmentation scheme comprising a Bayesian classifier and template matching. For a total of 41 samples, the graphbased features, in conjunction with a Support Vector Machine classifier, achieve a classification accuracy of 89.50%. Our method is also compared against the popular Varma-Zisserman (VZ) texton-based classifier, which achieves a maximum accuracy of 62.50%. Visualization of the low dimensional manifold of the LI complex via Graph Embedding shows the presence of three distinct stages of LI.