Using features from tumor subregions of breast DCE-MRI for estrogen receptor status prediction

In breast cancer, tumor heterogeneity is a reflection of differing tumor subtypes, which may display markedly different genotypes and clinical phenotypes. Although pathological and qualitative (based on contrast enhancement patterns) studies suggest the presence of clinical and molecular predictive tumor subregions, this has not been fully investigated. Our goal is to develop a novel algorithm to utilize the potential information available in different tumor subregions (periphery and core) by extracting textural kinetic features, for the purpose of estrogen receptor (ER) classification. We show that features from different tumor subregions, at appropriate scales and quantization levels, can be used to better classify ER subtypes than features averaged from the whole tumor. We analyzed representative two dimensional (2D) slices from twenty breast tumors with volumetric dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) available by extracting multi-parametric textural kinetic features from the periphery, core and whole tumor. The utility of the features from different subregions are evaluated using six meta-classifiers (feature selector and classifier pairs), formed from two feature selectors and three classifiers. Classification accuracy approached 94%.