Feature Driven Local Cell Graph (FeDeG): Predicting Overall Survival in Early Stage Lung Cancer

The local spatial arrangement of nuclei in histopathology image has been shown to have prognostic value in the context of different cancers. In order to capture the nuclear architectural information locally, local cell cluster graph based measurements have been proposed. However, conventional ways of cell graph construction only utilize nuclear spatial proximity, and do not differentiate different cell types while constructing a cell graph. In this paper, we present feature driven local cell graph (FeDeG), a new approach to constructing local cell graphs by simultaneously considering spatial proximity and attributes of the individual nuclei (e.g. shape, size, texture). In addition, we designed a new set of quantitative graph derived metrics to be extracted from FeDeGs, in turn capturing the interplay between different local cell clusters. We evaluated the efficacy of FeDeG features in a digitized H&E stained tissue micro-array (TMA) images cohort consists of 434 early stage non-small cell lung cancer for predicting short-term (<5 years) vs long-term (>5 years) survival. Across a 100 runs of 10-fold cross-validation, a linear discriminant classifier in conjunction with the 15 most predictive FeDeG features identified via the Wilcoxon Rank Sum Test (WRST) yielded an average of AUC = 0.68. By comparison, four state-of-the-art pathomic and a deep learning based classifier had a corresponding AUC of 0.56, 0.54, 0.61, 0.62, and 0.55 respectively.