Integrative analysis based on quantitative representation of whole slide images (WSIs) in a large histology cohort may provide predictive models of clinical outcome. On one hand, the efficiency and effectiveness of such representation is hindered as a result of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. On the other hand, perceptual interpretation/validation of important multi-variate phenotypic signatures are often difficult due to the loss of visual information during feature transformation in hyperspace. To address these issues, we propose a novel approach for integrative analysis based on cellular morphometric context, which is a robust representation of WSI, with the emphasis on tumor architecture and tumor heterogeneity, built upon cellular level morphometric features within the spatial pyramid matching (SPM) framework. The proposed approach is applied to The Cancer Genome Atlas (TCGA) lower grade glioma (LGG) cohort, where experimental results (i) reveal several clinically relevant cellular morphometric types, which enables both perceptual interpretation/validation and further investigation through gene set enrichment analysis; and (ii) indicate the significantly increased survival rates in one of the cellular morphometric context subtypes derived from the cellular morphometric context.

• Ju Han, Yunfu Wang, Weidong Cai, Alexander Borowsky, Bahram Parvin and Hang Chang. 2016. Integrative Analysis of Cellular Morphometric Context Reveals Clinically Relevant Signatures in Lower Grade Glioma. International Conference on Medical Image Computing and Computer Assisted Intervention, in press.
• Hang Chang, Ju Han, Alexander Borowsky, Leandro Loss, Jow W. Gray, Paul T. Spellman and Bahram Parvin. "Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association." IEEE Trans. on Medical Imaging, 32 4 (2013): 670-682.
• Hang Chang, Alexander Borowsky, Paul T. Spellman and Bahram Parvin, "Classification of Tumor Histology via Morphometric Context," Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, Portland, OR, 2013, pp. 2203-2210.