Gene expression-guided selection of histopathology image features

Histopathology imaging and gene expression profiling are two fundamental investigative techniques which allow the analysis of biological specimens from different perspectives. Given their apparent divergence in data representation, they are usually used separately, being connected only at the higher levels of data analysis. In this work we demonstrate how gene expression can be used directly for guiding the selection of prognostically-relevant imaging features. Our method is applied to the analysis of a breast cancer data set, but is not limited to this pathology.

[1]  A. Madabhushi,et al.  Histopathological Image Analysis: A Review , 2009, IEEE Reviews in Biomedical Engineering.

[2]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[3]  A. Moor,et al.  PRO_10 – A New Tissue-Based Prognostic Multigene Marker in Patients with Early Estrogen Receptor-Positive Breast Cancer , 2011, Pathobiology.

[4]  Fabio A. González,et al.  Histopathology Image Classification Using Bag of Features and Kernel Functions , 2009, AIME.

[5]  M. Salto‐Tellez,et al.  Molecular pathology – The value of an integrative approach , 2014, Molecular oncology.

[6]  M. Fernö,et al.  Combination of the proliferation marker cyclin A, histological grade, and estrogen receptor status in a new variable with high prognostic impact in breast cancer , 2011, Breast Cancer Research and Treatment.

[7]  A. Ruifrok,et al.  Quantification of histochemical staining by color deconvolution. , 2001, Analytical and quantitative cytology and histology.

[8]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[9]  Robert Tibshirani,et al.  Estimating the number of clusters in a data set via the gap statistic , 2000 .

[10]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[12]  Rolf Jaggi,et al.  Molecular risk assessment of BIG 1-98 participants by expression profiling using RNA from archival tissue , 2010, BMC Cancer.

[13]  Zlatko Trajanoski,et al.  Histopathologic-based prognostic factors of colorectal cancers are associated with the state of the local immune reaction. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[14]  Emmanuelle Gouillart,et al.  scikit-image: image processing in Python , 2014, PeerJ.

[15]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[16]  Luis Pedro Coelho,et al.  Mahotas: Open source software for scriptable computer vision , 2012, ArXiv.

[17]  Loris Nanni,et al.  Survey on LBP based texture descriptors for image classification , 2012, Expert Syst. Appl..