Classification of breast cancer stroma as a tool for prognosis

Sara Reis; Patrycja Gazinska; John H. Hipwell; Thomy Mertzanidou; Kalnisha Naidoo; Sarah Pinder; David J. Hawkes

doi:10.1117/12.2216520

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23 March 2016 Classification of breast cancer stroma as a tool for prognosis

Sara Reis, Patrycja Gazinska, John H. Hipwell, Thomy Mertzanidou, Kalnisha Naidoo, Sarah Pinder, David J. Hawkes

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Proceedings Volume 9791, Medical Imaging 2016: Digital Pathology; 979105 (2016) https://doi.org/10.1117/12.2216520
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

It has been shown that the tumour microenvironment plays a crucial role in regulating tumour progression by a number of different mechanisms, including the remodeling of collagen fibres in tumour-associated stroma. It is still unclear, however, if these stromal changes are of benefit to the host or the tumour. We hypothesise that stromal maturity is an important reflection of tumour biology, and thus can be used to predict prognosis. The aim of this study is to develop a texture analysis methodology which will automatically classify stromal regions from images of hematoxylin and eosin-stained (H and E) sections into two categories: mature and immature. Subsequently we will investigate whether stromal maturity could be used as a predictor of survival and also as a means to better understand the relationship between the radiological imaging signal and the underlying tissue microstructure. We present initial results for 118 regions-of-interest from a dataset of 39 patients diagnosed with invasive breast cancer.

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Sara Reis, Patrycja Gazinska, John H. Hipwell, Thomy Mertzanidou, Kalnisha Naidoo, Sarah Pinder, and David J. Hawkes "Classification of breast cancer stroma as a tool for prognosis", Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 979105 (23 March 2016); https://doi.org/10.1117/12.2216520

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