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Proceedings Paper

Identification of immune cell infiltration in hematoxylin-eosin stained breast cancer samples: texture-based classification of tissue morphologies
Author(s): Riku Turkki; Nina Linder; Panu E. Kovanen; Teijo Pellinen; Johan Lundin
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Paper Abstract

The characteristics of immune cells in the tumor microenvironment of breast cancer capture clinically important information. Despite the heterogeneity of tumor-infiltrating immune cells, it has been shown that the degree of infiltration assessed by visual evaluation of hematoxylin-eosin (H and E) stained samples has prognostic and possibly predictive value. However, quantification of the infiltration in H and E-stained tissue samples is currently dependent on visual scoring by an expert. Computer vision enables automated characterization of the components of the tumor microenvironment, and texture-based methods have successfully been used to discriminate between different tissue morphologies and cell phenotypes. In this study, we evaluate whether local binary pattern texture features with superpixel segmentation and classification with support vector machine can be utilized to identify immune cell infiltration in H and E-stained breast cancer samples. Guided with the pan-leukocyte CD45 marker, we annotated training and test sets from 20 primary breast cancer samples. In the training set of arbitrary sized image regions (n=1,116) a 3-fold cross-validation resulted in 98% accuracy and an area under the receiver-operating characteristic curve (AUC) of 0.98 to discriminate between immune cell -rich and - poor areas. In the test set (n=204), we achieved an accuracy of 96% and AUC of 0.99 to label cropped tissue regions correctly into immune cell -rich and -poor categories. The obtained results demonstrate strong discrimination between immune cell -rich and -poor tissue morphologies. The proposed method can provide a quantitative measurement of the degree of immune cell infiltration and applied to digitally scanned H and E-stained breast cancer samples for diagnostic purposes.

Paper Details

Date Published: 23 March 2016
PDF: 7 pages
Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 979110 (23 March 2016); doi: 10.1117/12.2217040
Show Author Affiliations
Riku Turkki, Univ. of Helsinki (Finland)
Nina Linder, Univ. of Helsinki (Finland)
Panu E. Kovanen, Univ. of Helsinki (Finland)
Teijo Pellinen, Univ. of Helsinki (Finland)
Johan Lundin, Univ. of Helsinki (Finland)
Karolinska Institutet (Sweden)

Published in SPIE Proceedings Vol. 9791:
Medical Imaging 2016: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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