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Gray-Level Co-Occurrence Matrix Texture Analysis of Breast Tumor Images in Prognosis of Distant Metastasis Risk

Published online by Cambridge University Press:  10 April 2015

Tijana Vujasinovic ,
Jelena Pribic ,
Ksenija Kanjer ,
Nebojsa T. Milosevic ,
Zorica Tomasevic ,
Zorka Milovanovic ,
Dragica Nikolic-Vukosavljevic  and
Marko Radulovic
Tijana Vujasinovic
Affiliation:
Department of Experimental Oncology, Institute for Oncology and Radiology, 11000 Belgrade, Serbia
Jelena Pribic
Affiliation:
Department of Experimental Oncology, Institute for Oncology and Radiology, 11000 Belgrade, Serbia
Ksenija Kanjer
Affiliation:
Department of Experimental Oncology, Institute for Oncology and Radiology, 11000 Belgrade, Serbia
Nebojsa T. Milosevic
Affiliation:
Department of Biophysics, School of Medicine, University of Belgrade, Višegradska 26/2, 11000 Belgrade, Serbia
Zorica Tomasevic
Affiliation:
Daily Chemotherapy Hospital, Institute for Oncology and Radiology, 11000 Belgrade, Serbia
Zorka Milovanovic
Affiliation:
Department of Pathology and Cytology, Institute for Oncology and Radiology, 11000 Belgrade, Serbia
Dragica Nikolic-Vukosavljevic
Affiliation:
Department of Experimental Oncology, Institute for Oncology and Radiology, 11000 Belgrade, Serbia
Marko Radulovic*
Affiliation:
Department of Experimental Oncology, Institute for Oncology and Radiology, 11000 Belgrade, Serbia
*
*Corresponding author. [email protected]
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Abstract

Owing to exceptional heterogeneity in the outcome of invasive breast cancer it is essential to develop highly accurate prognostic tools for effective therapeutic management. Based on this pressing need, we aimed to improve breast cancer prognosis by exploring the prognostic value of tumor histology image analysis. Patient group (n=78) selection was based on invasive breast cancer diagnosis without systemic treatment with a median follow-up of 147 months. Gray-level co-occurrence matrix texture analysis was performed retrospectively on primary tumor tissue section digital images stained either nonspecifically with hematoxylin and eosin or specifically with a pan-cytokeratin antibody cocktail for epithelial malignant cells. Univariate analysis revealed stronger association with metastasis risk by texture analysis when compared with clinicopathological parameters. The combination of individual clinicopathological and texture variables into composite scores resulted in further powerful enhancement of prognostic performance, with an accuracy of up to 90%, discrimination efficiency by the area under the curve [95% confidence interval (CI)] of 0.94 (0.87–0.99) and hazard ratio (95% CI) of 20.1 (7.5–109.4). Internal validation was successfully performed by bootstrap and split-sample cross-validation, suggesting that the models are generalizable. Whereas further validation is needed on an external set of patients, this preliminary study indicates the potential use of primary breast tumor histology texture as a highly accurate, simple, and cost-effective prognostic indicator of distant metastasis risk.

Keywords

breast cancertumorgray-level co-occurrence matrixmetastasispan-cytokeratin
Type
Biological Applications
Information
Microscopy and Microanalysis , Volume 21 , Issue 3 , June 2015 , pp. 646 - 654
Copyright
© Microscopy Society of America 2015 

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Footnotes

a

These authors contributed equally to this work.

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