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

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.

The Pan-Cytokeratin Staining Intensity and Fractal Computational Analysis of Breast Tumor Malignant Growth Patterns Prognosticate the Occurrence of Distant Metastasis

“…Whereas the prognostic value of malignant cell distribution in breast tumors has been previously established (14–16), this is the first investigation of the intensity of their immunostaining for pan-cytokeratin. We found that the mean intensity of immunostaining significantly associated with low-risk.…”

“…Epithelial cells within breast tumors immunostained for pan-cytokeratins have been previously prognostically evaluated using GLCM and binary monofractal algorithms (14–16). This study extends the previous reports by undertaking a much wider investigation by utilizing the monofractal, multifractal and intensity analysis of binary and grayscale images of pan-cytokeratin stained malignant cell clusters.…”

Fractal Parameters of Tumour Microscopic Images as Prognostic Indicators of Clinical Outcome in Early Breast Cancer

“…By marking epithelial cells, this kind of staining directs the fractal analysis primarily to the outline of malignant tissue growth patterns, while our analysis was performed on unspecifically stained H&E tissue sections. Furthermore, high tumour grade as an indicator of increased risk also reflected high histomorphological disorder and complexity [18].…”

Effects of different preprocessing algorithms on the prognostic value of breast tumour microscopic images

“…The pixel distance was set to 1 and isotropic spatial orientation obtained by averaging the feature values at 0°, 90°, 180° and 270° angles. Five statistical GLCM features were calculated: contrast, correlation, entropy , inverse difference moment ( IDM ) and angular second moment (ASM) by use of the Texture Analyzer plugin by Julio E. Cabrera for Fiji / ImageJ 1.51i according to formulas indicated in our previous report (Vujasinovic et al ., ) and based on the original description (Haralick et al ., ).…”