Using tissue texture surrounding calcification clusters to predict benign vs malignant outcomes

Abstract

The positive predictive value of mammography is between 20% and 25% for clustered microcalcifications. For very early cancers there is often a lack of concordance between mammographic signs and pathology. This study examines the usefulness of computer texture analysis to improve the accuracy of malignant diagnosis. Texture analysis of the breast tissue surrounding microcalcifications on digitally acquired images during stereotactic biopsy is used in this study to predict malignant vs benign outcomes. 54 biopsy proven cases (36 benign, 18 malignant) are used. The texture analysis calculates statistical features from gray level co-occurrence matrices and fractal geometry for equal probability and linear quantizations of the image data. Discriminant models are generated using linear discriminant analysis and logistic discriminant analysis. Results do not differ significantly by method of quantization or discriminant analysis. Jackknife results misclassify 2 of 18 malignant cases (sensitivity 89%) and 6 of 36 benign cases (specificity 83%) for logistic discriminant analysis. From this preliminary study, texture analysis appears to show significant discriminatory power between benign and malignant tissue, which may be useful in resolving problems of discordance between pathological and mammographic findings, and may ultimately reduce the number of benign biopsies.

Composition Dependence of Inhomogeneous Hydrogen Bonding Structures in a-SiGe:H

“…Furthermore, fractal analysis has been investigated for mammographic breast parenchymal density assessment as well as automated detection and diagnosis of malignancies (Priebe et al 1994, Byng et al 1996, Lefebvre et al 1995, Zheng and Chan 2001, Bocchi et al 2004, Velanovich 1998, Thiele et al 1996, Pohlman et al 1996. However, a previous study has also highlighted several shortcomings in the clinical applicability of fractal analysis for radiographic images (Veenland et al 1996).…”

Wavelet-Based Multifractal Formalism to Assist in Diagnosis in Digitized Mammograms

“…Currently most of these methods are often combined to detect and classify clusters of microcalcifications (MC) which is an important mammographic sign of early (in situ) breast cancer despite the fact that several benign diseases show MC as well. In the middle nineties, fractal methods have also been applied to the analysis of radiographic images with some success in improving the performances of previous CAD schemes (Priebe et al, 1994;Lefebvre et al, 1995;Thiele et al, 1996). But most of these methods have been intrinsically elaborated on the prerequesite that the background roughness fluctuations of normal breast texture are statistically homogenous (i.e.…”

Heterogeneity of Focal Breast Lesions and Surrounding Tissue Assessed by Mammographic Texture Analysis: Preliminary Evidence of an Association with Tumor Invasion and Estrogen Receptor Status

“…Up till now research in computer-based TA of mammographic lesions has been focused on detection and classification of masses as benign and malignant (Gupta and Undrill, 1995; Wei et al, 1995; Byng et al, 1996; Thiele et al, 1996; Sahiner et al, 1998, 2001; Brake et al, 2000; Mudigonda et al, 2000, 2001; Gulsrud and Huso, 2001; Rangayyan et al, 2008; Guo et al, 2009). Many of the features like shape, border, density, spiculation, type of micro-calcification, are used by CAD algorithms to classify lesions (Erickson and Bartholomai, 2002).…”

“…In mammography, CAD is mostly employed for automated detection of lesions, whilst leaving the decision of the nature of the lesion to a radiologist. Image analysis methodologies that underpin CAD systems for mammography have included density variations within masses, two-step scheme of pixel-level detection, region-level classification, automated feature-based micro-calcification extraction, fractal dimensions, lacunarity analysis and support vector machines, gradient and flow-based texture analysis (TA; Gupta and Undrill, 1995; Wei et al, 1995; Byng et al, 1996; Thiele et al, 1996; Sahiner et al, 1998, 2001; Brake et al, 2000; Mudigonda et al, 2000, 2001; Gulsrud and Huso, 2001; Rangayyan et al, 2008; Guo et al, 2009). …”