Parenchymal texture analysis in digital breast tomosynthesis for breast cancer risk estimation: a preliminary study.

RATIONALE AND OBJECTIVES Studies have demonstrated a relationship between mammographic parenchymal texture and breast cancer risk. Although promising, texture analysis in mammograms is limited by tissue superposition. Digital breast tomosynthesis (DBT) is a novel tomographic x-ray breast imaging modality that alleviates the effect of tissue superposition, offering superior parenchymal texture visualization compared to mammography. The aim of this study was to investigate the potential advantages of DBT parenchymal texture analysis for breast cancer risk estimation. MATERIALS AND METHODS DBT and digital mammographic (DM) images of 39 women were analyzed. Texture features, shown in previous studies with mammograms to correlate with cancer risk, were computed from the retroareolar breast region. The relative performances of the DBT and DM texture features were compared in correlating with two measures of breast cancer risk: (1) the Gail and Claus risk estimates and (2) mammographic breast density. Linear regression was performed to model the association between texture features and increasing levels of risk. RESULTS No significant correlation was detected between parenchymal texture and the Gail and Claus risk estimates. Significant correlations were observed between texture features and breast density. Overall, the DBT texture features demonstrated stronger correlations with breast percent density than DM features (P < or = .05). When dividing the study population into groups of increasing breast percent density, the DBT texture features appeared to be more discriminative, having regression lines with overall lower P values, steeper slopes, and higher R(2) estimates. CONCLUSION Although preliminary, the results of this study suggest that DBT parenchymal texture analysis could provide more accurate characterization of breast density patterns, which could ultimately improve breast cancer risk estimation.

[1]  Andrew D. A. Maidment,et al.  Evaluating the Effect of Tomosynthesis Acquisition Parameters on Image Texture: A Study Based on an Anthropomorphic Breast Tissue Software Model , 2008, Digital Mammography / IWDM.

[2]  Maryellen L. Giger,et al.  Computerized texture analysis of mammographic parenchymal patterns of digitized mammograms , 2004, CARS.

[3]  Andrew D. A. Maidment,et al.  Analysis of percent density estimates from digital breast tomosynthesis projection images , 2007, SPIE Medical Imaging.

[4]  N. Boyd,et al.  Mammographic density and the risk and detection of breast cancer. , 2007, The New England journal of medicine.

[5]  Laurie L Fajardo,et al.  Breast tomosynthesis: present considerations and future applications. , 2007, Radiographics : a review publication of the Radiological Society of North America, Inc.

[6]  T. Sellers,et al.  Mammographic density, breast cancer risk and risk prediction , 2007, Breast Cancer Research.

[7]  Li Lan,et al.  Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment. , 2007, Academic radiology.

[8]  Berkman Sahiner,et al.  Computerized image analysis: estimation of breast density on mammograms , 2000, Medical Imaging: Image Processing.

[9]  W. Thompson,et al.  Relationship between breast histopathology and family history of breast cancer , 1993, Cancer.

[10]  Stanton A. Glantz,et al.  Primer of biostatistics : statistical software program version 6.0 , 1981 .

[11]  Karla Kerlikowske,et al.  Using Clinical Factors and Mammographic Breast Density to Estimate Breast Cancer Risk: Development and Validation of a New Predictive Model , 2008, Annals of Internal Medicine.

[12]  Constantine Gatsonis,et al.  Screening women at high risk for breast cancer with mammography and magnetic resonance imaging , 2005, Cancer.

[13]  C. Gatsonis,et al.  Cancer yield of mammography, MR, and US in high-risk women: prospective multi-institution breast cancer screening study. , 2007, Radiology.

[14]  Caroline Sabin,et al.  Medical Statistics at a Glance , 2000 .

[15]  Barbara L. Smith,et al.  Evaluation of hereditary risk in a mammography population. , 2005, Clinical breast cancer.

[16]  Robert King,et al.  Textural features corresponding to textural properties , 1989, IEEE Trans. Syst. Man Cybern..

[17]  Andrew D. A. Maidment,et al.  Analysis of parenchymal texture properties in breast tomosynthesis images , 2007, SPIE Medical Imaging.

[18]  David G. Stork,et al.  Pattern Classification , 1973 .

[19]  Elizabeth A Rafferty,et al.  Digital mammography: novel applications. , 2007, Radiologic clinics of North America.

[20]  A. McTiernan,et al.  Biological significance of interventions that change breast density. , 2003, Journal of the National Cancer Institute.

[21]  N. Boyd,et al.  The quantitative analysis of mammographic densities. , 1994, Physics in medicine and biology.

[22]  Jinbo Chen,et al.  Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density. , 2006, Journal of the National Cancer Institute.

[23]  J. Heine,et al.  Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography. Part 2. Serial breast tissue change and related temporal influences. , 2002, Academic radiology.

[24]  Maryellen L. Giger,et al.  Computerized Texture Analysis of Mammographic Parenchymal Patterns of Digitized Mammograms1 , 2005 .

[25]  M. Yaffe,et al.  Characterisation of mammographic parenchymal pattern by fractal dimension. , 1990, Physics in medicine and biology.

[26]  D Spiegelman,et al.  Validation of the Gail et al. model of breast cancer risk prediction and implications for chemoprevention. , 2001, Journal of the National Cancer Institute.

[27]  Andrew Karellas.  Mammographic pattern analysis: an emerging risk assessment tool. , 2007, Academic radiology.

[28]  J. Kaufhold,et al.  A calibration approach to glandular tissue composition estimation in digital mammography. , 2002, Medical physics.

[29]  Andrew D. A. Maidment,et al.  Texture in digital breast tomosynthesis: a comparison between mammographic and tomographic characterization of parenchymal properties , 2008, SPIE Medical Imaging.

[30]  Beth A Reboussin,et al.  Postmenopausal hormone therapy and change in mammographic density. , 2003, Journal of the National Cancer Institute.

[31]  Grégoire Toussaint,et al.  Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas. , 2003, Magnetic resonance imaging.

[32]  N. Boyd,et al.  Analysis of mammographic density and breast cancer risk from digitized mammograms. , 1998, Radiographics : a review publication of the Radiological Society of North America, Inc.

[33]  J. Heine,et al.  Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography. Part 1. Tissue and related risk factors. , 2002, Academic radiology.

[34]  M Souto,et al.  Computer-assisted diagnosis: the classification of mammographic breast parenchymal patterns. , 1995, Physics in medicine and biology.

[35]  F. Cendes,et al.  Texture analysis of medical images. , 2004, Clinical radiology.

[36]  M. Giger,et al.  Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: effect of ROI size and location. , 2004, Medical physics.

[37]  Martin J. Yaffe,et al.  Characterization Of Mammographic Parenchymal Pattern By Fractal Dimension , 1989, Medical Imaging.

[38]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[39]  Lubomir M. Hadjiiski,et al.  A comparative study of limited-angle cone-beam reconstruction methods for breast tomosynthesis. , 2006, Medical physics.

[40]  Tor D Tosteson,et al.  Digital breast tomosynthesis: initial experience in 98 women with abnormal digital screening mammography. , 2007, AJR. American journal of roentgenology.

[41]  Karla Kerlikowske,et al.  The mammogram that cried Wolfe. , 2007, The New England journal of medicine.

[42]  Maryellen L. Giger,et al.  Power Spectral Analysis of Mammographic Parenchymal Patterns for Breast Cancer Risk Assessment , 2008, Journal of Digital Imaging.

[43]  J. Wolfe,et al.  Mammographic features and breast cancer risk: effects with time, age, and menopause status. , 1995, Journal of the National Cancer Institute.

[44]  S. Cummings,et al.  Mammographic Breast Density and the Gail Model for Breast Cancer Risk Prediction in a Screening Population , 2005, Breast Cancer Research and Treatment.

[45]  Heang-Ping Chan,et al.  Mammographic density measured with quantitative computer-aided method: comparison with radiologists' estimates and BI-RADS categories. , 2006, Radiology.

[46]  D. Kopans,et al.  Digital tomosynthesis in breast imaging. , 1997, Radiology.

[47]  S. Cummings,et al.  Critical assessment of new risk factors for breast cancer: considerations for development of an improved risk prediction model. , 2007, Endocrine-related cancer.

[48]  M. Giger,et al.  Volumetric texture analysis of breast lesions on contrast‐enhanced magnetic resonance images , 2007, Magnetic resonance in medicine.

[49]  B. Kimler,et al.  Mammographic density: use in risk assessment and as a biomarker in prevention trials. , 2006, The Journal of nutrition.

[50]  Jacob D. Furst,et al.  CO-OCCURRENCE MATRICES FOR VOLUMETRIC DATA , 2004 .

[51]  Constance D Lehman,et al.  Mammographic Density Correlation with Gail Model Breast Cancer Risk Estimates and Component Risk Factors , 2006, Cancer Epidemiology Biomarkers & Prevention.

[52]  J. Wolfe.  Risk for breast cancer development determined by mammographic parenchymal pattern , 1976, Cancer.

[53]  Jennifer A Harvey,et al.  Quantitative assessment of mammographic breast density: relationship with breast cancer risk. , 2004, Radiology.

[54]  V. McCormack,et al.  Breast Density and Parenchymal Patterns as Markers of Breast Cancer Risk: A Meta-analysis , 2006, Cancer Epidemiology Biomarkers & Prevention.

[55]  Bradley D. Clymer,et al.  Three-dimensional texture analysis of cancellous bone cores evaluated at clinical CT resolutions , 2006, Osteoporosis International.

[56]  Daniel B Kopans,et al.  Basic physics and doubts about relationship between mammographically determined tissue density and breast cancer risk. , 2008, Radiology.

[57]  N. Boyd,et al.  Mammographic density. Potential mechanisms of breast cancer risk associated with mammographic density: hypotheses based on epidemiological evidence , 2008, Breast Cancer Research.

[58]  Ronald Rousseau,et al.  Jaccard Similarity Leads to the Marczewski-Steinhaus Topology for Information Retrieval , 1998, Inf. Process. Manag..

[59]  M. Gail,et al.  Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. , 1989, Journal of the National Cancer Institute.

[60]  M. Giger,et al.  Computerized analysis of mammographic parenchymal patterns for breast cancer risk assessment: feature selection. , 2000, Medical physics.

[61]  Maria Petrou,et al.  Texture anisotropy in 3-D images , 1999, IEEE Trans. Image Process..

[62]  C. Isaacs,et al.  Management of women at increased risk for hereditary breast cancer. , 2007, Breast disease.

[63]  Ann-Katherine Carton,et al.  Breast percent density estimation from 3D reconstructed digital breast tomosynthesis images , 2008, SPIE Medical Imaging.

[64]  Tao Wu,et al.  A comparison of reconstruction algorithms for breast tomosynthesis. , 2004, Medical physics.

[65]  Zhimin Huo,et al.  Computerized analysis of digitized mammograms of BRCA1 and BRCA2 gene mutation carriers. , 2002, Radiology.

[66]  L. Ford,et al.  Tamoxifen for the prevention of breast cancer: current status of the National Surgical Adjuvant Breast and Bowel Project P-1 study. , 2005, Journal of the National Cancer Institute.

[67]  Karla Kerlikowske,et al.  Prospective breast cancer risk prediction model for women undergoing screening mammography. , 2006, Journal of the National Cancer Institute.