Mammographic Parenchymal Texture Analysis for Estrogen-Receptor Subtype Specific Breast Cancer Risk Estimation

We investigate the potential of mammographic parenchymal texture as a surrogate marker of the risk to develop Estrogen Receptor (ER) sub-type specific breast cancer. A case-control study was performed, including 118 cancer cases stratified by ER receptor status and 354 age-matched controls. Digital mammographic (DM) images were retrospectively collected and analyzed under HIPAA and IRB approval. The performance of the texture features was compared to that of the standard mammographic density measures. We observed that breast percent density PD% and parenchymal texture features can both distinguish between cancer cases and controls (Az > 0.70). However, for ER subtype-specific classification, PD% alone does not provide sufficient classification (Az = 0.60), while texture features have significant classification performance (Az = 0.70). Combining breast density with texture features achieves the best performance (Az = 0.71). These findings suggest that mammographic texture analysis may have value for sub-type specific breast cancer risk assessment.

[1]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[2]  K. Gelmon,et al.  Patterns of relapse in breast cancer: changes over time , 2010, Breast Cancer Research and Treatment.

[3]  Yan Wang,et al.  Adaptive Multi-cluster Fuzzy C-Means Segmentation of Breast Parenchymal Tissue in Digital Mammography , 2011, MICCAI.

[4]  George C. Kagadis,et al.  Assessing Estrogen Receptors' Status by Texture Analysis of Breast Tissue Specimens and Pattern Recognition Methods , 2007, CAIP.

[5]  Sunil Arya,et al.  An optimal algorithm for approximate nearest neighbor searching fixed dimensions , 1998, JACM.

[6]  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.

[7]  R A Young,et al.  The Gaussian derivative model for spatial vision: I. Retinal mechanisms. , 1988, Spatial vision.

[8]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

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

[10]  T. Byers,et al.  Differences in Estrogen Receptor Subtype According to Family History of Breast Cancer among Hispanic, but not Non-Hispanic White Women , 2008, Cancer Epidemiology Biomarkers & Prevention.

[11]  S. Cummings,et al.  Mammographic density and estrogen receptor status of breast cancer. , 2004, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[12]  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.

[13]  Gopal Karemore,et al.  Anisotropic diffusion tensor applied to temporal mammograms: An application to breast cancer risk assessment , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[14]  L. Costaridou,et al.  Texture analysis of tissue surrounding microcalcifications on mammograms for breast cancer diagnosis. , 2007, The British journal of radiology.

[15]  Joachim Weickert,et al.  Coherence-Enhancing Diffusion Filtering , 1999, International Journal of Computer Vision.

[16]  Tao Qin,et al.  Feature selection for ranking , 2007, SIGIR.

[17]  L. Savage.  Researchers wonder why high-risk women are not taking chemoprevention drugs. , 2007, Journal of the National Cancer Institute.

[18]  M. Nielsen,et al.  A novel and automatic mammographic texture resemblance marker is an independent risk factor for breast cancer. , 2011, Cancer epidemiology.

[19]  N. Boyd,et al.  Mammographic density, response to hormones, and breast cancer risk. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[20]  Emily White,et al.  Association between Mammographic Breast Density and Breast Cancer Tumor Characteristics , 2005, Cancer Epidemiology Biomarkers & Prevention.

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

[22]  C. Benz,et al.  Risk factors for estrogen receptor-positive breast cancer. , 2005, Archives of surgery.