A Novel Breast Cancer Risk Assessment Scheme Design Using Dual View Mammograms

Computer aided diagnosis CADx schemes based on dual view mammograms are able to provide extra information compared to single view schemes. To explore an efficient and effective way for combining the information from different views, a new breast cancer risk analysis scheme was developed and tested in this study. 120 pairs of dual view mammograms from 120 women were used in this study. Three different groups of texture features and density features were extracted from both MLO view and CC view mammograms. The asymmetry score that measures the asymmetry levels of these two view mammograms was considered in our proposed scheme.i?ź91 computational features on each view and 3 asymmetry measurements were computed and used for the proposed scheme. Three classifiers were used in our proposed scheme, one for each of the dual view mammograms, and the third one combined dual view scores with asymmetry measurements. The highest area under the curve AUC we obtained was 0.753i?ź±i?ź0.039.

[1]  W. E. Hoogendoorn,et al.  Modeling familial clustered breast cancer using published data. , 2003, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[2]  P. Langenberg,et al.  Breast Imaging Reporting and Data System: inter- and intraobserver variability in feature analysis and final assessment. , 2000, AJR. American journal of roentgenology.

[3]  Karla Kerlikowske,et al.  Benign breast disease, mammographic breast density, and the risk of breast cancer. , 2013, Journal of the National Cancer Institute.

[4]  Wenqing Sun,et al.  Prediction of near-term risk of developing breast cancer using computerized features from bilateral mammograms , 2014, Comput. Medical Imaging Graph..

[5]  D. Vanel.  The American College of Radiology (ACR) Breast Imaging and Reporting Data System (BI-RADS): a step towards a universal radiological language? , 2007, European journal of radiology.

[6]  Jacob D. Furst,et al.  RUN-LENGTH ENCODING FOR VOLUMETRIC TEXTURE , 2004 .

[7]  D. Berry,et al.  Determining carrier probabilities for breast cancer-susceptibility genes BRCA1 and BRCA2. , 1998, American journal of human genetics.

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

[9]  J. Wolfe.  Breast patterns as an index of risk for developing breast cancer. , 1976, AJR. American journal of roentgenology.

[10]  Wenqing Sun,et al.  Improving the efficacy of mammography screening: the potential and challenge of developing new computer-aided detection approaches , 2015, Expert review of medical devices.

[11]  Michael Unser,et al.  Sum and Difference Histograms for Texture Classification , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  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.

[13]  N Risch,et al.  Genetic analysis of breast cancer in the cancer and steroid hormone study. , 1991, American journal of human genetics.

[14]  A. Jemal,et al.  Cancer statistics, 2014 , 2014, CA: a cancer journal for clinicians.

[15]  Wenqing Sun,et al.  Using multiscale texture and density features for near-term breast cancer risk analysis. , 2015, Medical physics.