It’s October 1^st and the beginning of Breast Cancer Awareness Month.  I am reminded that both my mother and grandmother had breast cancer, two close friends have died from breast cancer, and my daughter’s best friend was diagnosed at the age of 34.   When my sister and I investigated BRCA genetic testing, we were discouraged because if we had a genetic risk of getting cancer ourselves, and our health insurance companies found out, they may drop our coverage. 

We have five  JDI online-first articles with Breast Imaging topics I want to draw to your attention.  The first one is entitled “Effect of Dose Reduction on the Ability of Digital Mammography to Detect Simulated Microcalcifications”  by Yakabe, Sakai, Yabuuchi, Matsuo, Kamitani, Setoguchi, Cho, Masuda and Sasaki.  In their article, their research suggests that a certain level of dose reduction in digital mammography may be an option.  The second article is entitled “A Statistical Approach for Breast Density Segmentation” by Oliver,  Llado, Perez, Pont, Denton, Freixenet, and Marti.   Their research centers around evaluating the density of a breast by segmenting its internal parenchyma in either fatty or dense  surrounding tissue.  The third article is entitled “A New Fast Fractal Modeling Approach for the Detection of Microcalcifications in Mammograms” by Sankar and Thomas.  As the title suggests, they describe their fast method for modeling mammograms by using deterministic fractal coding to enhance microcalcifications.    The fourth article is entitled “Effect of Pixel Resolution on Texture Features of Breast Masses in Mammograms” by Rangayyan, Nguyen, ayres, and Nandi.  This group analyzed breast masses at various pixel sizes to discriminate mammographic breast lesions as benign masses or malignant tumors.   And last but not least, the fifth article is entitled “Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast Cancer” by Woods, Oliphant, Shinki, Page, Shavlik, and Burnside.   The purpose of the study was to identify and quantify the association between high breast mass density and breast malignancy using inductive logic programming and conditional probabilities.  Their results show that both measures indicate that mass density is an important adjunct predictor of malignancy.  This article is also open access, provided by SIIM for articles deemed to be of high interest to the SIIM community.

All women readers should consider joining the Army of Women, www.armyofwomen.org.  This organization has one research goal – to prevent breast cancer.  They need women without breast cancer as well as those who are newly diagnosed or who are survivers.