PURPOSE: To evaluate, by using computer image analysis, the mammographic density patterns of women with germ-line mutations in BRCA1 and BRCA2 genes in comparison with those of women at low risk of developing breast cancer. MATERIALS AND METHODS: Mammograms from 30 carriers of BRCA1 and BRCA2 mutations and from 142 low-risk women were collected retrospectively and digitized. In addition, 60 of the 142 low-risk women were randomly selected and age matched at 5-year intervals with the 30 mutation carriers. Mammographic features were extracted from the central regions of the breast images to characterize the mammographic density and heterogeneity of dense portions of the breast. These features were then merged into a single value related to the risk of breast cancer by using linear discriminant analysis. The applicability of these computer-extracted features and the output from linear discriminant analysis to differentiate between the carriers of BRCA1 and BRCA2 mutations and the low-risk women in the entire database and in an age-matched group were evaluated by using receiver operating characteristic analysis. RESULTS: Quantitative analysis of mammograms demonstrated that carriers of BRCA1 and BRCA2 mutations tended to have dense breast tissue, and their mammographic patterns tended to be low in contrast, with a coarse texture. Linear discriminant analysis resulted in values of the areas under the receiver operating characteristic curve of 0.91 and 0.92 in distinguishing between the BRCA1 and BRCA2 mutation carriers and the low-risk women in the entire database and the age-matched group, respectively. CONCLUSION: The computerized analysis of mammograms suggests that mammographic patterns in carriers of BRCA1 and BRCA2 mutations differ from those of women at low risk for breast cancer. Our computer-extracted features may be useful as radiographic markers for identifying women at high risk for breast cancer. Copyright RSNA, 2002
PURPOSE: To evaluate, by using computer image analysis, the mammographic density patterns of women with germ-line mutations in BRCA1 and BRCA2 genes in comparison with those of women at low risk of developing breast cancer. MATERIALS AND METHODS: Mammograms from 30 carriers of BRCA1 and BRCA2 mutations and from 142 low-risk women were collected retrospectively and digitized. In addition, 60 of the 142 low-risk women were randomly selected and age matched at 5-year intervals with the 30 mutation carriers. Mammographic features were extracted from the central regions of the breast images to characterize the mammographic density and heterogeneity of dense portions of the breast. These features were then merged into a single value related to the risk of breast cancer by using linear discriminant analysis. The applicability of these computer-extracted features and the output from linear discriminant analysis to differentiate between the carriers of BRCA1 and BRCA2 mutations and the low-risk women in the entire database and in an age-matched group were evaluated by using receiver operating characteristic analysis. RESULTS: Quantitative analysis of mammograms demonstrated that carriers of BRCA1 and BRCA2 mutations tended to have dense breast tissue, and their mammographic patterns tended to be low in contrast, with a coarse texture. Linear discriminant analysis resulted in values of the areas under the receiver operating characteristic curve of 0.91 and 0.92 in distinguishing between the BRCA1 and BRCA2 mutation carriers and the low-risk women in the entire database and the age-matched group, respectively. CONCLUSION: The computerized analysis of mammograms suggests that mammographic patterns in carriers of BRCA1 and BRCA2 mutations differ from those of women at low risk for breast cancer. Our computer-extracted features may be useful as radiographic markers for identifying women at high risk for breast cancer. Copyright RSNA, 2002
Authors: Brad M Keller; Andrew Oustimov; Yan Wang; Jinbo Chen; Raymond J Acciavatti; Yuanjie Zheng; Shonket Ray; James C Gee; Andrew D A Maidment; Despina Kontos Journal: J Med Imaging (Bellingham) Date: 2015-04-03
Authors: Hui Li; William A Weiss; Milica Medved; Hiroyuki Abe; Gillian M Newstead; Gregory S Karczmar; Maryellen L Giger Journal: J Med Imaging (Bellingham) Date: 2016-12-28
Authors: Josep M Sabaté; Antonio Gómez; Sofía Torrubia; Carme Blancas; Gloria Sánchez; M C Alonso; E Lerma Journal: Eur Radiol Date: 2005-10-06 Impact factor: 5.315