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13 March 2017 Prediction of near-term breast cancer risk using local region-based bilateral asymmetry features in mammography
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This study proposed a near-term breast cancer risk assessment model based on local region bilateral asymmetry features in Mammography. The database includes 566 cases who underwent at least two sequential FFDM examinations. The ‘prior’ examination in the two series all interpreted as negative (not recalled). In the “current” examination, 283 women were diagnosed cancers and 283 remained negative. Age of cancers and negative cases completely matched. These cases were divided into three subgroups according to age: 152 cases among the 37-49 age-bracket, 220 cases in the age-bracket 50- 60, and 194 cases with the 61-86 age-bracket. For each image, two local regions including strip-based regions and difference-of-Gaussian basic element regions were segmented. After that, structural variation features among pixel values and structural similarity features were computed for strip regions. Meanwhile, positional features were extracted for basic element regions. The absolute subtraction value was computed between each feature of the left and right local-regions. Next, a multi-layer perception classifier was implemented to assess performance of features for prediction. Features were then selected according stepwise regression analysis. The AUC achieved 0.72, 0.75 and 0.71 for these 3 age-based subgroups, respectively. The maximum adjustable odds ratios were 12.4, 20.56 and 4.91 for these three groups, respectively. This study demonstrate that the local region-based bilateral asymmetry features extracted from CC-view mammography could provide useful information to predict near-term breast cancer risk.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yane Li, Ming Fan, Lihua Li, and Bin Zheng "Prediction of near-term breast cancer risk using local region-based bilateral asymmetry features in mammography", Proc. SPIE 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, 101380L (13 March 2017);

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