title: Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer Risk.
abstract: The purpose of this study was to develop and assess a new quantitative four-view mammographic image feature based fusion model to predict the near-term breast cancer risk of the individual women after a negative screening mammography examination of interest. The dataset included fully-anonymized mammograms acquired on 870 women with two sequential full-field digital mammography examinations. For each woman, the first "prior" examination in the series was interpreted as negative (not recalled) during the original image reading. In the second "current" examination, 430 women were diagnosed with pathology verified cancers and 440 remained negative ("cancer-free"). For each of four bilateral craniocaudal and mediolateral oblique view images of left and right breasts, we computed and analyzed eight groups of global mammographic texture and tissue density image features. A risk prediction model based on three artificial neural networks was developed to fuse image features computed from two bilateral views of four images. The risk model performance was tested using a ten-fold cross-validation method and a number of performance evaluation indices including the area under the receiver operating characteristic curve (AUC) and odds ratio (OR). The highest AUC = 0.725 ± 0.026 was obtained when the model was trained by gray-level run length statistics texture features computed on dense breast regions, which was significantly higher than the AUC values achieved using the model trained by only two bilateral one-view images (p < 0.02). The adjustable OR values monotonically increased from 1.0 to 11.8 as model-generated risk score increased. The regression analysis of OR values also showed a significant increase trend in slope (p < 0.01). As a result, this preliminary study demonstrated that a new four-view mammographic image feature based risk model could provide useful and supplementary image information to help predict the near-term breast cancer risk.
authors: School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK, 73019, USA. maxine.y.tan-1@ou.edu.
authors: Maxine
authors: M
authors: Tan
authors: Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
authors: Jiantao
authors: J
authors: Pu
authors: School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK, 73019, USA.
authors: Samuel
authors: S
authors: Cheng
authors: School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK, 73019, USA.
authors: Hong
authors: H
authors: Liu
authors: School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK, 73019, USA.|Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
authors: Bin
authors: B
authors: Zheng
mesh_terms: D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D008954:Models, Biological; D016571:Neural Networks, Computer; D012189:Retrospective Studies
keywords: Breast cancer; Computer-aided detection (CAD); Full-field digital mammography (FFDM); Mammographic density feature analysis; Near-term breast cancer risk stratification