A new near-term breast cancer risk prediction scheme based on the quantitative analysis of ipsilateral view mammograms. (March 2018)

• Record Type: Journal Article
• Authors: Sun, Wenqing; Tseng, Tzu-Liang(Bill); Qian, Wei; Saltzstein, Edward C.; Zheng, Bin; Yu, Hui; Zhou, Shi
• Is Part Of: Computer methods and programs in biomedicine. Volume 155(2018)
• Page Start: 29
• Page End: 38
• Publication Date: 2018-03
• Subjects: Breast cancer risk -- Digital mammography -- Texture features -- Quantitative image feature analysis based risk prediction models -- Analysis of ipsilateral view mammograms; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.11.019
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The first computerized breast cancer risk analysis system using ipsilateral view mammograms. Used similarity test to control the feature redundancy. Developed new and effective concurrent features incorporated the ipsilateral view features. The results are significantly higher than using single view mammogram. Abstract: Purpose: To help improve efficacy of screening mammography and eventually establish an optimal personalized screening paradigm, this study aimed to develop and test a new near-term breast cancer risk prediction scheme based on the quantitative analysis of ipsilateral view of the negative screening mammograms. Methods: The dataset includes digital mammograms acquired from 392 women with two sequential full-field digital mammography examinations. All the first ("prior") sets of mammograms were interpreted as negative during the original reading. In the sequential ("current") screening, 202 were proved positive and 190 remained negative/benign. For each pair of the "prior" ipsilateral mammograms, we adaptively fused the image features computed from two views. Using four different types of image features, we built four elastic net support vector machine (EnSVM) based classifiers. Then, the initial prediction scores form the 4 EnSVMs were combined to build a final artificial neural network (ANN) classifier that produces the final risk prediction score. The performance of the new scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). Results: A total number of 466 features were initially extracted from each pair of ipsilateral mammograms. Among them, 51 were selected to build the EnSVM based prediction scheme. The AUC = 0.737 ± 0.052 was yielded using the new scheme. Applying an optimal operating threshold, the prediction sensitivity was 60.4% (122 of 202) and the specificity was 79.0% (150 of 190). Conclusion: The study results showed moderately high positive association between computed risk scores using the "prior" negative mammograms and the actual outcome of the image-detectable breast cancers in the next subsequent screening examinations. The study also demonstrated that quantitative analysis of the ipsilateral views of the mammograms enabled to provide useful information in predicting near-term breast cancer risk.