Using multiscale texture and density features for near‐term breast cancer risk analysis. Issue 6 (18th May 2015)

• Record Type: Journal Article
• Authors: Sun, Wenqing; Tseng, Tzu‐Liang (Bill); Qian, Wei; Zhang, Jianying; Saltzstein, Edward C.; Zheng, Bin; Lure, Fleming; Yu, Hui; Zhou, Shi
• Is Part Of: Medical physics. Volume 42:Issue 6(2015)Part 1
• Page Start: 2853
• Page End: 2862
• Publication Date: 2015-05-18
• Subjects: cancer -- feature selection -- image classification -- image texture -- mammography -- medical image processing -- sensitivity analysis -- support vector machines; Digital mammography -- Cancer; Biological material, e.g. blood, urine; Haemocytometers -- In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Analysis of texture -- Inference methods or devices; breast cancer risk -- multiscale features -- digital mammography -- texture features; Cancer -- Biomedical modeling -- Tissues -- Multiscale methods -- Entropy -- Image analysis -- Digital mammography -- Radiologists; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4919772
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: To help improve efficacy of screening mammography by eventually establishing a new optimal personalized screening paradigm, the authors investigated the potential of using the quantitative multiscale texture and density feature analysis of digital mammograms to predict near‐term breast cancer risk. Methods: The authors' dataset includes digital mammograms acquired from 340 women. Among them, 141 were positive and 199 were negative/benign cases. The negative digital mammograms acquired from the "prior" screening examinations were used in the study. Based on the intensity value distributions, five subregions at different scales were extracted from each mammogram. Five groups of features, including density and texture features, were developed and calculated on every one of the subregions. Sequential forward floating selection was used to search for the effective combinations. Using the selected features, a support vector machine (SVM) was optimized using a tenfold validation method to predict the risk of each woman having image‐detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) was used as the performance assessment index. Results: From a total number of 765 features computed from multiscale subregions, an optimal feature set of 12 features was selected. Applying this feature set, a SVM classifier yielded performance of AUC = 0.729 ± 0.021. The positive predictive value was 0.657 (92 of 140) and the negative predictive value was 0.755 (151 of 200). Conclusions: The study results demonstrated a moderately high positive association between risk prediction scores generated by the quantitative multiscale mammographic image feature analysis and the actual risk of a woman having an image‐detectable breast cancer in the next subsequent examinations.