title: Using multiscale texture and density features for near-term breast cancer risk analysis.
abstract: OBJECTIVE
  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.
authors.affiliation: College of Engineering, University of Texas at El Paso, El Paso, Texas 79968.
authors.forename: Wenqing
authors.initials: W
authors.lastname: Sun
authors.affiliation: College of Engineering, University of Texas at El Paso, El Paso, Texas 79968.
authors.forename: Tzu-Liang Bill
authors.initials: TL
authors.lastname: Tseng
authors.affiliation: College of Engineering, University of Texas at El Paso, El Paso, Texas 79968 and Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang 110819, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Qian
authors.affiliation: Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang 110819, China and College of Biological Sciences, University of Texas at El Paso, El Paso, Texas 79968.
authors.forename: Jianying
authors.initials: J
authors.lastname: Zhang
authors.affiliation: University Breast Care Center at the Texas Tech University Health Sciences, El Paso, Texas 79905.
authors.forename: Edward C
authors.initials: EC
authors.lastname: Saltzstein
authors.affiliation: Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang 110819, China and College of Engineering, University of Oklahoma, Norman, Oklahoma 73019.
authors.forename: Bin
authors.initials: B
authors.lastname: Zheng
authors.affiliation: College of Engineering, University of Texas at El Paso, El Paso, Texas 79968 and Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang 110819, China.
authors.forename: Fleming
authors.initials: F
authors.lastname: Lure
authors.affiliation: Department of Radiology, Affiliated Hospital of Guiyang Medical University, Guiyang 550004, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Yu
authors.affiliation: Department of Radiology, Affiliated Hospital of Guiyang Medical University, Guiyang 550004, China.
authors.forename: Shi
authors.initials: S
authors.lastname: Zhou
mesh_terms: D001940:Breast / Q000166:cytology* / Q000473:pathology*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008327:Mammography; D011237:Predictive Value of Tests; D012372:ROC Curve; D011856:Radiographic Image Enhancement; D018570:Risk Assessment; D060388:Support Vector Machine