title: The Added Value of Statistical Modeling of Backscatter Properties in the Management of Breast Lesions at US.
abstract: OBJECTIVE
  To develop a classification method based on the statistical backscatter properties of tissues that can be used as an ancillary tool to the usual Breast Imaging Reporting and Data System (BI-RADS) classification for solid breast lesions identified at ultrasonography (US).
  
  
  METHODS
  This study received institutional review board approval, and all subjects provided informed consent. Eighty-nine women (mean age, 50 years; age range, 22-82 years) with 96 indeterminate solid breast lesions (BI-RADS category 4-5; mean size, 13.2 mm; range, 2.6-44.7 mm) were enrolled. Prior to biopsy, additional radiofrequency US images were obtained, and a 3-second cine sequence was used. The research data were analyzed at a later time and were not used to modify patient management decisions. The lesions were segmented manually, and parameters of the homodyned K distribution (α, k, and μn values) were extracted for three regions: the intratumoral zone, a 3-mm supratumoral zone, and a 5-mm infratumoral zone. The Mann-Whitney rank sum test was used to identify parameters with the best discriminating value, yielding intratumoral α, supratumoral k, and infratumoral μn values.
  
  
  RESULTS
  The 96 lesions were classified as follows: 48 BI-RADS category 4A lesions, 16 BI-RADS category 4B lesions, seven BI-RADS category 4C lesions, and 25 BI-RADS category 5 lesions. There were 24 cancers (25%). The area under the receiver operating characteristic curve was 0.76 (95% confidence interval: 0.65, 0.86). Overall, 24% of biopsies (in 17 of 72 lesions) could have been spared. By limiting analysis to lesions with a lower likelihood of malignancy (BI-RADS category 4A-4B), this percentage increased to 26% (16 of 62 lesions). Among benign lesions, the model was used to correctly classify 10 of 38 fibroadenomas (26%) and three of seven stromal fibroses (43%).
  
  
  CONCLUSIONS
  The statistical model performs well in the classification of solid breast lesions at US, with the potential of preventing one in four biopsies without missing any malignancy.
authors.affiliation: From the Department of Radiology, Breast Imaging Center (I.T., M.E.K.), Department of Surgical Oncology, Breast Care Center (A.R.), and Department of Pathology (L.G.), Centre Hospitalier de l'Université de Montréal, 3840 Saint-Urbain, Montreal, QC, Canada H2W 1T8; Department of Radiology, Radio-Oncology and Nuclear Medicine (I.T., M.E.K., G.C.) and Institute of Biomedical Engineering (G.C.), Université de Montréal, Montreal, Quebec, Canada; and Laboratory of Biorheology and Medical Ultrasonics, Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, Quebec, Canada (F.D., L.A., B.C., G.C.).
authors.forename: Isabelle
authors.initials: I
authors.lastname: Trop
authors.forename: François
authors.initials: F
authors.lastname: Destrempes
authors.forename: Mona
authors.initials: M
authors.lastname: El Khoury
authors.forename: André
authors.initials: A
authors.lastname: Robidoux
authors.forename: Louis
authors.initials: L
authors.lastname: Gaboury
authors.forename: Louise
authors.initials: L
authors.lastname: Allard
authors.forename: Boris
authors.initials: B
authors.lastname: Chayer
authors.forename: Guy
authors.initials: G
authors.lastname: Cloutier
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000145:classification* / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D011446:Prospective Studies; D016217:Ultrasonography, Mammary; D055815:Young Adult