title: Differentiation of Breast Lesions by Use of HyperSPACE: Hyper-Spectral Analysis for Characterization in Echography.
abstract: Early diagnosis represents the cornerstone in breast cancer control. Ultrasound is still a valid tool because of its low invasiveness, reduced costs and reduced risk of harm, but better exploitation of its potential is necessary to extract information on tissue features. The proposed method, HyperSPACE (hyper-spectral analysis for characterization in echography), which processes the ultrasonic radiofrequency signal in an N-dimension spectral hyperspace to define several characteristic parameters of the tissue under investigation, was used with the aim of differentiating two types of breast lesion: infiltrating ductal carcinoma and fibroadenoma. The analyzed data set consisted of 2000 radiofrequency frames related to 200 sections of pathologic breast nodules: 104 infiltrating ductal carcinomas and 96 fibroadenomas. The algorithm was trained on single radiofrequency frames related to 50 sections (26 carcinomas, 24 fibroadenomas) to recognize the two pathologies considered, and all the radiofrequency frames related to the other 150 sections were classified, yielding a sensitivity of 92.2%, specificity of 93%, positive predictive value of 93.2% and negative predictive value of 91%. The results were compared with those of RULES (radiofrequency ultrasonic local estimators), a processing method set developed by our group and used by other researchers in clinical and laboratory environments.
authors.affiliation: Department of Information Engineering (DINFO), University of Florence, Florence, Italy.
authors.forename: Simona
authors.initials: S
authors.lastname: Granchi
authors.affiliation: Department of Information Engineering (DINFO), University of Florence, Florence, Italy.
authors.forename: Enrico
authors.initials: E
authors.lastname: Vannacci
authors.affiliation: Department of Information Engineering (DINFO), University of Florence, Florence, Italy. Electronic address: elena.biagi@unifi.it.
authors.forename: Elena
authors.initials: E
authors.lastname: Biagi
authors.affiliation: Scientific Committee, El.En. S.p.A., Florence, Italy.
authors.forename: Leonardo
authors.initials: L
authors.lastname: Masotti
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001943:Breast Neoplasms / Q000145:classification / Q000000981:diagnostic imaging*; D018270:Carcinoma, Ductal, Breast / Q000145:classification / Q000000981:diagnostic imaging*; D003937:Diagnosis, Differential; D005260:Female; D018226:Fibroadenoma / Q000145:classification / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016217:Ultrasonography, Mammary / Q000379:methods*; D055815:Young Adult
keywords: Breast characterization; Cancer detection; Clustering; Diagnostic tools; Early diagnosis; Image processing; Radiofrequency signal; Signal processing; Spectral analysis; Tissue characterization; Ultrasound