title: Quantitative ultrasound characterization of locally advanced breast cancer by estimation of its scatterer properties.
abstract: OBJECTIVE
  Tumor grading is an important part of breast cancer diagnosis and currently requires biopsy as its standard. Here, the authors investigate quantitative ultrasound parameters in locally advanced breast cancers that can potentially separate tumors from normal breast tissue and differentiate tumor grades.
  
  
  METHODS
  Ultrasound images and radiofrequency data from 42 locally advanced breast cancer patients were acquired and analyzed. Parameters related to the linear regression of the power spectrum--midband fit, slope, and 0-MHz-intercept--were determined from breast tumors and normal breast tissues. Mean scatterer spacing was estimated from the spectral autocorrelation, and the effective scatterer diameter and effective acoustic concentration were estimated from the Gaussian form factor. Parametric maps of each quantitative ultrasound parameter were constructed from the gated radiofrequency segments in tumor and normal tissue regions of interest. In addition to the mean values of the parametric maps, higher order statistical features, computed from gray-level co-occurrence matrices were also determined and used for characterization. Finally, linear and quadratic discriminant analyses were performed using combinations of quantitative ultrasound parameters to classify breast tissues.
  
  
  RESULTS
  Quantitative ultrasound parameters were found to be statistically different between tumor and normal tissue (p < 0.05). The combination of effective acoustic concentration and mean scatterer spacing could separate tumor from normal tissue with 82% accuracy, while the addition of effective scatterer diameter to the combination did not provide significant improvement (83% accuracy). Furthermore, the two advanced parameters, including effective scatterer diameter and mean scatterer spacing, were found to be statistically differentiating among grade I, II, and III tumors (p = 0.014 for scatterer spacing, p = 0.035 for effective scatterer diameter). The separation of the tumor grades further improved when the textural features of the effective scatterer diameter parametric map were combined with the mean value of the map (p = 0.004).
  
  
  CONCLUSIONS
  Overall, the binary classification results (tumor versus normal tissue) were more promising than tumor grade assessment. Combinations of advanced parameters can further improve the separation of tumors from normal tissue compared to the use of linear regression parameters. While the linear regression parameters were sufficient for characterizing breast tumors and normal breast tissues, advanced parameters and their textural features were required to better characterize tumor subtypes.
authors.affiliation: Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5, Canada and Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, Ontario M5G 2M9, Canada.
authors.forename: Hadi
authors.initials: H
authors.lastname: Tadayyon
authors.affiliation: Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5, Canada; Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, Ontario M5G 2M9, Canada; Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5, Canada; and Department of Radiation Oncology, Faculty of Medicine, University of Toronto, Toronto, Ontario M5T 1P5, Canada.
authors.forename: Ali
authors.initials: A
authors.lastname: Sadeghi-Naini
authors.affiliation: Department of Physics, Ryerson University, Toronto, Ontario M5B 2K3, Canada.
authors.forename: Lauren
authors.initials: L
authors.lastname: Wirtzfeld
authors.affiliation: Division of Surgical Oncology, Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5, Canada.
authors.forename: Frances C
authors.initials: FC
authors.lastname: Wright
authors.affiliation: Physical Sciences, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5, Canada; Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, Ontario M5G 2M9, Canada; Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5, Canada; and Department of Radiation Oncology, Faculty of Medicine, University of Toronto, Toronto, Ontario M5T 1P5, Canada.
authors.forename: Gregory
authors.initials: G
authors.lastname: Czarnota
mesh_terms: D000328:Adult; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D005260:Female; D006801:Humans; D008875:Middle Aged; D060787:Neoplasm Grading; D016217:Ultrasonography, Mammary / Q000379:methods*