title: Computer-aided diagnosis for dynamic contrast-enhanced breast MRI of mass-like lesions using a multiparametric model combining a selection of morphological, kinetic, and spatiotemporal features.
abstract: OBJECTIVE
  Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a radiological tool for the detection and discrimination of breast lesions. The aim of this study is to evaluate a computer-aided diagnosis (CAD) system for discriminating malignant from benign breast lesions at DCE-MRI by the combined use of morphological, kinetic, and spatiotemporal lesion features.
  
  
  METHODS
  Fifty-four malignant and 19 benign breast lesions in 51 patients were retrospectively evaluated. Images were acquired at two centers at 1.5 T. Mass-like lesions were automatically segmented after image normalization and elastic coregistration of contrast-enhanced frames. For each lesion, a set of 28 3D features were extracted: ten morphological (related to shape, margins, and internal enhancement distribution); nine kinetic (computed from signal-to-time curves); and nine spatiotemporal (related to the variation of the signal between adjacent frames). A support vector machine (SVM) was trained with feature subsets selected by a genetic search. Best subsets were composed of the most frequent features selected by majority rule. The performance was measured by receiver operator characteristics analysis with a stratified tenfold cross-validation and bootstrap method for confidence intervals.
  
  
  RESULTS
  SVM training by the three separated classes of features resulted in an area under the curve (AUC) of 0.90 ± 0.04 (mean ± standard deviation), 0.87 ± 0.06, and 0.86 ± 0.06 for morphological, kinetic, and spatiotemporal feature, respectively. Combined training with all 28 features resulted in AUC of 0.96 ± 0.02 obtained with a selected feature subset composed by two morphological, one kinetic, and two spatiotemporal features.
  
  
  CONCLUSIONS
  Quantitative combination of morphological, kinetic, and spatiotemporal features is feasible and provides a higher discriminating power than using the three different classes of features separately.
authors.affiliation: im3D, Research and Development Department, Turin, Italy. silvano.agliozzo@i-m3d.com
authors.forename: S
authors.initials: S
authors.lastname: Agliozzo
authors.forename: M
authors.initials: M
authors.lastname: De Luca
authors.forename: C
authors.initials: C
authors.lastname: Bracco
authors.forename: A
authors.initials: A
authors.lastname: Vignati
authors.forename: V
authors.initials: V
authors.lastname: Giannini
authors.forename: L
authors.initials: L
authors.lastname: Martincich
authors.forename: L A
authors.initials: LA
authors.lastname: Carbonaro
authors.forename: A
authors.initials: A
authors.lastname: Bert
authors.forename: F
authors.initials: F
authors.lastname: Sardanelli
authors.forename: D
authors.initials: D
authors.lastname: Regge
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis*; D003198:Computer Simulation; D003287:Contrast Media; D019786:Gadolinium DTPA; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
chemical_list: D003287:Contrast Media; D019786:Gadolinium DTPA