title: Performance of a fully automatic lesion detection system for breast DCE-MRI.
abstract: OBJECTIVE
  To describe and test a new fully automatic lesion detection system for breast DCE-MRI.
  
  
  METHODS
  Studies were collected from two institutions adopting different DCE-MRI sequences, one with and the other one without fat-saturation. The detection pipeline consists of (i) breast segmentation, to identify breast size and location; (ii) registration, to correct for patient movements; (iii) lesion detection, to extract contrast-enhanced regions using a new normalization technique based on the contrast-uptake of mammary vessels; (iv) false positive (FP) reduction, to exclude contrast-enhanced regions other than lesions. Detection rate (number of system-detected malignant and benign lesions over the total number of lesions) and sensitivity (system-detected malignant lesions over the total number of malignant lesions) were assessed. The number of FPs was also assessed.
  
  
  RESULTS
  Forty-eight studies with 12 benign and 53 malignant lesions were evaluated. Median lesion diameter was 6 mm (range, 5-15 mm) for benign and 26 mm (range, 5-75 mm) for malignant lesions. Detection rate was 58/65 (89%; 95% confidence interval [CI] 79%-95%) and sensitivity was 52/53 (98%; 95% CI 90%-99%). Mammary median FPs per breast was 4 (1st-3rd quartiles 3-7.25).
  
  
  CONCLUSIONS
  The system showed promising results on MR datasets obtained from different scanners producing fat-sat or non-fat-sat images with variable temporal and spatial resolution and could potentially be used for early diagnosis and staging of breast cancer to reduce reading time and to improve lesion detection. Further evaluation is needed before it may be used in clinical practice.
authors.affiliation: Department of Radiology, IRCC - Institute for Cancer Research and Treatment, Candiolo, Italy. vignati.anna@gmail.com
authors.forename: Anna
authors.initials: A
authors.lastname: Vignati
authors.forename: Valentina
authors.initials: V
authors.lastname: Giannini
authors.forename: Massimo
authors.initials: M
authors.lastname: De Luca
authors.forename: Lia
authors.initials: L
authors.lastname: Morra
authors.forename: Diego
authors.initials: D
authors.lastname: Persano
authors.forename: Luca A
authors.initials: LA
authors.lastname: Carbonaro
authors.forename: Ilaria
authors.initials: I
authors.lastname: Bertotto
authors.forename: Laura
authors.initials: L
authors.lastname: Martincich
authors.forename: Daniele
authors.initials: D
authors.lastname: Regge
authors.forename: Alberto
authors.initials: A
authors.lastname: Bert
authors.forename: Francesco
authors.initials: F
authors.lastname: Sardanelli
mesh_terms: D001943:Breast Neoplasms / Q000175:diagnosis*; D003287:Contrast Media; D016208:Databases, Factual; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D007089:Image Enhancement; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media