title: [Self-organizing neural networks for automatic detection and classification of contrast (media) enhancement of lesions in dynamic MR-mammography].
abstract: OBJECTIVE
  Investigation and statistical evaluation of "Self-Organizing Maps," a special type of neural networks in the field of artificial intelligence, classifying contrast enhancing lesions in dynamic MR-mammography.
  
  
  METHODS
  176 investigations with proven histology after core biopsy or operation were randomly divided into two groups. Several Self-Organizing Maps were trained by investigations of the first group to detect and classify contrast enhancing lesions in dynamic MR-mammography. Each single pixel's signal/time curve of all patients within the second group was analyzed by the Self-Organizing Maps. The likelihood of malignancy was visualized by color overlays on the MR-images. At last assessment of contrast-enhancing lesions by each different network was rated visually and evaluated statistically.
  
  
  RESULTS
  A well balanced neural network achieved a sensitivity of 90.5 % and a specificity of 72.2 % in predicting malignancy of 88 enhancing lesions. Detailed analysis of false-positive results revealed that every second fibroadenoma showed a "typical malignant" signal/time curve without any chance to differentiate between fibroadenomas and malignant tissue regarding contrast enhancement alone; but this special group of lesions was represented by a well-defined area of the Self-Organizing Map.
  
  
  CONCLUSIONS
  Self-Organizing Maps are capable of classifying a dynamic signal/time curve as "typical benign" or "typical malignant." Therefore, they can be used as second opinion. In view of the now known localization of fibroadenomas enhancing like malignant tumors at the Self-Organizing Map, these lesions could be passed to further analysis by additional post-processing elements (e.g., based on T2-weighted series or morphology analysis) in the future.
authors.affiliation: Klinik und Poliklinik für Radiologie, Klinikum der Universität Mainz. vomweg@radiologie.klinik.uni-mainz.de
authors.forename: T W
authors.initials: TW
authors.lastname: Vomweg
authors.forename: A
authors.initials: A
authors.lastname: Teifke
authors.forename: H U
authors.initials: HU
authors.lastname: Kauczor
authors.forename: T
authors.initials: T
authors.lastname: Achenbach
authors.forename: O
authors.initials: O
authors.lastname: Rieker
authors.forename: W G
authors.initials: WG
authors.lastname: Schreiber
authors.forename: K R
authors.initials: KR
authors.lastname: Heitmann
authors.forename: T
authors.initials: T
authors.lastname: Beier
authors.forename: M
authors.initials: M
authors.lastname: Thelen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000145:classification / Q000473:pathology*; D003287:Contrast Media; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008327:Mammography / Q000379:methods; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity
chemical_list: D003287:Contrast Media