title: Artificial Neural Networks for differential diagnosis of breast lesions in MR-Mammography: a systematic approach addressing the influence of network architecture on diagnostic performance using a large clinical database.
abstract: OBJECTIVE
  Differential diagnosis of lesions in MR-Mammography (MRM) remains a complex task. The aim of this MRM study was to design and to test robustness of Artificial Neural Network architectures to predict malignancy using a large clinical database.
  
  
  METHODS
  For this IRB-approved investigation standardized protocols and study design were applied (T1w-FLASH; 0.1 mmol/kgBW Gd-DTPA; T2w-TSE; histological verification after MRM). All lesions were evaluated by two experienced (>500 MRM) radiologists in consensus. In every lesion, 18 previously published descriptors were assessed and documented in the database. An Artificial Neural Network (ANN) was developed to process this database (The-MathWorks/Inc., feed-forward-architecture/resilient back-propagation-algorithm). All 18 descriptors were set as input variables, whereas histological results (malignant vs. benign) was defined as classification variable. Initially, the ANN was optimized in terms of "Training Epochs" (TE), "Hidden Layers" (HL), "Learning Rate" (LR) and "Neurons" (N). Robustness of the ANN was addressed by repeated evaluation cycles (n: 9) with receiver operating characteristics (ROC) analysis of the results applying 4-fold Cross Validation. The best network architecture was identified comparing the corresponding Area under the ROC curve (AUC).
  
  
  RESULTS
  Histopathology revealed 436 benign and 648 malignant lesions. Enhancing the level of complexity could not increase diagnostic accuracy of the network (P: n.s.). The optimized ANN architecture (TE: 20, HL: 1, N: 5, LR: 1.2) was accurate (mean-AUC 0.888; P: <0.001) and robust (CI: 0.885-0.892; range: 0.880-0.898).
  
  
  CONCLUSIONS
  The optimized neural network showed robust performance and high diagnostic accuracy for prediction of malignancy on unknown data.
authors: Institute of Diagnostic and Interventional Radiology, Friedrich-Schiller-University Jena, Erlanger Allee 101, D-07740 Jena, Germany. dietzelmatthias2@hotmail.com
authors: Matthias
authors: M
authors: Dietzel
authors: Pascal A T
authors: PA
authors: Baltzer
authors: Andreas
authors: A
authors: Dietzel
authors: Ramy
authors: R
authors: Zoubi
authors: Tobias
authors: T
authors: Gröschel
authors: Hartmut P
authors: HP
authors: Burmeister
authors: Martin
authors: M
authors: Bogdan
authors: Werner A
authors: WA
authors: Kaiser
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology; D003287:Contrast Media; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D005260:Female; D019786:Gadolinium DTPA; D006801:Humans; D008279:Magnetic Resonance Imaging; D008327:Mammography; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012372:ROC Curve
chemical_list: D003287:Contrast Media; D019786:Gadolinium DTPA