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Publication Detail
Comparison between radiological and artificial neural network diagnosis in clinical screening
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Article
  • Authors:
    Degenhard A, Tanner C, Hayes C, Hawkes DJ, Leach MO
  • Publication date:
    11/2002
  • Pagination:
    727, 739
  • Journal:
    PHYSIOL MEAS
  • Volume:
    23
  • Issue:
    967-3334 (Print), 4
  • Print ISSN:
    0967-3334
  • Keywords:
    analysis, Breast, Breast Neoplasms, Cohort Studies, Comparative Study, diagnosis, Female, Humans, Magnetic Resonance Imaging, Neural Networks (Computer), pathology, radiography, Research, Research Support, Non-U.S.Gov't
  • Addresses:
    Cancer Research UK Clinical Magnetic Resonance Research Group, The Institute of Cancer Research and the Royal Marsden NHS Trust, Sutton, Surrey SM2 5PT, UK
  • Notes:
    DA - 20021126
Abstract
The imaging protocol of the UK multicentre magnetic resonance imaging study for screening in women at genetic risk of breast cancer aims to assist in detecting and diagnosing malignant breast lesions. In this paper, we evaluate a three-layer, feed-forward, backpropagation neural network as an artificial radiological classifier using receiver operating characteristic (ROC) curve analysis and compare the results with those obtained using a proposed radiological scoring system for the study which currently supplements the radiologist's clinical opinion, in comparison with histological diagnosis. Based on the 76 symptomatic cases evaluated, descriptive features scored by radiologists showed considerable overlap between benign and malignant, although some features such as irregular contours and heterogeneous enhancement were more often associated with malignant pathology. In this preliminary evaluation, ROC analysis showed that the proposed scoring scheme did not perform well, indicating further refinement is required. When all 23 features were used in the neural network, its performance was poorer than that of the scoring scheme. When only ten features were used, limited to descriptors of enhancement characteristics, the neural network performed similar to the scoring scheme. This comparison shows that the neural network approach to clinical diagnosis has considerable potential and warrants further development
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