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Prediction of nodal spread of breast cancer by using artificial neural network-based analyses of S100A4, nm23 and steroid receptor expression

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Abstract

The expression of tumour promoter gene S100A4, metastasis suppressor gene nm23, oestrogen and progesterone receptors, and tumour grade and size have been investigated for their potential to predict breast cancer progression. The molecular and cellular data have been analysed using artificial neural networks to determine the potential of these markers to predict the presence of metastatic tumour in the regional lymph nodes. This study shows that tumour grade and size are poor predictors. The relative expression of S100A4 and nm23 genes is the single most effective predictor of nodal status. Inclusion of oestrogen- and progesterone-receptor status with tumour grade and size markers improves prediction; however, there may be some overlap between steroid receptors and molecular markers. This study also underscores the power of artificial neural network techniques to predict the potential of primary breast cancers to spread to axillary lymph nodes. This could aid the clinician in determining whether invasive procedures of axially node dissection can be obviated and whether conservative forms of treatment might be appropriate in the management of the patient.

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Authors and Affiliations

  1. School of Electrical, Electronic and Computer Engineering, University of Newcastle upon Tyne, UK

    S.R. Grey, S.S. Dlay & G.V. Sherbet

  2. Ospedale di Monza, Monza, Italy

    B.E. Leone

  3. Istituto Di Patologia Generale, University of Milan, Italy

    F. Cajone

  4. Institute for Molecular Medicine, Huntington Beach, California, USA

    G.V. Sherbet

Authors
  1. S.R. Grey
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  2. S.S. Dlay
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  3. B.E. Leone
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  4. F. Cajone
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  5. G.V. Sherbet
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Correspondence to S.S. Dlay.

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Grey, S., Dlay, S., Leone, B. et al. Prediction of nodal spread of breast cancer by using artificial neural network-based analyses of S100A4, nm23 and steroid receptor expression. Clin Exp Metastasis 20, 507–514 (2003). https://doi.org/10.1023/A:1025846019656

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