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Improvement of breast cancer relapse prediction in high risk intervals using artificial neural networks

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Summary

The objective of this study is to compare the predictive accuracy of a neural network (NN) model versus the standard Cox proportional hazard model. Data about the 3811 patients included in this study were collected within the ‘El Álamo’ Project, the largest dataset on breast cancer (BC) in Spain. The best prognostic model generated by the NN contains as covariates age, tumour size, lymph node status, tumour grade and type of treatment. These same variables were considered as having prognostic significance within the Cox model analysis. Nevertheless, the predictions made by the NN were statistically significant more accurate than those from the Cox model (p<0.0001). Seven different time intervals were also analyzed to find that the NN predictions were much more accurate than those from the Cox model in particular in the early intervals between 1–10 and 11–20 months, and in the later one considered from 61 months to maximum follow-up time (MFT). Interestingly, these intervals contain regions of high relapse risk that have been observed in different studies and that are also present in the analyzed dataset.

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Acknowledgements

Support from APOMA (Málaga, Spain) under contract No. 8.06/47/2176 is acknowledged. The authors also thank Esther Mahillo (GEICAM) for her collaboration and coordination of this project.

Author information

Authors and Affiliations

  1. Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Málaga, Spain

    J.M. Jerez & L. Franco

  2. Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom

    L. Franco

  3. Servicio de Oncología Médica, Hospital Clínico Universitario Virgen de la Victoria, Málaga, Spain

    E. Alba & N. Ribelles

  4. Servicio de Oncología Médica, Instituto Valenciano de Oncología, Valencia, Spain

    A. Llombart-Cussac

  5. Servicio de Oncología Médica, Hospital General Universitario de Valencia, Valencia, Spain

    A. Lluch

  6. Servicio de Oncología Médica, Hospital Universitario La Fe, Valencia, Spain

    B. Munárriz

  7. Servicio de Oncología Médica, Hospital Clínico Universitario San Carlos, Madrid, Spain

    M. Martín

Authors
  1. J.M. Jerez
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  2. L. Franco
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  3. E. Alba
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  4. A. Llombart-Cussac
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  5. A. Lluch
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  6. N. Ribelles
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  7. B. Munárriz
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  8. M. Martín
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Corresponding author

Correspondence to L. Franco.

Additional information

Address for offprints and correspondence: Leonardo Franco, Depto. de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Campus de Teatinos S/N, 29071, Málaga, Spain; Tel.: +34-952-133304; Fax: +34-952-133397; E-mail: Leonardo.Franco@psy.ox.ac.uk

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Jerez, J., Franco, L., Alba, E. et al. Improvement of breast cancer relapse prediction in high risk intervals using artificial neural networks. Breast Cancer Res Treat 94, 265–272 (2005). https://doi.org/10.1007/s10549-005-9013-y

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  • DOI: https://doi.org/10.1007/s10549-005-9013-y

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