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Breast Cancer Research and Treatment

, Volume 94, Issue 3, pp 265–272 | Cite as

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

  • J.M. Jerez
  • L. Franco
  • E. Alba
  • A. Llombart-Cussac
  • A. Lluch
  • N. Ribelles
  • B. Munárriz
  • M. Martín
Article

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.

Keywords

breast cancer prognosis, Cox proportional hazard model, high peaks of relapse, neural networks, survival analysis 

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Notes

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.

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Copyright information

© Kluwer Academic Publishers 2005

Authors and Affiliations

  • J.M. Jerez
    • 1
  • L. Franco
    • 1
    • 2
  • E. Alba
    • 3
  • A. Llombart-Cussac
    • 4
  • A. Lluch
    • 5
  • N. Ribelles
    • 3
  • B. Munárriz
    • 6
  • M. Martín
    • 7
  1. 1.Departamento de Lenguajes y Ciencias de la ComputaciónUniversidad de MálagaMálagaSpain
  2. 2.Department of Experimental PsychologyUniversity of OxfordOxfordUnited Kingdom
  3. 3.Servicio de Oncología MédicaHospital Clínico Universitario Virgen de la VictoriaMálagaSpain
  4. 4.Servicio de Oncología MédicaInstituto Valenciano de OncologíaValenciaSpain
  5. 5.Servicio de Oncología MédicaHospital General Universitario de ValenciaValenciaSpain
  6. 6.Servicio de Oncología MédicaHospital Universitario La FeValenciaSpain
  7. 7.Servicio de Oncología MédicaHospital Clínico Universitario San CarlosMadridSpain

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