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Information Systems Frontiers

, Volume 19, Issue 6, pp 1233–1241 | Cite as

Revealing determinant factors for early breast cancer recurrence by decision tree

  • Jimin Guo
  • Benjamin C. M. Fung
  • Farkhund Iqbal
  • Peter J. K. Kuppen
  • Rob A. E. M. Tollenaar
  • Wilma E. Mesker
  • Jean-Jacques Lebrun
Article

Abstract

Early breast cancer recurrence is indicative of poor response to adjuvant therapy and poses threats to patients’ lives. Most existing prediction models for breast cancer recurrence are regression-based models and difficult to interpret. We apply a Decision Tree algorithm to the clinical information of a cohort of non-metastatic invasive breast cancer patients, to establish a classifier that categorizes patients based on whether they develop early recurrence and on similarities of their clinical and pathological diagnoses. The classifier predicts for whether a patient developed early disease recurrence; and is estimated to be about 70% accurate. For an independent validation cohort of 65 patients, the classifier predicts correctly for 55 patients. The classifier also groups patients based on intrinsic properties of their diseases; and for each subgroup lists the disease characteristics in a hierarchal order, according to their relevance to early relapse. Overall, it identifies pathological nodal stage, percentage of intra-tumor stroma and components of TGFβ-Smad signaling pathway as highly relevant factors for early breast cancer recurrence. Since most of the disease characteristics used by this classifier are results of standardized tests, routinely collected during breast cancer diagnosis, the classifier can easily be adopted in various research and clinical settings.

Keywords

Breast cancer Recurrence Decision tree Classifier Stroma TGFβ 

Notes

Acknowledgements

We would like to thank Drs. C. C. Engels, J. W. T. Dekker and E. M. de Kruijf for conducting immunohistochemistry staining, evaluating stroma percentage and recording original data; and Drs. A. Dibrov and Catalin Mihalcioiu for valuable discussions. J. Guo is supported by a Traineeship from the Breast Cancer Research Program of Congressionally Directed Medical Research Program (CDMRP). B. C. M. Fung is a Canada Research Chair in Data Mining for Cybersecurity. J.-J. Lebrun is a Sir William Dawson Research Chair of McGill University. This work was supported in part by grants from the Canadian Institutes for Health Research (CIHR) (fund codes 230670 and 233716 to J.-J. Lebrun), the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grants (fund code 356065-2013 to B. C. M. Fung), Canada Research Chairs Program (fund code 950-230623 to B. C. M. Fung), and Zayed University Research Incentive Fund and Research Cluster Award (fund codes R15048 and R16083 to F. Iqbal and B. C. M. Fung).

Contribution

J. Guo, B. C. M. Fung and J.-J. Lebrun designed the study, analyzed and interpreted the results. F. Iqbal participated in interpreting the results. P. J. K. Kuppen, R. A. E. M. Tollenaar and W. E. Mesker collected patient samples and designed the tumor tissue microarrays.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Jimin Guo
    • 1
    • 2
    • 3
  • Benjamin C. M. Fung
    • 4
  • Farkhund Iqbal
    • 5
  • Peter J. K. Kuppen
    • 6
  • Rob A. E. M. Tollenaar
    • 6
  • Wilma E. Mesker
    • 6
  • Jean-Jacques Lebrun
    • 1
  1. 1.Division of Medical OncologyMcGill University Health CenterMontrealCanada
  2. 2.Department of Biomedical InformaticsHarvard Medical SchoolBostonUSA
  3. 3.John A. Paulson School of Engineering and Applied SciencesHarvard UniversityCambridgeUSA
  4. 4.School of Information StudiesMcGill UniversityMontrealCanada
  5. 5.Zayed UniversityAbu DhabiUnited Arab Emirates
  6. 6.Department of SurgeryLeiden University Medical CenterLeidenThe Netherlands

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