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 () and Jean-Jacques Lebrun ()
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Jimin Guo: McGill University Health Center
Benjamin C. M. Fung: McGill University
Farkhund Iqbal: Zayed University
Peter J. K. Kuppen: Leiden University Medical Center
Rob A. E. M. Tollenaar: Leiden University Medical Center
Wilma E. Mesker: Leiden University Medical Center
Jean-Jacques Lebrun: McGill University Health Center

Information Systems Frontiers, 2017, vol. 19, issue 6, No 2, 1233-1241

Abstract: 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 (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10796-017-9764-0

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