Prognostic factors for metachronous contralateral breast cancer: A comparison of the linear Cox regression model and its artificial neural network extension

L. Mariani, D. Coradini, E. Biganzoli, P. Boracchi, E. Marubini, S. Pilotti, B. Salvadori, R. Silvestrini, U. Veronesi, R. Zucali, F. Rilke

Research output: Contribution to journalArticlepeer-review

Abstract

The purpose of the present study was to assess prognostic factors for metachronous contralateral recurrence of breast cancer (CBC). Two factors were of particular interest, namely estrogen (ER) and progesterone (PgR) receptors assayed with the biochemical method in primary tumor tissue. Information was obtained from a prospective clinical database for 1763 axillary node-negative women who had received curative surgery, mostly of the conservative type, and followed-up for a median of 82 months. The analysis was performed based on both a standard (linear) Cox model and an artificial neural network (ANN) extension of this model proposed by Faraggi and Simon. Furthermore, to assess the prognostic importance of the factors considered, model predictive ability was computed. In agreement with already published studies, the results of our analysis confirmed the prognostic role of age at surgery, histology, and primary tumor site, in that young patients (≤ 45 years) with tumors of lobular histology or located at inner/central mammary quadrants were at greater risk of developing CBC. ER and PgR were also shown to have a prognostic role. Their effect, however, was not simple in relation to the presence of interactions between ER and age, and between PgR and histology. In fact, ER appeared to play a protective role in young patients, whereas the opposite was true in older women. Higher levels of PgR implied a greater hazard of CBC occurrence in infiltrating duct carcinoma or tumors with an associated extensive intraductal component, and a lower hazard in infiltrating lobular carcinoma or other histotypes. In spite of the above findings, the predictive value of both the standard and ANN Cox models was relatively low, thus suggesting an intrinsic limitation of the prognostic variables considered, rather than their suboptimal modeling. Research for better prognostic variables should therefore continue.

Original languageEnglish
Pages (from-to)167-178
Number of pages12
JournalBreast Cancer Research and Treatment
Volume44
Issue number2
DOIs
Publication statusPublished - 1997

Keywords

  • Breast neoplasms
  • Models
  • Multiple primary
  • Neural networks (computer)
  • Prognosis
  • Statistical analysis

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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