A prognostic model that makes quantitative estimates of probability of relapse for breast cancer patients

Michelino De Laurentiis, Sabino De Placido, Angelo R. Bianco, Gary M. Clark, Peter M. Ravdin

Research output: Contribution to journalArticlepeer-review


Tumor-node-matastasis (TNM) staging is the standard system for the estimation of prognosis of breast cancer patients. However, this system does not exploit information yielded by markers of the biological aggressiveness of breast cancer and is clearly unsatisfactory for optimal-treatment decision-making and for patient counseling. We have developed a prognostic model, based on a few routinely evaluated prognostic variables, that produces quantitative estimates for risk of relapse of individual breast cancer patients. We used data concerning 2441 of 2990 consecutive breast cancer patients to develop an artificial neural network (ANN) for the prediction of the probability of relapse over 5 years. The prognostic variables used were: patient age, tumor size, number of axillary metastases, estrogen and progesterone receptor levels, S-phase fraction, and tumor ploidy. Performances of the model were evaluated in terms of discrimination ability and quantitative precision. Predictions were validated on an independent series of 310 patients from an institution in another country. The ANN discriminated patients according to their risk of relapse better than the TNM classification (P = 0.0015). The quantitative precision of the model's estimates was accurate and was confirmed on the series from the second institution. The 5-year relapse risk yielded by the model varied greatly within the same TNM class, particularly for patients with four or more nodal metastases. The model discriminates prognosis better than the TNM classification and is able to identify patients with strikingly different risks of relapse within each TNM class.

Original languageEnglish
Pages (from-to)4133-4139
Number of pages7
JournalClinical Cancer Research
Issue number12
Publication statusPublished - Dec 1999

ASJC Scopus subject areas

  • Cancer Research
  • Oncology


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