A simplified breast cancer prognostic score: comparison with the AJCC clinical prognostic staging system

Academic Article

Abstract

  • There have been many breast cancer prognostic models proposed in the last few decades, varying in their methods of development and validation, predictors, outcomes, and patients included. Most models were developed to assess prognostic outcomes for early breast cancers. In this study, we established a simplified prognostic score to predict survival outcomes in all breast cancer patients. A total of 36,152 breast cancer patients diagnosed between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were used as the training dataset. Multivariate analyses were performed to identify independent factors for disease-specific survival (DSS). A prognostic score was calculated by summing the point values based on the magnitude of the hazard ratio for all independent factors. The authors institutional cohort (n = 4982) was used as the validation dataset. The prognostic score model consisting of histologic grade, ER, PR, HER2, and TNM status demonstrated a similar predictive power when compared to the revised 8th AJCC Clinical Prognostic Staging system in both training and validation datasets, whereas the addition of age and race did not facilitate stratification of prognostic groups. Pairwise comparison of hazard ratios showed a significant difference in all categories when compared to their proximate groups in both prognostic schemes in the SEER database, while the prognostic score model demonstrated a slightly better discriminating power in the validation dataset. Thus, the proposed prognostic score showed at least a comparable predicting power for survival outcomes in breast cancer patients receiving standard-of-care treatment when compared to the AJCC Clinical Prognostic Stage. This prognostic model provides a convenient and alternative modality in clinical practice thus warranting further validation using larger cohorts with longer follow-up.
  • Published In

  • Modern Pathology  Journal
  • Digital Object Identifier (doi)

    Author List

  • Fei F; Zhang K; Siegal GP; Wei S