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Incorporating progesterone receptor expression into the PREDICT breast prognostic model.

Isabelle Grootes1, Renske Keeman2, Fiona M Blows1

  • 1University of Cambridge, Centre for Cancer Genetic Epidemiology, Department of Oncology, Cambridge, CB1 8RN, UK.

European Journal of Cancer (Oxford, England : 1990)
|August 7, 2022
PubMed
Summary

Adding progesterone receptor (PR) status to the PREDICT Breast tool improves accuracy for early invasive breast cancer prognostication. This enhancement offers more precise treatment benefit predictions for individual patients.

Keywords:
PREDICT BreastProgesterone receptorPrognosisbreast cancer

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Area of Science:

  • Oncology
  • Biostatistics
  • Clinical Prognostication

Background:

  • PREDICT Breast is an established tool for early invasive breast cancer prognostication.
  • The current version (2.2) does not incorporate progesterone receptor (PR) status.

Purpose of the Study:

  • To integrate the prognostic impact of PR status into a new version of the PREDICT Breast tool.
  • To evaluate the performance of the updated PREDICT Breast model against the existing version.

Main Methods:

  • Analysis of data from 45,088 European breast cancer patients across 49 studies.
  • Utilized Cox proportional hazard models to determine the hazard ratio for PR status.
  • External validation performed on 11,365 early invasive breast cancer patients from New Zealand.
  • Model performance assessed using calibration and discrimination metrics.

Main Results:

  • PR-positive tumors were associated with a 23% (ER-negative) and 28% (ER-positive) reduced risk of breast cancer mortality.
  • The addition of PR status improved the area under the ROC curve in the New Zealand cohort.
  • Discrimination improved for both ER-negative (0.807 to 0.809) and ER-positive (0.898 to 0.902) tumors.
  • Model calibration was modest, with 940 observed deaths versus 1151 predicted.

Conclusions:

  • Incorporating PR status enhances the PREDICT Breast model's performance.
  • The updated model provides more accurate absolute treatment benefit predictions for individual patients.
  • Further research is needed to assess the necessity of recalibrating the baseline hazard function.