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Inclusion of KI67 significantly improves performance of the PREDICT prognostication and prediction model for early breast cancer.


Type

Article

Change log

Authors

Wishart, Gordon C 
Rakha, Emad 
Green, Andrew 
Ellis, Ian 
Ali, Hamid Raza 

Abstract

BACKGROUND: PREDICT (http://www.predict.nhs.uk) is a prognostication and treatment benefit tool for early breast cancer (EBC). The aim of this study was to incorporate the prognostic effect of KI67 status in a new version (v3), and compare performance with the Predict model that includes HER2 status (v2). METHODS: The validation study was based on 1,726 patients with EBC treated in Nottingham between 1989 and 1998. KI67 positivity for PREDICT is defined as >10% of tumour cells staining positive. ROC curves were constructed for Predict models with (v3) and without (v2) KI67 input. Comparison was made using the method of DeLong. RESULTS: In 1274 ER+ patients the predicted number of events at 10 years increased from 196 for v2 to 204 for v3 compared to 221 observed. The area under the ROC curve (AUC) improved from 0.7611 to 0.7676 (p=0.005) in ER+ patients and from 0.7546 to 0.7595 (p=0.0008) in all 1726 patients (ER+ and ER-). CONCLUSION: Addition of KI67 to PREDICT has led to a statistically significant improvement in the model performance for ER+ patients and will aid clinical decision making in these patients. Further studies should determine whether other markers including gene expression profiling provide additional prognostic information to that provided by PREDICT.

Description

Keywords

Adult, Area Under Curve, Breast Neoplasms, Female, Humans, Ki-67 Antigen, Lymphatic Metastasis, Middle Aged, Models, Theoretical, Predictive Value of Tests, Prognosis, ROC Curve, Receptor, ErbB-2, Receptors, Estrogen, Tumor Burden

Journal Title

BMC Cancer

Conference Name

Journal ISSN

1471-2407
1471-2407

Volume Title

14

Publisher

Springer Science and Business Media LLC
Sponsorship
Cancer Research UK (16942)
Cancer Research Uk (None)
Cancer Research Uk (None)
SEARCH was funded through a programme grant from Cancer Research UK (C490/A10124) and this work is supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge.