Gordon C. Wishart
Inclusion of KI67 significantly improves performance of the PREDICT prognostication and prediction model for early breast cancer
Wishart, Gordon C.; Rakha, Emad; Green, Andrew; Ellis, Ian; Ali, Hamid Raza; Provenzano, Elena; Blows, Fiona M.; Caldas, Carlos; Pharoah, Paul D.P.
EMAD RAKHA Emad.Rakha@nottingham.ac.uk
Professor of Breast Cancer Pathology
ANDREW GREEN firstname.lastname@example.org
Professor IAN ELLIS IAN.ELLIS@NOTTINGHAM.AC.UK
Professor of Cancer Pathology
Hamid Raza Ali
Fiona M. Blows
Paul D.P. Pharoah
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).
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.
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-).
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.
|Journal Article Type||Article|
|Peer Reviewed||Peer Reviewed|
|APA6 Citation||Wishart, G. C., Rakha, E., Green, A., Ellis, I., Ali, H. R., Provenzano, E., …Pharoah, P. D. (2014). Inclusion of KI67 significantly improves performance of the PREDICT prognostication and prediction model for early breast cancer. BMC Cancer, 14(1), doi:10.1186/1471-2407-14-908|
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