Medline ® Abstracts for References 18-21
of 'Adjuvant chemotherapy for HER2-negative breast cancer'
Validity of the online PREDICT tool in older patients with breast cancer: a population-based study.
de Glas NA, Bastiaannet E, Engels CC, de Craen AJ, Putter H, van de Velde CJ, Hurria A, Liefers GJ, Portielje JE
Br J Cancer. 2016 Feb;114(4):395-400. Epub 2016 Jan 19.
BACKGROUND: Predicting breast cancer outcome in older patients is challenging, as it has been shown that the available tools are not accurate in older patients. The PREDICT tool may serve as an alternative tool, as it was developed in a cohort that included almost 1800 women aged 65 years or over. The aim of this study was to assess the validity of the online PREDICT tool in a population-based cohort of unselected older patients with breast cancer.
METHODS: Patients were included from the population-based FOCUS-cohort. Observed 5- and 10-year overall survival were estimated using the Kaplan-Meier method, and compared with predicted outcomes. Calibration was tested by composing calibration plots and Poisson Regression. Discriminatory accuracy was assessed by composing receiver-operator-curves and corresponding c-indices.
RESULTS: In all 2012 included patients, observed and predicted overall survival differed by 1.7%, 95% confidence interval (CI)=-0.3-3.7, for 5-year overall survival, and 4.5%, 95% CI=2.3-6.6, for 10-year overall survival. Poisson regression showed that 5-year overall survival did not significantly differ from the ideal line (standardised mortality ratio (SMR)=1.07, 95% CI=0.98-1.16, P=0.133), but 10-year overall survival was significantly different from the perfect calibration (SMR=1.12, 95% CI=1.05-1.20, P=0.0004). The c-index for 5-year overall survival was 0.73, 95% CI=0.70-0.75, and 0.74, 95% CI=0.72-0.76, for 10-year overall survival.
CONCLUSIONS: PREDICT can accurately predict 5-year overall survival in older patients with breast cancer. Ten-year predicted overall survival was, however, slightly overestimated.
Department of Surgery, Leiden University Medical Centre, PO Box 9600, 2300RC Leiden, The Netherlands.
PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer.
Wishart GC, Azzato EM, Greenberg DC, Rashbass J, Kearins O, Lawrence G, Caldas C, Pharoah PD
Breast Cancer Res. 2010;12(1):R1. Epub 2010 1 6.
INTRODUCTION: The aim of this study was to develop and validate a prognostication model to predict overall and breast cancer specific survival for women treated for early breast cancer in the UK.
METHODS: Using the Eastern Cancer Registration and Information Centre (ECRIC) dataset, information was collated for 5,694 women who had surgery for invasive breast cancer in East Anglia from 1999 to 2003. Breast cancer mortality models for oestrogen receptor (ER) positive and ER negative tumours were derived from these data using Cox proportional hazards, adjusting for prognostic factors and mode of cancer detection (symptomatic versus screen-detected). An external dataset of 5,468 patients from the West Midlands Cancer Intelligence Unit (WMCIU) was used for validation.
RESULTS: Differences in overall actual and predicted mortality were<1% at eight years for ECRIC (18.9% vs. 19.0%) and WMCIU (17.5% vs. 18.3%) with area under receiver-operator-characteristic curves (AUC) of 0.81 and 0.79 respectively. Differences in breast cancer specific actual and predicted mortality were<1% at eight years for ECRIC (12.9% vs. 13.5%) and<1.5% at eight years for WMCIU (12.2% vs. 13.6%) with AUC of 0.84 and 0.82 respectively. Model calibration was good for both ER positive and negative models although the ER positive model provided better discrimination (AUC 0.82) than ER negative (AUC 0.75).
CONCLUSIONS: We have developed a prognostication model for early breast cancer based on UK cancer registry data that predicts breast cancer survival following surgery for invasive breast cancer and includes mode of detection for the first time. The model is well calibrated, provides a high degree of discrimination and has been validated in a second UK patient cohort.
Cambridge Breast Unit, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 2QQ, UK. email@example.com
A population-based validation of the prognostic model PREDICT for early breast cancer.
Wishart GC, Bajdik CD, Azzato EM, Dicks E, Greenberg DC, Rashbass J, Caldas C, Pharoah PD
Eur J Surg Oncol. 2011 May;37(5):411-7. Epub 2011 Mar 2.
INTRODUCTION: Predict (www.predict.nhs.uk) is a prognostication and treatment benefit tool developed using UK cancer registry data. The aim of this study was to compare the 10-year survival estimates from Predict with observed 10-year outcome from a British Columbia dataset and to compare the estimates with those generated by Adjuvant! (www.adjuvantonline.com).
METHOD: The analysis was based on data from 3140 patients with early invasive breast cancer diagnosed in British Columbia, Canada, from 1989-1993. Demographic, pathologic, staging and treatment data were used to predict 10-year overall survival (OS) and breast cancer specific survival (BCSS) using Adjuvant! and Predict models. Predicted outcomes from both models were then compared with observed outcomes.
RESULTS: Calibration of both models was excellent. The difference in total number of deaths estimated by Predict was 4.1 percent of observed compared to 0.7 percent for Adjuvant!. The total number of breast cancer specific deaths estimated by Predict was 3.4 percent of observed compared to 6.7 percent for Adjuvant! Both models also discriminate well with similar AUC for Predict and Adjuvant! respectively for both OS (0.709 vs 0.712) and BCSS (0.723 vs 0.727). Neither model performed well in women aged 20-35.
CONCLUSION: In summary Predict provided accurate overall and breast cancer specific survival estimates in the British Columbia dataset that are comparable with outcome estimates from Adjuvant! Both models appear well calibrated with similar model discrimination. This study provides further validation of Predict as an effective predictive tool following surgery for invasive breast cancer.
Cambridge Breast Unit, Addenbrooke's Hospital, Hills Road, Cambridge, CB2 2QQ, UK.
PREDICT Plus: development and validation of a prognostic model for early breast cancer that includes HER2.
Wishart GC, Bajdik CD, Dicks E, Provenzano E, Schmidt MK, Sherman M, Greenberg DC, Green AR, Gelmon KA, Kosma VM, Olson JE, Beckmann MW, Winqvist R, Cross SS, Severi G, Huntsman D, Pylkäs K, Ellis I, Nielsen TO, Giles G, Blomqvist C, Fasching PA, Couch FJ, Rakha E, Foulkes WD, Blows FM, Bégin LR, van't Veer LJ, Southey M, Nevanlinna H, Mannermaa A, Cox A, Cheang M, Baglietto L, Caldas C, Garcia-Closas M, Pharoah PD
Br J Cancer. 2012 Aug;107(5):800-7. Epub 2012 Jul 31.
BACKGROUND: Predict (www.predict.nhs.uk) is an online, breast cancer prognostication and treatment benefit tool. The aim of this study was to incorporate the prognostic effect of HER2 status in a new version (Predict+), and to compare its performance with the original Predict and Adjuvant!.
METHODS: The prognostic effect of HER2 status was based on an analysis of data from 10 179 breast cancer patients from 14 studies in the Breast Cancer Association Consortium. The hazard ratio estimates were incorporated into Predict. The validation study was based on 1653 patients with early-stage invasive breast cancer identified from the British Columbia Breast Cancer Outcomes Unit. Predicted overall survival (OS) and breast cancer-specific survival (BCSS) for Predict+, Predict and Adjuvant! were compared with observed outcomes.
RESULTS: All three models performed well for both OS and BCSS. Both Predict models provided better BCSS estimates than Adjuvant!. In the subset of patients with HER2-positive tumours, Predict+ performed substantially better than the other two models for both OS and BCSS.
CONCLUSION: Predict+ is the first clinical breast cancer prognostication tool that includes tumour HER2 status. Use of the model might lead to more accurate absolute treatment benefit predictions for individual patients.
Cambridge Breast Unit, Addenbrookes Hospital, Cambridge, UK.