Information for professionals
Welcome to PREDICT, an online prognostication and treatment benefit tool designed to help clinicians and patients make informed decisions about treatment following breast cancer surgery. The survival estimates, presented both with and without adjuvant therapy (hormone therapy, chemotherapy and trastuzumab), are provided for 5 and 10 years following surgery. Development of the model was a collaborative project between the Cambridge Breast Unit, University of Cambridge Department of Oncology and the Eastern Cancer Information and Registration Centre (ECRIC) and was supported by an unrestricted educational grant from Pfizer Limited.
We welcome any feedback you may have about PREDICT. If you have questions about its development or there are features you would like to have added to the model please let us know by emailing us at email@example.comUsing PREDICT
Model extension: HER2 status
Model extension: KI67 status
PREDICT and Oncotype DX
Use the interactive PREDICT tool to estimate breast cancer survival and the benefits of hormone therapy, chemotherapy and trastuzumab.
The model is easy to use following data entry for an individual patient including patient age, tumour size, tumour grade, number of positive nodes, ER status, HER2 status, KI67 status and mode of detection. Survival estimates, with and without adjuvant therapy, are presented in visual and text formats. Treatment benefits for hormone therapy and chemotherapy are calculated by applying relative risk reductions from the Oxford overview to the breast cancer specific mortality. Predicted mortality reductions are available for both second generation (anthracycline-containing, >4 cycles or equivalent) and third generation (taxane-containing) chemotherapy regimens.
The Cambridge Breast Unit (UK) uses the absolute 10-year survival benefit from chemotherapy to guide decision making for adjuvant chemotherapy as follows: <3% no chemotherapy; 3-5% chemotherapy discussed as a possible option; >5% chemotherapy recommended.
The relative risk reduction for hormone therapy is based on 5 years of tamoxifen.
The model was derived from cancer registry information on 5,694 women treated in East Anglia from 1999-2003. Breast cancer mortality models for ER positive and ER negative tumours were constructed using Cox proportional hazards, adjusted for known prognostic factors and mode of detection (symptomatic versus screen-detected). The survival estimates for an individual patient are based on the average co morbidity for women with breast cancer of a similar age. Further information about the model is provided in a paper published in Breast Cancer Research in January 2010.
The clinical validity of a prediction model can be defined as the accuracy of the model to predict future events. The two key measures of clinical validity are calibration and discrimination.
Calibration is how well the model predicts the total number of events in a given data set. A perfectly calibrated model is one where the observed (or actual) number of events in a given patient cohort is the same as the number of events predicted by the model.
Discrimination is how well the model predicts the occurrence of an event in individual patients. The discrimination statistic is a number between zero and one. It is generally obtained from the area under a receiver-operator characteristic curve. If a random pair of patients is selected from a dataset - one being a survivor and the other a non-survivor - the discrimination is the probability that the non-survivor will have a higher predicted risk than the survivor.
PREDICT was originally validated using a dateset of over 5000 breast cancer patients from the West Midlands Cancer Intelligence Unit.
We also validated PREDICT using a dataset from British Columbia that had been previously used for a validation of Adjuvant! Online. Predict provided overall and breast cancer specific survival estimates that were at least as accurate as estimates from Adjuvant! The results of this validation were published in the European Journal of Surgical Oncology.
Model extension: HER2 status
The model was updated in October 2011 to include HER2 status. Estimates for the prognostic effect of HER2 status were based on analysis of 10,179 cases collected by the Breast Cancer Association Consortium (BCAC). A validation of the new model in the British Columbia dataset was published in the British Journal of Cancer. This showed that inclusion of HER2 status in the model improved the estimates of breast cancer-specific mortality, especially in HER2 positive patients.
The benefit of trastuzumab is based on the relative risk reduction of 31 per cent in mortality up to five years in published trials.
Model extension: KI67 status
More recently we have added KI67 status to the model. The prognostic effect of KI67 was taken from published data showing that ER positive tumours that express KI67 are associated with a 30 per cent poorer relative survival.
KI67 positivity for the PREDICT model was defined as greater than 10 per cent of tumour cells staining positive.
The discrimination and calibration of the PREDICT model including KI67 has not yet been externally validated. The reasons for this are the lack of an appropriate validation set. Sets of research samples are generally unsuitable for model validation of novel markers as the cases are often highly selected and not representative of average cases. Cancer registry data are also unsuitable because it is generally not possible to get data on new markers. It is thus almost impossible to obtain data for an optimal validation. However, the relative hazard associated with KI67 positivity has been estimated many times in many independent data sets. That it is prognostic is clearly validated. And so, provided the estimate of the hazard ratio that is used in the prediction model is reasonably close to the "ground truth", the addition of the marker to the model will inevitably improve model performance. We just cannot say by how much.
PREDICT and Oncotype DX
Oncotype DX is a prognostic model (breast cancer recurrence) based on a test of gene expression profiles in tumours. It has recently been recommended by NICE (DG10) for use in women with oestrogen receptor positive (ER+), lymph node negative (LN-) and human epidermal growth factor receptor 2 negative (HER2-) early breast cancer to guide chemotherapy decisions if the person is assessed as being at intermediate risk, and where the information on the biological features of the cancer provided by oncotype DX is likely to help in predicting the course of the disease.
The incremental improvement in discrimination for oncotype DX over the established prognostic factors is not known. One of the key genes evaluated by oncotype DX is HER2. HER2 is already incorporated into PREDICT. The largest clinical validation of oncotype DX was published in 2008 . In this paper it is stated that "If the model [Cox regression] included only 389 patients with HER2/neu negative-tumours, RS [recurrence score] was not predictive". It is thus unlikely that the information on the biological features of the cancer provided by oncotype DX would help in predicting the course of the disease in HER2 negative breast cancer. Moreover, it has also been shown that the prognostic value of multi-gene expression signatures such as oncotype DX derive mainly from three genes; ER, HER2 and AURKA (a proliferation marker) . Thus, it is unlikely that oncotype DX would add much to the performance of the current version of PREDICT, particularly if KI67 is inlcuded.
 Goldstein LJ, Gray R, Badve S, Childs BH, Yoshizawa C, Rowley S, et al. Prognostic utility of the 21-gene assay in hormone receptor-positive operable breast cancer compared with classical clinicopathologic features. J Clin Oncol 2008;26(25):4063-71.
 Haibe-Kains B, Desmedt C, Loi S, Culhane AC, Bontempi G, Quackenbush J, et al. A Three-Gene Model to Robustly Identify Breast Cancer Molecular Subtypes. J Natl Cancer Inst 2012;104(4):311-25.