PREDICT breast model development and validation improved accuracy for breast cancer mortality, suggesting enhanced clinical utility.
There were 172,208 eligible cases randomly split 50:50 into model development and validation data sets. Cox proportional hazards models were derived for estrogen receptor negative and estrogen receptor positive cancer for breast cancer specific mortality with a third model for non-breast cancer mortality. In cases with at least 5 years follow-up and censored at 10 years, the model was well-calibrated with a less than 5% difference between observed and predicted breast cancer deaths. Model discrimination was also good with AUCs in the validation data of 0.735 and 0.794 for ER negative and ER positive cases respectively. Calibration and discrimination were slightly improved compared to PREDICT breast v3.1.
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Pharoah et al. (2025) studied this question.
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