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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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PREDICT breast v4.0: an update to the PREDICT breast prognostic model.
Paul D P Pharoah1, Yi-Wen Hsiao2, Gordon C Wishart3
1Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA. paul.pharoah@cshs.org.
BMC Research Notes
|November 14, 2025
Summary
The updated PREDICT breast model (v4.0) shows improved accuracy in predicting breast cancer mortality using a larger UK dataset. This enhanced prognostic tool offers better calibration and discrimination than the previous version (v3.1).
Area of Science:
- Oncology
- Biostatistics
- Epidemiology
Background:
- The PREDICT breast model is a prognostic tool for breast cancer outcomes.
- Previous versions (e.g., v3.1) were developed using regional data.
- Clinical decisions often rely on 10-year outcome predictions.
Purpose of the Study:
- To reparameterize the PREDICT breast model using a comprehensive UK dataset.
- To develop and validate the new version (v4.0) of the PREDICT breast model.
- To compare the performance of PREDICT breast v4.0 against v3.1.
Main Methods:
- Utilized a large dataset of 172,208 eligible breast cancer cases across the UK.
- Employed Cox proportional hazards models for estrogen receptor-negative and -positive breast cancer mortality, and non-breast cancer mortality.
- Randomly split data into development (50%) and validation (50%) sets for robust analysis.
Main Results:
- The new model (v4.0) demonstrated good calibration (<5% difference between observed and predicted deaths) and discrimination (AUCs of 0.735 for ER-negative and 0.794 for ER-positive cases).
- Performance metrics showed slight improvements in calibration and discrimination compared to PREDICT breast v3.1.
- Models were well-calibrated for breast cancer-specific mortality up to 10 years post-diagnosis.
Conclusions:
- The reparameterized PREDICT breast v4.0 model, based on a larger UK dataset, offers improved prognostic accuracy.
- This updated model provides clinicians with more reliable predictions for breast cancer outcomes.
- Enhanced calibration and discrimination in v4.0 support its use in clinical decision-making for breast cancer patients.
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