PREDICT breast v4.0: an update to the PREDICT breast prognostic model

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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Publication Details

Journal
BMC Research Notes
Published
2025-11-14
DOI
https://doi.org/10.1186/s13104-025-07552-1
Primary Topic
Breast Cancer Treatment Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

PREDICT breast v4.0: an update to the PREDICT breast prognostic model

Yi-Wen Hsiao, Pei-Chen Peng, Paul D.P. Pharoah, Gordon Wishart
BMC Research Notes
Breast Cancer Treatment Studies
article

PREDICT breast v4.0: an update to the PREDICT breast prognostic model

Yi-Wen Hsiao, Pei-Chen Peng, Paul D.P. Pharoah, Gordon Wishart
article en

Abstract

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.

BMC Research NotesVol. 18(1)
Cedars-Sinai Medical Center (US), Anglia Ruskin University (GB)
Cedars-Sinai Medical Center
Openalex Percentile: Top 58%
Breast Cancer Treatment Studies
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PREDICT breast v4.0: an update to the PREDICT breast prognostic model — Yi-Wen Hsiao, Pei-Chen Peng, et al. · BMC Research Notes (2025) | TGRS Research Map | TGRS