Scholarlink

Introduction <p>Outcome prediction research in early-onset breast cancer (EoBC) is limited. This study evaluated the predictive performance of NHS PREDICT v2.1 and developed two prediction models for 5-year and 10-year all-cause mortality in a cohort of EoBC patients in Alberta, Canada.</p> Methods <p>Adults &lt; 40&#xa0;years diagnosed with invasive breast cancer in Alberta, Canada from 2004 to 2020 were included. Patient data were entered into PREDICT v2.1 and mortality estimates at 5 and 10&#xa0;years were extracted. Two prediction models were developed for all-cause mortality: multivariable Cox regression with LASSO penalization (LASSO Cox) and random survival forests (RSF). Internal validation of the developed models was performed using nested tenfold cross-validation repeated 200 times. Model performance was assessed using receiver operator characteristic and calibration curves for mortality at 5 and 10&#xa0;years.</p> Results <p>In total, 1827 patients with EoBC were eligible for inclusion. At 5&#xa0;years, PREDICT had an area under the curve of 0.78 (95%CI 0.74–0.82) and overestimated mortality by 2.4% (95%CI 0.70–4.33) in the overall cohort. No differences in observed and predicted mortality by PREDICT were observed at 10&#xa0;years. The LASSO Cox model showed better discrimination at 5 and 10&#xa0;years than the RSF model, but both had poor calibration and underestimated mortality.</p> Conclusion <p>PREDICT&#xa0;v2.1&#xa0;tended to overestimate 5-year&#xa0;mortality in those with &gt;&#xa0;30% predicted risks and 10-year mortality in those with &gt; 50% predicted risks for EoBC in Alberta, Canada. We did not identify additional models that would be clinically useful by applying machine learning. More follow-up data and emerging systemic treatment variables are required to study outcome prediction in modern cohorts.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Evaluating PREDICT and developing outcome prediction models in early-onset breast cancer using data from Alberta, Canada

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月12日

查看全文
  • Robert B. Basmadjian,
  • Yuan Xu,
  • May Lynn Quan,
  • Sasha Lupichuk,
  • Winson Y. Cheung,
  • Darren R. Brenner

摘要

Introduction

Outcome prediction research in early-onset breast cancer (EoBC) is limited. This study evaluated the predictive performance of NHS PREDICT v2.1 and developed two prediction models for 5-year and 10-year all-cause mortality in a cohort of EoBC patients in Alberta, Canada.

Methods

Adults < 40 years diagnosed with invasive breast cancer in Alberta, Canada from 2004 to 2020 were included. Patient data were entered into PREDICT v2.1 and mortality estimates at 5 and 10 years were extracted. Two prediction models were developed for all-cause mortality: multivariable Cox regression with LASSO penalization (LASSO Cox) and random survival forests (RSF). Internal validation of the developed models was performed using nested tenfold cross-validation repeated 200 times. Model performance was assessed using receiver operator characteristic and calibration curves for mortality at 5 and 10 years.

Results

In total, 1827 patients with EoBC were eligible for inclusion. At 5 years, PREDICT had an area under the curve of 0.78 (95%CI 0.74–0.82) and overestimated mortality by 2.4% (95%CI 0.70–4.33) in the overall cohort. No differences in observed and predicted mortality by PREDICT were observed at 10 years. The LASSO Cox model showed better discrimination at 5 and 10 years than the RSF model, but both had poor calibration and underestimated mortality.

Conclusion

PREDICT v2.1 tended to overestimate 5-year mortality in those with > 30% predicted risks and 10-year mortality in those with > 50% predicted risks for EoBC in Alberta, Canada. We did not identify additional models that would be clinically useful by applying machine learning. More follow-up data and emerging systemic treatment variables are required to study outcome prediction in modern cohorts.