Risk prediction for breast Cancer in Han Chinese women based on a cause-specific Hazard model

Lu Wang1, Liyuan Liu2,3, Zhen Lou4

  • 1Department of Biostatistics, School of Public Health, Shandong University, 44 Wen Hua Xi Road, Jinan, 250012, China.

BMC Cancer
|February 9, 2019
PubMed

Insights

A new breast cancer risk prediction model was developed for Han Chinese women, identifying high-risk individuals for targeted prevention. Key factors include fertility history, benign breast disease, and body mass index (BMI).

Area of Science:

  • Oncology
  • Epidemiology
  • Biostatistics

Background:

  • Lack of efficient breast cancer prediction models for general population screening in China.
  • Need for targeted primary prevention strategies for breast cancer among Han Chinese women.

Purpose of the Study:

  • To develop and validate a breast cancer risk prediction model for Han Chinese women.
  • To identify high-risk populations for enhanced breast cancer screening and prevention.

Main Methods:

  • Utilized a cause-specific competing risk model for model development.
  • Employed data from the Shandong Case-Control Study and Taixing Prospective Cohort Study for development and validation.
  • Evaluated model calibration and discriminative accuracy using expected/observed (E/O) ratio and C-statistic.

Main Results:

  • Identified significant risk factors: number of abortions (RR=6.3), age at first live birth (RR=3.6), history of benign breast disease (RR=4.3), body mass index (BMI) (RR=1.9), family history of breast cancer (RR=3.3), and life satisfaction scores (RR=2.4).
  • The developed model demonstrated good calibration (E/O ratio=1.03) and discriminatory accuracy (C-statistic=0.64).

Conclusions:

  • A novel risk prediction model for breast cancer was successfully developed for the Han Chinese population.
  • The model incorporates fertility status, disease history, and modifiable risk factors.
  • The model exhibits satisfactory calibration and discrimination, suitable for clinical application in risk assessment.
Abstract

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