Development and performance of female breast cancer incidence risk prediction models: a systematic review and meta-analysis.

ABSTRACT:

Introduction

Accurate breast cancer risk prediction is essential for early detection and personalized prevention strategies. While traditional models, such as Gail and Tyrer-Cuzick, are widely utilized, machine learning-based approaches may offer enhanced predictive performance. This systematic review and meta-analysis compare the accuracy of traditional statistical models and machine learning models in breast cancer risk prediction.

Methods

A total of 144 studies from 27 countries were systematically reviewed, incorporating genetic, clinical, and imaging data. Pooled C-statistics were calculated to assess model discrimination, while observed-to-expected (O/E) ratios were used to evaluate calibration. Subgroup and sensitivity analyses were conducted to examine heterogeneity

SUBMITTER: Liu L 

PROVIDER: S-EPMC12278464 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature