Scientists use machine learning to unveil new predictors of post-menopausal breast cancer BreastCancer Predictors PostMenopausal Health PolygenicRisk MachineLearning Medicine RiskPrediction StatisticalModels CancerResearch SciReports
By Dr. Priyom Bose, Ph.D.Jun 11 2023Reviewed by Benedette Cuffari, M.Sc. One of the most common types of cancer affecting women worldwide is breast cancer. Multiple predictors of this disease have been identified, including inherited genetic factors, reproductive factors, and lifestyle.
Background Machine learning methods can analyze large datasets on predictors and process complex non-linear relationships. Although previous studies have used ML for breast cancer risk prediction, they were not used to identify predictors. Previously, breast cancer PRS has been combined with risk prediction models, such as the Tyrer-Cuzick model and the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm . Although the interaction between PRS and phenotypic features like gene-environment interactions have been analyzed for breast cancer, contradictory findings have been reported.
Post-menopausal women between the ages of 40 and 69 at baseline were recruited due to the aforementioned etiological heterogeneity by menopausal status. The incidence of breast cancer was identified using the International Classification of Diseases codes, in which PRS313 and PRS120k were considered as potential genetic features.
The newly identified predictors were strongly associated with the incidence of post-menopausal breast cancer. In the future, more research is needed to understand whether these are potentially modifiable risk factors for breast cancer.
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