Article Text
Abstract
Background Breast cancer (BC) is the most common cancer and the second leading cause of cancer death in women; an estimated one in eight women in the USA will develop BC during her lifetime. However, current methods of BC screening, including clinical breast exams, mammograms, biopsies and others, are often underused due to limited access, expense and a lack of risk awareness, causing 30% (up to 80% in low-income and middle-income countries) of patients with BC to miss the precious early detection phase.
Methods This study creates a key step to supplement the current BC diagnostic pipeline: a prescreening platform, prior to traditional detection and diagnostic steps. We have developed BREast CAncer Risk Detection Application (BRECARDA), a novel framework that personalises BC risk assessment using artificial intelligence neural networks to incorporate relevant genetic and non-genetic risk factors. A polygenic risk score (PRS) was enhanced by employing AnnoPred and validated by fivefolds cross-validation, outperforming three existing state-of-the-art PRS methods.
Results We used data from 97 597 female participants of the UK BioBank to train our algorithm. Using the enhanced PRS thus trained together with non-genetic information, BRECARDA was evaluated in a testing dataset with 48 074 UK Biobank female participants and achieved a high accuracy of 94.28% and area under the curve of 0.7861. Our optimised AnnoPred outperformed other state-of-the-art methods on quantifying genetic risk, indicating its potential for supplementing the current BC detection tests, population screening and risk evaluation.
Conclusion BRECARDA can enhance disease risk prediction, identify high-risk individuals for BC screening, facilitate disease diagnosis and improve population-level screening efficiency. It can serve as a valuable and supplemental platform to assist doctors in BC diagnosis and evaluation.
- genetic research
- genetics
- genetics, medical
- genomics
Data availability statement
Data are available in a public, open access repository.
Statistics from Altmetric.com
Data availability statement
Data are available in a public, open access repository.
Footnotes
Twitter @HongyuZhao2
Contributors LRT performed the integrative analysis and wrote the manuscript; YY cleaned the data and built the PRS; HZ and YY supervised the study. All authors contributed to the study design, data interpretation and manuscript editing. HZ is the guarantor for the overall content and data access.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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