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Swiss-PROMPT Swiss Personalized Breast Cancer Risk Prediction study

Research Project
 | 
01.08.2016
 - 31.12.2020

Hintergrund: Breast cancer affects about 12% of Swiss women. Predictive models are important in personalized medicine because they contribute to early identification of high-risk individuals, which in turn facilitates stratification of preventive interventions and individualized clinical management. However, existing models have limited discriminatory accuracy (0.6-0.7) and do not include some non-modifiable and modifiable breast cancer risk factors, e.g., mammography density and obesity. Zielsetzung: The purpose of the study is to provide clinical decision support for accurate, reproducible, and more reliable individualized forecasting of the absolute risk for breast cancer compared to currently used models e.g., Gail model and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA). Design / Methode: We employed six different model-free machine-learning methods to predict absolute risk of breast cancer. Using independent training and testing data we quantified and compared the performance of machine-learning methods to the performance of the Gail model and BOADICEA using the following datasets (1) simulated, with no signal; (2) simulated, with artificial signal; (3) a random population-based sample of US breast cancer patients and their cancer-free female relatives (N=1232); and (4) a clinic-based sample of Swiss breast cancer patients and cancer-free women seeking genetic evaluation and/or testing at the Geneva University Hospitals (N=1700). Managing the massive, multi-source, incongruent and heterogeneous data includes data harmonization, model-free predictive analytics, and quantitative comparison of forecasting reliability. Erwarteter Nutzen / Relevanz (z.B. für Public Health): Advanced data-processing protocols are powerful tools to forecast personalized breast cancer risk and can help develop new and updated predictive models specified for Swiss women.

Collaborations & Cooperations

2021 - Participation or Organization of Collaborations within own University
Dellas, Sophie, MD, University Hospital Basel, Research cooperation
2021 - Participation or Organization of Collaborations on a national level
Chappuis, Pierre O, Prof. Dr., Geneva University Hospitals (HUG), Research cooperation
2020 - Participation or Organization of Collaborations on an international level
Dinov, Ivo D, Prof. Dr., Department of Computational Medicine and Bioinformatics, & Michigan Institute for Data Science, University of Michigan, Research cooperation

Publications

Ming, Chang et al. (2020) ‘Letter to the editor: Response to Giardiello D, Antoniou AC, Mariani L, Easton DF, Steyerberg EW’, Breast cancer research. 10.04.2020, 22(1), p. 35. Available at: https://doi.org/10.1186/s13058-020-01274-x.

URLs
URLs

Members (5)

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Maria Katapodi

Principal Investigator
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Chang Ming

Principal Investigator
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Nicole Probst Hensch

Co-Investigator
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Ivo D Dinov

Co-Investigator
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Pierre O Chappuis

Co-Investigator