Swiss-PROMPT Swiss Personalized Breast Cancer Risk Prediction study
Project funded by own resources
Project title Swiss-PROMPT Swiss Personalized Breast Cancer Risk Prediction study
Principal Investigator(s) Katapodi, Maria
Ming, Chang
Co-Investigator(s) Chappuis, Pierre O
Probst Hensch, Nicole
Dinov, Ivo D
Project Members Viassolo, Valeria
Winkens-Nothers, Judith Elisabeth
Organisation / Research unit Departement Public Health / Pflegewissenschaft (Katapodi)
Project start 01.08.2016
Probable end 31.12.2020
Status Active
Abstract

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.

Keywords personalized breast cancer risk; prediction model, machine learning
Financed by University funds

Cooperations ()

  ID Kreditinhaber Kooperationspartner Institution Laufzeit - von Laufzeit - bis
3977920  Katapodi, Maria  Chappuis, Pierre O, Prof. Dr.  Geneva University Hospitals (HUG)  01.04.2016  31.12.2021 
3978028  Katapodi, Maria  Dinov, Ivo D, Prof. Dr.  Department of Computational Medicine and Bioinformatics, & Michigan Institute for Data Science, University of Michigan  01.09.2016  31.12.2020 
3978032  Katapodi, Maria  Dellas, Sophie, MD  University Hospital Basel  01.01.2018  31.12.2021 
   

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14/08/2020