The MamoRisk project, a collaborative initiative led by the Santiago de Compostela Health Research Institute (IDIS), the Spanish National Cancer Research Centre (CNIO), and the Galicia Sur Health Research Institute (IISGS), has launched a study to refine breast cancer screening through personalized risk prediction. Funded by Spain’s Ministry of Science, Innovation and Universities with €1.2 million, the project aims to categorize women into low, intermediate, or high-risk groups over two- and five-year periods to optimize early detection and preventive measures.
Predicting Breast Cancer Risk with the BOADICEA Algorithm
At the core of the MamoRisk study is the validation of the BOADICEA—Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation—algorithm. According to the Centro Nacional de Investigaciones Oncológicas (CNIO), this model integrates genetic, family, clinical, and epidemiological information to forecast an individual’s probability of developing breast and ovarian cancer. The research team intends to apply this model to a cohort of 10,000 women across 14 Spanish autonomous communities, comparing the model’s predictions against actual health outcomes observed over a six-year monitoring period.
Did you know?
A lifestyle that prioritizes regular physical activity, low alcohol consumption, and the avoidance of postmenopausal obesity is associated with a 20% reduction in the risk of developing breast cancer, according to data provided by the CNIO.
Genetic Analysis and Artificial Intelligence Integration
The study methodology involves a multi-layered data approach. Participants provide saliva samples for genetic analysis, which is conducted by Anna González-Neira and Guillermo Pita at the CNIO. This analysis accounts for hundreds of genetic variants, ranging from high-risk mutations in BRCA1 and BRCA2 to moderate-risk variants and those with smaller individual effects. Simultaneously, José Esteban Castelao of the IISGS employs artificial intelligence to analyze mammograms, integrating visual data with clinical and epidemiological profiles to strengthen the risk-prediction model.
Future Implications for Personalized Screening Programs
If the MamoRisk model proves effective, it could fundamentally shift how national health systems approach breast cancer screening. Instead of uniform monitoring, health authorities could offer intensive surveillance to women identified as high-risk. Manuela Gago, who coordinates participant recruitment and data collection at IDIS, oversees the selection of 4,000 women diagnosed with breast cancer and 6,000 healthy women for the study. The international collaboration includes Antonis Antoniou, Professor of Cancer Risk Prediction at the University of Cambridge, providing a global perspective on the validation of these risk-prediction tools.
Frequently Asked Questions
How does the MamoRisk project classify individual risk?
The project acts as a calculator that processes each woman’s genetic, clinical, and epidemiological data to produce a classification of low, intermediate, or high risk over two- and five-year windows.
Which regions in Spain are participating in the study?
The study is being conducted across 14 autonomous communities, including Galicia, Madrid, Cantabria, Castilla-La Mancha, Murcia, Extremadura, Asturias, Castilla y León, Andalusia, Aragón, Catalonia, La Rioja, the Valencian Community, and the Canary Islands.
What role does artificial intelligence play in this research?
Artificial intelligence is used by the research team to analyze mammograms, which, when combined with genetic information and clinical data, helps refine the accuracy of the risk-prediction model.
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