Identifying risk factors for breast cancer through machine learning
Principal Investigator
Name
Daniella Araujo
Degrees
Ph.D student
Institution
Universidade Federal de Minas Gerais
Position Title
Research Associate of Artificial Intelligence Lab
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-853
Initial CDAS Request Approval
Nov 3, 2021
Title
Identifying risk factors for breast cancer through machine learning
Summary
In Brazil, every women aged 40+ should go through annual breast cancer screening with mammograms. Because there is high inequality between Brazil's regions, some health systems can not support this screening process. Besides, recurrent mammograms can increase future risk of breast cancer, specially for women with BRCA1 or BRCA2 mutations. Thus, given the relevance of early identification of breast cancer, we propose to create a cheap and minimum invasive pre-screening process using only demographics and /or plasma biomarkers data.
Using a machine learning methodology developed in Artificial Intelligence Lab in UFMG, we have already had relevant results using plasma data in prodromal Alzheimer's disease identification; COVID-19 prognosis and diagnosis (similar to RT-PCR); polycystic ovary syndrome (PCOS) identification. This methodology has four steps: data engineering; large scale exploration; feature selection and interpretability.
Aims
- To predict the risk of breast cancer using only basic and cheap data (such as demographics data and/or plasma analytes). This model could be further used as a pre-screening process for mammograms;
- To understand the patterns involved in the model decision making, that could grasp general patterns about breast cancer pathogenesis.
Collaborators
Prof. Adriano Alonso Veloso - Advisor