Development of a ML algorithm to predict ovarian cancer risk
Principal Investigator
Name
Nicolas Martelin
Degrees
M.B.A., Ph.D.
Institution
Prostperia SAS
Position Title
Managing Director
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-903
Initial CDAS Request Approval
Jan 27, 2022
Title
Development of a ML algorithm to predict ovarian cancer risk
Summary
Ovarian cancer risk remains difficult to predict and detect. Therefore, 160.000 women worldwide die of ovarian cancer every year(Lozano et al, 2012). Moreover, even in those women who survive ovarian cancer, the consequences of surgery (hysterectomy, salpingoophorectomy, oophorectomy) negatively impact their quality of life.
We want to study how several personal variables (e.g. medical history, family history, lifestyle, etc.) could interact with each other and build a Machine-Learning model around those variables to see whether such a model could improve the prediction of ovarian cancers.
We want to study how several personal variables (e.g. medical history, family history, lifestyle, etc.) could interact with each other and build a Machine-Learning model around those variables to see whether such a model could improve the prediction of ovarian cancers.
Aims
- Check whether personal variables such as medical history, family history, lifestyle, etc. can have some predictive power for ovarian cancer when taken altogether;
- If yes, test whether a predictive score of ovarian cancer risk could be calculated;
- If yes, the project should answer whether such a score could potentially improve ovarian cancer screening.
Collaborators
Nicolas Martelin, Ph.D. - Prostperia SAS
Benjamin Chen - Prostperia SAS