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Early detection of ovarian cancer via biomarkers using Machine Learning techniques

Principal Investigator
Name
Kritika Murali
Degrees
BTech
Institution
MIT Art Design and Technology University
Position Title
Student
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCO-1124
Initial CDAS Request Approval
Dec 8, 2022
Title
Early detection of ovarian cancer via biomarkers using Machine Learning techniques
Summary
CA-125 cannot detect ovarian cancer unless it is too late in more than 40% of cases. The aim of this project is to find and use other, more sensitive and specific, biomarkers and a machine learning approach to find a better way to detect it. I'm trying to use non invasive biomarkers so that a full fledged tissue biopsy will not be required to obtain the biomarker in the patient.
Aims

1) Find more specific and sensitive biomarkers
2) Biomarkers should be non invasive (like blood biomarkers)
3) It should be a biomarker that appears at least as early as stage 1
4) Use the biomarker for a machine learning rooted approach for diagnosing ovarian cancer
5) Find the best machine learning based technique for prediction of ovarian cancer

Collaborators

Aneesh Deshmukh - MIT Art Design and Technology University
Poorvi Patil - MIT Art Design and Technology University
Yash Barkale - MIT Art Design and Technology University