Predictive model ( AI/ML) of post surgery prognosis of breast cancer using PLCO dataset patient data
Principal Investigator
Name
Juri Baruah
Degrees
MD
Institution
Quantum Biosciences
Position Title
Principal investigator
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-1572
Initial CDAS Request Approval
May 29, 2024
Title
Predictive model ( AI/ML) of post surgery prognosis of breast cancer using PLCO dataset patient data
Summary
Quantum Biosciences is developing an AI/ML powered predictive model on precision treatment of post primary surgery of breast cancer (stage 2A/2B). 95% of breast cancer patients get chemotherapy to avoid a relapse. Existing information suggests that only 15% of these patients benefit from the adjuvant chemotherapy and it interferes with a woman's reproductive health in the childbearing age group. We are correlating the histological type and grade of the tumor with the lymph node status of the patient at surgery to create a predictive model for prognosis, hence determining the need for adjuvant chemotherapy. We will take into account the presence of the tumor marker (Ki 67), hormone receptors and the Her2 gene. Our goal is to provide health care providers with an effective tool to make an informed decision regarding adjuvant chemotherapy in a breast cancer patient (stage 2A/2B), which can get a woman the most precious few years she needs to complete her family.
Aims
1.Develop an AI/ML powered predictive model for prognosis of breast cancer ( stage 2A/2B).
2.Correlate the histological type, grade of the tissue and the TNM stage of the cancer to assess prognosis for recurrence.
3.Also assess the hormone, HER2+ receptor status and the presence/ absence of Ki 67 on prognosis .
4.Use Oncotype Dx test scoring to validate the predictive model
Collaborators
Subu Gupta, AI/ML Computer Scientist, subu@quantumbiosciences.ai