Diagnostic for Screening and/or Predicting Women “at risk” for the Development and Progression Endometrial Cancer

This invention describes a novel method to use metabolic markers in samples obtained from cervicovaginal lavage (CVL) and machine-learning algorithms to detect patients “at risk” for endometrial cancer (EMC).
Background:
Endometrial cancer (EMC) is the most common gynecological cancer. The American Cancer Society estimates that in 2021 about 66,570 new cases will be diagnosed, with an estimated 12,940 deaths. Diagnosis typically requires invasive sample collection for biopsies and can only be carried out after the disease has progressed significantly.

In this technology, the inventors describe a method by which to screen women for being “at risk” of having development or progression of endometrial cancer. This method is far less invasive and utilizes metabolic biomarker panels and machine-learning algorithms. Additionally, this test can be used in the clinical setting as part of the annual well woman exam.
Applications:

  • Diagnostic method for women at high risk of developing endometrial cancer

Advantages:

  • Less invasive method using cervicovaginal lavage
  • Uses machine learning algorithms and validated metabolic biomarkers
  • Convenient, can be conducted as part of the well woman exam

Website

https://arizona.technologypublisher.com/tech?title=Diagnostic_for_Screening_and%2for_Predicting_Women_%22at_risk%22_for_the_Development_and_Progression_Endometrial_Cancer

Contact Information

TTO Home Page: https://arizona.technologypublisher.com

Name: Katherine Kuhns

Title: Licensing Manager, UAHS-TLA

Email: katiekuhns@email.arizona.edu