GAIN-EC
Non-invasive test for endometrial cancer detection using genome sequencing and artificial intelligence
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Endometrial cancer is one of the tumors with highest incidence. The standard strategy to diagnose it consists of pelvic ultrasonography plus endometrial biopsy, in those case of increased endometrial thickness. The lack of ultrasound specificity (≈50%) exposes a high proportion of healthy women to invasive tests. Furthermore, endometrial biopsies are not informative or impracticable in 1/3 of women. These limitations are more accentuated in bleeding post-menopausal women. This results in a significant financial burden on the healthcare system, consumes time, and a personal cost to women.
Solution
This non-invasive tool is able to identify endometrial cancer patients and its molecular subtype based on the evaluation of somatic mutations and computerized analysis of cytologic images obtained from urine samples. It not only provides a positive or negative result, but also identifies the molecular subgroup, which is recommended in current guidelines. This test allows early identification of the most appropiate clinical approach and disease monitoring.
Objective
To improve the diagnosis of endometrial cancer patients using a non-invasive urine test, highly specific and sensitive based on the analysis of somatic mutations and cytologic images.