We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

LabMedica

Download Mobile App
Recent News Expo Medica 2024 Clinical Chem. Molecular Diagnostics Hematology Immunology Microbiology Pathology Technology Industry Focus

Ovarian Cancer Blood Test Distinguishes Between Cancerous and Benign Pelvic Masses With 91% Accuracy

By LabMedica International staff writers
Posted on 12 Oct 2023

High-grade serous ovarian carcinoma (HGSOC) is the most frequently occurring form of ovarian cancer and is also the deadliest. One reason for its lethality is the lack of effective early-stage screening methods. When a woman has a pelvic mass, or unusual growth in her lower abdomen, it's tough to know ahead of surgery whether the mass is cancerous or benign. Biopsies, common in diagnosing other cancers, are usually not feasible here, complicating the choice of treatment options. Now, a new liquid biopsy blood test that detects specific nucleic acids circulating in the blood could change that.

Researchers from the University of Southern California (USC, Los Angeles, CA, USA) have led a study using human tissue and plasma that demonstrated the ovarian cancer blood test, named OvaPrint, can differentiate between cancerous and non-cancerous pelvic masses with an accuracy rate of up to 91%. According to preclinical research published in the Clinical Cancer Research journal, OvaPrint outperforms other commercially available tests. The test employs a cell-free DNA methylation liquid biopsy technique, an emerging method for early-stage cancer detection. It identifies circulating DNA that has undergone methylation at specific nucleic acids. Methylation is a complex process that can change how genes are expressed and serve as a disease indicator. OvaPrint aims to detect HGSOC in its early, more treatable stages, something most existing ovarian cancer tests fail to do consistently.


Image: High-grade serous ovarian carcinoma (HGSOC) is the most common type of ovarian cancer (Photo courtesy of USC)
Image: High-grade serous ovarian carcinoma (HGSOC) is the most common type of ovarian cancer (Photo courtesy of USC)

Having this information ahead of the surgery could guide the choice of surgeon and surgical method, benefiting the overall treatment plan for patients diagnosed with a pelvic mass. The research team is also studying whether OvaPrint could be a useful screening tool for detecting early-stage ovarian cancer in women who show no symptoms. Detecting ovarian cancer early vastly improves survival rates; over 90% of patients will live for five years or longer if diagnosed early, compared to less than 40% for late-stage diagnoses. The USC team is now planning a larger study to confirm these findings. If successful, a commercially viable version of the test could be available for clinical use within the next two years. The researchers are also considering whether OvaPrint can be adapted to identify other ovarian cancer subtypes and hope to refine the test for broader population screening eventually.

“The test has the potential to improve treatment, because the surgical approach to removing a pelvic mass differs depending on whether it’s benign or not,” said Bodour Salhia, PhD, the study’s corresponding author. “Right now, doctors essentially have to take their best guess.”

“Early detection saves lives,” added Salhia. “If we can accurately identify early-stage ovarian cancer, we can change the outcome of the disease and really crank up survival rates.”

Related Links:
USC


Gold Member
Fully Automated Cell Density/Viability Analyzer
BioProfile FAST CDV
Antipsychotic TDM AssaysSaladax Antipsychotic Assays
New
Thyroxine ELISA
T4 ELISA
New
17 Beta-Estradiol Assay
17 Beta-Estradiol Assay

Latest Molecular Diagnostics News

Non-Invasive AI-Powered Urine Test Enables Early Bladder Cancer Detection

New Multi-Biomarker Class Approach Improves Cancer Detection

AI Urine Test Predicts COPD Flares Before Symptoms Appear