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Early Detection of Ovarian Cancer Using Plasma Cell-free DNA Fragmentomics (Retrospective Study)

NCT05693974 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 130

Last updated 2023-01-23

No results posted yet for this study

Summary

The purpose of this study is to enable non-invasive early detection of ovarian cancer in high-risk populations through the establishment of a multimodal machine learning model using plasma cell-free DNA fragmentomics. Plasma cell-free DNA from early stage ovarian cancer patients and healthy individuals will be subjected to whole-genome sequencing. Five diferent feature types, including Fragment Size Coverage (FSC), Fragment Size Distribution (FSD), EnD Motif (EDM), BreakPoint Motif (BPM), and Copy Number Variation (CNV) will be assessed to generate this model.

Conditions

Sponsors & Collaborators

  • Nanjing Geneseeq Technology Inc.

    collaborator INDUSTRY

  • Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

    lead OTHER

Principal Investigators

  • Bingzhong Zhang, MD · Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Eligibility

Min Age
18 Years
Sex
FEMALE
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2022-10-01
Primary Completion
2023-01-31
Completion
2023-04-30

Countries

  • China

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05693974 on ClinicalTrials.gov