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

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.

Inclusion Criteria:Age minimum 18 yearsPatients with I-IV ovarian cancer or benign tumor confirmed by pathological examination.Ability to understand and the willingness to sign a written informed consent documentNon-cancer controls are sex- and age-matched individuals without presence of any tumors or nodules or any other severe chronic diseases through systematic screeningExclusion Criteria:Participants must not be pregnant or breastfeedingParticipants must not have prior cancer histories or a second non-ovarian malignancyParticipants must not have had any form of cancer treatment before enrollment or plasma collection, including surgery, chemotherapy, radiotherapy, targeted therapy and immunotherapyParticipants must not present medical conditions of fever or have acute or immunological diseases that required treatment 14 days before plasma collectionParticipants who underwent organ transplant or allogenic bone marrow or hematopoietic stem cell transplantationParticipants with clinically important abnormalities or conditions unsuitable for blood collectionAny other disease or clinical condition of participants that the researcher believes may affect the compliance of the protocol, or affect the patient's signing of the informed consent form (ICF), which is not suitable to participate in this clinical trial.