This study is a multi-center, observational study aiming at developing a machine learning-based early detection model using prospectively collected liquid biopsy samples from newly diagnosed ovarian cancer.
Peripheral blood samples from ovarian cancer (OC) patients will be prospectively collected to identify cancer-specific circulating signals by analyzing cell free DNA. Based on the comprehensive molecular profiling, a machine learning-driven noninvasive test will be trained and validated through a two-stage approach in clinically annotated individuals. Approximately 168 stage I-II OC patients will be enrolled in this study. Age-matched female controls included in model development were recruited in another study, which are volunteers without a cancer diagnosis after routine medical screening.
Study Type
OBSERVATIONAL
Enrollment
168
Blood sample will be collected
Sun Yat-sen Memorial Hospital
Guangzhou, Guangdong, China
NOT_YET_RECRUITINGLiaoning Cancer Hospital & Institute
Shenyang, Liaoning, China
NOT_YET_RECRUITINGFudan University Shanghai Cancer Center
Shanghai, Shanghai Municipality, China
RECRUITINGThe performance of cfDNA methylation-based model for discriminating ovarian cancer versus non-cancer.
Sensitivities of cfDNA methylation-based model in detecting OC at specificity of 99% and 95%, respectively.
Time frame: 12 months
The performance of model using multi-omics data for discriminating ovarian cancer versus non-cancer
Sensitivities of multi-omics model which combines methylation signature and fragmentomic features in detecting OC at specificity of 99% and 95%, respectively.
Time frame: 12 months
The performance of pre-defined model in clinical sub-groups of interest
Sensitivity of pre-defined model in different pathological subtypes or different age groups or tumor marker-negative cases.
Time frame: 12 months
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