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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.
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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.
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168 participants in 1 patient group
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Hao Wen, M.D., Ph.D.
Data sourced from clinicaltrials.gov
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