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This study is a multi-center, case-control study aiming at developing and blinded testing machine learning-based multiple cancers early detection model by prospectively collecting blood samples from newly diagnosed cancer patients and individuals without confirmed cancer diagnosis.
Full description
Blood samples from newly diagnosed cancer patients and individuals without confirmed cancer diagnosis will be prospectively collected to identify cancer-specific circulating signals through integrative multi-omic analysis. Based on the comprehensive molecular profiling, a machine learning-driven model will be trained and blinded validated independent through a two-stage approach in clinically annotated individuals. Approximately 10327 cancer patients will be enrolled in this study and early-stage cancer patients will be enriched to improve the model sensitivity on distinguishing cancers with favorable prognosis. Approximately 6339 age and sex matched controls will be included in model development, which are volunteers without a cancer diagnosis after routine cancer screening tests.
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Inclusion and exclusion criteria
Inclusion Criteria for Case Arm Participants:
Exclusion Criteria for Case Arm Participants:
Inclusion Criteria for Control Arm Participants:
Exclusion Criteria for Control Arm Participants:
16,666 participants in 2 patient groups
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Central trial contact
Yong Qin
Data sourced from clinicaltrials.gov
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