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| Name | Class |
|---|---|
| Peking University People's Hospital | OTHER |
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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.
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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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Case arm | Participants with newly diagnosed cancer of lung, breast, digestive tract, urinary tract and etc. | ||
| Control arm | Participants without a cancer diagnosis after routine cancer screening tests. |
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| Measure | Description | Time Frame |
|---|---|---|
| The performance of cfDNA methylation-based multiple cancers early detection model in case-control study | The sensitivity, specificity and tissue origin accuracy of cfDNA methylation-based multiple cancers early detection model in detecting cancer or non-cancer at 95% confidence interval. | 12 months |
| Measure | Description | Time Frame |
|---|---|---|
| The performance of cfDNA methylation-based multiple cancers early detection model in early stage cancer cases | The sensitivity and tissue origin accuracy of cfDNA methylation-based multiple cancers early detection model in detecting stage I to II cancer at 95% confidence interval. | 12 months |
| The performance of multi-omic-based multiple cancers early detection model in case-control study |
| Measure | Description | Time Frame |
|---|---|---|
| To develop a questionnaire to evaluate the risk factors in the multi-cancer early screening | To develop a questionnaire to evaluate the high-risk factors in the multi-cancer early screening, including lung cancer, gastrointestinal cancer, gynecological cancer, urogenital neoplasms, etc. | 12 months |
| To evaluate the performance of multi-omics early detection models in the population with suspected cancer |
Inclusion Criteria for Case Arm Participants:
Exclusion Criteria for Case Arm Participants:
Inclusion Criteria for Control Arm Participants:
Exclusion Criteria for Control Arm Participants:
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Confirmed cancers or individuals without confirmed cancer will be invited to participate in this case-control study by a designated consenting professional.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Yong Qin | Contact | +86 186 2629 2273 | qinyong@bytedance.com |
| Name | Affiliation | Role |
|---|---|---|
| Jun Wang | Peking University People's Hospital | Principal Investigator |
| Xiaohui Wu | Shanghai Weihe Medical Laboratory Co., Ltd. | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Peking University People's Hospital | Recruiting | Beijing | Beijing Municipality | 100044 | China |
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| ID | Term |
|---|---|
| D009369 | Neoplasms |
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blood
The sensitivity, specificity and tissue origin accuracy of multi-omic-based multiple cancers early detection model in detecting cancer or non-cancer at 95% confidence interval. |
| 12 months |
| The performance of different multi-cancer early detection models in different subgroups | The sensitivity and specificity of cfDNA methylation-based or multi-omic-based multiple cancers early detection model in different subgroups of the population (such as age, gender, cancer pathological classification, and clinical stage) at 95% confidence interval. | 12 months |
The sensitivity, specificity and tissue origin accuracy of multi-omic-based multiple cancers early detection model in in the population with suspected cancer at 95% confidence interval. |
| 12 months |
| To simulate the positive predictive value and negative predictive value of different multi-cancer early detection models based on the cancer prevalence and staging data of individuals aged 40-75 years in China using multiple models | To simulate the positive predictive value and negative predictive value of different multi-cancer early detection models(cfDNA methylation-based or multi-omic-based),based on the sensitivity, specificity and tissue origin accuracy,according to multi cancer prevalence and staging data of individuals aged 40-75 years in China. | 12 months |
| To simulate the benefits of clinical utility and health economics using different multi-cancer early detection models | To simulate the stage-shift and incremental cost-effective ratio (ICER) benefit when compared to usual care (SOC screening) using Markov model based on MCED test performance | 12 months |
| To explore biomarkers for cancer screening and construct a multimodal machine learning model based on multi-omics data | Exploring biomarkers in methylomics and fragmentomics,and constructing multimodal for multi-cancer early detection based on multiomics analysis | 12 months |
| Peking University Cancer Hospital and Institute | Not yet recruiting | Beijing | Beijing Municipality | 100083 | China |
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