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| Name | Class |
|---|---|
| Shanghai Weihe Medical Laboratory Co., Ltd. | INDUSTRY |
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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.
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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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Ovarian cancer | Participants with new diagnosis of ovarian cancer, from whom a peripheral blood sample will be collected. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Blood collection | Other | Blood sample will be collected |
|
| Measure | Description | Time Frame |
|---|---|---|
| The 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. | 12 months |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 12 months |
| The performance of pre-defined model in clinical sub-groups of interest |
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Inclusion Criteria:
Exclusion Criteria:
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Patients diagnosed ovarian cancer or individuals with a high suspicion for OC will be invited to participate in this proof-of-concept study
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Hao Wen, M.D., Ph.D. | Contact | +8618017317873 | wenhao_fdc@163.com |
| Name | Affiliation | Role |
|---|---|---|
| Hao Wen, M.D., Ph.D. | Fudan University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Sun Yat-sen Memorial Hospital | Not yet recruiting | Guangzhou | Guangdong | 110042 | China |
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| ID | Term |
|---|---|
| D010051 | Ovarian Neoplasms |
| ID | Term |
|---|---|
| D004701 | Endocrine Gland Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D010049 | Ovarian Diseases |
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| ID | Term |
|---|---|
| D001800 | Blood Specimen Collection |
| ID | Term |
|---|---|
| D013048 | Specimen Handling |
| D019411 | Clinical Laboratory Techniques |
| D019937 | Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
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blood sample
Sensitivity of pre-defined model in different pathological subtypes or different age groups or tumor marker-negative cases. |
| 12 months |
| Liaoning Cancer Hospital & Institute | Not yet recruiting | Shenyang | Liaoning | 110042 | China |
|
| Fudan University Shanghai Cancer Center | Recruiting | Shanghai | Shanghai Municipality | 200032 | China |
|
| D000291 |
| Adnexal Diseases |
| D005831 | Genital Diseases, Female |
| D052776 | Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D005833 | Genital Neoplasms, Female |
| D014565 | Urogenital Neoplasms |
| D000091662 | Genital Diseases |
| D004700 | Endocrine System Diseases |
| D006058 | Gonadal Disorders |
| D011677 | Punctures |
| D013514 | Surgical Procedures, Operative |
| D008919 | Investigative Techniques |