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Through the research of this project, we expect to achieve the cfDNA fragment omics liquid biopsy technology based on deep learning, verify the accuracy of the TuFEst model in predicting the tumor burden status of breast cancer lesions and lymph nodes in newly diagnosed breast cancer patients and those receiving neoadjuvant therapy, and provide a theoretical basis for large-scale clinical application in the future
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 1 | Breast cancer patients who have undergone radical surgery and have not received neoadjuvant therapy |
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| 2 | Patients with newly diagnosed invasive breast cancer and confirmed axillary lymph node metastasis, who are willing to undergo radical surgery after treatment (Exploratory Analysis Cohort) |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| No Intervention: Observational Cohort | Other | No Intervention: Observational Cohort |
|
| Measure | Description | Time Frame |
|---|---|---|
| Negative predictive value (NPV) of the TuFEst-based classifier for predicting pathologic node-negative status (pN0) | Validate the accuracy of the TuFEst model in predicting breast cancer lesion and lymph node tumor burden status among patients with primary breast cancer and those undergoing neoadjuvant therapy. | up to 2 weeks |
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Inclusion Criteria:
Exclusion Criteria:
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Patients who have been treated at the Second Affiliated Hospital of Zhejiang University School of Medicine (Other Centers) from the date of ethical approval until December 31, 2027
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Detect tumor-related cfDNA fragments in plasma
| ID | Term |
|---|---|
| D001943 | Breast Neoplasms |
| ID | Term |
|---|---|
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D001941 | Breast Diseases |
| D012871 | Skin Diseases |
| D017437 | Skin and Connective Tissue Diseases |
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