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This study aims to develop a multimodal deep learning model integrating MRI, ultrasound, digital pathology and clinical information based on multicenter retrospective data. To externally validate the model in an independent prospective cohort, and evaluate its accuracy in predicting pathological complete response (pCR), 3-year and 5-year disease-free survival (DFS). To establish visual tools such as nomograms, assisting clinicians in identifying patients with chemoresistance and facilitating individualized de-escalation or escalation treatment strategies.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Prediction of Neoadjuvant Therapy Efficacy and Prognosis for Breast Cancer Based on Multimodal Data | Experimental | To develop a multimodal deep learning model integrating MRI, ultrasound, digital pathology and clinical information based on multicenter retrospective data. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| To explore the value of a multimodal deep learning model integrating MRI, ultrasound, digital pathology and clinical information in predicting pCR and long-term prognosis. | Diagnostic Test | MRI and ultrasound were performed in addition to conventional treatment regimens |
| Measure | Description | Time Frame |
|---|---|---|
| Predictive value of multimodal data for neoadjuvant therapy efficacy in breast cancer | Combined with preoperative multimodal MRI and ultrasound imaging parameters, pathological baseline data and clinical data, a prediction model for neoadjuvant therapy efficacy in breast cancer is constructed. Taking postoperative pathological response results as the evaluation basis, the predictive efficacy of multimodal data for neoadjuvant therapy complete response and non-complete response is evaluated. | From enrollment to the end of surgery |
| Measure | Description | Time Frame |
|---|---|---|
| Prognostic predictive value of multimodal data for breast cancer | Follow up the long-term prognosis of breast cancer patients after neoadjuvant therapy and surgery, record key prognostic indicators including disease-free survival (DFS) and overall survival (OS). Analyze the correlation between multimodal imaging and clinical pathological data and patient prognosis, and verify the prognostic prediction ability of multimodal data for breast cancer patients. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Yu Xie | Contact | 13708445492 | xieyu@kmmu.edu.cn | |
| Zhenhui LI | Contact | 13698736132 | lizhenhui@kmmu.edu.cn |
| Name | Affiliation | Role |
|---|---|---|
| Lianhua Ye | Ethics Committee of Yunnan Provincial Cancer Hospital | Study Director |
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| From enrollment to the end of surgery |
| 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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| ID | Term |
|---|---|
| D014463 | Ultrasonography |
| ID | Term |
|---|---|
| D003952 | Diagnostic Imaging |
| D019937 | Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
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