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.
Histopathologically confirmed invasive breast cancer;
Planned to receive a full course of neoadjuvant therapy;
Complete baseline imaging data (MRI/ultrasound/mammography) and core needle pathology results available.
Previous history of ipsilateral breast cancer or chest radiotherapy;
Distant metastasis (Stage IV);
Poor image quality or missing clinical data exceeding 20%.
Single group
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 testMRI and ultrasound were performed in addition to conventional treatment regimens
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.
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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