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Prediction of Neoadjuvant Therapy Efficacy and Prognosis for Breast Cancer Based on Multimodal Data
Sponsor: Yunnan Cancer Hospital
Summary
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.
Official title: Prediction of Neoadjuvant Therapy Efficacy and Prognosis for Breast Cancer Based on Multimodal Data: A Multicenter Retrospective and Prospective Validation
Key Details
Gender
FEMALE
Age Range
18 Years - 80 Years
Study Type
INTERVENTIONAL
Enrollment
1800
Start Date
2026-06-01
Completion Date
2029-06-30
Last Updated
2026-06-26
Healthy Volunteers
No
Conditions
Interventions
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.
MRI and ultrasound were performed in addition to conventional treatment regimens