Prediction of Neoadjuvant Therapy Efficacy and Prognosis for Breast Cancer Based on Multimodal Data

Starting soon · Not applicable

Conditions studied: Breast Carcinoma

In brief

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.

Key facts

Study ID

NCT07671690

Run by

Yunnan Cancer Hospital

People needed

1800

Starts

2026-06-01

Expected to finish

2029-06-30

Last updated by the study team

2026-06-26

Who can join

Age: 18 and older, up to 80. Sex: female. Healthy volunteers: not accepted.

You may qualify if…

• 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.

You may not qualify if…

• Previous history of ipsilateral breast cancer or chest radiotherapy;
• Distant metastasis (Stage IV);
• Poor image quality or missing clinical data exceeding 20%.

Full record on ClinicalTrials.gov

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