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

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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Key information

Conditions

Breast Carcinoma Breast Diseases Breast Neoplasms Neoplasms Neoplasms by Site Skin Diseases Skin and Connective Tissue Diseases

Age range

18 year–80 year

Sex eligibility

Female

Study type

Interventional

Phase

Not applicable

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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.

Exclusion criteria

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

Treatment and study plan

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

Primary outcomes

Predictive value of multimodal data for neoadjuvant therapy efficacy in breast cancer

Time frame: From enrollment to the end of surgery

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.

Secondary outcomes

Prognostic predictive value of multimodal data for breast cancer

Time frame: From enrollment to the end of surgery

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.

Study contacts

Contact information is provided by the study sponsor or research team.

Yu Xie

CONTACT

[email protected]

13708445492

Zhenhui LI

CONTACT

[email protected]

13698736132

Sponsors and collaborators

Lead sponsor

Yunnan Cancer Hospital

Other

Registry information

Official study title

Prediction of Neoadjuvant Therapy Efficacy and Prognosis for Breast Cancer Based on Multimodal Data: A Multicenter Retrospective and Prospective Validation

Important dates

Study start: 2026
Primary completion: 2026
Study completion: 2029
First posted: Jun 26, 2026
Registry last updated: Jun 26, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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