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

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

Status

Not yet recruiting

Phases

Unknown

Study type

Interventional

Source

ClinicalTrials.gov

Registry ID

NCT07671690

Enrollment

1800

Registered

2026-06-26

Start date

2026-06-01

Completion date

2029-06-30

Last updated

2026-06-26

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Breast Carcinoma

Brief 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.

Interventions

DIAGNOSTIC_TESTTo 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

Study design

Allocation

Intervention model

SINGLE_GROUP

Primary purpose

DIAGNOSTIC

Masking

NONE

Eligibility

Sex/Gender

FEMALE

Age

18 Years to 80 Years

Healthy volunteers

Inclusion criteria

1. Histopathologically confirmed invasive breast cancer; 2. Planned to receive a full course of neoadjuvant therapy; 3. Complete baseline imaging data (MRI/ultrasound/mammography) and core needle pathology results available.

Exclusion criteria

1. Previous history of ipsilateral breast cancer or chest radiotherapy; 2. Distant metastasis (Stage IV); 3. Poor image quality or missing clinical data exceeding 20%.

Design outcomes

Primary

Measure	Time frame	Description
Predictive value of multimodal data for neoadjuvant therapy efficacy in breast cancer	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

Measure	Time frame	Description
Prognostic predictive value of multimodal data for breast cancer	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.

Contacts

CONTACTYu Xie

xieyu@kmmu.edu.cn13708445492

CONTACTZhenhui LI

lizhenhui@kmmu.edu.cn13698736132

STUDY_DIRECTORLianhua Ye

Ethics Committee of Yunnan Provincial Cancer Hospital

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Jun 27, 2026