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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Breast Carcinoma
• Intervention / Treatment: Diagnostic test: 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.

• Study Type: Interventional
• Enrollment (Estimated): 1800
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Zhenhui LI; Phone Number: 13698736132; Email: lizhenhui@kmmu.edu.cn
• Study Contact Backup: Name: Yu Xie; Phone Number: 13708445492; Email: xieyu@kmmu.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

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%.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Prediction of Neoadjuvant Therapy Efficacy and Prognosis for Breast Cancer Based on Multimodal Data; To develop a multimodal deep learning model integrating MRI, ultrasound, digital pathology and clinical information based on multicenter retrospective data.	Diagnostic test: 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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	From enrollment to the end of surgery

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	From enrollment to the end of surgery

Collaborators and Investigators

• Sponsor: Yunnan Cancer Hospital
• Investigators: Study Director: Lianhua Ye, Ethics Committee of Yunnan Provincial Cancer Hospital

Study record dates

Study Major Dates

• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): June 30, 2029

Study Registration Dates

• First Submitted: May 19, 2026
• First Submitted That Met QC Criteria: June 22, 2026
• First Posted (Actual): June 26, 2026

Study Record Updates

• Last Update Posted (Actual): June 26, 2026
• Last Update Submitted That Met QC Criteria: June 22, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Skin Diseases; Breast Diseases; Skin and Connective Tissue Diseases; Breast Neoplasms; Diagnostic Techniques and Procedures; Diagnosis; Diagnostic Imaging; Ultrasonography
• Other Study ID Numbers: KYLX2026-205

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No