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NCT07880600

Pre-Treatment Breast DCE-MRI AI Model to Predict Neoadjuvant Chemotherapy Benefit in Locally Advanced Triple-Negative Breast Cancer

Sponsor: Fujian Cancer Hospital

View on ClinicalTrials.gov

Summary

This study is a multicenter, prospective, observational cohort study to evaluate the predictive performance of pre-treatment DCE-MRI-based artificial intelligence (AI) models for neoadjuvant chemotherapy benefit in locally advanced triple-negative breast cancer. The study plans to enroll eligible locally advanced triple-negative breast cancer patients receiving routine standard neoadjuvant chemotherapy and stratify participants into high-benefit and low-benefit subgroups via the established AI model based on baseline breast DCE-MRI images. All enrolled patients will undergo systematic collection of baseline clinical-pathological data, pre-treatment DCE-MRI scans, neoadjuvant chemotherapy regimens, pathological complete response (pCR) status, objective response rate (ORR), and long-term survival endpoints including disease-free survival (DFS) and overall survival (OS). The primary objective compares the pCR rate between AI-defined high-benefit patients and published historical control data; secondary analyses compare ORR, pCR proportion, DFS and OS between AI-stratified high-benefit and low-benefit subgroups to comprehensively verify the clinical value of this imaging AI model for individualized neoadjuvant chemotherapy selection.

Official title: Multicenter Prospective Observational Cohort Study of Pre-Treatment Breast DCE-MRI-Based Artificial Intelligence Model for Predicting Populations Benefiting From Neoadjuvant Chemotherapy in Locally Advanced Triple-Negative Breast Cancer

Key Details

Gender

FEMALE

Age Range

18 Years - Any

Study Type

OBSERVATIONAL

Enrollment

120

Start Date

2026-10-08

Completion Date

2027-10-31

Last Updated

2026-10-19

Healthy Volunteers

Conditions

Breast Neoplasm Female Triple -Negative Breast Cancer

Interventions

DIAGNOSTIC_TEST

Pre-treatment DCE-MRI-based AI model

Preoperative dynamic contrast-enhanced MRI images are input into an artificial intelligence prediction model to stratify triple-negative breast cancer patients into high and low neoadjuvant chemotherapy benefit subgroups.

Inclusion Criteria: 1. Female patients aged ≥ 18 years. 2. Histologically confirmed invasive triple-negative breast cancer; patients with ER/PR 1%-10% positive and HER2-negative status are also eligible given their biological features resembling triple-negative breast cancer. 3. Clinical stage T2-3N0-3M0 or T1N1-3M0 according to AJCC 8th edition, with potential indication for neoadjuvant chemotherapy. 4. Availability of both MRI and pathological biopsy materials; standardized breast DCE-MRI examination and pathological biopsy completed prior to neoadjuvant chemotherapy, with image quality satisfying requirements for model analysis. 5. Patients planned to receive standard neoadjuvant chemotherapy followed by surgical treatment. Patients who are undergoing standard neoadjuvant chemotherapy or have completed neoadjuvant chemotherapy awaiting surgery are also eligible. 6. Post-operative pathology available to assess neoadjuvant chemotherapy response (pCR or non-pCR). 7. Eastern Cooperative Oncology Group (ECOG) performance status 0-1; adequate function of major organs to tolerate planned therapy. 8. Voluntary participation and provision of written informed consent. Exclusion Criteria: 1. Prior systemic anti-tumor therapy for breast cancer other than neoadjuvant chemotherapy. 2. Inflammatory breast cancer or distant metastatic disease (M1). 3. Concurrent active malignancy during neoadjuvant chemotherapy or surgical period. 4. History of autoimmune diseases, such as systemic lupus erythematosus, acanthosis and other related disorders. 5. Documented history of neurological or psychiatric disorders, including epilepsy, dementia, etc. 6. Contraindications to MRI or unacceptable image quality for analysis. 7. No definitive surgery performed or incomplete postoperative pathological reports. 8. Severe comorbidities that preclude chemotherapy or surgical intervention. 9. Missing required clinical information, MRI images, or pathological biopsy specimens for analysis. 10. Other conditions rendering the subject inappropriate for enrollment, in the investigator's judgment.

Locations (5)

Fujian Cancer Hospital

Fuzhou, Fujian, China

Fujian Provincial Hospital

Fuzhou, Fujian, China

Ningde First Hospital

Ningde, Fujian, China

The Second Affiliated Hospital of Fujian Medical University

Quanzhou, Fujian, China

Sanming Second Hospital

Sanming, Fujian, China