Clinical trial Recruiting at 1 site Observational Ages 18 and up Women only

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

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

Run by Fujian Cancer Hospital · NCT07880600 · Data updated 2026-11-12

This study tests an AI model using pre-treatment breast MRI to see if it can identify which locally advanced triple-negative breast cancer patients will benefit most from neoadjuvant chemotherapy.

Quick facts

Study typeObservational
Ages18 Years and up
SexWomen only
Enrollment120 (estimated)
SponsorFujian Cancer Hospital
Government
Drugs / interventionschemotherapy
Locations5 sites (Fuzhou, Fujian and 4 other locations)
StatusRecruiting
StartedOctober 8, 2026
Main results expectedAugust 31, 2027
Study endsOctober 31, 2027
Registry record updatedOctober 19, 2026
Trial IDNCT07880600 on ClinicalTrials.gov

Is this trial a fit for you?

Likely a good fit

Adult female patients (18+) with histologically confirmed invasive triple-negative breast cancer or TNBC-like biology (ER/PR 1-10% and HER2-negative) eligible for neoadjuvant chemotherapy and with available pre-treatment DCE-MRI and biopsy material.

Probably not a fit

Patients whose tumors do not manifest AI-defined high-benefit features or whose outcomes align with historical controls may not see a benefit from this predictive approach.

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This is a multicenter, prospective, observational cohort study enrolling women with locally advanced triple-negative breast cancer who are planned for standard neoadjuvant chemotherapy. Baseline breast DCE-MRI scans will be analyzed with an AI model to classify patients as high-benefit or low-benefit from neoadjuvant therapy. Researchers will collect clinical-pathological data, treatment regimens, and responses (pCR, ORR) along with long-term outcomes such as DFS and OS. The primary aim is to compare pCR rates between AI-defined high-benefit patients and published historical controls. The study will help determine if MRI-based AI can guide treatment decisions in this patient group.

Why it matters

Potential benefit: If successful, this approach could help tailor neoadjuvant chemotherapy decisions to improve response rates and avoid unnecessary treatment.

How similar studies have gone: AI-assisted imaging has shown promise in predicting chemotherapy response in breast cancer, but this exact pre-treatment DCE-MRI AI model in locally advanced TNBC is investigational and not yet proven.

Who can join: the full rules

Show full inclusion / exclusion criteria
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.

Where it's running

  • Fujian Cancer Hospital
    Fuzhou, Fujian, China
    Recruiting
  • Fujian Provincial Hospital
    Fuzhou, Fujian, China
    Not yet recruiting
  • Ningde First Hospital
    Ningde, Fujian, China
    Not yet recruiting
  • The Second Affiliated Hospital of Fujian Medical University
    Quanzhou, Fujian, China
    Not yet recruiting
  • Sanming Second Hospital
    Sanming, Fujian, China
    Not yet recruiting

Study contacts

Similar studies

Other studies for the same condition or treatment. Recruiting studies are listed first.

How to take part

  1. Review the eligibility criteria above with your treating physician.
  2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
  3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.
Last reviewed 2026-11-14 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.