AI-powered pre-treatment MRI to predict chemotherapy response in HR+/HER2- breast cancer
Official title: A Multicenter Prospective Observational Cohort Study: Predicting Neoadjuvant Chemotherapy Response Using Pre-Treatment DCE-MRI-Based AI Models in HR+/HER2- Breast Cancer
Run by Fujian Cancer Hospital · NCT07702708 · Data updated 2026-11-12
This study tests whether AI analysis of baseline DCE-MRI can forecast which HR+/HER2- breast cancer patients will benefit most from neoadjuvant chemotherapy.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years and up |
| Sex | Women only |
| Enrollment | 100 (estimated) |
| Sponsor | Fujian Cancer Hospital Government |
| Drugs / interventions | chemotherapy |
| Locations | 5 sites (Fuzhou, Fujian and 4 other locations) |
| Status | Recruiting |
| Started | June 1, 2026 |
| Main results expected | April 30, 2027 |
| Study ends | June 30, 2027 |
| Registry record updated | July 14, 2026 |
| Trial ID | NCT07702708 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates are women aged 18 or older with confirmed HR+/HER2- invasive breast cancer (stage II-III) planning neoadjuvant chemotherapy, who have pre-treatment quality DCE-MRI suitable for AI analysis.
✕Probably not a fit
Patients not showing a clear high-benefit signal from the AI model or with contraindications to MRI may not experience a predictive advantage.
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 study enrolling women with HR+/HER2- breast cancer who will receive standard neoadjuvant chemotherapy. Baseline DCE-MRI scans will be analyzed by AI models to classify patients as high-benefit or low-benefit. Researchers will collect clinical data, treatment regimens, and outcomes such as residual cancer burden, response rate, disease-free survival, and overall survival to compare AI-defined high-benefit patients with historical controls. The primary endpoint is the rate of residual cancer burden 0-1 in the high-benefit group versus published data; secondary analyses explore additional correlations and long-term outcomes.
Why it matters
Potential benefit: If successful, the AI model could help tailor neoadjuvant treatment decisions for HR+/HER2- breast cancer.
How similar studies have gone: Prior studies in imaging-based AI have shown promise in predicting chemotherapy response in breast cancer, but this approach is still being validated.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: 1. Female patients aged ≥ 18 years old. 2. Histopathologically confirmed invasive breast carcinoma. 3. Hormone receptor positive (ER and/or PR ≥1%), HER2-negative status (IHC 0-1+, or IHC 2+ with negative FISH result). 4. Clinical stage II-III breast cancer per the 8th AJCC staging system, with clinical indication for neoadjuvant chemotherapy or primary surgery. 5. Standard pre-treatment breast DCE-MRI performed before neoadjuvant chemotherapy, with image quality eligible for AI model analysis. 6. ECOG performance status 0 or 1; adequate function of major vital organs to tolerate planned clinical treatment. 7. Voluntary participation with written informed consent obtained. Exclusion Criteria: 1. Prior systemic anti-tumor therapy for breast cancer other than planned neoadjuvant chemotherapy. 2. Inflammatory breast cancer or distant metastatic disease (M1). 3. Concurrent active malignant tumors of other origins. 4. Contraindications to MRI examination or unqualified MRI images that cannot support model analysis. 5. Severe comorbidities incompatible with neoadjuvant chemotherapy or surgical resection. 6. Any other conditions judged ineligible for enrollment by the investigator.
Where it's running
-
Fujian Cancer Hospital
Fuzhou, Fujian, China Recruiting -
Fujian Provincial Hospital
Fuzhou, Fujian, China Recruiting -
The Second Affiliated Hospital of Fujian Medical University
Quanzhou, Fujian, China Recruiting -
Ningde First Hospital
Ningde, Ningde, China Recruiting -
Sanming Second Hospital
Sanming, Sanming, China Recruiting
Study contacts
- Principal investigator: Chuangui Song, doctor — Fujian Cancer Hospital
- Study coordinator: Chuangui Song, doctor
- Email: songcg1971@outlook.com
- Phone: 13960709993
Similar studies
Other studies for the same condition or treatment. Recruiting studies are listed first.
-
Dalpiciclib plus hormone therapy for early HR+/HER2- breast cancer
This trial tests whether adding dalpiciclib at two different doses and durations to standard hormone therapy helps people with early HR+/HER2- breast cancer.
-
Predicting outcomes for advanced breast cancer patients treated with CDK4/6 inhibitors and endocrine therapy
This study is trying to see how well different CDK4/6 inhibitors combined with hormone therapy work for people with advanced breast cancer and how safe they are.
-
Improved Facial Skin Quality After VYC-12L for Breast Cancer Survivors
This study tests whether intradermal injections of VYC-12L can improve facial skin quality and quality of life in adults who have completed breast cancer treatment.
-
PRT13722 in advanced HR+/HER2- breast cancer
This early study tests PRT13722, by itself and with standard therapies, to see if it is safe and has any signs of activity in adults with HR+/HER2- breast cancer who have progressed on prior treatments.
-
Breast cancer subtypes and outcomes in Colombian women
This study looks back at medical records of women with breast cancer to see how different molecular subtypes relate to survival and recurrence in a single Colombian hospital.
-
Multimodal psychosomatic symptom care for breast cancer: personalized acupuncture care guided by shared decision-making
This study tests whether personalized acupuncture options (manual acupuncture, ear acupuncture, or both) can help women with breast cancer who have anxiety, sleep problems, or fatigue, compared to routine nursing care.
How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.