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NCT Number: NCT07702708

Pre-Treatment DCE-MRI AI Models Predict Neoadjuvant Chemotherapy Response in HR+/HER2- Breast Cancer

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 HR+/HER2- breast cancer. The study plans to enroll eligible HR+/HER2- 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, postoperative residual cancer burden (RCB) classification, objective response rate (ORR), and long-term survival endpoints including disease-free survival (DFS) and overall survival (OS). The primary objective compares the rate of RCB 0-1 between AI-defined high-benefit patients and published historical control data; secondary analyses compare ORR, RCB 0-1 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.

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Key information

Conditions

HR+/HER2- Breast Cancer Breast Diseases Breast Neoplasms Neoplasms Neoplasms by Site Skin Diseases Skin and Connective Tissue Diseases

Age range

18 year and older

Sex eligibility

Female

Study type

Observational

Primary location

Fujian Cancer Hospital, Fuzhou, Fujian, China

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Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Female patients aged ≥ 18 years old.
  • Histopathologically confirmed invasive breast carcinoma.
  • Hormone receptor positive (ER and/or PR ≥1%), HER2-negative status (IHC 0-1+, or IHC 2+ with negative FISH result).
  • Clinical stage II-III breast cancer per the 8th AJCC staging system, with clinical indication for neoadjuvant chemotherapy or primary surgery.
  • Standard pre-treatment breast DCE-MRI performed before neoadjuvant chemotherapy, with image quality eligible for AI model analysis.
  • ECOG performance status 0 or 1; adequate function of major vital organs to tolerate planned clinical treatment.
  • Voluntary participation with written informed consent obtained.

Exclusion criteria

  • Prior systemic anti-tumor therapy for breast cancer other than planned neoadjuvant chemotherapy.
  • Inflammatory breast cancer or distant metastatic disease (M1).
  • Concurrent active malignant tumors of other origins.
  • Contraindications to MRI examination or unqualified MRI images that cannot support model analysis.
  • Severe comorbidities incompatible with neoadjuvant chemotherapy or surgical resection.
  • Any other conditions judged ineligible for enrollment by the investigator.

Treatment and study plan

Pre-treatment DCE-MRI-based AI model

Diagnostic Test

Preoperative dynamic contrast-enhanced MRI images are input into an artificial intelligence prediction model to stratify HR+/HER2- breast cancer patients into high and low neoadjuvant chemotherapy benefit subgroups.

Primary outcomes

  1. Incidence of Residual Cancer Burden (RCB) 0-1

    Time frame: After completion of neoadjuvant chemotherapy and definitive surgery (approximately 3-6 months after enrollment)

    Compare the incidence of RCB 0-1 among HR+/HER2- breast cancer patients stratified as high chemotherapy benefit by pre-treatment DCE-MRI AI model against published historical control data to verify the predictive value of the imaging AI model.

Secondary outcomes

  1. Objective response rate (ORR) of AI-defined high neoadjuvant chemotherapy benefit group

    Time frame: Imaging assessment after completion of neoadjuvant chemotherapy and prior to surgery

    Compare the objective response rate (ORR) assessed by imaging after neoadjuvant chemotherapy before surgery in patients of AI-identified high chemotherapy benefit subgroup with historical control data.

  2. Between-subgroup differences in RCB 0-1 rate

    Time frame: RCB classification obtained after definitive surgical resection, approximately 3-6 months after enrollment

    Compare RCB 0-1 incidence between AI-stratified high benefit subgroup and low benefit subgroup.

  3. Between-subgroup differences in objective response rate (ORR)

    Time frame: ORR imaging assessment after neoadjuvant chemotherapy before surgery

    Compare ORR between AI-stratified high benefit subgroup and low benefit subgroup.

  4. Disease-free survival (DFS) between high and low chemotherapy benefit subgroups

    Time frame: From the date of surgery until the first recurrence, metastasis, or death, whichever came first, assessed up to 60 months

    Compare DFS (time interval from the date of surgery to first recurrence, metastasis or death) between AI-stratified high and low chemotherapy benefit subgroups to explore the correlation between AI imaging stratification and long-term survival prognosis.

  5. Overall survival (OS) between high and low chemotherapy benefit subgroups

    Time frame: From the date of surgery until death from any cause, assessed up to 60 months

    Compare OS between AI-stratified high and low chemotherapy benefit subgroups to explore the correlation between AI imaging stratification and long-term survival prognosis.

Study contacts

Contact information is provided by the study sponsor or research team.

Chuangui Song, doctor

CONTACT

[email protected]

13960709993

Sponsors and collaborators

Lead sponsor

Fujian Cancer Hospital

Other Gov

Registry information

Official study title

A Multicenter Prospective Observational Cohort Study: Predicting Neoadjuvant Chemotherapy Response Using Pre-Treatment DCE-MRI-Based AI Models in HR+/HER2- Breast Cancer

Important dates

Study start
2026
Primary completion
2027
Study completion
2027
First posted
Jul 14, 2026
Registry last updated
Jul 14, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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