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Recruiting NCT07702708

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

— Observational HR+/HER2- Breast Cancer Breast Neoplasms

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: Pre-treatment DCE-MRI-based AI model.
Who it may be relevant to
Registry conditions: HR+/HER2- Breast Cancer, Breast Neoplasms. Basic parameters: from 18 years · Female.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

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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

Overview

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.

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 HR+/HER2- breast cancer patients into high and low neoadjuvant chemotherapy benefit subgroups.

Primary outcome measures

  • Incidence of Residual Cancer Burden (RCB) 0-1 [Time frame: After completion of neoadjuvant chemotherapy and definitive surgery (approximately 3-6 months after enrollment)]
Secondary outcome measures (5)
  • 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]
  • Between-subgroup differences in RCB 0-1 rate [Time frame: RCB classification obtained after definitive surgical resection, approximately 3-6 months after enrollment]
  • Between-subgroup differences in objective response rate (ORR) [Time frame: ORR imaging assessment after neoadjuvant chemotherapy before surgery]
  • 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]
  • 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]

Eligibility criteria

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.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model
Cohort

Study locations

China · 5 centers
  • Fujian Cancer Hospital — Fuzhou
  • Fujian Provincial Hospital — Fuzhou
  • The Second Affiliated Hospital of Fujian Medical University — Quanzhou
  • Ningde First Hospital — Ningde
  • Sanming Second Hospital — Sanming

Identifiers

NCT: NCT07702708 · K2026-219-01

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗