Pre-Treatment DCE-MRI AI Models Predict Neoadjuvant Chemotherapy Response in HR+/HER2- Breast Cancer
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexFemale
Age18+
SponsorFujian Cancer Hospital
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
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.
Disqualifiers
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.
Trial population
This study population consists of female patients aged 18 years or older with histologically confirmed stage II-III HR+/HER2-negative invasive breast cancer according to the 8th AJCC staging system. All participants receive routine standard neoadjuvant chemotherapy in multi-center breast cancer departments, complete standardized pre-treatment DCE-MRI with qualified imaging data, have ECOG performance status 0-1 and intact vital organ function. Subjects must satisfy all inclusion criteria, without meeting any exclusion criteria, and sign written informed consent voluntarily. Approximately 100 eligible patients will be consecutively enrolled from participating hospitals.
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
Pre-treatment DCE-MRI-based AI model
Diagnostic testPreoperative 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.
Treatment groups
100 Participants
are divided into 1 treatment groupGroup A: HR+/HER2- Breast Cancer Cohort Receiving Neoadjuvant Chemotherapy1 intervention
Trial outcomes
Primary outcomes
1
Incidence of Residual Cancer Burden (RCB) 0-1
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.
Time frame
After completion of neoadjuvant chemotherapy and definitive surgery (approximately 3-6 months after enrollment)Secondary outcomes
1
Objective response rate (ORR) of AI-defined high neoadjuvant chemotherapy benefit group
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.
Time frame
Imaging assessment after completion of neoadjuvant chemotherapy and prior to surgery2
Between-subgroup differences in RCB 0-1 rate
Compare RCB 0-1 incidence between AI-stratified high benefit subgroup and low benefit subgroup.
Time frame
RCB classification obtained after definitive surgical resection, approximately 3-6 months after enrollment3
Between-subgroup differences in objective response rate (ORR)
Compare ORR between AI-stratified high benefit subgroup and low benefit subgroup.
Time frame
ORR imaging assessment after neoadjuvant chemotherapy before surgery4
Disease-free survival (DFS) between high and low chemotherapy benefit subgroups
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.
Time frame
From the date of surgery until the first recurrence, metastasis, or death, whichever came first, assessed up to 60 monthsSponsors and contacts
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Source ClinicalTrials.gov