Breast Neoplasms, HR+/HER2- Breast Cancer
Conditions
Brief summary
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_TESTPre-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.
Sponsors
Fujian Cancer Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Incidence of Residual Cancer Burden (RCB) 0-1 | 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
| Measure | Time frame | Description |
|---|---|---|
| Objective response rate (ORR) of AI-defined high neoadjuvant chemotherapy benefit group | 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. |
| Between-subgroup differences in RCB 0-1 rate | 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. |
| Between-subgroup differences in objective response rate (ORR) | ORR imaging assessment after neoadjuvant chemotherapy before surgery | Compare ORR between AI-stratified high benefit subgroup and low benefit subgroup. |
| Disease-free survival (DFS) between high and low chemotherapy benefit subgroups | 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. |
| Overall survival (OS) between high and low chemotherapy benefit subgroups | 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. |
Countries
China
Contacts
CONTACTChuangui Song, doctor
PRINCIPAL_INVESTIGATORChuangui Song, doctor
Fujian Cancer Hospital
Outcome results
None listed