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.
Study Type
OBSERVATIONAL
Enrollment
100
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.
Fujian Cancer Hospital
Fuzhou, Fujian, China
RECRUITINGFujian Provincial Hospital
Fuzhou, Fujian, China
RECRUITINGThe Second Affiliated Hospital of Fujian Medical University
Quanzhou, Fujian, China
RECRUITINGNingde First Hospital
Ningde, Ningde, China
RECRUITINGSanming Second Hospital
Sanming, Sanming, China
RECRUITINGIncidence 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)
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 surgery
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 enrollment
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 surgery
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 months
Overall survival (OS) between high and low chemotherapy benefit subgroups
Compare OS 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 death from any cause, assessed up to 60 months
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