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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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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| HR+/HER2- Breast Cancer Cohort Receiving Neoadjuvant Chemotherapy | Multicenter prospective observational cohort of patients with HR+/HER2- invasive breast cancer who receive routine standard neoadjuvant chemotherapy. All participants undergo pre-treatment DCE-MRI scanning, and an MRI-based AI model is applied to stratify patients into high and low chemotherapy benefit subgroups. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | After completion of neoadjuvant chemotherapy and definitive surgery (approximately 3-6 months after enrollment) |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | Imaging assessment after completion of neoadjuvant chemotherapy and prior to surgery |
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Inclusion Criteria:
Exclusion Criteria:
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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.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Chuangui Song, doctor | Contact | 13960709993 | songcg1971@outlook.com |
| Name | Affiliation | Role |
|---|---|---|
| Chuangui Song, doctor | Fujian Cancer Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Fujian Cancer Hospital | Recruiting | Fuzhou | Fujian | China |
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| ID | Term |
|---|---|
| D001943 | Breast Neoplasms |
| ID | Term |
|---|---|
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D001941 | Breast Diseases |
| D012871 | Skin Diseases |
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Formalin-fixed paraffin-embedded (FFPE) core needle biopsy and postoperative surgical pathological specimens, as well as peripheral blood samples collected before the initial cycle of neoadjuvant chemotherapy.
| Between-subgroup differences in RCB 0-1 rate | Compare RCB 0-1 incidence between AI-stratified high benefit subgroup and low benefit subgroup. | 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. | 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. | 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. | From the date of surgery until death from any cause, assessed up to 60 months |
| Fujian Provincial Hospital | Recruiting | Fuzhou | Fujian | China |
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| The Second Affiliated Hospital of Fujian Medical University | Recruiting | Quanzhou | Fujian | China |
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| Ningde First Hospital | Recruiting | Ningde | Ningde | China |
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| Sanming Second Hospital | Recruiting | Sanming | Sanming | China |
|
| D017437 |
| Skin and Connective Tissue Diseases |