Pre-Treatment DCE-MRI AI Models Predict Neoadjuvant Chemotherapy Response in HR+/HER2- Breast Cancer
2026년 7월 13일 업데이트: Fujian Cancer Hospital
A Multicenter Prospective Observational Cohort Study: Predicting Neoadjuvant Chemotherapy Response Using Pre-Treatment DCE-MRI-Based AI Models in HR+/HER2- Breast Cancer
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.
연구 개요
연구 유형
관찰
등록 (추정된)
100
연락처 및 위치
이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.
연구 연락처
- 이름: Chuangui Song, doctor
- 전화번호: 13960709993
- 이메일: songcg1971@outlook.com
연구 장소
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Fujian
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Fuzhou, Fujian, 중국
- 모병
- Fujian Cancer Hospital
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연락하다:
- Chuangui Song, doctor
- 전화번호: 13960709993
- 이메일: songcg1971@outlook.com
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Fuzhou, Fujian, 중국
- 모병
- Fujian Provincial Hospital
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연락하다:
- ruijuan wang, doctor
- 전화번호: 13799367490
- 이메일: Rjwang2025@126.com
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Quanzhou, Fujian, 중국
- 모병
- The Second Affiliated Hospital of Fujian Medical University
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연락하다:
- kaiyan Huang, doctor
- 전화번호: 15905059388
- 이메일: kaiyanhuang@fjmu.edu.cn
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Ningde
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Ningde, Ningde, 중국
- 모병
- Ningde First Hospital
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연락하다:
- zirong jiang, doctor
- 전화번호: 15892129077
- 이메일: zirongjiang@outlook.com
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Sanming
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Sanming, Sanming, 중국
- 모병
- Sanming Second Hospital
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연락하다:
- junxiao wang, doctor
- 전화번호: 15159110696
- 이메일: 25985991@qq.com
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참여기준
연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.
자격 기준
공부할 수 있는 나이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
아니
샘플링 방법
비확률 샘플
연구 인구
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.
설명
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.
공부 계획
이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.
연구는 어떻게 설계됩니까?
디자인 세부사항
코호트 및 개입
그룹/코호트 |
개입 / 치료 |
|---|---|
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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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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.
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연구는 무엇을 측정합니까?
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Incidence of Residual Cancer Burden (RCB) 0-1
기간: After completion of neoadjuvant chemotherapy and definitive surgery (approximately 3-6 months after enrollment)
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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.
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After completion of neoadjuvant chemotherapy and definitive surgery (approximately 3-6 months after enrollment)
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2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Objective response rate (ORR) of AI-defined high neoadjuvant chemotherapy benefit group
기간: Imaging assessment after completion of neoadjuvant chemotherapy and prior to surgery
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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.
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Imaging assessment after completion of neoadjuvant chemotherapy and prior to surgery
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Between-subgroup differences in RCB 0-1 rate
기간: RCB classification obtained after definitive surgical resection, approximately 3-6 months after enrollment
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Compare RCB 0-1 incidence between AI-stratified high benefit subgroup and low benefit subgroup.
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RCB classification obtained after definitive surgical resection, approximately 3-6 months after enrollment
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Between-subgroup differences in objective response rate (ORR)
기간: ORR imaging assessment after neoadjuvant chemotherapy before surgery
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Compare ORR between AI-stratified high benefit subgroup and low benefit subgroup.
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ORR imaging assessment after neoadjuvant chemotherapy before surgery
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Disease-free survival (DFS) between high and low chemotherapy benefit subgroups
기간: From the date of surgery until the first recurrence, metastasis, or death, whichever came first, assessed up to 60 months
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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.
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From the date of surgery until the first recurrence, metastasis, or death, whichever came first, assessed up to 60 months
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Overall survival (OS) between high and low chemotherapy benefit subgroups
기간: From the date of surgery until death from any cause, assessed up to 60 months
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Compare OS between AI-stratified high and low chemotherapy benefit subgroups to explore the correlation between AI imaging stratification and long-term survival prognosis.
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From the date of surgery until death from any cause, assessed up to 60 months
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공동 작업자 및 조사자
여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.
수사관
- 수석 연구원: Chuangui Song, doctor, Fujian Cancer Hospital
연구 기록 날짜
이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.
연구 주요 날짜
연구 시작 (실제)
2026년 6월 1일
기본 완료 (추정된)
2027년 4월 30일
연구 완료 (추정된)
2027년 6월 30일
연구 등록 날짜
최초 제출
2026년 7월 6일
QC 기준을 충족하는 최초 제출
2026년 7월 13일
처음 게시됨 (실제)
2026년 7월 14일
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
2026년 7월 14일
QC 기준을 충족하는 마지막 업데이트 제출
2026년 7월 13일
마지막으로 확인됨
2026년 7월 1일
추가 정보
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