MRI-Driven Precision Typing and Response Prediction in Luminal Breast Cancer

MRI-driven Multiomics Research on Precise Typing and Response Prediction of Luminal Breast Cancer

Luminal breast cancer is characterized by marked heterogeneity, resulting in diverse treatment responses and long-term outcomes. This project aims to integrate MRI and multiomics data to achieve non-invasive molecular typing and precise response prediction. By linking imaging phenotypes with underlying molecular and pathological characteristics, the investigators will develop predictive models for treatment resistance, recurrence, and metastasis, ultimately supporting personalized treatment strategies and precision oncology.

Study Overview

• Status: Active, not recruiting
• Conditions: HR Positive/HER-2 Negative Breast Cancer

Detailed Description

Luminal breast cancer represents the most common type of breast cancer, characterized by its intricate tumor heterogeneity that poses a significant challenge in clinical management due to resistance to endocrine therapy and high risk of long-term recurrence. It is significant for the accurate prediction of molecular subtypes and treatment response for luminal breast cancer. Our team has previously identified four molecular subtypes and seven pivotal molecules associated with luminal breast cancer utilizing multiomics techniques. The investigators posit that the integration of MRI-driven multiomics studies holds promise in achieving precise typing and response prediction for luminal breast cancer. This project intends to use multiomics molecular subtypes and key molecules as the gold standard to extract comprehensive quantitative features from diverse regions and levels utilizing MRI, thus facilitating non-invasive diagnosis. Additionally, our approach involves correlating MRI data with multiomics information to unveil the biological significance of imaging models at both pathological and molecular levels. Finally, the investigators aim to construct response prediction models through the fusion of multi-temporal MRI features and multiomics data across various scales, enabling precise forecasts of treatment resistance, recurrence, and metastasis. This initiative aims to enhance treatment decision-making and promote application transformation. This study will include a large-scale real-world retrospective and prospective population to validate and improve the effectiveness of model.

• Study Type: Observational
• Enrollment (Estimated): 2000

Contacts and Locations

Study Locations

• China
  • Shanghai Municipality
    • Shanghai, Shanghai Municipality, China, 200032
      • Fudan University Shanghai Cancer Center

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

Patients with invasive luminal breast cancer (HR+/HER2-)

Description

Inclusion Criteria:

1. Histopathologically confirmed invasive luminal breast cancer (HR+/HER2-);
2. Patients who underwent breast MRI examination.

Exclusion Criteria:

1. Pathological biopsy performed prior to the baseline MRI examination;
2. Patients have received any form of prior treatment for the breast cancer;
3. History of other malignancies;
4. Incomplete or poor-quality MRI and/or pathological images;
5. Missing clinical data.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Diagnostic performance of breast MRI for molecular subtyping of luminal breast cancer, with comparison to multiomics	The primary outcome is the diagnostic performance of AI-assisted analysis for molecular subtyping of luminal breast cancer on contrast-enhanced breast MRI. Quantitative radiomic features and deep learning features are extracted from DCE-MRI, followed by classification into multiomics-defined molecular subtypes. Performance metrics include sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the receiver operating characteristic curve (AUC). Participants must have undergone both breast MRI and multiomics profiling of tumor tissue. Performance metrics will be compared with those obtained from multiomics classification within the same participants to evaluate the relative diagnostic performance.	1 year

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Predictive Performance of Multiomics Model for Pathological Complete Response (pCR) in Luminal Breast Cancer	The model integrates multiomics data, including breast MRI, pathological features, and other relevant molecular and clinical variables, to predict pathological complete response (ypT0/is ypN0) following neoadjuvant therapy in patients with luminal breast cancer. Performance metrics include sensitivity, specificity, positive predictive value, negative predictive value, accuracy, area under the receiver operating characteristic curve (AUC), C-index, and time-dependent AUC. Participants must have undergone neoadjuvant therapy with available pathological response assessment.	1 years

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Predictive Performance of Multiomics Model for Disease-Free Survival (DFS) in Luminal Breast Cancer	The model integrates multiomics data, including breast MRI, pathological features, and other relevant molecular and clinical variables, to predict disease-free survival in luminal breast cancer, defined as time from surgery to first documented disease recurrence, distant metastasis, or death from any cause. Performance metrics include sensitivity, specificity, positive predictive value, negative predictive value, accuracy, area under the receiver operating characteristic curve (AUC), C-index, and time-dependent AUC. Participants must have undergone surgery and completed 5 years follow-up.	5 years

Collaborators and Investigators

• Sponsor: Fudan University

Study record dates

Study Major Dates

• Study Start (Actual): January 6, 2026
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2029

Study Registration Dates

• First Submitted: June 26, 2026
• First Submitted That Met QC Criteria: July 2, 2026
• First Posted (Actual): July 9, 2026

Study Record Updates

• Last Update Posted (Actual): July 10, 2026
• Last Update Submitted That Met QC Criteria: July 8, 2026
• Last Verified: July 1, 2026

More Information

Terms related to this study

• Keywords: Magnetic resonance imaging; Multiomics; Response prediction; HR positive/HER-2 negative breast cancer; Precise typing
• Other Study ID Numbers: 2410-Exp103-KY; 82430061 (Other Grant/Funding Number: Key Project of National Natural Science Foundation of China)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No