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

NCT07692230 | Active Not Recruiting DMC

• 2 other identifiers: 2410-Exp103-KY82430061
• Study Type: observational
• Target: 2,000
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

HR Positive/HER-2 Negative Breast Cancer

Keywords

HR positive/HER-2 negative breast cancer; Magnetic resonance imaging; Multiomics; Precise typing; Response prediction

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (1)

• Predictive Performance of Multiomics Model for Pathological Complete Response (pCR) in Luminal Breast Cancer

  1 years

Other Outcomes (1)

• Predictive Performance of Multiomics Model for Disease-Free Survival (DFS) in Luminal Breast Cancer

  5 years

Eligibility Criteria

• Gender Eligibility Details: Eligibility is determined by biological sex (female), not gender identity. Self-identified genders eligible: cisgender women and transgender men with intact breast tissue.
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

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

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Fudan University: lead

Study Sites (1)

Fudan university Shanghai Cancer Center

Shanghai, Shanghai Municipality, 200032, China

Location

Biospecimen

Retention: SAMPLES WITHOUT DNA

Samples with mRNA

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Target Duration: 3 Years
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Director, Head of Radiology, Principal Investigator, Clinical Professor

Study Record Dates

• First Submitted: June 26, 2026
• First Posted: July 9, 2026
• Study Start: January 6, 2026
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2029
• Last Updated: July 21, 2026

Record last verified: 2026-07

Locations

CN (1)