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NCT Number: NCT07692230

MRI-Driven Precision Typing and Response Prediction in 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.

Active, Not Recruiting

This study is active but is not currently recruiting participants.

Key information

Conditions

HR Positive/HER-2 Negative Breast Cancer

Sex eligibility

Female

Study type

Observational

Primary location

Fudan university Shanghai Cancer Center

Shanghai, Shanghai Municipality, 200032, China

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

  • 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.

Treatment and study plan

Primary outcomes

  1. Diagnostic performance of breast MRI for molecular subtyping of luminal breast cancer, with comparison to multiomics

    Time frame: 1 year

    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.

Secondary outcomes

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

    Time frame: 1 years

    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.

Other outcomes

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

    Time frame: 5 years

    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.

Sponsors and collaborators

Lead sponsor

Fudan University

Other

Registry information

Official study title

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

Important dates

Study start
2026
Primary completion
2027
Study completion
2029
First posted
Jul 9, 2026
Registry last updated
Jul 21, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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