Clinical trial · Observational
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
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| HR Positive/HER-2 Negative Breast Cancer | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Diagnostic performance of breast MRI for molecular subtyping of luminal breast cancer, with comparison to multiomics
- timeFrame
- 1 year
- description
- 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)
- measure
- Predictive Performance of Multiomics Model for Pathological Complete Response (pCR) in Luminal Breast Cancer
- timeFrame
- 1 years
- description
- 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.
Eligibility
Eligibility (as posted)
- Sex
- Female
Show eligibility criteria text
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.
References
Publications (0)
Data not yet available