Clinical trial · Interventional
Prediction of Neoadjuvant Therapy Efficacy and Prognosis for Breast Cancer Based on Multimodal Data
Prediction of Neoadjuvant Therapy Efficacy and Prognosis for Breast Cancer Based on Multimodal Data: A Multicenter Retrospective and Prospective Validation
- 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)
This study aims to develop a multimodal deep learning model integrating MRI, ultrasound, digital pathology and clinical information based on multicenter retrospective data. To externally validate the model in an independent prospective cohort, and evaluate its accuracy in predicting pathological complete response (pCR), 3-year and 5-year disease-free survival (DFS). To establish visual tools such as nomograms, assisting clinicians in identifying patients with chemoresistance and facilitating individualized de-escalation or escalation treatment strategies.
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 |
|---|---|---|---|
| Breast Carcinoma | Breast Carcinoma | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| To explore the value of a multimodal deep learning model integrating MRI, ultrasound, digital pathology and clinical information in predicting pCR and long-term prognosis. | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- type
- EXPERIMENTAL
- label
- Prediction of Neoadjuvant Therapy Efficacy and Prognosis for Breast Cancer Based on Multimodal Data
- description
- To develop a multimodal deep learning model integrating MRI, ultrasound, digital pathology and clinical information based on multicenter retrospective data.
- interventionNames
- Diagnostic Test: To explore the value of a multimodal deep learning model integrating MRI, ultrasound, digital pathology and clinical information in predicting pCR and long-term prognosis.
Primary outcomes (1)
- measure
- Predictive value of multimodal data for neoadjuvant therapy efficacy in breast cancer
- timeFrame
- From enrollment to the end of surgery
- description
- Combined with preoperative multimodal MRI and ultrasound imaging parameters, pathological baseline data and clinical data, a prediction model for neoadjuvant therapy efficacy in breast cancer is constructed. Taking postoperative pathological response results as the evaluation basis, the predictive efficacy of multimodal data for neoadjuvant therapy complete response and non-complete response is evaluated.
Secondary outcomes (1)
Eligibility
Eligibility (as posted)
- Sex
- Female
- Minimum age
- 18 Years
- Maximum age
- 80 Years
Show eligibility criteria text
Inclusion Criteria: 1. Histopathologically confirmed invasive breast cancer; 2. Planned to receive a full course of neoadjuvant therapy; 3. Complete baseline imaging data (MRI/ultrasound/mammography) and core needle pathology results available. Exclusion Criteria: 1. Previous history of ipsilateral breast cancer or chest radiotherapy; 2. Distant metastasis (Stage IV); 3. Poor image quality or missing clinical data exceeding 20%.
References
Publications (0)
Data not yet available