Clinical trial · Interventional
Research on the Whole-Process Intelligent Diagnosis and Treatment of Digital Breast Tomosynthesis Based on Deep Learning
Research on the Whole-Process Intelligent Diagnosis and Treatment of Digital Breast Tomosynthesis Based on Deep Learning: 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 construct a multi-task deep learning model system to mine deep features in DBT images, so as to achieve accurate detection of breast lesions, differential diagnosis of benign and malignant (especially for the challenging BI-RADS 4A category), prediction of molecular subtypes, and evaluation of neoadjuvant chemotherapy (NAC) efficacy, providing an imaging basis for precision medicine.
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 digital breast tomosynthesis based on deep learning in the diagnosis of breast cancer | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- type
- EXPERIMENTAL
- label
- The application value of digital breast tomosynthesis in the accurate diagnosis of breast cancer
- description
- This study aims to construct a multi-task deep learning model system to mine deep features in DBT images, so as to achieve accurate detection of breast lesions, differential diagnosis of benign and malignant (especially for the challenging BI-RADS 4A category), prediction of molecular subtypes, and evaluation of neoadjuvant chemotherapy (NAC) efficacy, providing an imaging basis for precision medicine.
- interventionNames
- Diagnostic Test: To explore the value of digital breast tomosynthesis based on deep learning in the diagnosis of breast cancer
Primary outcomes (1)
- measure
- The accuracy of the multi-task deep learning-based intelligent diagnostic model in differentiating benign and malignant breast lesions on digital breast tomosynthesis (DBT) images.
- timeFrame
- 1day
- description
- Taking surgical or puncture histopathological results as the gold standard, the accuracy of the multi-task deep learning-based intelligent diagnostic model in differentiating benign and malignant breast lesions on digital breast tomosynthesis (DBT) images was evaluated. It focuses on challenging BI-RADS 4A lesions, covering retrospective multi-center validation sets and prospective multi-center validation sets to ensure the representativeness and rigor of the indicator.
Eligibility
Eligibility (as posted)
- Sex
- Female
- Minimum age
- 18 Years
- Maximum age
- 80 Years
Show eligibility criteria text
Inclusion Criteria 1. Female patients aged ≥ 18 years. 2. Complete bilateral digital breast tomosynthesis (DBT) images available, including craniocaudal (CC) and mediolateral oblique (MLO) views. 3. Confirmed pathological diagnosis (core needle biopsy or surgical resection) serving as the reference standard; or benign lesions with stable findings on follow-up for more than 2 years. 4. (For the efficacy prediction subgroup) Patients who received complete neoadjuvant therapy and had postoperative pathological results. 2\. Exclusion Criteria 1. Poor image quality with severe artifacts that precluded reliable analysis. 2. History of previous breast surgery or radiotherapy (except for the recurrence risk subgroup). 3. Incomplete clinical or pathological data.
References
Publications (0)
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