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Key Technologies for Predicting Sentinel Lymph Node Metastasis in Breast Cancer Based on Deep Fusion of Multimodal Imaging and Clinicopathological Features Using Transformer Architecture

Key Technologies for Predicting Sentinel Lymph Node Metastasis in Breast Cancer Based on Deep Fusion of Multimodal Imaging and Clinicopathological Features Using Transformer Architecture

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600127549
Enrollment
Unknown
Registered
2026-07-02
Start date
2025-08-18
Completion date
Unknown
Last updated
2026-07-13

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Breast cancer

Interventions

SLNB positive group:None
SLNB negative group:none

Sponsors

Guangdong Provincial Hospital of Chinese Medicine (Guangdong Provincial Academic of Chinese Medicine Science; Second Clinical Medical College, Guangzhou University of Traditional Chinese Medicine)
Lead Sponsor

Eligibility

Sex/Gender
Female

Inclusion criteria

Inclusion criteria: 1.Histologically confirmed diagnosis of breast cancer; 2.Underwent sentinel lymph node biopsy during breast surgery; 3.Received preoperative dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging MRI (DWI-MRI), apparent diffusion coefficient-weighted imaging (ADC-MRI), B-ultrasound, and mammography; 4.Time interval between B-ultrasound, mammography, MRI examinations, and surgery was less than 3 weeks;

Exclusion criteria

Exclusion criteria: 1.Absence of key clinical and pathological findings; 2.Lack of imaging examination for a specific modality or scan coverage not including the axillary region; 3.Received neoadjuvant chemotherapy or endocrine therapy prior to surgery; 4.Bilateral breast cancer;

Design outcomes

Primary

MeasureTime frame
Area under curve (AUC);

Secondary

MeasureTime frame
Specificity;Sensitivity;Positive Predictive Value;Negative Predictive Value;

Countries

China

Contacts

Public ContactQianjun Chen

Guangdong Provincial Hospital of Chinese Medicine (Guangdong Provincial Academic of Chinese Medicine Science; Second Clinical Medical College, Guangzhou University of Traditional Chinese Medicine)

cqj55@163.com+86 18688883505

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Jul 23, 2026