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Deep learning-enabled fully automated pipeline system for segmentation and classification of breast lesions using contrast enhanced mammography

Deep learning-enabled fully automated pipeline system for segmentation and classification of breast lesions using contrast enhanced mammography

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2200063444
Enrollment
Unknown
Registered
2022-09-07
Start date
2022-09-01
Completion date
Unknown
Last updated
2023-04-17

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

Conditions

Breast cancer

Interventions

Gold Standard:pathological result
Index test:Draw ROC curve to evaluate model performance

Sponsors

Yantai Yuhuangding Hospital
Lead Sponsor

Eligibility

Sex/Gender
Female

Inclusion criteria

Inclusion criteria: 1. The patient was examined by CEM before surgery and had available clinical information; 2. Have complete surgical and pathological results; 3. The image quality is clear.

Exclusion criteria

Exclusion criteria: 1. A history of prior chemoradiotherapy or malignancy; 2. Multiple lesions on both sides; 3. Incomplete clinical or pathological information.

Design outcomes

Primary

MeasureTime frame
receiver operating characteristic curve;—

Secondary

MeasureTime frame
sensitivity;specificity;—

Countries

China

Contacts

Public ContactMao Ning

Yantai Yuhuangding Hospital

maoning@pku.edu.cn+86 13105351972

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 11, 2026