Skip to content

Deep learning with convolutional neural network for differentiation of breast cancer molecular subtypes at ultrasound imaging

Deep learning with convolutional neural network for differentiation of breast cancer molecular subtypes at ultrasound imaging

Status
Active, not recruiting
Phases
Early Phase 1
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2000028872
Enrollment
Unknown
Registered
2020-01-05
Start date
2020-01-20
Completion date
Unknown
Last updated
2020-01-06

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:Post-operative histopathology test
ultrasound&#32

Sponsors

Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
Lead Sponsor

Eligibility

Sex/Gender
Female

Inclusion criteria

Inclusion criteria: 1. Surgical resection was performed for the target tumor; 2. The tumor was pathologically proven breast cancer, and the immunohistochemical markers of estrogen receptor (ER), progesterone receptor (PR), Ki-67and human epidermal growth factor receptor 2 (HER2) status could be obtained; 3. The US examination obtained within 1 month before surgery.

Exclusion criteria

Exclusion criteria: 1. Any preoperative intervention and therapy (radiotherapy, chemotherapy, radiofrequency ablation or biopsy) before US examination; 2. The target tumor was unclear or had no visible region of interest on US images due to artifacts; 3. Clinical and pathological data were incomplete.

Design outcomes

Primary

MeasureTime frame
breast cancer 2d-ultrasound image;SEN, SPE, ACC, AUC of ROC;

Countries

China

Contacts

Public ContactMeng Jiang

Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology

jiangmenghust@163.com+86 15387042819

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026