The study of application of TransUNet deep neural network model based on full volume three dimensional ultrasound in diagnosing of breast cancer

Status

Active, not recruiting

Phases

Early Phase 1

Study type

Interventional

Source

ChiCTR

Registry ID

ChiCTR2100052477

Enrollment

Unknown

Registered

2021-10-28

Start date

2022-01-01

Completion date

Unknown

Last updated

2022-10-17

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

Conditions

Breast Cancer

Interventions

Breast cancer group:Histopathological results

breast benign mass group:Histopathological results

Eligibility

Sex/Gender

Female

Age

20 Years to 85 Years

Inclusion criteria

Inclusion criteria: 1. Cases with surgical and pathological results; 2. Cases with histopathological results who underwent ultrasound-guided needle biopsy without surgery; 3. Although no surgery and needle biopsy, but after more than 1 year of X-ray mammography or MRI follow-up confirmed benign or normal cases.

Exclusion criteria

Exclusion criteria: 1. Cases with incomplete full-volume 3D ultrasound data due to various reasons; 2. Cases with confirmed benign (or malignant) breast lumps receiving invasive or drug treatment; 3. Patients with malignant tumors in other parts of the body who received chemotherapy.

Design outcomes

Primary

Measure	Time frame
age;height;weight;Mass size;Mass shape;Mass border;Mass location;Mass number;Mass margin;Mass echogenicity;	—

Secondary

Measure	Time frame
Disease history;Smoking history;Drinking history;	—

Countries

China

Contacts

Public ContactChen Lin

Huadong Hospital, Fudan University

cl_point@126.com+86 13817917826

Outcome results

None listed

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