The study of spatiotemporal expression deep learning network model based on full volume three-dimensional ultrasound in diagnosing of breast cancer

Status

Active, not recruiting

Phases

Early Phase 1

Study type

Observational

Source

ChiCTR

Registry ID

ChiCTR2000036274

Enrollment

Unknown

Registered

2020-08-22

Start date

2020-10-01

Completion date

Unknown

Last updated

2020-09-07

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:Breast malignancies: histopathological findings

Benign breast tumors: histopathological findings or clinical follow-up.

Index test:Full&#32

volume&#32

three-dimensional&#32

ultrasound

&#32

Spatiotemporal&#32

expression&#32

and&#32

deep&#32

learning&#32

network&#32

model

Eligibility

Sex/Gender

All

Age

16 Years to 90 Years

Inclusion criteria

Inclusion criteria: 1. Cases with surgical pathological results; 2. Cases without surgery but with histopathological results after ultrasound-guided biopsy; 3. Cases without surgery and biopsy but confirmed as benign or normal after more than 2 years of follow-up with X-ray mammography target or MRI.

Exclusion criteria

Exclusion criteria: 1. Cases with incomplete full volume three-dimensional ultrasound data due to various reasons; 2. Cases with benign (or malignant) breast masses and receiving invasive or drug treatment; 3. Cases with malignant tumors in other parts of the body and receiving chemotherapy.

Design outcomes

Primary

Measure	Time frame
shap;size;border;margin;internal structure;Age;Weight;SEN, SPE, ACC, AUC of ROC;	—

Countries

China

Contacts

Public ContactLin Chen

Huadong Hospital

cl_point@126.com+86 021-62483180

Outcome results

None listed

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