Using Deep Learning and Radiomics to Diagnose Benign and Malignant Breast Lesions Based on Ultrasound

Ma Zhe

Status: Completed

Conditions: Breast Diseases

Study type: Observational

Funder types: Other

Identifiers

NCT06069921

YXLL-KY-2023(045)

Details and patient eligibility

About

This retrospective study aimed to create a prediction model using deep learning and radiomics features extracted from intratumoral and peritumoral regions of breast lesions in ultrasound images, to diagnose benign and malignant breast lesions with BI-RADS 4 classification.

Materials and methods: Patients who visited in The First Affiliated Hospital of Shandong First Medical University ＆ Shandong Provincial Qianfoshan Hospital were collected. Their general clinical features, information on preoperative ultrasound diagnosis, and postoperative pathologic data were reviewed.

Enrollment: 400 patients

Sex: Female

Ages: 15 to 80 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• female patients with US-visible solid breast masses who underwent biopsy and/or surgical resection, and were classified as having BI-RADS 4 lesions in medical US reports.

Exclusion criteria

• preoperative endocrine therapy, chemotherapy, or radiotherapy, preoperative invasive breast operation, insufficient image quality, and no pathological results.

Trial design

400 participants in 2 patient groups

maligant

Description:

female patients with US-visible solid maligant breast masses who underwent biopsy and/or surgical resection.

benign

Description:

female patients with US-visible solid benign breast masses who underwent biopsy and/or surgical resection.