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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

400 participants

Study Classification

OBSERVATIONAL

Study Start Date

2015-01-01

Study Completion Date

2022-12-30

Brief Summary

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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.

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Detailed Description

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Conditions

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Breast Diseases

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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maligant

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

No interventions assigned to this group

benign

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

No interventions assigned to this group

Eligibility Criteria

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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.

Minimum Eligible Age

15 Years

Maximum Eligible Age

80 Years

Eligible Sex

FEMALE

Accepts Healthy Volunteers

No