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

Completed

• Conditions: Breast Diseases

• Registration Number: NCT06069921
• Lead Sponsor: Ma Zhe

Brief Summary

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2015-01-01
• Primary Completion: 2022-12-30
• Study Completion: 2022-12-30
• Study First Submitted: 2023-09-29
• Study First Submitted QC: 2023-09-29
• Study First Posted: 2023-10-06
• Status Verified: 2024-06
• Last Update Submitted: 2024-06-23
• Last Update Posted: 2024-06-25

Recruitment & Eligibility

• Status: COMPLETED
• Sex: Female
• Target Recruitment: 400

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
radiomcis prediction model and the model evaluation	Immediately evaluated after the radiomcis prediction model was built	three radiomics models were established using the support vector machines algorithm based on features extracted from the intratumoral, peritumoral, and combined regions of the breast lesions.The models were evaluated using various metrics, including AUC, accuracy, sensitivity, specificity, PPV, and NPV

Secondary Outcome Measures

Name	Time	Method
deep learning prediction model and the model evaluation	Immediately evaluated after the deep learning prediction model was built	three deep learning models were established using the support vector machines algorithm based on features extracted from the intratumoral, peritumoral, and combined regions of the breast lesions.The models were evaluated using various metrics, including AUC, accuracy, sensitivity, specificity, PPV, and NPV

Trial Locations

Locations (1)

QianfoshanH; 🇨🇳 Jinan, Shandong, China