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

Breast Diseases Clinical Trial

Official title:

Ultrasound-based Deep Learning Signature and Radiomics Signature Nomogram for Diagnosis of Benign and Malignant Breast Lesions of BI-RADS Category 4 Using Intratumoral and Peritumoral Regions

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT06069921
• Other study ID #: YXLL-KY-2023(045)
• Status: Completed
• Start date: January 1, 2015
• Est. completion date: December 30, 2022

Study information

• Verified date: September 2023
• Source: Qianfoshan Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 400
• Est. completion date: December 30, 2022
• Est. primary completion date: December 30, 2022
• Accepts healthy volunteers: No
• Gender: Female
• Age group: 15 Years to 80 Years

Eligibility

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.

Related Conditions & MeSH terms

• Breast Diseases

Locations

Country	Name	City	State
China	QianfoshanH	Jinan	Shandong

Sponsors (1)

Lead Sponsor	Collaborator
Ma Zhe

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	the combination prediction model and the model evaluation	the combination model was established using clinical features , deep learning score and radiomics score.The models were evaluated using various metrics, including AUC, accuracy, sensitivity, specificity, PPV, and NPV	Immediately evaluated after the combination prediction model was built
Primary	radiomcis prediction model and the model evaluation	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	Immediately evaluated after the radiomcis prediction model was built
Secondary	deep learning prediction model and the model evaluation	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	Immediately evaluated after the deep learning prediction model was built