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Completed

NCT Number: NCT06069921

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

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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Key information

Conditions

Breast Diseases Skin Diseases Skin and Connective Tissue Diseases

Age range

15 year–80 year

Sex eligibility

Female

Study type

Observational

Primary location

QianfoshanH

Jinan, Shandong, 250014, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

  1. radiomcis prediction model and the model evaluation

    Time frame: 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 outcomes

  1. deep learning prediction model and the model evaluation

    Time frame: 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

Other outcomes

  1. the combination prediction model and the model evaluation

    Time frame: Immediately evaluated after the combination prediction model was built

    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

Sponsors and collaborators

Lead sponsor

Ma Zhe

Other

Registry information

Official study 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

Important dates

Study start
2015
Primary completion
2022
Study completion
2022
First posted
Oct 6, 2023
Registry last updated
Jun 25, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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