CompletedNot Applicable

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

China400 participantsStarted 2015-01-01

Plain-language 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.

Who can participate

Age range15 Years – 80 Years

SexFEMALE

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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.

What they're measuring

radiomcis prediction model and the model evaluation

Timeframe: Immediately evaluated after the radiomcis prediction model was built

Trial details

NCT IDNCT06069921

SponsorMa Zhe

Sponsor typeOTHER

Study typeOBSERVATIONAL

Primary completion2022-12-30

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