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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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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| maligant | female patients with US-visible solid maligant breast masses who underwent biopsy and/or surgical resection. | ||
| benign | female patients with US-visible solid benign breast masses who underwent biopsy and/or surgical resection. |
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| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
Inclusion Criteria:
Exclusion Criteria:
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Patients with breast lesions attending the First Affiliated Hospital of Shandong First Medical University were selected.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| QianfoshanH | Jinan | Shandong | 250014 | China |
Lack of resources or infrastructure: Sharing IPDs requires resources and infrastructure to ensure data security, manage access requests and provide necessary documentation. Currently these resources are not well developed, so it may be difficult to share IPDs.
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| ID | Term |
|---|---|
| D001941 | Breast Diseases |
| ID | Term |
|---|---|
| D012871 | Skin Diseases |
| D017437 | Skin and Connective Tissue Diseases |
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