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
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 400
- 试验地点
- 1
- 主要终点
- radiomcis prediction model and the model evaluation
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 15 Years 至 80 Years（Child, Adult, Older Adult）
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- •preoperative endocrine therapy, chemotherapy, or radiotherapy, preoperative invasive breast operation, insufficient image quality, and no pathological results.
结局指标
主要结局
radiomcis prediction model and the model evaluation
时间窗: 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
次要结局
- deep learning prediction model and the model evaluation(Immediately evaluated after the deep learning prediction model was built)
研究者
Ma Zhe
Director of Ultrasound
Qianfoshan Hospital