整合内和周放射学与深度转移学习用于DCE-MRI乳腺损伤差异化:一项多中心研究,与放射科医生比较性能
Tao Yu1, Renqiang Yu2, Mengqi Liu2
1Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China; State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing 400016, China.
European journal of radiology
|June 14, 2024
概括
这项研究融合了乳腺MRI的放射学和深度学习特征,以准确区分良性和恶性瘤. 先进的融合模型优于放射科医生,提高了乳腺癌的诊断准确度.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 准确区分良性和恶性乳腺瘤对于有效的患者管理至关重要.
- 乳腺癌诊断通常依赖于成像解释,这可能是主观的.
研究的目的:
- 开发和验证一个融合模型,将放射学和来自乳腺DCE-MRI瘤内和瘤周区域的深度转移学习特征结合起来.
- 将这种融合模型的诊断准确性与经验丰富的放射科医生进行比较.
主要方法:
- 对330名女性 (138名良性,192名恶性) 的DCE-MRI的回顾性分析.
- 从内 (ITR) 和周 (PTR) 区域提取的特征.
- 放射学和深度转移学习功能的融合.
- 基于最小绝对收缩和选择操作员 (LASSO) 的支持矢量机器分类器.
- 使用内部 (270名患者) 和外部 (60名患者) 测试套件进行验证.
主要成果:
- 融合模型实现了高诊断性能,AUC为0.950 (内部) 和0.921 (外部).
- 这种性能超过了仅使用放射学或深度学习功能的模型.
- 与两个经验丰富的放射科医生相比,融合模型显示出更高的准确性.
结论:
- 融合模型整合了多区域放射学和深度转移学习特征,有效地区分了良性和恶性乳腺瘤.
- 这种模型显示了作为一个有价值的工具来提高乳腺瘤诊断的准确性和可靠性的潜力.
- 融合模型的性能超过了经验丰富的放射科医生,这表明其在临床决策支持中的实用性.
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