深度学习为三类分类在非增强的胸部CT上地面玻璃结节的CT:CNN架构的多中心比较研究
Meihua Shao1, Jian Wang2, Lin Zhu1
1Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
European journal of radiology open
|October 23, 2025
概括
这项研究开发了深度学习模型,用于在CT扫描上对肺结节进行分类. Res2Net 3D 模型在区分地面玻璃结节亚型,特别是侵入性腺癌方面表现出高准确性.
科学领域:
- 医疗成像中的人工智能
- 放射学和诊断成像 放射学和诊断成像
- 瘤学和癌症检测 癌症检测
背景情况:
- 在非对比的胸部CT扫描中,地面玻璃结节 (GGNs) 需要准确的分类,以实现最佳的患者管理.
- 区分亚型,如腺瘤性增生/adenocarcinoma in situ (AAH/AIS),微侵袭性腺癌 (MIA) 和侵袭性腺癌 (IA) 是至关重要的.
研究的目的:
- 为GGN分类开发,验证和比较四个3D卷积神经网络 (CNN) 模型.
- 评估深度学习模型在区分AAH/AIS,MIA和IA亚型方面的表现.
主要方法:
- 对4284名病理确诊的GGN亚型患者进行了回顾性多中心研究.
- 四个3D深度学习模型 (Res2Net 3D,DenseNet3D,ResNet50 3D,Vision Transformer 3D) 被实施用于细分和分类.
- Res2Net 3D的变种包含了临床和CT功能,以提高性能.
主要成果:
- Res2Net 3D表现出高AUC的优异性能:0.91 (AAH/AIS),0.88 (MIA) 和0.92 (IA). 通过使用Res2Net 3D,我们可以获得高AUC的效果.
- 在Res2Net 3D中,AAH/AIS,MIA和IA分别获得了0.416的F1分,0.500和0.929的F1分.
- 所有Res2Net变体都显示了0.83-0.84.8之间的精度.
结论:
- 在Res2Net的3D模型有效地区分了在非对比CT扫描中的GGN亚型.
- 这种深度学习方法显示出高性能,特别是用于识别侵入性腺癌.
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