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开发一种基于深度学习的成像诊断框架,PVDNet,用于区分肺动脉肉瘤和肺血栓栓塞:一个多中心观察性研究

Linfeng Xi^1,2, Anqi Liu^3,4, Han Kang⁵

¹China-Japan Friendship Hospital, Capital Medical University, Beijing, 100069, China.

The Lancet regional health. Western Pacific

|September 11, 2025

概括

一个新的深度学习模型,PVDNet,可以在CT肺血管造影 (CTPA) 扫描上区分肺动脉肉瘤 (PAS) 和肺血栓栓塞 (PTE). 它的性能接近专家放射科医生的性能,为诊断提供了一个有前途的工具.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
医疗成像医学成像

背景情况:

使用CT肺血管造影 (CTPA) 将肺动脉肉瘤 (PAS) 与肺血栓栓塞 (PTE) 区分开来是具有挑战性的.
深度学习 (DL) 为提高诊断准确性提供了潜在的解决方案.

研究的目的:

开发和验证一个基于DL的模型,PVDNet,用于在CTPA上区分PAS和PTE.
与放射科医生相比,评估PVDNet的诊断性能.

主要方法:

来自CHOICE研究的952名患者 (PAS,急性PTE [APE],慢性PTE [CPE]) 的CTPA数据集的回顾性分析.
PVDNet DL框架的开发和内部/外部验证.
与四位不同专业知识的放射科医生进行PVDNet性能比较.

主要成果:

在内部测试组中,PVDNet实现了高AUC值 (PAS为0.972,APE为0.902,CPE为0.900).
在外部验证中,PVDNet的AUC与高级放射科医生 (0.973比0.943) 的AUC相当.
在PVDNet中,与高级放射科医生达成中度一致 (kappa = 0.651).

结论:

该PVDNet模型有效地将PAS与PTE区分开来,接近高级放射科医生的熟练程度.
对于PVDNet在区分APE和CPE方面的表现,需要进一步优化.

关键词:

计算机断层扫描肺血管扫描肺血管扫描深度学习是一种深度学习.肺动脉肉瘤是一种肺动脉肉瘤.肺动脉血栓塞栓症肺动脉血栓塞栓症

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