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Radiology：对比增强CT图像深度学习系统在肝硬化分级的价值

2019-04-03 shaosai MedSci原创

对比增强CT深度学习肝硬化
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本研究旨在建立并验证CT图像深度学习系统（DLS）在肝硬化分级中的价值。

本研究旨在建立并验证CT图像深度学习系统（DLS）在肝硬化分级中的价值。

利用包括门静脉期CT图像的7461例病理证实为肝硬化患者的数据建立基于CT肝硬化分级的DLS。利用额外891例患者数据进行验证DLS的诊断能力。利用Logistic回归分析评价患者特征及CT技术对DLS分级准确性的影响。在421例患者的亚组中，利用ROC曲线下面积和Obuchowski指数比较DLS与放射科医生、转氨酶-血小板比例指数（APRI0）肝硬化-4指数对肝硬化分级的诊断能力。

结果为，在测试组，DLS的分级准确率为79.4% (707 of 891)，对诊断明显肝硬化(F2-4)、肝硬化晚期 (F3-4)、重度肝硬化(F4)的ROC曲线下面积分别为0.96、0.97、0.95。在多元分析中，仅有病理纤维化分级能够影响F1和F2 DLS的准确性(与F4比较，F1与F2的P值分别为.016 、.013)，而CT技术和肝脏基础病变并不能影响DLS分级诊断性。DLS(Obuchowski指数为0.94)要优于放射科医生的评价、APRI和肝硬化-4指数(Obuchowski指数范围为0.71-0.81； P ？ .001)。

本研究表明，CT图像深度学习系统能够准确对肝硬化进行分级。

原始出处：


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    2019-07-11 guguangxiang

    #深度学习系统#

    0

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    2019-07-23 bnurmamat

    #增强CT#

    0

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    2019-10-12 shijzhiewnjhch

    #对比增强CT#

    0

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    2019-04-04 183****7028

    学习

    0

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    2019-04-04 guging

    谢谢！最新的信息读起来就是收获大

    0