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Deep Convolutional Neural Network for the Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma

机译:深度卷积神经网络用于肝细胞癌和肝内胆管癌的分类

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Liver cancer is the second leading cause of cancer-related death worldwide.1 Hepatocellular carcinoma (HCC) is t he most common primary liver cancer accounting for approximately 80% of cases. Intrahepatic cholangiocarcinoma (ICC) is a rare liver cancer, arising in patients with the same risk factors as HCC, but treatment options and prognosis differ. The diagnosis of HCC is based primarily on imaging but distinguishing between HCC and ICC is challenging due to common radiographic features.The aim of the present study is to classify HCC and ICC in portal venous phase CT. 107 patients with resected ICC and 116 patients with resected HCC were included in our analysis. We developed a deep neural network by modifying a pre-trained Inception network by retraining the final layers. The proposed method achieved the best accuracy and area under the receiver operating characteristics curve of 69.70% and 0.72, respectively on the test data.
机译:肝癌是全世界与癌症相关的死亡的第二大主要原因。1肝细胞癌(HCC)是最常见的原发性肝癌,约占病例的80%。肝内胆管癌(ICC)是一种罕见的肝癌,发生在具有与HCC相同的危险因素的患者中,但是治疗选择和预后不同。肝癌的诊断主要基于影像学,但由于常见的影像学特征,很难区分肝癌和ICC。本研究的目的是对门静脉期CT中的肝癌和ICC进行分类。我们的分析包括107例ICC切除患者和116例HCC切除患者。我们通过重新训练最终层来修改预训练的Inception网络,从而开发了深度神经网络。在测试数据上,该方法在接收器工作特性曲线下分别达到了69.70%和0.72的最佳精度和面积。

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