Deep convolutional neural network for the classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma

Abhishek Midya; Jayasree Chakraborty; Linda M. Pak M.D.; Jian Zheng M.D.; William R. Jarnagin M.D.; Richard K. G. Do M.D.; Amber L. Simpson

doi:10.1117/12.2293683

27 February 2018 Deep convolutional neural network for the classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma

Abhishek Midya, Jayasree Chakraborty, Linda M. Pak M.D., Jian Zheng M.D., William R. Jarnagin M.D., Richard K. G. Do M.D., Amber L. Simpson

Author Affiliations +

Proceedings Volume 10575, Medical Imaging 2018: Computer-Aided Diagnosis; 1057528 (2018) https://doi.org/10.1117/12.2293683
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Liver cancer is the second leading cause of cancer-related death worldwide.¹ Hepatocellular carcinoma (HCC) is the 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.^2-4 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.

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Abhishek Midya, Jayasree Chakraborty, Linda M. Pak M.D., Jian Zheng M.D., William R. Jarnagin M.D., Richard K. G. Do M.D., and Amber L. Simpson "Deep convolutional neural network for the classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057528 (27 February 2018); https://doi.org/10.1117/12.2293683

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