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Deep convolutional neural network for the classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma
Author(s): Abhishek Midya; Jayasree Chakraborty; Linda M. Pak; Jian Zheng; William R. Jarnagin; Richard K. G. Do; Amber L. Simpson
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Paper 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.

Paper Details

Date Published: 27 February 2018
PDF: 6 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057528 (27 February 2018); doi: 10.1117/12.2293683
Show Author Affiliations
Abhishek Midya, Memorial Sloan-Kettering Cancer Ctr. (United States)
Jayasree Chakraborty, Memorial Sloan-Kettering Cancer Ctr. (United States)
Linda M. Pak, Memorial Sloan-Kettering Cancer Ctr. (United States)
Jian Zheng, Memorial Sloan-Kettering Cancer Ctr. (United States)
William R. Jarnagin, Memorial Sloan-Kettering Cancer Ctr. (United States)
Richard K. G. Do, Memorial Sloan-Kettering Cancer Ctr. (United States)
Amber L. Simpson, Memorial Sloan-Kettering Cancer Ctr. (United States)


Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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