Intrahepatic cholangiocarcinoma (IHC) is an aggressive liver cancer with a five-year survival rate of less than 10%. Surgery is the only curative treatment. However, most patients die of disease recurrence, with more than 50% recurring within 2 years. The liver is the most common site. Recurrence at liver within a short period after surgery is common and eventually leads to death. Currently, there is no way to assess the risk of early recurrence or death in these patients. Methods to predict these risks would help physicians select the best treatment plan for individual patients; patients at high risk of recurrence could be treated early or at the time of surgery with chemotherapy or radiation. Such changes in patient management would greatly impact patients’ prospects of survival. The objective of the present study is to identify preoperative computed tomography (CT)-based quantitative imaging predictors of early hepatic recurrence. Two hundred fifty four texture features were extracted from CT-tumor and future liver remnant (FLR) along with tumor size. With features selected using minimum redundancy maximum relevance method and AdaBoost classifier, we obtained an area under the receiver operating characteristic curve of 0.78 using a 3-fold cross-validation for a cohort of 139 patients with IHC.
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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