Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related death worldwide. The high probability ofmetastasis makes its prognosis very poor even after potentially curative treatment. Detecting high metastatic HCC willallow for the development of effective approaches to reduce HCC mortality. The mechanism of HCC metastasis has beenstudied using gene profiling analysis, which indicated that HCC with different metastatic capability was differentiable.However, it is time consuming and complex to analyze gene expression level with conventional method. To distinguishHCC with different metastatic capabilities, we proposed a deep learning based method with microscopic images inanimal models. In this study, we adopted convolutional neural networks (CNN) to learn the deep features of microscopicimages for classifying each image into low metastatic HCC or high metastatic HCC. We evaluated our proposedclassification method on the dataset containing 1920 white-light microscopic images of frozen sections from threetumor-bearing mice injected with HCC-LM3 (high metastasis) tumor cells and another three tumor-bearing mice injectedwith SMMC-7721(low metastasis) tumor cells. Experimental results show that our method achieved an average accuracyof 0.85. The preliminary study demonstrated that our deep learning method has the potential to be applied to microscopicimages for metastasis of HCC classification in animal models.
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