Treatment Response Prediction of Hepatocellular Carcinoma Patients from Abdominal CT Images with Deep Convolutional Neural Networks
Prediction of treatment responses of hepatocellular carcinoma (HCC) patients, such as local control (LC) and overall survival (OS), from CT images, has been of importance for treatment planning of radiotherapy for HCC. In this paper, we propose a deep learning method to predict LC and OS responses of HCC from abdominal CT images. To improve the prediction efficiency, we constructed a prediction model that learns both the intratumoral information and contextual information between the tumor and the liver. In our model, two convolutional neural networks (CNNs) are trained on each of the tumor image patch and the context image patch, and the features extracted from these two CNNs are combined to train a random forest classifier for predicting the LC and OS responses. In the experiments, we observed that (1) the CNN outperformed the conventional hand-crafted radiomic feature approaches for both the LC and OS prediction tasks, and (2) the contextual information is useful not only individually, but also in combination with the conventional intratumoral information in the proposed model.
KeywordsComputed tomography Hepatocellular carcinoma Prediction model Treatment response Deep learning
This work was supported by Radiation Technology R&D program through the NRF of Korea (NRF-2017M2A2A7A02070427).
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