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.
|Title of host publication||Predictive Intelligence in Medicine - 2nd International Workshop, PRIME 2019, Held in Conjunction with MICCAI 2019, Proceedings|
|Editors||Islem Rekik, Ehsan Adeli, Sang Hyun Park|
|Number of pages||9|
|Publication status||Published - 2019|
|Event||2nd International Workshop on Predictive Intelligence in Medicine, PRIME 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China|
Duration: 2019 Oct 13 → 2019 Oct 13
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||2nd International Workshop on Predictive Intelligence in Medicine, PRIME 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019|
|Period||19/10/13 → 19/10/13|
Bibliographical noteFunding Information:
Acknowledgments. This work was supported by Radiation Technology R&D program through the NRF of Korea (NRF-2017M2A2A7A02070427).
© Springer Nature Switzerland AG 2019.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)