1. Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma
- Author
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Gu, Jiapan, Zhao, Ziyuan, Zeng, Zeng, Wang, Yuzhe, Qiu, Zhengyiren, Veeravalli, Bharadwaj, Goh, Brian Kim Poh, Bonney, Glenn Kunnath, Madhavan, Krishnakumar, Ying, Chan Wan, Choon, Lim Kheng, Hua, Thng Choon, and Chow, Pierce KH
- Subjects
Quantitative Biology - Quantitative Methods ,Computer Science - Computer Vision and Pattern Recognition ,Electrical Engineering and Systems Science - Image and Video Processing ,Quantitative Biology - Genomics - Abstract
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the fourth most common cause of cancer-related death worldwide. Understanding the underlying gene mutations in HCC provides great prognostic value for treatment planning and targeted therapy. Radiogenomics has revealed an association between non-invasive imaging features and molecular genomics. However, imaging feature identification is laborious and error-prone. In this paper, we propose an end-to-end deep learning framework for mutation prediction in APOB, COL11A1 and ATRX genes using multiphasic CT scans. Considering intra-tumour heterogeneity (ITH) in HCC, multi-region sampling technology is implemented to generate the dataset for experiments. Experimental results demonstrate the effectiveness of the proposed model., Comment: Accepted version to be published in the 42nd IEEE Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2020, Montreal, Canada
- Published
- 2020
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