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.
Jiapan Gu, Ziyuan Zhao, Zeng Zeng, Yuzhe Wang, Zhengyiren Qiu, Bharadwaj Veeravalli, Brian Kim Poh Goh, Glenn Kunnath Bonney, Krishnakumar Madhavan, Chan Wan Ying, Lim Kheng Choon, Thng Choon Hua, Pierce KH Chow