Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma

• URL: http://arxiv.org/abs/2005.04069v1
• Date: Fri, 8 May 2020 14:36:59 GMT
• Title: Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma
• Authors: 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
• Abstract summary: We propose an end-to-end deep learning framework for mutation prediction in APOB, COL11A1 and ATRX genes using multiphasic CT scans.
• Score: 7.621860963237023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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