@INPROCEEDINGS{9176677, author={J. {Gu} and Z. {Zhao} and Z. {Zeng} and Y. {Wang} and Z. {Qiu} and B. {Veeravalli} and B. K. {Poh Goh} and G. {Kunnath Bonney} and K. {Madhavan} and C. W. {Ying} and L. {Kheng Choon} and T. C. {Hua} and P. {KH Chow}}, booktitle={2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC)}, title={Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma}, year={2020}, volume={}, number={}, pages={5814-5817},}
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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Funding Info:
The work was supported by Singapore-China NRF-NSFC Grant (Grant No. NRF2016NRF-NSFC001-111) and Pre-GAP Grant, Singapore (Grant No. ACCL/19-GAP023-R20H) with special acknowledgement to all collaborators in the NMRC Translational and Clinical Research (TCR) Flagship Programme (NMRC/TCR/015-NCC/2016).