3 research outputs found
Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma
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, Canad