论文标题
多相跨模式学习，用于肝细胞癌中非侵入性基因突变预测
Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma
论文作者
论文摘要
肝细胞癌（HCC）是最常见的原发性肝癌类型，也是全球与癌症相关死亡的第四大原因。了解HCC中的潜在基因突变为治疗计划和靶向治疗提供了巨大的预后价值。放射基因组学揭示了非侵入性成像特征与分子基因组学之间的关联。但是，成像功能识别是费力且容易出错的。在本文中，我们提出了使用多相CT扫描的APOB，COL11A1和ATRX基因突变预测的端到端深度学习框架。考虑到HCC中的群众内异质性（ITH），实施了多区域采样技术来生成用于实验的数据集。实验结果证明了所提出的模型的有效性。
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.
