Development of Molecular Magnetic Resonance Imaging Tools for Risk Stratification of Carotid Atherosclerotic Disease Using Dual-Targeted Microparticles of Iron Oxide.

Identification of patients with high-risk asymptomatic carotid plaques remains a challenging but crucial step in stroke prevention. Inflammation is the key factor that drives plaque instability. Currently, there is no imaging tool in routine clinical practice to assess the inflammatory status within atherosclerotic plaques. We have developed a molecular magnetic resonance imaging (MRI) tool to quantitatively report the inflammatory activity in atherosclerosis using dual-targeted microparticles of iron oxide (DT-MPIO) against P-selectin and VCAM-1 as a smart MRI probe. A periarterial cuff was used to generate plaques with varying degree of phenotypes, inflammation and risk levels at specific locations along the same single carotid artery in an Apolipoprotein-E-deficient mouse model. Using this platform, we demonstrated that in vivo DT-MPIO-enhanced MRI can (i) target high-risk vulnerable plaques, (ii) differentiate the heterogeneity (i.e. high vs intermediate vs low-risk plaques) within the asymptomatic plaque population and (iii) quantitatively report the inflammatory activity of local plaques in carotid artery. This novel molecular MRI tool may allow characterisation of plaque vulnerability and quantitative reporting of inflammatory status in atherosclerosis. This would permit accurate risk stratification by identifying high-risk asymptomatic individual patients for prophylactic carotid intervention, expediting early stroke prevention and paving the way for personalised management of carotid atherosclerotic disease.

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.

Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma.

Gene mutation prediction in hepatocellular carcinoma (HCC) is of great diagnostic and prognostic value for personalized treatments and precision medicine. In this paper, we tackle this problem with multi-instance multi-label learning to address the difficulties on label correlations, label representations, etc. Furthermore, an effective oversampling strategy is applied for data imbalance. Experimental results have shown the superiority of the proposed approach.