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.

Clinics in diagnostic imaging (193). Sporadic Creutzfeldt-Jakob disease (sCJD)

A 68-year-old man presented with a three-week history of rapidly progressive dementia, gait ataxia and myoclonus. Subsequent electroencephalography showed periodic sharp wave complexes, and cerebrospinal fluid assay revealed the presence of a 14-3-3 protein. A probable diagnosis of sporadic Creutzfeldt-Jakob disease was made, which was further supported by magnetic resonance (MR) imaging of the brain showing asymmetric signal abnormality in the cerebral cortices and basal ganglia. The aetiology, clinical features, diagnostic criteria, various MR imaging patterns and radiologic differential diagnosis of sporadic Creutzfeldt-Jakob disease are discussed in this article.