Cardiovascular resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV). By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images. On a short-axis image test set of 600 subjects, it achieves an average Dice metric of 0.94 for the LV cavity, 0.88 for the LV myocardium and 0.90 for the RV cavity. The mean absolute difference between automated measurement and manual measurement is 6.1 mL for LVEDV, 5.3 mL for LVESV, 6.9 gram for LVM, 8.5 mL for RVEDV and 7.2 mL for RVESV. On long-axis image test sets, the average Dice metric is 0.93 for the LA cavity (2-chamber view), 0.95 for the LA cavity (4-chamber view) and 0.96 for the RA cavity (4-chamber view). The performance is comparable to human inter-observer variability. We show that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinically relevant measures.

Purpose: Although microRNAs have recently been recognized as riboregulators of gene expression, little is known about microRNA expression profiles in serous ovarian carcinoma. We assessed the expression of microRNA and the association between microRNA expression and the prognosis of serous ovarian carcinoma. Experimental Design: Twenty patients diagnosed with serous ovarian carcinoma and eight patients treated for benign uterine disease between December 2000 and September 2003 were enrolled in this study. The microRNA expression profiles were examined using DNA microarray and Northern blot analyses. Results: Several microRNAs were differentially expressed in serous ovarian carcinoma compared with normal ovarian tissues, including miR-21, miR-125a, miR-125b, miR-100, miR-145, miR-16, and miR-99a, which were each differentially expressed in >16 patients. In addition, the expression levels of some microRNAs were correlated with the survival in patients with serous ovarian carcinoma. Higher expression of miR-200, miR-141, miR-18a, miR-93, and miR-429, and lower expression of let-7b, and miR-199a were significantly correlated with a poor prognosis (P < 0.05). Conclusion: Our results indicate that dysregulation of microRNAs is involved in ovarian carcinogenesis and associated with the prognosis of serous ovarian carcinoma.

Kim PK, et al. Korean J Radiol. 2017 Jan-Feb;18(1):113-131. https://doi.org/10.3348/kjr.2017.18.1.113

Approximately 80% of hepatocellular carcinoma (HCC) cases arise in sub-Saharan Africa and Eastern Asia, following a similarly high prevalence of chronic hepatitis B virus (HBV) carriers in these regions. The etiology and epidemiology of HCC have recently changed worldwide. Although HBV infection is the main contributor to HCC development, a slow but continuous decline in HBV infection rates has been reported since 1990. Owing to the widespread use of direct-acting antivirals, the incidence of hepatitis C virus-related HCC has remarkably decreased in Japan and European countries. In Korea, Taiwan, and Singapore, the incidence of HBV-related HCC has significantly decreased owing to vaccination against HBV. Globally, while HBV accounted for more than half of HCCs in 1990, this had decreased to 42% in 2019. In contrast, the proportion of patients with alcoholic- and nonalcoholic steatohepatitis (NASH) increased from 13% to 18% and from 5% to 6%, respectively. NASH-related HCC has characteristics that differ from those of virus-associated HCC. Compared with other etiologies, patients with NASHassociated HCC are older, have a higher body mass index, and have higher rates of type 2 diabetes mellitus, hypertension, hyperlipidemia, and cardiovascular disease. Nonalcoholic fatty liver disease (NAFLD)-associated HCC is also known to develop in the absence of cirrhosis, unlike alcohol-related and autoimmune liver diseases. Because patients with NAFLD usually have diabetes or obesity, surveying this population is challenging. Optimal selection of the target population and surveillance tools among patients with NAFLD needs to be determined.

The functions of many long non-coding RNAs (lncRNAs) in human cancers remain to be clarified. The lncRNA Hox transcript antisense intergenic RNA (HOTAIR) has been reported to reprogram chromatin organization and promote breast and colorectal cancer metastasis, the involvement of lncRNAs in cervical cancer is just beginning to be studied. In the present study, we examined the expression and the functional role of HOTAIR in cervical cancer. HOTAIR expression was determined in cervical cancer tissues (n=111) and corresponding normal tissues (n=40) by using real-time polymerase chain reaction, and its correlation with clinical parameters and prognosis were analyzed. To determine the effect of HOTAIR knockdown and overexpression in cervical cancer cell lines, we used the CCK-8 assay, wound healing migration and matrigel invasion assay. The expression level of HOTAIR in cervical cancer tissues was higher than that in corresponding non-cancerous tissues. High HOTAIR expression correlated with lymph node metastasis, and reduced overall survival. A multivariate analysis showed that HOTAIR was a prognostic factor for predicting cervical cancer recurrence. Knockdown of HOTAIR reduced cell proliferation, migration, and invasion in cervical cancer cell lines. Moreover, HOTAIR regulated the expression of vascular endothelial growth factor, matrix metalloproteinase-9 and epithelial-to-mesenchymal transition (EMT)-related genes, which are important for cell motility and metastasis. Therefore, HOTAIR may promote tumor aggressiveness through the upregulation of VEGF and MMP-9 and EMT-related genes. These findings indicate that HOTAIR may represent a novel biomarker for predicting recurrence and prognosis and serve as a promising therapeutic target in cervical cancer.