Nonalcoholic fatty liver disease and hepatocellular carcinoma:Insights in epidemiology, pathogenesis, imaging, prevention and therapy.

Hepatocellular carcinoma (HCC) is estimated to be the third leading cause of cancer-related mortality and is characterized by low survival rates. Nonalcoholic fatty liver disease (NAFLD) is emerging as a leading cause of HCC, whose rates are increasing, owing to the increasing prevalence of NAFLD. The pathogenesis of NAFLD-associated HCC is multifactorial: insulin resistance, obesity, diabetes and the low-grade hepatic inflammation, which characterizes NAFLD, seem to play key roles in the development and progression of HCC. The diagnosis of NAFLD-associated HCC is based on imaging in the presence of liver cirrhosis, preferably computerized tomography or magnetic resonance imaging, but liver biopsy for histological confirmation is usually required in the absence of liver cirrhosis. Some preventive measures have been recommended for NAFLD-associated HCC, including weight loss, cessation of even moderate alcohol drinking and smoking, as well as the use of metformin, statins and aspirin. However, these preventive measures are mainly based on observational studies, thus they need validation in trials of different design before introducing in clinical practice. The treatment of NAFLD should be tailored on an individual basis and should be ideally determined by a multidisciplinary team. In the last two decades, new medications, including tyrosine kinase inhibitors and immune checkpoints inhibitors, have improved the survival of patients with advanced HCC, but trials specifically designed for patients with NAFLD-associated HCC are scarce. The aim of this review was to overview evidence on the epidemiology and pathophysiology of NAFLD-associated HCC, then to comment on imaging tools for its appropriate screening and diagnosis, and finally to critically summarize the currently available options for its prevention and treatment.

Corrigendum to "A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning" [IBRO Neurosci Rep. 15 (2023) 77-89].

[This corrects the article DOI: 10.1016/j.ibneur.2023.06.008.].

A characteristic cerebellar biosignature for bipolar disorder, identified with fully automatic machine learning.

Background: Transcriptomic profile differences between patients with bipolar disorder and healthy controls can be identified using machine learning and can provide information about the potential role of the cerebellum in the pathogenesis of bipolar disorder.With this aim, user-friendly, fully automated machine learning algorithms can achieve extremely high classification scores and disease-related predictive biosignature identification, in short time frames and scaled down to small datasets. Method: A fully automated machine learning platform, based on the most suitable algorithm selection and relevant set of hyper-parameter values, was applied on a preprocessed transcriptomics dataset, in order to produce a model for biosignature selection and to classify subjects into groups of patients and controls. The parent GEO datasets were originally produced from the cerebellar and parietal lobe tissue of deceased bipolar patients and healthy controls, using Affymetrix Human Gene 1.0 ST Array. Results: Patients and controls were classified into two separate groups, with no close-to-the-boundary cases, and this classification was based on the cerebellar transcriptomic biosignature of 25 features (genes), with Area Under Curve 0.929 and Average Precision 0.955. The biosignature includes both genes connected before to bipolar disorder, depression, psychosis or epilepsy, as well as genes not linked before with any psychiatric disease. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed participation of 4 identified features in 6 pathways which have also been associated with bipolar disorder. Conclusion: Automated machine learning (AutoML) managed to identify accurately 25 genes that can jointly - in a multivariate-fashion - separate bipolar patients from healthy controls with high predictive power. The discovered features lead to new biological insights. Machine Learning (ML) analysis considers the features in combination (in contrast to standard differential expression analysis), removing both irrelevant as well as redundant markers, and thus, focusing to biological interpretation.

Posterior cerebral artery dissection after excessive caffeine consumption in a teenager.

Arterial ischemic stroke is a rare but significant cause of neurological deficits in childhood. Even though there is a variety of risk factors, identifying the etiology can sometimes be a hard diagnostic challenge. Arteriopathies in general, and more specifically, arterial dissection is one of the uncommon pathologies that can cause incidents of pediatric stroke. We report a rare case of a young adolescent with posterior cerebral artery dissection after excessive consumption of caffeine, contained in energy drinks, only hours before the onset of neurological symptoms. A complete neuroimaging evaluation (MRI, intracranial US and digital subtraction angiography) at the admission and during the follow-ups supported the diagnosis of arterial dissection possibly caused by caffeine overconsumption.

Current Imaging Diagnosis of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer related death worldwide. Radiology has traditionally played a central role in HCC management, ranging from screening of high-risk patients to non-invasive diagnosis, as well as the evaluation of treatment response and post-treatment follow-up. From liver ultrasonography with or without contrast to dynamic multiple phased CT and dynamic MRI with diffusion protocols, great progress has been achieved in the last decade. Throughout the last few years, pathological, biological, genetic, and immune-chemical analyses have revealed several tumoral subtypes with diverse biological behavior, highlighting the need for the re-evaluation of established radiological methods. Considering these changes, novel methods that provide functional and quantitative parameters in addition to morphological information are increasingly incorporated into modern diagnostic protocols for HCC. In this way, differential diagnosis became even more challenging throughout the last few years. Use of liver specific contrast agents, as well as CT/MRI perfusion techniques, seem to not only allow earlier detection and more accurate characterization of HCC lesions, but also make it possible to predict response to treatment and survival. Nevertheless, several limitations and technical considerations still exist. This review will describe and discuss all these imaging modalities and their advances in the imaging of HCC lesions in cirrhotic and non-cirrhotic livers. Sensitivity and specificity rates, method limitations, and technical considerations will be discussed.

Imaging of nonalcoholic fatty liver disease and its clinical utility.

The prevalence of nonalcoholic fatty liver disease has been continuously rising over the last three decades and is projected to become the most common indication for liver transplantation in the near future. Its pathophysiology and complex interplay with diabetes and the metabolic syndrome are not as yet fully understood despite growing scientific interest and research. Modern imaging techniques offer significant assistance in this field by enabling the study of the liver noninvasively and evaluation of the degree of both steatosis and fibrosis, and even in attempting to diagnose the presence of inflammation (steatohepatitis). The derived measurements are highly precise, accurate and reproducible, performing better than biopsy in terms of quantification. In this article, these imaging techniques are overviewed and their performance regarding diagnosis, stratification and monitoring are evaluated. Their expanding role both in the research arena and in clinical practice along with their limitations is also discussed.