Objectives The primary objective was to compare the performance of 3 different abbreviated MRI (AMRI) sets extracted from a complete gadoxetate-enhanced MRI obtained for hepatocellular carcinoma (HCC) screening. Secondary objective was to perform a preliminary cost-effectiveness analysis, comparing each AMRI set to published ultrasound performance for HCC screening in the USA. Methods This retrospective study included 237 consecutive patients (M/F, 146/91; mean age, 58 years) with chronic liver disease who underwent a complete gadoxetate-enhanced MRI for HCC screening in 2017 in a single institution. Two radiologists independently reviewed 3 AMRI sets extracted from the complete exam: non-contrast (NC-AMRI: T2-weighted imaging (T2wi)+diffusion-weighted imaging (DWI)), dynamic-AMRI (Dyn-AMRI: T2wi+DWI+dynamic T1wi), and hepatobiliary phase AMRI (HBP-AMRI: T2wi+DWI+T1wi during the HBP). Each patient was classified as HCC-positive/HCC-negative based on the reference standard, which consisted in all available patient data. Diagnostic performance for HCC detection was compared between sets. Estimated set characteristics, including historical ultrasound data, were incorporated into a microsimulation model for cost-effectiveness analysis. ResultsThe reference standard identified 13/237 patients with HCC (prevalence, 5.5%; mean size, 33.7 ± 30 mm). Pooled sensitivities were 61.5% for NC-AMRI (95% confidence intervals, 34.4-83%), 84.6% for Dyn-AMRI (60.8-95.1%), and 80.8% for HBP-AMRI (53.6-93.9%), without difference between sets (p range, 0.06-0.16). Pooled specificities were 95.5% (92.4-97.4%), 99.8% (98.4-100%), and 94.9% (91.6-96.9%), respectively, with a significant difference between Dyn-AMRI and the other sets (p < 0.01). All AMRI methods were effective compared with ultrasound, with life-year gain of 3-12 months against incremental costs of US$ < 12,000.

BaCKgRoUND aND aIMS: Mutations in TERT (telomerase reverse transcriptase) promoter are established gatekeepers in early hepatocarcinogenesis, but little is known about other molecular alterations driving this process. Epigenetic deregulation is a critical event in early malignancies. Thus, we aimed to (1) analyze DNA methylation changes during the transition from preneoplastic lesions to early HCC (eHCC) and identify candidate epigenetic gatekeepers, and to (2) assess the prognostic potential of methylation changes in cirrhotic tissue. appRoaCH aND ReSUltS: Methylome profiling was performed using Illumina HumanMethylation450 (485,000 cytosine-phosphateguanine, 96% of known cytosinephosphateguanine islands), with data available for a total of 390 samples: 16 healthy liver, 139 cirrhotic tissue, 8 dysplastic nodules, and 227 HCC samples, including 40 eHCC below 2cm. A phylo-epigenetic tree derived from the Euclidean distances between differentially DNA-methylated sites (n = 421,997) revealed a gradient of methylation changes spanning healthy liver, cirrhotic tissue, dysplastic nodules, and HCC with closest proximity of dysplasia to HCC. Focusing on promoter regions, we identified epigenetic gatekeeper candidates with an increasing proportion of hypermethylated samples (beta value > 0.5) from cirrhotic tissue (<1%), to dysplastic nodules (≥25%), to eHCC (≥50%), and confirmed inverse correlation between DNA methylation and gene expression for TSPYL5 (testis-specific Y-encoded-like protein 5), KCNA3 (potassium voltage-gated channel, shaker-related subfamily, member 3), LDHB (lactate dehydrogenase B), and SPINT2 (serine peptidase inhibitor, Kunitz type 2) (all P < 0.001). Unsupervised clustering of genome-wide methylation profiles of cirrhotic tissue identified two clusters, M1 and M2, with 42% and 58% of patients, respectively, which correlates with survival (P < 0.05), independent of etiology.CoNClUSIoNS: Genome-wide DNA-methylation profiles accurately discriminate the different histological stages of human hepatocarcinogenesis. We report on epigenetic gatekeepers in the transition between dysplastic nodules and eHCC. DNA-methylation changes in cirrhotic tissue correlate with clinical outcomes. (Hepatology 2021;74:183-199).H CC, the most common form of primary liver cancer, typically occurs in patients with chronic liver disease, primarily cirrhosis. HBV and HCV virus infection, alcohol use disorder, and NAFLD are the major etiologies. (1) In the USA, the liver cancer death rate increased 43% between 2000 and 2016. (2) There is a limited understanding of the molecular events leading to malignant transformation in the context of cirrhosis. (3) Specifically, little is known about the molecular mechanisms that govern the transition from cirrhosis to dysplasia and early HCC (eHCC). TERT (telomerase reverse transcriptase) promoter mutations are the only bone fide early molecular events in hepatocarcinogenesis.

The American Society of Anesthesiologist recommends peripheral physiological monitoring during general anesthesia, which offers no information regarding the effects of anesthetics on the brain. Since no "gold standard" method exists for this evaluation, such a technique is needed to ensure patient comfort, procedure quality and safety. In this study we investigated functional near infrared spectroscopy (fNIRS) as possible monitor of anesthetic effects on the prefrontal cortex. Anesthetic drugs, such as sevoflurane, suppress the cerebral metabolism and alter the cerebral blood flow. We hypothesize that fNIRS derived features carry information on the effects of anesthetics on the prefrontal cortex (PFC) that can be used for the classification of the anesthetized state. In this study, patients were continuously monitored using fNIRS, BIS and standard monitoring during surgical procedures under sevoflurane general anesthesia. Maintenance and emergence states were identified and fNIRS features were identified and compared between states. Linear and non-linear machine learning algorithms were investigated as methods for the classification of maintenance/emergence. The results show that changes in oxygenated (HbO) and deoxygenated hemoglobin (HHb) concentration and blood volume measured by fNIRS were associated with the transition between maintenance and emergence that occurs as a result of sevoflurane washout. We observed that during maintenance the signal is relatively more stable than during emergence. Maintenance and emergence states were classified with 94.7% accuracy with a non-linear model using the locally derived mean total hemoglobin, standard deviation of HbO, minimum and range of HbO and HHb as features. These features were found to be correlated with the effects of sevoflurane and to carry information that allows real time and automatic classification of the anesthetized state with high accuracy.

The standard-of-care guidelines published by the American Society of Anesthesiologists (ASA) recommend monitoring of pulse oximetry, blood pressure, heart rate, and end tidal CO2 during the use of anesthesia and sedation. This information can help to identify adverse events that may occur during procedures. However, these parameters are not specific to the effects of anesthetics or sedatives, and therefore they offer little, to no, real time information regarding the effects of those agents and do not give the clinician the lead-time necessary to prevent patient “awareness.” Since no “gold-standard” method is available to continuously, reliably, and effectively monitor the effects of sedatives and anesthetics, such a method is greatly needed. Investigation of the use of functional near-infrared spectroscopy (fNIRS) as a method for anesthesia or sedation monitoring and for the assessment of the effects of various anesthetic drugs on cerebral oxygenation has started to be conducted. The objective of this paper is to provide a thorough review of the currently available published scientific studies regarding the use of fNIRS in the fields of anesthesia and sedation monitoring, comment on their findings, and discuss the future work required for the translation of this technology to the clinical setting.