Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network.

PurposeTo assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and apply this to enable automation of liver biometry.MethodsWe trained a 2D U-Net CNN for liver segmentation in two stages using 330 abdominal MRI and CT exams acquired at our institution. First, we trained the neural network with non-contrast multi-echo spoiled-gradient-echo (SGPR)images with 300 MRI exams to provide multiple signal-weightings. Then, we used transfer learning to generalize the CNN with additional images from 30 contrast-enhanced MRI and CT exams.We assessed the performance of the CNN using a distinct multi-institutional data set curated from multiple sources (n = 498 subjects). Segmentation accuracy was evaluated by computing Dice scores. Utilizing these segmentations, we computed liver volume from CT and T1-weighted (T1w) MRI exams, and estimated hepatic proton- density-fat-fraction (PDFF) from multi-echo T2*w MRI exams. We compared quantitative volumetry and PDFF estimates between automated and manual segmentation using Pearson correlation and Bland-Altman statistics.ResultsDice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1w MR, and 0.92 ± 0.05 for T2*w MR (n = 169). Liver volume measured by manual and automated segmentation agreed closely for CT (95% limit-of-agreement (LoA) = [-298 mL, 180 mL]) and T1w MR (LoA = [-358 mL, 180 mL]). Hepatic PDFF measured by the two segmentations also agreed closely (LoA = [-0.62%, 0.80%]).ConclusionsUtilizing a transfer-learning strategy, we have demonstrated the feasibility of a CNN to be generalized to perform liver segmentations across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization

Agreement Between Magnetic Resonance Imaging Proton Density Fat Fraction Measurements and Pathologist-assigned Steatosis Grades of Liver Biopsies from Adults with Nonalcoholic Steatohepatitis

Background & Aims We assessed the diagnostic performance of magnetic resonance imaging (MRI) proton density fat fraction (PDFF) in grading hepatic steatosis and change in hepatic steatosis in adults with nonalcoholic steatohepatitis (NASH) in a multi-center study, using central histology as reference. Methods We collected data from 113 adults with NASH participating in a multi-center, randomized, double-masked, placebo-controlled, phase 2b trial to compare the efficacy cross-sectionally and longitudinally of obeticholic acid vs placebo. Hepatic steatosis was assessed at baseline and after 72 weeks of obeticholic acid or placebo by liver biopsy and MRI (scanners from different manufacturers, at 1.5T or 3T). We compared steatosis estimates by PDFF vs histology. Histologic steatosis grade was scored in consensus by a pathology committee. Cross-validated receiver operating characteristic (ROC) analyses were performed. Results At baseline, 34% of subjects had steatosis grade 0 or 1, 39% had steatosis grade 2, and 27% had steatosis grade 3; corresponding mean PDFF values were 9.8%±3.7%, 18.1%±4.3%, and 30.1%±8.1%. PDFF classified steatosis grade 0–1 vs 2–3 with an area under the ROC curve (AUROC) of 0.95 (95% CI, 0.91–0.98), and grade 0–2 vs grade 3 steatosis with an AUROC of 0.96 (95% CI, 0.93–0.99). PDFF cut-off values at 90% specificity were 16.3% for grades 2–3 and 21.7% for grade 3, with corresponding sensitivities of 83% and 84%. After 72 weeks' of obeticholic vs placebo, 42% of subjects had a reduced steatosis grade (mean reduction in PDFF from baseline of 7.4%±8.7%), 49% had no change in steatosis grade (mean increase in PDFF from baseline of 0.3%±6.3%), and 9% had an increased steatosis grade (mean increase in PDFF from baseline of 7.7%±6.0%). PDFF change identified subjects with reduced steatosis grade with an AUROC of 0.81 (95% CI, 0.71–0.91) and increased steatosis grade with an AUROC of 0.81 (95% CI, 0.63–0.99). A PDFF reduction of 5.15% identified subjects with reduced steatosis grade with 90% specificity and 58% sensitivity, whereas a PDFF increase of 5.6% identified those with increased steatosis grade with 90% specificity and 57% sensitivity. Conclusions Based on data from a phase 2 randomized controlled trial of adults with NASH, PDFF estimated by MRI scanners of different field strength and at different sites, accurately classifies grades and changes in hepatic steatosis when histologic analysis of biopsies is used as a reference

Bioinformatics-Driven Identification and Examination of Candidate Genes for Non-Alcoholic Fatty Liver Disease

ObjectiveCandidate genes for non-alcoholic fatty liver disease (NAFLD) identified by a bioinformatics approach were examined for variant associations to quantitative traits of NAFLD-related phenotypes.Research Design and MethodsBy integrating public database text mining, trans-organism protein-protein interaction transferal, and information on liver protein expression a protein-protein interaction network was constructed and from this a smaller isolated interactome was identified. Five genes from this interactome were selected for genetic analysis. Twenty-one tag single-nucleotide polymorphisms (SNPs) which captured all common variation in these genes were genotyped in 10,196 Danes, and analyzed for association with NAFLD-related quantitative traits, type 2 diabetes (T2D), central obesity, and WHO-defined metabolic syndrome (MetS).Results273 genes were included in the protein-protein interaction analysis and EHHADH, ECHS1, HADHA, HADHB, and ACADL were selected for further examination. A total of 10 nominal statistical significant associations (P<0.05) to quantitative metabolic traits were identified. Also, the case-control study showed associations between variation in the five genes and T2D, central obesity, and MetS, respectively. Bonferroni adjustments for multiple testing negated all associations.ConclusionsUsing a bioinformatics approach we identified five candidate genes for NAFLD. However, we failed to provide evidence of associations with major effects between SNPs in these five genes and NAFLD-related quantitative traits, T2D, central obesity, and MetS

Traumatic brain injury patient volume and mortality in neurosurgical intensive care units: a Finnish nationwide study

Background: Differences in outcomes after traumatic brain injury (TBI) between neurosurgical centers exist, although the reasons for this are not clear. Thus, our aim was to assess the association between the annual volume of TBI patients and mortality in neurosurgical intensive care units (NICUs).Methods: We collected data on all patients treated in the five Finnish university hospitals to examine all patients with TBI treated in NICUs in Finland from 2009 to 2012. We used a random effect logistic regression model to adjust for important prognostic factors to assess the independent effect of ICU volume on 6-month mortality. Subgroup analyses were performed for patients with severe TBI, moderate-to-severe TBI, and those who were undergoing mechanical ventilation or intracranial pressure monitoring.Results: Altogether 2,328 TBI patients were treated during the study period in five NICUs. The annual TBI patient volume ranged from 61 to 206 patients between the NICUs. Univariate analysis, showed no association between the NICUs' annual TBI patient volume and 6-month mortality (p = 0.063). The random effect model showed no independent association between the NICUs' annual TBI patient volume and 6-month mortality (OR = 1.000, 95% CI = 0.996-1.004, p = 0.876). None of the pre-defined subgroup analyses indicated any association between NICU volume and patient mortality (p > 0.05 for all).Discussion and Conclusion: We did not find any association between annual TBI patient volume and 6-month mortality in NICUs. These findings should be interpreted taking into account that we only included NICUs, which by international standards all treated high volumes of TBI patients, and that we were not able to study the effect of NICU volume on neurological outcome

Elevated Expression of Osteopontin May Be Related to Adipose Tissue Macrophage Accumulation and Liver Steatosis in Morbid Obesity

OBJECTIVE—Osteopontin (OPN) plays an important role in the development of insulin resistance and liver complications in dietary murine models. We aimed to determine the expression pattern of OPN and its receptor CD44 in obese patients and mice according to insulin resistance and liver steatosis

The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population

BACKGROUND: Fatty liver (FL) is the most frequent liver disease in Western countries. We used data from the Dionysos Nutrition & Liver Study to develop a simple algorithm for the prediction of FL in the general population. METHODS: 216 subjects with and 280 without suspected liver disease were studied. FL was diagnosed by ultrasonography and alcohol intake was assessed using a 7-day diary. Bootstrapped stepwise logistic regression was used to identify potential predictors of FL among 13 variables of interest [gender, age, ethanol intake, alanine transaminase, aspartate transaminase, gamma-glutamyl-transferase (GGT), body mass index (BMI), waist circumference, sum of 4 skinfolds, glucose, insulin, triglycerides, and cholesterol]. Potential predictors were entered into stepwise logistic regression models with the aim of obtaining the most simple and accurate algorithm for the prediction of FL. RESULTS: An algorithm based on BMI, waist circumference, triglycerides and GGT had an accuracy of 0.84 (95%CI 0.81–0.87) in detecting FL. We used this algorithm to develop the "fatty liver index" (FLI), which varies between 0 and 100. A FLI < 30 (negative likelihood ratio = 0.2) rules out and a FLI ≥ 60 (positive likelihood ratio = 4.3) rules in fatty liver. CONCLUSION: FLI is simple to obtain and may help physicians select subjects for liver ultrasonography and intensified lifestyle counseling, and researchers to select patients for epidemiologic studies. Validation of FLI in external populations is needed before it can be employed for these purposes

Reference range of liver corrected T1 values in a population at low risk for fatty liver disease-a UK Biobank sub-study, with an appendix of interesting cases

Purpose: Corrected T1 (cT1) value is a novel MRI-based quantitative metric for assessing a composite of liver inflammation and fibrosis. It has been shown to distinguish between non-alcoholic fatty liver disease (NAFL) and non-alcoholic steatohepatitis. However, these studies were conducted in patients at high risk for liver disease. This study establishes the normal reference range of cT1 values for a large UK population, and assesses interactions of age and gender. Methods: MR data were acquired on a 1.5T system as part of the UK Biobank Imaging Enhancement study. Measures for Proton Density Fat Fraction and cT1 were calculated from the MRI data using a multi-parametric MRI software application. Data that did not meet quality criteria were excluded from further analysis. Inter and intra-reader variability was estimated in a set of data. A cohort at low risk for NAFL was identified by excluding individuals with BMI ≥ 25kg/m2 and PDFF ≥ 5%. Of the 2816 participants with data of suitable quality, 1037 (37%) were classified as at low risk. Results: The cT1 values in the low risk population ranged from 573 to 852 ms with a median of 666 ms and interquartile range from 643-694 ms. Iron correction of T1 was necessary in 36.5% of this reference population. Age and gender had minimal effect on cT1 values. Conclusion: The majority of cT1 values are tightly clustered in a population at low risk for NAFL; suggesting it has the potential to serve as a new quantitative imaging biomarker for studies of liver health and disease

Державне регулювання системи факторів оцінки та мінімізації ризиків легалізації коштів, одержаних злочинним шляхом в процесі фінансового моніторингу комерційних банків України

Основною ціллю данної статті є формулювання важливості впливу чітко визначених факторів, які впливають на процеси фінансового моніторингу в комерційних банків України. Виходячи з поставлених цілей, завданнями даної статті є розроблення моделі оцінки ризиків банківської установи щодо протидії легалізації коштів одержаних злочинним шляхом в системі внутрішньобанківського фінансового моніторингу, та використання в подальшому запропонованих стратегій управління наслідками даних ризиків