The many faces of cirrhosis

Schwope, Ryan B.; Katz, Matthew; Russell, T. S.; Reiter, Michael J.; Lisanti, Christopher J.

doi:10.1007/s00261-019-02095-z

Cited by 4 publications

(5 citation statements)

References 60 publications

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“…IBD comprising ulcerative and Crohn’s disease is a chronic inflammatory disorder of the GI tract, and in developing countries has an ascending prevalence ( Zuo and Ng, 2018 ). The terminal phase of chronic liver disease is cirrhosis which accounts for the main cause of death in the West and has several causes like alcohol abuse, chronic viral infection, or nonalcoholic fatty liver disease ( Schwope et al., 2020 ). GIB especially upper GIB is a frequently seen problem in elderly patients with a case fatality percentage between 5% and 14% ( Valkhoff et al., 2012 ).…”

Section: Metabolic Disorders As Risk Factors For Covid-19mentioning

confidence: 99%

“…In patients with COVID-19 found in critical state digestive manifestations occur in 36%–50.5% ( Gulen and Satar, 2020 ). In cirrhosis GI wall thickening or the misuse of alcohol is indistinctly linked to the gut microbiota ( Bajaj, 2019 ; Schwope et al., 2020 ). SARS-CoV-2 related liver damage reported in infected patients, is mostly due to enhanced systemic inflammation and immune immobility, causing profound immunological dysfunctions or even increased mortality rate ( Rela et al., 2020 ; Iavarone et al., 2020 ).…”

Section: Metabolic Disorders As Risk Factors For Covid-19mentioning

confidence: 99%

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Coronavirus Disease (COVID-19) Caused by (SARS-CoV-2) Infections: A Real Challenge for Human Gut Microbiota

Vodnar

Mitrea

Teleky

et al. 2020

Front. Cell. Infect. Microbiol.

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The current COVID-19 pandemic is a great challenge for worldwide researchers in the human microbiota area because the mechanisms and long-term effects of the infection at the GI level are not yet deeply understood. In the current review, scientific literature including original research articles, clinical studies, epidemiological reports, and review-type articles concerning human intestinal infection with SARS-CoV-2 and the possible consequences on the microbiota were reviewed. Moreover, the following aspects pertaining to COVID-19 have also been discussed: transmission, resistance in the human body, the impact of nutritional status in relation to the intestinal microbiota, and the impact of comorbid metabolic disorders such as inflammatory bowel disease (IBS), obesity, and type two diabetes (T2D). The articles investigated show that health, age, and nutritional status are associated with specific communities of bacterial species in the gut, which could influence the clinical course of COVID-19 infection. Fecal microbiota alterations were associated with fecal concentrations of SARS-CoV-2 and COVID-19 severity. Patients suffering from metabolic and gastrointestinal (GI) disorders are thought to be at a moderate-to-high risk of infection with SARS-CoV-2, indicating the direct implication of gut dysbiosis in COVID-19 severity. However, additional efforts are required to identify the initial GI symptoms of COVID-19 for possible early intervention.

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Section: Metabolic Disorders As Risk Factors For Covid-19mentioning

confidence: 99%

Section: Metabolic Disorders As Risk Factors For Covid-19mentioning

confidence: 99%

Coronavirus Disease (COVID-19) Caused by (SARS-CoV-2) Infections: A Real Challenge for Human Gut Microbiota

Vodnar

Mitrea

Teleky

et al. 2020

Front. Cell. Infect. Microbiol.

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“…This may be due to less distinctiveness between imaging criteria indicating different causes of the disease compared to image criteria distinguishing a diseased organ from a non-diseased organ. For instance, it has been described that a hypertrophic appearance of the central hepatic parenchyma/caudate lobe is expected in alcohol-related cirrhosis, but also in primary sclerosing cholangitis and Budd-Chiari syndrome related cirrhosis 11 .…”

Section: Discussionmentioning

confidence: 99%

“…As described above, hypertrophies of the caudate lobe and central hepatic areas are more frequently observed in patients with alcohol-related diseases, but are also seen in other etiologies. To the best of our knowledge, there are currently no studies presenting metrics for the diagnostic accuracy of cirrhosis etiology based on such imaging criteria 11 , 12 . However, a very recent work investigated a radiomics approach that relates imaging features to the etiology of liver cirrhosis, and also achieved promising results 21 .…”

Section: Discussionmentioning

confidence: 99%

“…Although it has been described that the micro- and macroscopic appearance of cirrhosis in medical imaging varies to some extent according to the underlying etiology, the use of imaging features as a means to determine the cause of the disease has not been established yet 9 , 11 , 12 . In a previous study, a convolutional neural network (CNN) was already shown to be able to detect liver cirrhosis based on standard clinical MRI sequences with expert-level accuracy irrespective of etiology 13 .…”

Section: Introductionmentioning

confidence: 99%

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Deep learning supports the differentiation of alcoholic and other-than-alcoholic cirrhosis based on MRI

Luetkens

Nowak

Mesropyan

et al. 2022

Sci Rep

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Although CT and MRI are standard procedures in cirrhosis diagnosis, differentiation of etiology based on imaging is not established. This proof-of-concept study explores the potential of deep learning (DL) to support imaging-based differentiation of the etiology of liver cirrhosis. This retrospective, monocentric study included 465 patients with confirmed diagnosis of (a) alcoholic (n = 221) and (b) other-than-alcoholic (n = 244) cirrhosis. Standard T2-weighted single-slice images at the caudate lobe level were randomly split for training with fivefold cross-validation (85%) and testing (15%), balanced for (a) and (b). After automated upstream liver segmentation, two different ImageNet pre-trained convolutional neural network (CNN) architectures (ResNet50, DenseNet121) were evaluated for classification of alcohol-related versus non-alcohol-related cirrhosis. The highest classification performance on test data was observed for ResNet50 with unfrozen pre-trained parameters, yielding an area under the receiver operating characteristic curve of 0.82 (95% confidence interval (CI) 0.71–0.91) and an accuracy of 0.75 (95% CI 0.64–0.85). An ensemble of both models did not lead to significant improvement in classification performance. This proof-of-principle study shows that deep-learning classifiers have the potential to aid in discriminating liver cirrhosis etiology based on standard MRI.

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