Hepatocellular carcinoma in non-alcoholic fatty liver disease with and without cirrhosis

Onzi, Georgia; Moretti, F.; Balbinot, Raul Angelo; Soldera, Jonathan

doi:10.20517/2394-5079.2018.114

Cited by 6 publications

(3 citation statements)

References 42 publications

(82 reference statements)

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“…HCC is a common indication for LT, ranking fifth among the most prevalent malignancies and being the third leading cause of cancer-related mortality worldwide[ 90 - 92 ]. LT offers a promising therapeutic option for long-term survival in HCC cases by addressing both advanced liver disease and HCC itself[ 93 , 94 ].…”

Section: Discussionmentioning

confidence: 99%

Use of machine learning models for the prognostication of liver transplantation: A systematic review

Chongo,

Soldera

2024

World J Transplant

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BACKGROUND Liver transplantation (LT) is a life-saving intervention for patients with end-stage liver disease. However, the equitable allocation of scarce donor organs remains a formidable challenge. Prognostic tools are pivotal in identifying the most suitable transplant candidates. Traditionally, scoring systems like the model for end-stage liver disease have been instrumental in this process. Nevertheless, the landscape of prognostication is undergoing a transformation with the integration of machine learning (ML) and artificial intelligence models. AIM To assess the utility of ML models in prognostication for LT, comparing their performance and reliability to established traditional scoring systems. METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, we conducted a thorough and standardized literature search using the PubMed/MEDLINE database. Our search imposed no restrictions on publication year, age, or gender. Exclusion criteria encompassed non-English studies, review articles, case reports, conference papers, studies with missing data, or those exhibiting evident methodological flaws. RESULTS Our search yielded a total of 64 articles, with 23 meeting the inclusion criteria. Among the selected studies, 60.8% originated from the United States and China combined. Only one pediatric study met the criteria. Notably, 91% of the studies were published within the past five years. ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values (ranging from 0.6 to 1) across all studies, surpassing the performance of traditional scoring systems. Random forest exhibited superior predictive capabilities for 90-d mortality following LT, sepsis, and acute kidney injury (AKI). In contrast, gradient boosting excelled in predicting the risk of graft-versus-host disease, pneumonia, and AKI. CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT, marking a significant evolution in the field of prognostication.

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Section: Discussionmentioning

confidence: 99%

Use of machine learning models for the prognostication of liver transplantation: A systematic review

Chongo,

Soldera

2024

World J Transplant

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“…Liver cirrhosis and HCC are complications of NASH when this condition is not properly treated. It is interesting to note that HCC is reported also in non-cirrhotic NASH patients [15].…”

Section: Non-alcoholic Fatty Liver Disease and Non-alcoholic Steatohe...mentioning

confidence: 99%

Underlying Liver Disease

Pellicelli

2022

Hepatocellular Carcinoma

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“…Furthermore, being frequently associated with type II diabetes mellitus, NAFLD is a direct effect of peripheral insulin resistance . (4) Even though over the years numerous studies covered the pathophysiology of NAFLD/NASH, there is a multitude of mechanisms that remain unknown or poorly understood. These two physiological activities of adiponutrin suggest its double metabolic roles, as a catabolic lipase, which catalyses the breakdown of triglycerides into fatty acids and glycerol, and also as an anabolic lipogenic enzyme .…”

Section: Etiology and Pathophysiologymentioning

confidence: 99%

Insights into Non-Alcoholic Fatty Liver Disease and Non-Alcoholic Steatohepatitis

et al. 2022

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Non-alcoholic fatty liver disease (NAFLD) constitutes a common pathological condition of the liver, the prevalence of which is currently increasing in western countries. NAFLD is frequently diagnosed in males and its incidence is higher in individuals with type 2 diabetes mellitus and obesity. Hence, the disease is considered to be the hepatic manifestation of the metabolic syndrome. A multitude of interconnected risk factors have been described over the years – genetic, hormonal and nutritional, which play important roles in the development of NAFLD. Insulin resistance is considered to be the central pathophysiological condition that promotes the disease in diabetic patients, whereas dyslipidemia and cardiovascular comorbidities (arterial hypertension, ischaemic heart disease) are frequently associated conditions. Although there are currently numerous pathophysiological mechanisms involved in NAFLD that are still unknown or poorly understood, there has been some advancements concerning the pathogenesis of the disease and its progression towards its severe form, known as non-alcoholic steatohepatitis (NASH). In the absence of a clear diagnosis and carefully controlled treatment, NAFLD/NASH may evolve towards liver cirrhosis, liver failure or hepatocellular carcinoma. However, the disease may also generate systemic effects, including the development of chronic kidney disease (CKD). The diagnosis of NAFLD/NASH is based both on its clinical manifestations, revealed by a carefully conducted patient history and physical examination of the patient, and on other investigations; histopathological findings upon liver biopsy, liver ultrasonography and the use of transient elastography (or FibroScan) are some of the most important investigations in NAFLD/NASH. The understanding of the most important risk factors and pathogenic mechanisms of the disease is fundamental for the elaboration of the most efficient treatment, to prevent chronic liver disease or the development of hepatocellular carcinoma.

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Hepatocellular carcinoma in non-alcoholic fatty liver disease with and without cirrhosis

Cited by 6 publications

References 42 publications

Use of machine learning models for the prognostication of liver transplantation: A systematic review

Use of machine learning models for the prognostication of liver transplantation: A systematic review

Underlying Liver Disease

Insights into Non-Alcoholic Fatty Liver Disease and Non-Alcoholic Steatohepatitis

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