2021
DOI: 10.3390/cancers13184567
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Liver Stiffness-Based Risk Prediction Model for Hepatocellular Carcinoma in Patients with Nonalcoholic Fatty Liver Disease

Abstract: Non-alcoholic fatty liver disease (NAFLD) is associated with an increased hepatocellular carcinoma (HCC) risk. We established and validated a liver stiffness (LS)-based risk prediction model for HCC development in patients with NAFLD. A total of 2666 and 467 patients with NAFLD were recruited in the training and validation cohorts, respectively. NAFLD was defined as controlled attenuated parameter ≥238 dB/m by transient elastography. Over a median of 64.6 months, HCC developed in 22 (0.8%) subjects in the trai… Show more

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Cited by 12 publications
(7 citation statements)
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References 37 publications
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“…Changes in ECM composition are one of the characterizing features of the TME and it is widely accepted that this actively contributes to disease progression in different tumors 20,21 . This significance of altered ECM is seen in HCC patients, where increases in liver stiffness are associated with hepatic dysfunction, poor prognosis, and survival [22][23][24] . Recent studies have even shown that increased liver stiffness is a predictor for mortality and need for mechanical ventilation among patients hospitalized with COVID-19 25 .…”
Section: Discussionmentioning
confidence: 99%
“…Changes in ECM composition are one of the characterizing features of the TME and it is widely accepted that this actively contributes to disease progression in different tumors 20,21 . This significance of altered ECM is seen in HCC patients, where increases in liver stiffness are associated with hepatic dysfunction, poor prognosis, and survival [22][23][24] . Recent studies have even shown that increased liver stiffness is a predictor for mortality and need for mechanical ventilation among patients hospitalized with COVID-19 25 .…”
Section: Discussionmentioning
confidence: 99%
“…Unlike previous studies [ 37 , 38 ], age was not a predictor for patients with AoCLD in our study, which could be attributed to study population heterogeneity. In previous studies [ 37 , 39 , 40 ] including all age groups, age is a parameter of the prognostic indicator, usually as a dichotomous or multi-categorical variable. However, our model was based primarily on data from people >60 years of age.…”
Section: Discussionmentioning
confidence: 99%
“…Therefore, integrating multiple factors will improve the accuracy of risk assessment. Lee et al [ 45 ] proposed a risk prediction model consisting of age, platelet count, and liver stiffness. This showed relatively good prediction performance in the validation cohort ( i.e., area under the receiver operating characteristic curve (AUROC), 0.78)[ 45 ].…”
Section: Clinical Risk Factorsmentioning
confidence: 99%
“…Lee et al [ 45 ] proposed a risk prediction model consisting of age, platelet count, and liver stiffness. This showed relatively good prediction performance in the validation cohort ( i.e., area under the receiver operating characteristic curve (AUROC), 0.78)[ 45 ]. Ioannou et al [ 46 ] reported another prediction model with a similar predictive value (AUROC, 0.75), consisting of age, sex, diabetes, BMI, platelet count, serum albumin, and AST/√ALT ratio[ 46 ].…”
Section: Clinical Risk Factorsmentioning
confidence: 99%