METHODS: The NFS was calculated and LSM obtained for consecutive adult NAFLD patients scheduled for liver biopsy. The accuracy of predicting advanced fibrosis using either modality and in combination were assessed. An algorithm combining the NFS and LSM was developed from a training cohort and subsequently tested in a validation cohort.
RESULTS: There were 101 and 46 patients in the training and validation cohort, respectively. In the training cohort, the percentages of misclassifications using the NFS alone, LSM alone, LSM alone (with grey zone), both tests for all patients and a 2-step approach using LSM only for patients with indeterminate and high NFS were 5.0, 28.7, 2.0, 2.0 and 4.0 %, respectively. The percentages of patients requiring liver biopsy were 30.7, 0, 36.6, 36.6 and 18.8 %, respectively. In the validation cohort, the percentages of misclassifications were 8.7, 28.3, 2.2, 2.2 and 8.7 %, respectively. The percentages of patients requiring liver biopsy were 28.3, 0, 41.3, 43.5 and 19.6 %, respectively.
CONCLUSIONS: The novel 2-step approach further reduced the number of patients requiring a liver biopsy whilst maintaining the accuracy to predict advanced fibrosis. The combination of NFS and LSM for all patients provided no apparent advantage over using either of the tests alone.
AIM: To determine how to use CAP in interpreting liver stiffness measurements.
METHODS: This is a secondary analysis of data from an individual patient data meta-analysis on CAP. The main exclusion criteria for the current analysis were unknown aetiology, unreliable elastography measurement and data already used for the same research question. Aetiology-specific liver stiffness measurement cut-offs were determined and used to estimate positive and negative predictive values (PPV/NPV) with logistic regression as functions of CAP.
RESULTS: Two thousand and fifty eight patients fulfilled the inclusion criteria (37% women, 18% NAFLD/NASH, 42% HBV, 40% HCV, 51% significant fibrosis ≥ F2). Youden optimised cut-offs were only sufficient for ruling out cirrhosis (NPV of 98%). With sensitivity and specificity-optimised cut-offs, NPV for ruling out significant fibrosis was moderate (70%) and could be improved slightly through consideration of CAP. PPV for significant fibrosis and cirrhosis were 68% and 55% respectively, despite specificity-optimised cut-offs for cirrhosis.
CONCLUSIONS: Liver stiffness measurement values below aetiology-specific cut-offs are very useful for ruling out cirrhosis, and to a lesser extent for ruling out significant fibrosis. In the case of the latter, Controlled Attenuation Parameter can improve interpretation slightly. Even if cut-offs are very high, liver stiffness measurements are not very reliable for ruling in fibrosis or cirrhosis.
METHODS: The study comprised 106 chronic kidney disease (CKD) patients and 203 control subjects. Conventional ultrasound was performed to measure the kidney length and cortical thickness. SWE imaging was performed to measure renal parenchymal stiffness. Diagnostic performance of SWE and conventional ultrasound were correlated with serum creatinine, urea levels and eGFR.
RESULTS: Pearson's correlation coefficient revealed a negative correlation between YM measurements and eGFR (r = -0.576, p < 0.0001). Positive correlations between YM measurements and age (r = 0.321, p < 0.05), serum creatinine (r = 0.375, p < 0.0001) and urea (r = 0.287, p < 0.0001) were also observed. The area under the receiver operating characteristic curve for SWE (0.87) was superior to conventional ultrasound alone (0.35-0.37). The cut-off value of less or equal to 4.31 kPa suggested a non-diseased kidney (80.3% sensitivity, 79.5% specificity).
CONCLUSION: SWE was superior to renal length and cortical thickness in detecting CKD. A value of 4.31 kPa or less showed good accuracy in determining whether a kidney was diseased or not. Advances in knowledge: On SWE, CKD patients show greater renal parenchymal stiffness than non-CKD patients. Determining a cut-off value between normal and diseased renal parenchyma may help in early non-invasive detection and management of CKD.
METHODS: Adult patients with chronic liver disease who had a liver biopsy and examination with both the M and XL probes were included. Previously defined optimal cut-offs for CAP using the M probe were used for the diagnosis of steatosis grades ≥S1, ≥S2, and S3 (248, 268, and 280 dB/m, respectively).
RESULTS: Data for 180 patients were analyzed (mean age 53.7 ± 10.8 years; central obesity 84.5%; non-alcoholic fatty liver disease 86.7%). The distribution of steatosis grades was S0, 9.4%; S1, 28.3%; S2, 43.9%, and S3, 18.3%. The sensitivity, specificity, positive predictive value, and negative predictive value of CAP using the M/XL probe for the diagnosis of steatosis grade ≥S1 was 93.9%/93.3%, 58.8%/58.8%, 95.6%/95.6%, and 50.0%/47.6%, respectively. These values were 94.6%/94.6%, 41.2%/44.1%, 72.6%/73.6%, and 82.4%/83.3%, respectively, for ≥S2, and 87.9%/87.9%, 27.2%/27.9%, 21.3%/21.5%, and 90.9%/91.1%, respectively, for S3.
CONCLUSION: The same cut-off values for CAP may be used for the M and XL probes for the diagnosis of hepatic steatosis grade.
METHODS: Patients with solid pancreatic lesions ≤ 15 mm in size and a definite diagnosis were included. Lesion stiffness relative to the surrounding pancreatic parenchyma, as qualitatively assessed and documented at the time of EUS elastography, was retrospectively compared with the final diagnosis obtained by fine-needle aspiration/biopsy or surgical resection.
RESULTS: 218 patients were analyzed. The average size of the lesions was 11 ± 3 mm; 23 % were ductal adenocarcinoma, 52 % neuroendocrine tumors, 8 % metastases, and 17 % other entities; 66 % of the lesions were benign. On elastography, 50 % of lesions were stiffer than the surrounding pancreatic parenchyma (stiff lesions) and 50 % were less stiff or of similar stiffness (soft lesions). High stiffness of the lesion had a sensitivity of 84 % (95 % confidence interval 73 % - 91 %), specificity of 67 % (58 % - 74 %), positive predictive value (PPV) of 56 % (50 % - 62 %), and negative predictive value (NPV) of 89 % (83 % - 93 %) for the diagnosis of malignancy. For the diagnosis of pancreatic ductal adenocarcinoma, the sensitivity, specificity, PPV, and NPV were 96 % (87 % - 100 %), 64 % (56 % - 71 %), 45 % (40 % - 50 %), and 98 % (93 % - 100 %), respectively.
CONCLUSIONS: In patients with small solid pancreatic lesions, EUS elastography can rule out malignancy with a high level of certainty if the lesion appears soft. A stiff lesion can be either benign or malignant.
METHODS: The proposed method uses a 2D contourlet transform and a set of texture features that are efficiently extracted from the transformed image. Then, the combination of a kernel discriminant analysis (KDA)-based feature reduction technique and analysis of variance (ANOVA)-based feature ranking technique was used, and the images were then classified into various stages of liver fibrosis.
RESULTS: Our 2D contourlet transform and texture feature analysis approach achieved a 91.46% accuracy using only four features input to the probabilistic neural network classifier, to classify the five stages of liver fibrosis. It also achieved a 92.16% sensitivity and 88.92% specificity for the same model. The evaluation was done on a database of 762 ultrasound images belonging to five different stages of liver fibrosis.
CONCLUSIONS: The findings suggest that the proposed method can be useful to automatically detect and classify liver fibrosis, which would greatly assist clinicians in making an accurate diagnosis.
Methods: A cross-sectional study of government officers and their family members attending a health screening at a public healthcare facility was conducted. All subjects underwent clinical evaluation, biochemical testing, anthropometry, ultrasound carotid Doppler, and Fibroscan examination.
Results: Data for 251 subjects were analyzed (mean age 47.1 ± 12.4 years, 74.1% male). Prevalence of NAFLD and advanced fibrosis were 57.4 and 17.5%, respectively. Independent factors associated with NAFLD were waist circumference (odds ratio [OR] = 1.077, 95% confidence interval [CI] 1.038-1.118, P < 0.001) and serum alanine aminotransferase (ALT) (OR = 1.039, 95% CI 1.005-1.074, P = 0.024). Independent factors associated with advanced fibrosis were male gender (OR = 4.847, 95% CI 1.369-17.155, P = 0.014) and serum aspartate aminotransferase (AST) (OR = 1.057, 95% CI 1.003-1.113, P = 0.036). Prevalence of increased CIMT was 29.0%. Independent factor associated with increased CIMT was older age (OR = 1.146, 95% CI 1.067-1.231, P < 0.001). Of the subjects, 34.5% with NAFLD had increased CIMT compared to 19.1% of the subjects without NAFLD (P = 0.063). Advanced fibrosis was not associated with increased CIMT.
Conclusions: Prevalence of NAFLD, advanced liver fibrosis, and increased CIMT were high. NAFLD and advanced liver fibrosis appeared not to be associated with increased CIMT. However, a larger sample size is needed to demonstrate whether there is any association.
METHODS: This is a cross-sectional study of consecutive adult T2DM patients attending the Diabetes Clinic of a university hospital. Significant hepatic steatosis and advanced fibrosis was diagnosed based on transient elastography if the controlled attenuation parameter was ≥ 263 dB/m, and the liver stiffness measurement was ≥ 9.6 kPa using the M probe or ≥ 9.3 kPa using the XL probe, respectively. Patients with liver stiffness measurement ≥ 8 kPa were referred to the Gastroenterology and Hepatology Clinic for further assessment, including liver biopsy.
RESULTS: The data of 557 patients were analyzed (mean age 61.4 ± 10.8 years, male 40.6%). The prevalence of NAFLD and advanced fibrosis based on transient elastography was 72.4% and 21.0%, respectively. On multivariate analysis, independent factors associated with NAFLD were central obesity (OR 4.856, 95% confidence interval [CI] 2.749-8.577, P = 0.006), serum triglyceride (OR 1.585, 95% CI 1.056-2.381, P = 0.026), and alanine aminotransferase levels (OR 1.047, 95% CI 1.025-1.070, P
DESIGN: We prospectively recruited 496 patients with non-alcoholic fatty liver disease who underwent VCTE by both M and XL probes within 1 week before liver biopsy.
RESULTS: 391 (78.8%) and 433 (87.3%) patients had reliable liver stiffness measurement (LSM) (10 successful acquisitions and IQR:median ratio ≤0.30) by M and XL probes, respectively (p<0.001). The area under the receiver operating characteristic curves was similar between the two probes (0.75-0.88 for F2-4, 0.83-0.91 for F4). When used in the same patient, LSM by XL probe was lower than that by M probe (mean difference 2.3 kPa). In contrast, patients with BMI ≥30 kg/m2 had higher LSM regardless of the probe used. When M and XL probes were used in patients with BMI <30 and ≥30 kg/m2, respectively, they yielded nearly identical median LSM at each fibrosis stage and similar diagnostic performance. Severe steatosis did not increase LSM or the rate of false-positive diagnosis by XL probe.
CONCLUSION: High BMI but not severe steatosis increases LSM. The same LSM cut-offs can be used without further adjustment for steatosis when M and XL probes are used according to the appropriate BMI.