M. Lupsor

Spleen stiffness measurement using fibroscan for the noninvasive assessment of esophageal varices in liver cirrhosis patients

Background and Aim:  Splenomegaly in a common finding in liver cirrhosis that should determine changes in the spleens density because of portal and splenic congestion and/or because of tissue hyperplasia and fibrosis. These changes might be quantified by elastography, so the aim of the study was to investigate whether spleen stiffness measured by transient elastography varies as liver disease progresses and whether this would be a suitable method for the noninvasive evaluation of the presence of esophageal varices.

Acoustic Radiation Force Impulse Elastography for fibrosis evaluation in patients with chronic hepatitis C: An international multicenter study

AIM The aim of this international multicenter study was to evaluate the reliability of Acoustic Radiation Force Impulse (ARFI) elastography for predicting fibrosis severity, in patients with chronic hepatitis C. PATIENTS AND METHODS We compared ARFI to liver biopsy (LB) in 914 patients (10 centers, 5 countries) with chronic hepatitis C. In each patient LB (evaluated according to the METAVIR score) and ARFI measurements were performed (median of 5-10 valid measurements, expressed in meters/second - m/s). In 400 from the 914 patients, transient elastography (TE) was also performed (median of 6-10 valid measurements, expressed in kiloPascals - kPa). RESULTS Valid ARFI measurements were obtained in 911 (99.6%) of 914 cases. On LB 61 cases (6.7%) had F0, 241 (26.4%) had F1, 202 (22.1%) had F2, 187 (20.4%) had F3, and 223 (24.4%) had F4 fibrosis. A highly significant correlation (r=0.654) was found between ARFI measurements and fibrosis (p<0.0001). The predictive values of ARFI for various stages of fibrosis were: F ≥ 1 - cut-off>1.19 m/s (AUROC=0.779), F ≥ 2 - cut-off>1.33 m/s (AUROC=0.792), F ≥ 3 - cut-off>1.43 m/s (AUROC=0.829), F=4 - cut-off>1.55 m/s (AUROC=0.842). The correlation with histological fibrosis was not significantly different for TE in comparison with ARFI elastography: r=0.728 vs. 0.689, p=0.28. TE was better than ARFI for predicting the presence of liver cirrhosis (p=0.01) and fibrosis (F ≥ 1, METAVIR) (p=0.01). CONCLUSION ARFI elastography is a reliable method for predicting fibrosis severity in chronic hepatitis C patients.

The influence of aminotransferase levels on liver stiffness assessed by Acoustic Radiation Force Impulse Elastography: A retrospective multicentre study

BACKGROUND Acoustic Radiation Force Impulse Elastography is a new method for non-invasive evaluation of liver fibrosis. AIM To evaluate the impact of elevated alanine aminotransferase levels on liver stiffness assessment by Acoustic Radiation Force Impulse Elastography. METHODS A multicentre retrospective study including 1242 patients with chronic liver disease, who underwent liver biopsy and Acoustic Radiation Force Impulse. Transient Elastography was also performed in 512 patients. RESULTS The best Acoustic Radiation Force Impulse cut-off for predicting significant fibrosis was 1.29 m/s in cases with normal alanine aminotransferase levels and 1.44 m/s in patients with alanine aminotransferase levels>5 × the upper limit of normal. The best cut-off for predicting liver cirrhosis were 1.59 and 1.75 m/s, respectively. Acoustic Radiation Force Impulse cut-off for predicting significant fibrosis and cirrhosis were relatively similar in patients with normal alanine aminotransferase and in those with alanine aminotransferase levels between 1.1 and 5 × the upper limit of normal: 1.29 m/s vs. 1.36 m/s and 1.59 m/s vs. 1.57 m/s, respectively. For predicting cirrhosis, the Transient Elastography cut-offs were significantly higher in patients with alanine aminotransferase levels between 1.1 and 5 × the upper limit of normal compared to those with normal alanine aminotransferase: 12.3 kPa vs. 9.1 kPa. CONCLUSION Liver stiffness values assessed by Acoustic Radiation Force Impulse and Transient Elastography are influenced by high aminotransferase levels. Transient Elastography was also influenced by moderately elevated aminotransferase levels.

Is ARFI elastography reliable for predicting fibrosis severity in chronic HCV hepatitis

AIM To determine whether acoustic radiation force impulse (ARFI) elastography is a reliable method for predicting fibrosis severity in patients with chronic hepatitis C virus (HCV) hepatitis. METHODS We performed a multicenter study including 274 subjects with HCV chronic hepatitis in which we compared ARFI with liver biopsy (LB). In each patient we performed LB (evaluated according to the Metavir score) and ARFI measurements (using a Siemens Acuson S2000™ ultrasound system: 10 valid measurements were performed and median values were calculated and expressed in meters/second (m/s). RESULTS A direct, strong, correlation (Spearman r = 0.707) was found between ARFI measurements and fibrosis (P < 0.0001). For predicting the presence of fibrosis (F ≥ 1 Metavir), significant fibrosis (F ≥ 2), severe fibrosis (F ≥ 3) and cirrhosis (F = 4), the cut-off values of 1.19, 1.21, 1.58 and 1.82 m/s were determined, respectively, liver stiffness measurements had 73%, 84%, 84% and 91% Se respectively; 93%, 91%, 94%, 90% Sp, respectively; with AUROCs of 0.880, 0.893, 0.908 and 0.937, respectively. CONCLUSION ARFI measurement is a reliable method for predicting the severity of fibrosis in HCV patients.

Serum homocysteine levels, oxidative stress and cardiovascular risk in non-alcoholic steatohepatitis

INTRODUCTION Hyperhomocysteinemia is considered an independent risk factor for cardiovascular disease. Oxidative stress is one of the major pathogenic mechanisms in non-alcoholic fatty liver disease and atherosclerosis. AIM Our study aimed to evaluate serum homocysteine levels and oxidative stress in patients with biopsy-proven non-alcoholic steatohepatitis and possible association with cardiovascular risk measured by carotid artery intima-media thickness (c-IMT). PATIENTS AND METHODS 50 patients with non-alcoholic steatohepatitis and 30 healthy controls, age and gender matched, were recruited. Lipid profile, liver biochemical markers, serum homocysteine, vitamins B6 and B12, folic acid, glutathione (reduced and total), erythrocyte superoxide dismutase, whole blood glutathione peroxidase, malondialdehyde and carotid intima-media thickness were assayed. RESULTS Patients had an altered lipid profile and liver biochemical markers; carotid intima-media thickness and serum homocysteine levels were significantly higher compared to controls, but there were no differences in folate, B12 and B6 vitamins levels. Patients had significantly lower levels of glutathione peroxidase activity, total and reduced glutathione and higher levels of malondialdehyde, but unchanged superoxide dismutase activity compared to control group. Also, serum homocysteine level showed significant positive correlation with waist circumference, body mass index, free cholesterol, triglycerides, LDL-cholesterol, amino transferases and negative correlation with reduced and total glutathione, superoxide dismutase and γ-GT. CONCLUSION Non-alcoholic steatohepatitis is an independent cardiovascular risk factor, associated with elevated homocysteine levels, oxidative stress and c-IMT. c-IMT could be used as an indicator of early atherosclerotic changes initiated by dyslipidemia and oxidative stress, while higher level of homocysteine might be an effect of liver damage.

Abdominal Tumor Characterization and Recognition Using Superior-Order Cooccurrence Matrices, Based on Ultrasound Images

The noninvasive diagnosis of the malignant tumors is an important issue in research nowadays. Our purpose is to elaborate computerized, texture-based methods for performing computer-aided characterization and automatic diagnosis of these tumors, using only the information from ultrasound images. In this paper, we considered some of the most frequent abdominal malignant tumors: the hepatocellular carcinoma and the colonic tumors. We compared these structures with the benign tumors and with other visually similar diseases. Besides the textural features that proved in our previous research to be useful in the characterization and recognition of the malignant tumors, we improved our method by using the grey level cooccurrence matrix and the edge orientation cooccurrence matrix of superior order. As resulted from our experiments, the new textural features increased the malignant tumor classification performance, also revealing visual and physical properties of these structures that emphasized the complex, chaotic structure of the corresponding tissue.

Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C

OBJECTIVE Hepatic fibrosis, the principal pointer to the development of a liver disease within chronic hepatitis C, can be measured through several stages. The correct evaluation of its degree, based on recent different non-invasive procedures, is of current major concern. The latest methodology for assessing it is the Fibroscan and the effect of its employment is impressive. However, the complex interaction between its stiffness indicator and the other biochemical and clinical examinations towards a respective degree of liver fibrosis is hard to be manually discovered. In this respect, the novel, well-performing evolutionary-powered support vector machines are proposed towards an automated learning of the relationship between medical attributes and fibrosis levels. The traditional support vector machines have been an often choice for addressing hepatic fibrosis, while the evolutionary option has been validated on many real-world tasks and proven flexibility and good performance. METHODS AND MATERIALS The evolutionary approach is simple and direct, resulting from the hybridization of the learning component within support vector machines and the optimization engine of evolutionary algorithms. It discovers the optimal coefficients of surfaces that separate instances of distinct classes. Apart from a detached manner of establishing the fibrosis degree for new cases, a resulting formula also offers insight upon the correspondence between the medical factors and the respective outcome. What is more, a feature selection genetic algorithm can be further embedded into the method structure, in order to dynamically concentrate search only on the most relevant attributes. The data set refers 722 patients with chronic hepatitis C infection and 24 indicators. The five possible degrees of fibrosis range from F0 (no fibrosis) to F4 (cirrhosis). RESULTS Since the standard support vector machines are among the most frequently used methods in recent artificial intelligence studies for hepatic fibrosis staging, the evolutionary method is viewed in comparison to the traditional one. The multifaceted discrimination into all five degrees of fibrosis and the slightly less difficult common separation into solely three related stages are both investigated. The resulting performance proves the superiority over the standard support vector classification and the attained formula is helpful in providing an immediate calculation of the liver stage for new cases, while establishing the presence/absence and comprehending the weight of each medical factor with respect to a certain fibrosis level. CONCLUSION The use of the evolutionary technique for fibrosis degree prediction triggers simplicity and offers a direct expression of the influence of dynamically selected indicators on the corresponding stage. Perhaps most importantly, it significantly surpasses the classical support vector machines, which are both widely used and technically sound. All these therefore confirm the promise of the new methodology towards a dependable support within the medical decision-making.

Is it better to use two elastographic methods for liver fibrosis assessment

AIM To find out if by combining 2 ultrasound based elastographic methods: acoustic radiation force impulse (ARFI) elastography and transient elastography (TE), we can improve the prediction of fibrosis in patients with chronic hepatitis C. METHODS Our study included 197 patients with chronic hepatitis C. In each patient, we performed, in the same session, liver stiffness (LS) measurements by means of TE and ARFI, respectively, and liver biopsy (LB), assessed according to the Metavir score. 10 LS measurements were performed both by TE and ARFI; median values were calculated and expressed in kilopascals (kPa) and meters/second (m/s), respectively. Only TE and ARFI measurements with IQR < 30% and SR ≥ 60% were considered reliable. RESULTS On LB 13 (6.6%) patients had F0, 32 (16.2%) had F1, 52 (26.4%) had F2, 47 (23.9%) had F3, and 53 (26.9%) had F4. A direct, strong correlation was found between TE measurements and fibrosis (r = 0.741), between ARFI and fibrosis (r = 0.730) and also between TE and ARFI (r = 0.675). For predicting significant fibrosis (F ≥ 2), for a cut-off of 6.7 kPa, TE had 77.5% sensitivity (Se) and 86.5% specificity (Sp) [area under the receiver operating characteristic curve (AUROC) 0.87] and for a cut-off of 1.2 m/s, ARFI had 76.9% Se and 86.7% Sp (AUROC 0.84). For predicting cirrhosis (F = 4), for a cut-off of 12.2 kPa, TE had 96.2% Se and 89.6% Sp (AUROC 0.97) and for a cut-off of 1.8 m/s, ARFI had 90.4% Se and 85.6% Sp (AUROC 0.91). When both elastographic methods were taken into consideration, for predicting significant fibrosis (F ≥ 2), (TE ≥ 6.7 kPa and ARFI ≥ 1.2 m/s) we obtained 60.5% Se, 93.3% Sp, 96.8% positive predictive value (PPV), 41.4% negative predictive value (NPV) and 68% accuracy, while for predicting cirrhosis (TE ≥ 12.2 kPa and ARFI ≥ 1.8 m/s) we obtained 84.9% Se, 94.4% Sp, 84.9% PPV, 94.4% NPV and 91.8% accuracy. CONCLUSION TE used in combination with ARFI is highly specific for predicting significant fibrosis; therefore when the two methods are concordant, liver biopsy can be avoided.

Feature selection for a cooperative coevolutionary classifier in liver fibrosis diagnosis

This paper presents an automatic tool capable to learn from a patients data set with 24 medical indicators characterizing each sample and to subsequently use the acquired knowledge to differentiate between five degrees of liver fibrosis. The indicators represent clinical observations and the liver stiffness provided by the new, non-invasive procedure of Fibroscan. The proposed technique combines a hill climbing algorithm that selects subsets of important attributes for an accurate classification and a core represented by a cooperative coevolutionary classifier that builds rules for establishing the diagnosis for every new patient. The results of the novel method proved to be superior as compared to the ones obtained by other important classification techniques from the literature. Additionally, the proposed methodology extracts a set of the most meaningful attributes from the available ones.

Two or more synchronous combination of noninvasive tests to increase accuracy of liver fibrosis assessement in chronic hepatitis C; results from a cohort of 446 patients.

Background The prediction of fibrosis is an essential part of the assessment and management of patients with chronic liver disease. Non-invasive tests (NITs) have a number of advantages over the traditional standard of fibrosis assessment by liver biopsy, including safety, cost-effectiveness, and widespread accessibility. Objectives The aim of this study was to determine the accuracy of certain biomarkers and transient elastography (TE) alone or in combination to predict the stage of liver fibrosis in chronic hepatitis C (CHC). Also, we examined whether the combination of certain biomarkers and TE could increase the diagnostic accuracy of liver fibrosis assessment. Patients and Method A total of 446 patients who were previously diagnosed with CHC were included in the study. In the study group, 6 blood-based scores (APRI, Forns, Fib-4, Hepascore, FibroTest, and Fibrometer) were calculated, and TE was performed to validate the stage of fibrosis, compared with liver biopsy (LB) as the standard. Results Significant fibrosis (F ≥ 2) was predicted with an AUROC of 0.727, 0.680, 0.714, 0.778, 0.688, 0.797, and 0.751 for the APRI, Forns, Fib-4, FibroTest, Hepascore, and Fibrometer scores and TE (Fibroscan), respectively. Severe fibrosis (F ≥ 3) was predicted, with AUROCs ranging between 0.705 and 0.811 for Hepascore and Fibrometer, respectively. Of the biomarkers, Fibrometer had the highest AUROC value in predicting both significant and severe fibrosis. The combination of APRI or FIB-4 with Fibrometer increased the diagnostic accuracy for significant fibrosis (from 69.07 to 82.27 for APRI, P = 0.001 and from 57.74 to 81.33, P = 0.001 for Fib-4). Combining APRI or Fib-4 with TE also increased the diagnostic accuracy (from 69.07 to 80.70%, P = 0.001 for APRI and from 57.74 to 81.33%, P = 0.001 for Fib-4) for significant fibrosis. The association that included Fibrotest was also reliable for the improvement of diagnostic accuracy. These combinations were more accurate or the assessment of severe fibrosis. Conclusions The synchronous association between a simple, inexpensive score and a complex but expensive score or TE increases the diagnostic accuracy of non-invasive methods for the assessment of liver fibrosis stage.