Evaluating Diagnostic Accuracy of Noninvasive Tests in Assessment of Significant Liver Fibrosis in Chronic Hepatitis C Egyptian Patients

Abstract

Stage of liver fibrosis is critical for treatment decision and prediction of outcomes in chronic hepatitis C (CHC) patients. We evaluated the diagnostic accuracy of transient elastography (TE)-FibroScan and noninvasive serum markers tests in the assessment of liver fibrosis in CHC patients, in reference to liver biopsy. One-hundred treatment-naive CHC patients were subjected to liver biopsy, TE-FibroScan, and eight serum biomarkers tests; AST/ALT ratio (AAR), AST to platelet ratio index (APRI), age-platelet index (AP index), fibrosis quotient (FibroQ), fibrosis 4 index (FIB-4), cirrhosis discriminant score (CDS), King score, and Goteborg University Cirrhosis Index (GUCI). Receiver operating characteristic curves were constructed to compare the diagnostic accuracy of these noninvasive methods in predicting significant fibrosis in CHC patients. TE-FibroScan predicted significant fibrosis at cutoff value 8.5 kPa with area under the receiver operating characteristic (AUROC) 0.90, sensitivity 83%, specificity 91.5%, positive predictive value (PPV) 91.2%, and negative predictive value (NPV) 84.4%. Serum biomarkers tests showed that AP index and FibroQ had the highest diagnostic accuracy in predicting significant liver fibrosis at cutoff 4.5 and 2.7, AUROC was 0.8 and 0.8 with sensitivity 73.6% and 73.6%, specificity 70.2% and 68.1%, PPV 71.1% and 69.8%, and NPV 72.9% and 72.3%, respectively. Combined AP index and FibroQ had AUROC 0.83 with sensitivity 73.6%, specificity 80.9%, PPV 79.6%, and NPV 75.7% for predicting significant liver fibrosis. APRI, FIB-4, CDS, King score, and GUCI had intermediate accuracy in predicting significant liver fibrosis with AUROC 0.68, 0.78, 0.74, 0.74, and 0.67, respectively, while AAR had low accuracy in predicting significant liver fibrosis. TE-FibroScan is the most accurate noninvasive alternative to liver biopsy. AP index and FibroQ, either as individual tests or combined, have good accuracy in predicting significant liver fibrosis, and are better combined for higher specificity.

Introduction

Assessment of liver fibrosis is of utmost importance for antiviral therapy decision and follow-up in chronic hepatitis C (CHC) patients (3). Although liver biopsy is still the gold standard for liver fibrosis staging, it has documented limitations (8,35,37,38). In addition, liver biopsy cannot be used for mass screening in a country with a very high prevalence of Hepatitis C virus (HCV) as in Egypt.

Over the past decade, researchers developed several noninvasive methods; imaging techniques and serum biomarkers, hoping to replace liver biopsy. The ideal noninvasive marker for evaluating liver fibrosis should be available, simple, inexpensive, reliable, safe, and well proved. Also, it should be helpful in the assessment of liver disease progression (2,42,44).

Transient elastography (TE)-Fibroscan is a simple noninvasive imaging technique, well accepted by the patients, and can be frequently repeated according to the need (24).

Serum biomarkers are either direct or indirect; the direct biomarkers include proteins involved in the synthesis or degradation of hepatic extracellular matrix (ECM) during the fibrosis process (2,21,32,33), while the indirect biomarkers reflect changes in liver functions related to liver inflammation, but they do not correlate with ECM turnover (2,32). Indirect serum biomarkers involve simple laboratory tests, easily available and included in the routine monitoring and investigations of patients with chronic liver disease (19). Combinations of indirect biomarkers are used in calculations to create different score models to assess liver fibrosis.

In the current study, we evaluated the diagnostic accuracy of eight different serum biomarkers tests; “AST/ALT ratio (AAR), AST to platelet ratio index (APRI), age-platelet index (AP index), Fibrosis quotient (FibroQ), Fibrosis 4 index (FIB-4), cirrhosis discriminant score (CDS), King score, and Goteborg University Cirrhosis Index (GUCI)” and TE-FibroScan in the assessment of liver fibrosis stage in treatment-naive CHC Egyptian patients. Results were compared to liver fibrosis stage assessed histopathologically by METAVIR scoring system of liver biopsy samples.

Patients and Methods

One hundred treatment-naive CHC patients were included in this cross-sectional study. HCV infection was diagnosed by anti-HCV antibodies testing and detection of HCV-RNA by real time-polymerase chain reaction. Patients with previous anti-HCV therapy, HBV coinfection, decompensated liver disease, hepatocellular carcinoma, body mass index >30, and presence of absolute contraindication for liver biopsy were excluded from this study.

Informed consent was obtained from all patients before participation in the study. The study was approved by Faculty of Medicine, Cairo University Ethics Committee, and was conducted according to the ethics guidelines of the Declaration of Helsinki.

Patients were subjected to detailed history, thorough clinical examination, liver stiffness (LS) measurement by TE-FibroScan, liver biopsy, and routine laboratory tests, including complete blood picture (CBC), aspartate aminotransferase (AST), alanine aminotransferase (ALT), total bilirubin, serum albumin, and prothrombin time (PT)-international normalized ratio (INR).

LS measurement was performed by a single experienced operator blinded to patients' clinical and laboratory data using a TE-FibroScan instrument (EchosensTM, Paris, France). Interpretation of measurement as valid required more than 10 successful acquisitions, with a success rate of at least 60% and an interquartile range lower than 30% (9).

Ultrasound-guided liver biopsy was performed within a week of LS measurement. All samples were examined by a single pathologist, blinded to the results of the noninvasive methods. Fibrosis was staged according to the METAVIR scoring system from F0 to F4 (4). Significant liver fibrosis was defined as METAVIR fibrosis scores of ≥2 (F2, F3, and F4).

The following eight serum biomarkers tests were calculated according to previously published equations and evaluated; AAR (48), APRI (47), AP index (34), FibroQ (18), FIB-4 (46), CDS (5), King score (10), and GUCI (20).

The performance of a noninvasive diagnostic method was evaluated by calculating the area under the receiver operating characteristic (AUROC) curve, taking liver biopsy as the reference standard (17). AUROC values close to 1 indicate a high diagnostic accuracy (36).

Statistical analyses

All statistical calculations were done using computer programs SPSS (Statistical Package for the Social Science) for Microsoft Windows, Version 16.0. Chicago, SPSS, Inc. Continuous data are presented as mean ± standard deviation, while categorical data are presented as number (percent). p-Value less than 0.05 was considered statistically significant. Student t-test is used when appropriate. Spearman correlation was done among the METVIR score, serum biomarkers, and the elastography. The diagnostic performance of the TE-FibroScan and serum markers was assessed by measuring the AUROCs. Diagnostic accuracy was also evaluated by comparing the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Results

Patients' clinical data, laboratory data, and histopathological classification are shown in Table 1.

Table 1.

Clinical Data, Laboratory Data, and Histopathological Classification of the Studied Patients

	HCV patients (N = 100)
Age (years)	46.8 ± 12.8
Gender
Male	57 (57%)
Female	43 (43%)
BMI	25.6 ± 4.3
ALT (IU/mL)	59.9 ± 40.9
AST (IU/mL)	54.6 ± 27.6
Albumin (g/dL)	3.9 ± 0.4
T. Bil (mg/dL)	0.7 ± 0.3
PT-INR	1.09 ± 0.1
Platelet count ( × 10⁹/mL)	192 ± 69.5
HCV viral load (IU/mL × 10⁵)	45.8 ± 32.5
METAVIR score
F0	0 (0%)
F1	47 (47%)
F2	25 (25%)
F3	14 (14%)
F4	14 (14%)

Data are presented as number (%) and mean ± SD.

ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; HCV, hepatitis C virus; INR, international normalized ratio; PT, prothrombin time; T. Bil, total bilirubin.

The mean values of TE-FibroScan and indirect serum biomarkers tests showed statistically significant increase as the stage of liver fibrosis advanced, except for AAR which also increased as fibrosis advanced, but did not reach statistical significance (Table 2).

Table 2.

Relationship Between Stage of Liver Fibrosis and Noninvasive Tests (Transient Elastography-FibroScan and Indirect Serum Biomarkers)

	F4 (n = 47)	F3 (n = 25)	F2 (n = 14)	F1 (n = 14)
TE-FibroScan
Mean ± SD	5.8 ± 2.2^* ^, ^ ^, ^*	9.7 ± 6.3^a	12.5 ± 5.4	19 ± 10.7
Median (range)	5.8 (3.1–16)	8.8 (4.7–39)	11.8 (4.1–24.8)	14.2 (12.4–41.4)
AAR
Mean ± SD	1.01 ± 0.5	1.06 ± 0.5	1.03 ± 0.07	1.2 ± 0.23
Median (range)	0.95 (0.2–4.6)	0.96 (0.3–3.7)	1.07 (0.9–1.1)	1.07 (0.9–1.5)
APRI
Mean ± SD	0.6 ± 0.3^***	0.8 ± 0.4	0.9 ± 0.6	1.2 ± 0.5
Median (range)	0.6 (0.2–2)	0.8 (0.2–1.6)	0.8 (0.3–2.4)	1.6 (0.5–1.7)
AP index
Mean ± SD	3.1 ± 1.8^* ^, ^ ^, ^*	5 ± 1.5^a	5.5 ± 2.1	6.4 ± 1.1
Median (range)	3 (0–6)	5 (2 –7)	5 (3 –9)	7 (4 –8)
FibroQ
Mean ± SD	2.1 ± 1.03^* ^, ^***	3.1 ± 0.8^b	4.2 ± 2.8	6.2 ± 2.2
Median (range)	2.2 (0.4–4.6)	3.1 (0.5–4.5)	2.7 (1.7–10)	6 (3–12.4)
FIB-4
Mean ± SD	1.4 ± 0.6^* ^, ^***	2 ± 0.8^b	2.7 ± 2	3.7 ± 1.1
Median (range)	1.5 (0.5–2.5)	2 (0.7–3.2)	1.8 (1–7.6)	3.9 (2.4–6.4)
CDS
Mean ± SD	4.4 ± 1.3^***	5.2 ± 1.4^a	5.3 ± 1.9	6.6 ± 1.1
Median (range)	5 (1 –6)	6 (0–6)	5.5 (2 –8)	7 (4 –8)
King score
Mean ± SD	10.8 ± 6.8^* ^, ^***	18 ± 11.2^b	24.8 ± 23.5	36.5 ± 16.1
Median (range)	9.6 (2.6–32.1)	17.2 (2.8–39.6)	12.5 (6–75.9	39.7 (16.9–66.6)
GUCI
Mean ± SD	0.6 ± 0.3^***	0.9 ± 0.5^a	1.04 ± 0.8	1.5 ± 0.7
Median (range)	0.6 (0.3–2)	0.8 (0.2–1.8)	0.6 (0.3–2.8)	1.8 (0.7–2.6)

p-Value ≤0.05 is significant.

p < 0.05 relative to F4.

<0.01 relative to F4.

< 0.01 relative to F2, ^** p < 0.01 relative to F3, ^*** p < 0.01 relative to F4.

AAR, AST/ALT ratio; AP index, age-platelet index; APRI, AST to platelet ratio index; CDS, cirrhosis discriminate score; FIB-4, fibrosis 4 index; FibroQ, fibrosis quotient; GUCI, Goteborg University cirrhosis index; TE, transient elastography.

The ability of TE-FibroScan and indirect serum biomarkers tests to discriminate between nonsignificant (<F2) and significant (≥F2) liver fibrosis was statistically highly significant (p < 0.001) except for AAR (p = 0.4) (Table 3).

Table 3.

Transient Elastography-FibroScan and Indirect Serum biomarkers in Nonsignificant (<F2) and Significant (≥F2) Liver Fibrosis Patients' Groups

	<F2 (n = 47)	≥F2 (n = 53)	p
TE-FibroScan
Mean ± SD	5.8 ± 2.2	12.9 ± 8.3	0.001^*
Median (range)	5.8 (3.1–16)	10 (4.1–41.4)
AAR
Mean ± SD	1.01 ± 0.6	1.09 ± 0.4	0.4
Median (range)	0.95 (0.2–4.6)	1.06 (0.3–3.7)
APRI
Mean ± SD	0.6 ± 0.3	0.9 ± 0.5	0.001^*
Median (range)	0.6 (0.2–2)	0.9 (0.2–2.4)
AP index
Mean ± SD	3.2 ± 1.8	5.5 ± 1.7	0.001^*
Median (range)	3 (0–6)	5 (2 –9)
FIB-4
Mean ± SD	1.4 ± 0.6	2.6 ± 1.4	0.001^*
Median (range)	1.4 (0.5–2.5)	2.5 (0.7–7.6)
FibroQ
Mean ± SD	2.1 ± 1.03	4.2 ± 2.3	0.001^*
Median (range)	2.2 (0.4–4.6)	3.6 (0.5–12.4)
CDS
Mean ± SD	4.4 ± 1.3	5.6 ± 1.6	0.001^*
Median (range)	5 (1 –6)	5 (0–8)
King score
Mean ± SD	10.8 ± 6.8	24.7 ± 17.9	0.001^*
Median (range)	9.5 (2.6–32.7)	18.7 (2.8–75.9)
GUCI
Mean ± SD	0.6 ± 0.3	1.1 ± 0.7	0.001^*
Median (range)	0.6 (0.3–2)	0.8 (2–2.8)

p-Value ≤0.05 is significant.

Diagnostic accuracy of indirect serum biomarkers and TE-FibroScan in prediction of significant fibrosis (F ≥ 2) in 100 HCV patients is shown in Table 4 and Figures 1 –3. TE-FibroScan was found superior to other noninvasive markers tested, in prediction of significant fibrosis (F ≥ 2), with cutoff value of 8.5, 83% sensitivity, 91.5% specificity, PPV of 91.2%, and NPV of 84.4% with AUROC 0.90. AP index and FibroQ showed the highest diagnostic accuracy in prediction of significant fibrosis (F ≥ 2) among indirect serum biomarkers with AUROC 0.8, sensitivity 73.6%, and specificity 70.2% and 68.1%, respectively. Combined AP index and FibroQ further increased their diagnostic accuracy with AUROC 0.83, sensitivity 73.6%, and specificity 80.9%.

FIG. 1.

ROC curve for TE-FibroScan and combined AP index and FibroQ. AP index, age-platelet index; FibroQ, fibrosis quotient; ROC, receiver operating characteristic; TE, transient elastography.

FIG. 2.

ROC curve for APRI, AAR, AP index, and FibroQ. AAR, AST/ALT ratio; APRI, AST to platelet ratio index.

FIG. 3.

ROC curve for FIB-4, CDS, King score, and GUCI. FIB-4, fibrosis 4 index; GUCI, Goteborg University cirrhosis index.

Table 4.

Diagnostic Accuracy of Transient Elastography-FibroScan and Indirect Serum Markers of Fibrosis in Prediction of Significant Fibrosis (≥F2)

	Cutoff	SEN	SPE	PPV	NPV	LR (+)	LR (−)	AUROC	95% CI
TE-FibroScan	8.5	83%	91.5%	91.2%	84.4%	10.4	0.18	0.90	0.84–0.97
AAR	1.02	60.4%	59.6%	60%	60%	1.5	0.67	0.60	0.49–0.72
APRI	1.05	43.4%	93.6%	86%	62%	6.1	0.61	0.68	0.58–0.79
AP index	4.5	73.6%	70.2%	71.1%	72.9%	2.5	0.37	0.80	0.72–0.89
FIB-4	1.7	71.7%	74.5%	74.2%	72.8%	2.8	0.37	0.78	0.70–0.87
FibroQ	2.7	73.6%	68.1%	69.8%	72.3%	2.3	0.38	0.80	0.72–0.89
CDS	4.5	81.1%	46.8%	60.4%	71.2%	1.5	0.4	0.74	0.65–0.84
King score	11.3	69.8%	68.1%	68.6%	69.4%	2.2	0.4	0.74	0.65–0.84
GUCI	0.6	64.2%	61.7%	62.7%	63.3%	1.7	0.58	0.67	0.57–0.78
Combined AP index and FibroQ	7.4	73.6%	80.9%	79.6%	75.7%	3.89	0.32	0.83	0.75–0.91

AUROC, area under the receiver operating characteristic curve; LR (−), negative likelihood ratio; LR (+), positive likelihood ratio; NPV, negative predictive value; PPV, positive predictive value; SEN, sensitivity; SPE, specificity.

There was positive correlation between the stage of liver fibrosis and TE-FibroScan (r = 0.8, p = 0.001) and the eight studied serum biomarkers; AAR (r = 0.3, p = 0.007), APRI (r = 0.4, p = 0.001), AP index (r = 0.6, p = 0.001), FibroQ (r = 0.6, p = 0.001), FIB-4 (r = 0.6, p = 0.001), CDS (r = 0.5, p = 0.001), King score (r = 0.5, p = 0.001), and GUCI (r = 0.4, p = 0.001).

Discussion

Validation of noninvasive alternatives to liver biopsy for assessment of liver fibrosis stage is required especially in areas with hyperendemic HCV infection and in low-income countries. In addition to the high cost of liver biopsy (40), it is associated with being generally not accepted by patients especially when repeated biopsy is required for follow-up (14).

Up till now, no single noninvasive test is considered ideal alternative to liver biopsy. A possible explanation is the difficulty to achieve an area under receiver operating characteristic curve >0.90, even for a perfect marker when a range of accuracies of biopsy and a range of prevalence of fibrosis are taken into account (28).

In the present study, we evaluated diagnostic accuracy of TE-FibroScan and eight serum biomarkers tests in assessing the stage of liver fibrosis and in predicting significant fibrosis (F ≥ 2). The presence of significant fibrosis (F ≥ 2) is important in decision-making to start antiviral therapy in CHC patients (41).

TE-FibroScan at cutoff value 8.5 kPa diagnosed significant fibrosis (F ≥ 2) with sensitivity 83%, specificity 91.5%, PPV of 91.2%, and an NPV of 84.4% with AUROC 0.90. Our results are comparable to previous studies, including Egyptian CHC patients (13,29). Also, several multicenter studies reported the accuracy of TE-FibroScan in assessment of liver fibrosis (12,31).

We evaluated eight serum biomarkers tests (AAR, APRI, AP index, FibroQ, FIB-4, CDS, King score, and GUCI); including standard laboratory tests routinely performed to CHC patients that are available in every laboratory and of low cost. Cost effectiveness is very important in low-income countries as in Egypt; having the highest prevalence of HCV infection worldwide (30).

AP index and FibroQ had the highest diagnostic accuracy in predicting significant liver fibrosis (F ≥ 2) at cutoff 4.5 and 2.7, AUROC was 0.8 and 0.8 with sensitivity 73.6% and 73.6%, specificity 70.2% and 68.1%, PPV 71.1% and 69.8%, and NPV 72.9% and 72.3%, respectively. In accordance with our results, FibroQ was found superior to FIB 4, AAR, APRI, and Lok's model in predicting significant fibrosis in CHC patients (18,19). A recent study, including various noninvasive serum markers, reported diagnostic accuracy of AP index in predicting significant liver fibrosis with AUROC 0.75, but its accuracy was comparable to other studied parameters (22).

Several studies have attempted to combine different fibrosis tests to improve the diagnostic accuracy of liver fibrosis in CHC infection (7,25,43). In our study, combined AP index and FibroQ had AUROC 0.83 with sensitivity 73.6%, specificity 80.9%, PPV 79.6%, and NPV 75.7% for predicting significant liver fibrosis (F ≥ 2).

APRI, FIB-4, CDS, King score, and GUCI had intermediate accuracy in predicting significant liver fibrosis (≥F2) with AUROC 0.68, 0.78, 0.74, 0.74, and 0.67, respectively, while AAR had low accuracy in predicting significant liver fibrosis (≥F2). The intermediate accuracy of APRI (27), FIB-4 (16), and CDS (39) in assessing fibrosis in CHC has been reported. King score is applicable index for prediction of cirrhosis in CHC patients (6,10,11). AAR has been reported to have low diagnostic accuracy in detecting significant liver fibrosis (F ≥ 2) (1,22,23).

In conclusion, TE-FibroScan is the most accurate noninvasive alternative to liver biopsy. However, it cannot be used when patients are not suitable candidates (15,45). Also, TE-FibroScan availability is a considerable issue as it is limited to high-volume centers (26). Widespread adoption of noninvasive, easily available, and cost effective diagnostic tools is major contributor to healthcare improvement in low-income countries with limited resources, where cost is a limitation to proper medical care. AP index and FibroQ either as individual tests or combined have good accuracy in predicting significant liver fibrosis (F ≥ 2) and are better combined for higher specificity.

Footnotes

Acknowledgments

This research was supported by the Science and Technology Development Fund (STDF), Egypt, Grant No. 5380, basic and applied research.

Author Disclosure Statement

No competing financial interests exist.

References

Amorim

, Staub

, Lazzarotto

, et al. Validation and comparison of simple noninvasive models for the prediction of liver fibrosis in chronic hepatitis C. Ann Hepatol, 2012; 11:855–861.

Baranova

, Lal

, Birerdinc

, et al. Non-invasive markers for hepatic fibrosis. BMC Gastroenterol, 2011; 11:91–105.

Bedossa

, Dargère

, Paradis

. Sampling variability of liver fibrosis in chronic hepatitis C. Hepatology, 2003; 38:1449–1457.

Bedossa

, and Poynard

. The METAVIR cooperative study group. An algorithm for the grading of activity in chronic hepatitis C. Hepatology, 1996; 24:289–293.

Bonacini

, Hadi

, Govindarajan

, et al. Utility of a discriminant score for diagnosing advanced fibrosis or cirrhosis in patients with chronic hepatitis C virus infection. Am J Gastroenterol, 1997; 92:1302–1304.

Bota

, Sirli

, Sporea

, et al. A new scoring system for prediction of fibrosis in chronic hepatitis C. Hepat Mon, 2011; 11:548–555.

Bourliere

, Penaranda

, Renou

, et al. Validation and comparison of indexes for fibrosis and cirrhosis prediction in chronic hepatitis C patients: proposal for a pragmatic approach classification without liver biopsies. J Viral Hepat, 2006; 13:659–670.

Bravo

, Sheth

, Chopra

. Liver biopsy. N Engl J Med, 2001; 344:495–500.

Castera

, Forns

, Alberti

. Non-invasive evaluation of liver fibrosis using transient elastography. J Hepatol, 2008; 48:835–847.

10.

Cross

, Rizzi

, Berry

, et al. King's Score: an accurate marker of cirrhosis in chronic hepatitis C. Eur J Gastroenterol Hepatol, 2009; 21:730–738.

11.

Cross

TJS

, Calvaruso

, Maimone

, et al. Prospective comparison of Fibroscan, King's score and liver biopsy for the assessment of cirrhosis in chronic hepatitis C infection. J Viral Hepat, 2010; 17:546–554.

12.

Degos

, Perez

, Roche

, et al. Diagnostic accuracy of FibroScan and comparison to liver fibrosis biomarkers in chronic viral hepatitis: a multicenter prospective study (the FIBROSTIC study). J Hepatol, 2010; 53:1013–1021.

13.

El-Hariri

, Abd El Megid

, Ali T

, et al. Diagnostic value of transient elastography (Fibroscan) in the evaluation of liver fibrosis in chronic viral hepatitis C: comparison to liver biopsy. Egypt J Radiol Nucl Med, 2017; 48:329–337.

14.

El-Kamary

, Mohamed

, El-Raziky

, et al. Liver fibrosis staging through a stepwise analysis of non-invasive markers (FibroSteps) in patients with chronic hepatitis C infection. Liver Int, 2013; 33:982–990.

15.

Foucher

, Castéra

, Bernard

et al. Prevalence and factors associated with failure of liver stiffness measurement using FibroScan in a prospective study of 2114 examinations. Eur J Gastroenterol Hepatol, 2006; 18:411–412.

16.

Güzelbulut

, Çetınkaya

, Sezıklı

, et al. AST-platelet ratio index, Forns index and FIB-4 in the prediction of significant fibrosis and cirrhosis in patients with chronic hepatitis C. Turk J Gastroenterol, 2011; 22:279–285.

17.

Hanley

, McNeil

. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 1983; 143:29–36.

18.

Hsieh

, Tung

, Lee

, et al. FibroQ: an easy and useful noninvasive test for predicting liver fibrosis in patients with chronic viral hepatitis. Chang Gung Med J, 2009; 32:614–622.

19.

Hsieh

, Tung

, Lee

, et al. Routine blood tests to predict liver fibrosis in chronic hepatitis C. World J Gastroenterol, 2012; 18:746–753.

20.

Islam

, Antonsson

, Westin

, et al. Cirrhosis in hepatitis C virus infected patients can be excluded using an index of standard biochemical serum markers. Scand J Gastroenterol, 2005; 40:867–872.

21.

Jarcuska

, Janicko

, Veselíny

, et al. Circulating markers of liver fibrosis progression. Clin Chim Acta, 2010; 411:1009–1017.

22.

Kim

, Sohn

, Kim

, et al. Comparison of various noninvasive serum markers of liver fibrosisin chronic viral liver disease. Korean J Hepatol, 2009; 15:454–463.

23.

Lackner

, Struber

, Liegl

, et al. Comparison and validation of simple noninvasive tests for prediction of fibrosis in chronic hepatitis C. Hepatology, 2005; 41:1376–1382.

24.

Ledinghen

, Vergniol

. Transient elastography (FibroScan). Gastroenterol Clin Bio, 2008; 32:58–67.

25.

Leroy

, Hilleret

, Sturm

, et al. Prospective comparison of six non-invasive scores for the diagnosis of liver fibrosis in chronic hepatitis C. J Hepatol, 2007; 46:775–782.

26.

Lichtinghagen

, Pietsch

, Bantel

, et al. The enhanced liver fibrosis (ELF) score: normal values, influence factors and proposed cut-off values. J Hepatol, 2013; 59:236–242.

27.

Lin

, Xin

, Dong

, et al. Performance of the aspartate aminotransferase-to-platelet ratio index for the staging of hepatitis C-related fibrosis: an updated meta-analysis. Hepatology, 2011; 53:726–736.

28.

Mehta

, Lau

, Afdhal

, et al. Exceeding the limits of liver histology markers. J Hepatol, 2009; 50:36–41.

29.

Mobarak

, Nabeel

, Hassan

, et al. Real-time elastography as a noninvasive assessment of liver fibrosis in chronic hepatitis C Egyptian patients: a prospective study. Ann Gastroenterol, 2016; 29:358–362.

30.

Mohamed

, Elbedewy

, El-Serafy

, et al. Hepatitis C virus: a global view. World J Hepatol, 2015; 7:2676–2680.

31.

Myers

, Elkashab

, Ma

, et al. Transient elastography for the noninvasive assessment of liver fibrosis: a multicentre Canadian study. Can J Gastroenterol, 2010; 24:661–670.

32.

Pinzani

, Rombouts

, Colagrande

. Fibrosis in chronic liver diseases: diagnosis and management. Journal of Hepatology, 2005; 42:S22–S36.

33.

Pinzani

, Vizzutti

, Arena

, et al. Technology Insight: noninvasive assessment of liver fibrosis by biochemical scores and elastography. Nat Clin Pract Gastroenterol Hepatol, 2008; 5:95–106.

34.

Poynard

, Bedossa

. Age and platelet count: a simple index for predicting the presence of histological lesions in patients with antibodies to hepatitis C virus. METAVIR and CLINIVIR Cooperative Study Groups. J Viral Hepat, 1997; 4:199–208.

35.

Poynard

, Munteanu

, Imbert-Bismut

. Prospective analysis of discordant results between biochemical markers and biopsy in patients with chronic hepatitis C. Clin Chem, 2004; 50:1344–1355.

36.

Rath

, Hage

, Kügler

, et al. Serum proteome profiling identifies novel and powerful markers of cystic fibrosis liver disease. PLoS One, 2013; 8:e58955.

37.

Regev

, Berho

, Jeffers

, et al. Sampling error and intraobserver variation in liver biopsy in patients with chronic HCV infection. Am J Gastroenterol, 2002; 97:2614–2618.

38.

Rousselet

, Michalak

, Dupré

, et al. Sources of variability in histological scoring of chronic viral hepatitis. Hepatology, 2005; 41:257–264.

39.

Saadeh

, Cammell

, Carey

, et al. The role of liver biopsy in chronic hepatitis C. Hepatology, 2001; 33:196–200.

40.

Schiavon L de

, Narciso-Schiavon

, de Carvalho-Filho

. Non-invasive diagnosis of liver fibrosis in chronic hepatitis C. World J Gastroenterol, 2014; 20:2854–2866.

41.

Schmeltzer

, Talwalkar

. Noninvasive tools to assess hepatic fibrosis: ready for prime time?. Gastroenterol Clin North Am, 2011; 40:507–521.

42.

Sebastiani

, Alberti

. Non invasive fibrosis biomarkers reduce but not substitute the need for liver biopsy. World J Gastroenterol, 2006; 12:3682–3694.

43.

Sebastiani

, Halfon

, Castera

, et al. SAFE biopsy: a validated method for large-scale staging of liver fibrosis in chronic hepatitis C. Hepatology, 2009; 49:1821–1827.

44.

Sebastiani

. Non-invasive assessment of liver fibrosis in chronic liver diseases: implementation in clinical practice and decisional algorithms. World J Gastroenterol, 2009; 15:2190–2203.

45.

Stasi

, Arena

, Vizzutti

, et al. Transient elastography for the assessment of liver fibrosis in patients with chronic viral hepatitis: the missing tool?. Dig Liver Dis, 2009; 41:863–866.

46.

Sterling

, Lissen

, Clumeck

, et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology, 2006; 43:1317–1325.

47.

Wai

, Greenson

, Fontana

, et al. A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology, 2003; 38:518–526.

48.

Williams

, Hoofnagle

. Ratio of serum aspartate to alanine aminotransferase in chronic hepatitis. Relationship to cirrhosis. Gastroenterology, 1988; 95:734–739.