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Liver Biopsy Versus Noninvasive Testing in Chronic Hepatitis C: Where Do We Stand in 2008? John S. Aita, MD, and Stephen A. Harrison, MD

Corresponding author Stephen A. Harrison, MD Brooke Army Medical Center, 3841 Roger Brooke Drive, Fort Sam Houston, TX 78234, USA. E-mail: [email protected] Current Hepatitis Reports 2008, 7:51–59 Current Medicine Group LLC ISSN 1540-3416 Copyright © 2008 by Current Medicine Group LLC

The liver biopsy has historically been the diagnostic gold standard, albeit imperfect, for establishing the degree of inflammation and fibrosis in patients with chronic hepatitis C (CHC). However, this procedure is associated with the risk of complications, sampling variability, intra- and interobserver interpretation variability, and high cost. Recently, there has been significant interest in developing noninvasive modalities to accurately assess disease severity in CHC. Numerous scoring models have been proposed and validated, each with variable results depending on the study population assessed, the degree of significant fibrosis, and the type of liver biopsy scoring system and fibrosis markers used, making interpretation of the literature potentially confusing. Individual scoring algorithms have been shown to accurately distinguish F0-F1 versus F2-F4 fibrosis in about 50% of cases, with the rate improving somewhat for distinguishing more advanced fibrosis. Combination testing may further enhance the diagnostic accuracy of the tests. Imaging studies such as FibroScan (Echosens, Paris, France) and magnetic resonance elastography hold significant promise, but caution should be used when considering this modality in the setting of obesity, nonalcoholic fatty liver disease, ascites, or acute hepatitis.

Introduction It is estimated that 2.7 million Americans are infected with chronic hepatitis C (CHC), and up to 30% of these individuals will develop cirrhosis. The progression of CHC can be variable and may be associated with several factors, including age, gender, alcohol consumption, substance abuse, HIV or hepatitis B coinfection, and

insulin resistance or diabetes. A liver biopsy can assist in determining prognosis regarding risk of disease progression but also may be helpful in guiding initiation of therapy, excluding other liver diseases, and predicting antiviral therapy response [1]. This is supported by the 2002 National Institutes of Health Consensus Statement on Management of Hepatitis C [2], which states “…liver biopsy is useful in defining baseline abnormalities of liver disease and in enabling patients and healthcare providers to reach a decision regarding antiviral therapy.” Despite this large amount of helpful data, liver biopsies are associated with potential complications, and there are possibilities of sampling error and intra- and interobserver variation in histologic interpretation as well as cost considerations. An array of serum markers and imaging modalities recently have been studied and compared with liver biopsy. Serum tests are generally calculations ranging from simple ratios to more complex models based on linear regression. Imaging studies have also been employed and compared with both noninvasive serum tests and liver biopsy. In this article, we review and compare liver biopsy with some of the newer modalities of determining, through noninvasive means, the severity of CHC-associated liver disease.

The Liver Biopsy One of the primary indications for a CHC pretreatment liver biopsy is to determine the grade of inflammation and stage of fibrosis. The severity of fibrosis has been shown to be an inverse predictor of response to antiviral therapy, including peginterferon and ribavirin [1,3]. Furthermore, a liver biopsy may also serve to rule out other types of occult liver injury, such as increased iron storage or significant steatosis or steatohepatitis. The liver biopsy may not always be completely reflective of the degree of hepatic injury, however. There may be sampling variation based on biopsy location and intra- and interobserver variation by the pathologist. In one welldescribed prospective study that included 60 CHC patients undergoing right and left hepatic lobe paired biopsies, Skripenova et al. [4] showed there was a difference of one

52 Hepatitis C: Current and Future Therapies

Table 1. Clinical, biochemical, and serological g markers used to create fibrosis scoring panels Marker

Times used, n*

Age

5

Platelet count

5

B2-Macroglobulin

4

Aspartate aminotransferase

4

Hyaluronic acid

3

H-Glutamyl transferase

3

Gender

2

Bilirubin

2

Cholesterol level

1

Alanine aminotransferase

1

Haptoglobin

1

Apolipoprotein A-I

1

Prothrombin time

1

Urea

1

Procollagen type III N-terminal peptide

1

Matrix metalloproteinase-1

1

Tissue inhibitor of metalloproteinases-1

1

H-Globulin

1

*The total number of times the variable is incorporated into the various noninvasive serum tests.

grade or stage in 30% of biopsies, from both sampling and observer variability. Additionally, Regev et al. [5] performed a prospective study that included 124 CHC patients who underwent laparoscopic paired right and left hepatic lobe biopsies. When comparing each patient’s right and left hepatic lobe biopsies, 24.2% had a difference of at least one grade and 33.1% had a difference of at least one stage; however, the difference of two stages or grades was observed in only 2.4% and 1.6%, respectively. Liver biopsy size in both length and width may be highly variable from institution to institution but is critical to properly assess hepatic architecture. Colloredo et al. [6] examined the effects of random biopsy size in 161 liver biopsies from 130 chronic hepatitis patients. The incidence of mild fibrosis increased significantly in the shorter specimens: 59% in 3+ cm–length, 68.3% in 1.5 cm–length, and 80.1% in 1 cm–length specimens (P < 0.001). Also, the histologic grade and stage were significantly underscored in 1 mm–width samples (which was independent of specimen length). Similarly, Schiano et al. [7], using 100 CHC liver biopsy specimens, compared each original 20-mm or greater biopsy specimen with shorter biopsy segments measuring 5 mm, 10 mm, and 15 mm. The best agreement (regarding stage of the 20+-mm liver biopsy and the shorter biopsy) was with the 15-mm specimens. Subsequently it has been suggested that a biopsy length of

greater than 20 to 25 mm should be obtained for reliable grading and staging in chronic viral hepatitis [8,9]. Unfortunately, liver biopsies are invasive and may be associated with complications. It has been estimated that temporary pain is experienced in about 30%, severe complications in 0.57% to 3%, and death in 0.01% to 0.03% of patients [1,10–12]. Additionally, these risks may be prohibitive in certain patient populations, such as those who require chronic platelet inhibition or long-term dialysis or those who have von Willebrand disease or hemophilia. Another barrier may be cost, including both hospital expenses and time away from work. Finally, although the optimal biopsy length may be considered to be greater than 20 mm, this may be difficult to achieve in a single pass, and multiple passes may increase the risk of complications.

Noninvasive Serum Markers and Scoring Systems In the setting of CHC, the viral-mediated chronic inflammation promotes fibrogenesis. Matsuzaki et al. [13] showed that hepatitis C–associated chronic inflammation shifts hepatocyte transforming growth factor-C signaling from tumor suppression to fibrogenesis, which accelerates hepatic fibrosis and increases the risk of hepatocellular carcinoma. Hepatic fibrogenesis is dynamic and involves the development of an extracellular matrix (eg, glycoproteins, proteoglycans) in an intricate process. Ultimately, if left unchecked, it may lead to irreversible hepatic injury, architectural changes, and subsequent cirrhosis [14]. Recognizing this dynamic process, researchers have studied a variety of biochemical markers to determine their correlation with CHC-associated fibrosis or inflammation (Table 1). These can be classified into four general categories (Fig. 1): indirect markers without algorithms, indirect scoring algorithms that include indirect markers (the largest category), direct scoring algorithms that include direct markers, and mixed scoring algorithms including indirect and direct markers [15]. Table 2 details the original studies for each of the fibrosis scoring systems discussed.

Indirect markers without algorithms Aminotransferase-to-platelet ratio index The aminotransferase-to-platelet ratio index (APRI) represents the aspartate aminotransferase (AST)/the upper limit of normal × 100/platelet count (109/L). Wai et al. [16] originally studied 270 CHC patients, 47% of whom had significant fibrosis (Ishak score s 3). In the training set of 192 patients, the area under the curve (AUC) was 0.80 for detecting significant fibrosis and 0.89 for predicting cirrhosis. Based on the AUC, two cut points were obtained. An APRI value of c 0.50 was selected to exclude significant fibrosis with a positive predictive value (PPV) of 64% and a negative predictive value (NPV) of 90%. An APRI value of greater than 1.50 was selected for predicting the presence of significant fibrosis with a PPV of 91% and an NPV of 65%. A subsequent systematic review

Liver Biopsy Versus Noninvasive Testing in Chronic Hepatitis C Aita and Harrison

Figure 1. Noninvasive scoring algorithms assessing fibrosis severity. FibroTest is based on a statistical model licensed to BioPredictive (Houilles, France). FibroSpect II is licensed to Prometheus Laboratories (San Diego, CA).

Indirect algorithms using indirect markers of fibrosis • FibroTest • Forns index • FIB-4 • FibroIndex Indirect markers of fibrosis without algorithms • APRI

Noninvasive tests for assessing hepatic fibrosis stage

53

Mixed algorithms using both direct and indirect markers of fibrosis • Hepascore • Fibrometer

Direct algorithms using direct markers of fibrosis • FIBROSpectt II • MP3

of 22 studies (N = 4266) assessing the APRI, found the overall AUC for significant fibrosis and cirrhosis to be 0.76 and 0.82, respectively [17]. Using the original cut point of c 0.5 and a 47% prevalence of significant fibrosis, the NPV was 75%. When using a lower prevalence of fibrosis, the NPV improved to 80% to 86%. Overall, approximately one third of patients met the c 0.5 cut point and would have been correctly identified, thus potentially precluding biopsy in this subset of patients.

Indirect scoring algorithms based on indirect fibrosis markers FibroTest Assessed in more than 20 studies to date, FibroTest (scores of 0.0–1.0) is the most studied of all the scoring algorithms. It is based on a statistical model licensed to BioPredictive (Houilles, France) that combines serum concentrations of B2-macroglobulin, haptoglobin, H-glutamyl transferase (GGT), bilirubin, and apolipoprotein A-1, adjusted for gender and age. In the original study, 339 CHC patients were assessed (205 in the estimation group and 134 in the validation group) [18]. The prevalence of significant fibrosis was 40%. The overall AUCs were 0.836 and 0.87 in the estimation and validation groups, respectively. A score of c 0.1 was associated with an NPV of 100%, whereas a score of s 0.60 was associated with a PPV of greater than 90%. A recent systematic review of FibroTest (eight studies and 1503 CHC patients) found the AUC for predicting F2-F4 fibrosis to be 0.81 [19]. At a cutoff value of less than 0.20, significant fibrosis is largely excluded (sensitivity of 88%–96%). Cutoff values of greater than 0.60 were associated with a specificity of 92% to 98% and PPV of 77% to 90% for predicting advanced fibrosis. Forns index Forns index incorporates four variables that were identified on multivariate logistic regression analysis to be

independently predictive of significant fibrosis: age, GGT, cholesterol, and platelet count. The scoring system uses the following formula:

7.811–3.131× logg (platelet p count) + 0.781 × logg (GGT) + 3.467 × log (age) – 0.014 × (cholesterol). A total of 476 CHC patients were included (351 in the estimation group, 125 in the validation group) [20]. The prevalence of significant fibrosis was 25%. The overall AUC for predicting advanced fibrosis was 0.86 and 0.81 for the estimation group and the validation group, respectively. When a cutoff score less than 4.20 was used, the sensitivity was 94% and the NPV was 96% for excluding significant fibrosis. Alternatively, a score greater than 6.9 resulted in a specificity of 96% and a PPV of 79% for predicting significant fibrosis [21]. Others have attempted to validate this scoring system, with one study finding a lower NPV (89%) but similar PPV (77%) using the cutoffs of less than 4.2 and greater than 6.9 [20]. A retrospective study comparing the Forns index with the FibroTest found a lower AUC for the Forns index (0.78) compared with FibroTest (0.84; P = 0.02) [22]. FIB-4 Originally designed for HIV/HCV-coinfected patients, the FIB-4 is calculated using the AST, alanine aminotransferase (ALT), platelet count, and age in the following formula:

Age (years) × AST (units/liter)/platelets p (109/L) × [ALT (units/liter) ½]. In the first study with coinfected patients, the AUC for diff ferentiating between Ishak stages 0 to 3 and 4 to 6 was 0.737 and 0.765 in the estimation and validation sets, respectively [23]. When a cutoff of less than 1.45 was used, the NPV for excluding Ishak 4 to 6 fibrosis was 89% to 90%, and when a cutoff of greater than 3.25 was used, the PPV for predicting

205 351 240 555 294 194 117 383

FibroTest* [18]

Forns index [20]

FibroIndex [26]

FIB-4 [23]

FIBROSpect II§ [28]

MP3 [30]

Hepascore [32]

Fibrometer [34]

120

104

402

277

120

125

134

78

55

50

45

52

65†

51

25

40

47

Metavir F2-F4 fibrosis, %

0.88/0.89

0.85/0.82

0.82

0.83/0.82

0.74/0.77‡

0.83/0.82

0.86/0.81

0.836/0.87

0.80/0.88

AUC F2-F4

≥ 0.60

≥ 0.50

< 0.50

> 0.40

< 0.20

≥ 0.36

> 3.25

< 1.45

≥ 2.25

≤ 1.25

> 6.90

< 4.20

86.3

87–88

91

74.3

65

94.3

79

> 90

91

> 1.50 ≤ 0.10

64

PPV, %

≤ 0.50

Cut point

*Based on a statistical model licensed to BioPredictive, Houilles, France. †Ishak 2–6 fibrosis. ‡Ishak 0–3 vs 4–6. §Prometheus Laboratories, San Diego, CA. AUC—area under the curve; APRI—aminotransferase-to-platelet ratio index; NPV—negative predictive value; PPV—positive predictive value.

192

Estimation set, n

APRI [16]

Test

Validation set, n

Table 2. Original fibrosis scoring algorithm assessments F2-F4

77.6

95–98

88

75.8

89

62.4

96

100

65

90

NPV, %



advanced fibrosis was 65%. Overall, 87% of the patients with values meeting these cutoff points were correctly classified and 71% of the validation cohort (277 patients) would have avoided a liver biopsy. Subsequently, ValletPichard et al. [24] showed FIB-4 results to be similar to those of the FibroTest in the setting of CHC monoinfection, when outside the values of 1.45 to 3.25. A FIB-4 index less than 1.45 had an NPV of 94.7% to exclude F3-F4 fibrosis, and a value greater than 3.25 was associated with a PPV of 82.1% to confirm the existence of F3-F4 fibrosis. Additionally, this test correlated strongly to the FibroTest. The results of the FIB-4 test may not be as accurate in older patients, however [25]. FibroIndex Made up of three variables—AST, platelet count, and serum H-globulin, this nonproprietary algorithm (as compared with FibroTest) represents an equation of

1.738 – 0.064 (platelets p [× 104/mm3]) + 0.005 (AST [international units/liter]) + 0.463 (H-globulin [grams/deciliter]). Koda et al. [26] evaluated 240 CHC patients in an estimation set and then 120 in a validation set and found the AUCs for significant fibrosis to be 0.83 and 0.82 in the estimation and validation sets, respectively. The PPV for predicting significant fibrosis with a cutoff value of s 2.25 was 94.3%. This test is much less reliable for excluding significant fibrosis, however, as the NPV was only 62.4%. Overall, the authors estimated that approximately one third of liver biopsies could be eliminated if this scoring system were used. External validation by a separate group of 125 CHC patients (prevalence of significant fibrosis was 64%) determined that the AUC was 0.76 (CI, 0.68–0.83) for predicting significant fibrosis, less than either the estimation or validation group in the original study [27].

Direct scoring algorithms that include direct markers FIBROSpect II FIBROSpectt II (Prometheus Laboratories, San Diego, CA), with scores ranging from 0.0 to 1.0, consists of hyaluronic acid, tissue inhibitor of metalloproteinases-1 (TIMP-1), and B2-macroglobulin. This test was developed retrospectively from a large series of banked serum specimens and compared with liver biopsy tissue in which the prevalence of significant fibrosis was 52% and the mean liver biopsy length was 13 ± 0.22 mm [28]. Overall, the AUC for predicting significant fibrosis was 0.831. An index score s 0.36 was consistent with F2-F4 fibrosis and the reported PPV and NPV were 74.3 and 75.8, respectively. A subsequent prospective study by Zaman et al. [14] in 108 CHC patients found the PPV and NPV to be 60.9% and 82.3%, respectively, when using a slightly different cutoff of s 0.42. Interestingly, when two distinct threshold scores are used, one for

55

F0-F1 fibrosis and one for F2-F4 fibrosis, the likelihood of accurately detecting the degree of fibrosis increased. An index score less than 0.20 correctly identified F0-F1 fibrosis in 90% of the cases and an index score s 0.80 correctly identified F2-F4 fibrosis in about 80% of cases. Overall, FIBROSpectt II seems to identify CHC patients with no or mild fibrosis quite accurately with cutoff scores less than 0.20 and is also quite good at identifying CHC patients with advanced fibrosis (with index scores s 80). It is less reliable for determining intermediate fibrosis stages [29]. MP3 The MP3 fibrosis score incorporates procollagen type III N-terminal peptide (PIIINP), a marker of fibrogenesis, and metalloproteinase-1 (MMP-1), a marker of fibrolysis, into a statistical model as follows:

0.5903 log g PIIINP (nanograms/milliliter) g – 0.1749 log MMP-1 (nanograms/milliliter). These markers were identified in 194 CHC patients to be independently associated with fibrosis on multivariate logistic regression analysis [30]. The prevalence of significant fibrosis was 45%. Overall, the AUCs were 0.82 and 0.88 for predicting significant and extensive fibrosis, respectively. Using cutoff points, when a score less than 0.20 was used, the NPV was 88% and when a cutoff score greater than 0.40 was used, the PPV for predicting significant fibrosis was 91%. This same group attempted to validate this scoring system a few years later in 79 CHC patients; in this study, the percentage of patients with significant fibrosis was 66% (much higher than in the original study cohort) [31]. The overall AUCs were not as robust: 0.77 and 0.81 for significant and extensive fibrosis, respectively. Using the cutoff of less than 0.20, however, the NPV was 96%, and when using a cutoff of greater than 0.50, the PPV was 100%.

Mixed scoring algorithms including indirect and direct markers Hepascore Hepascore (range, 0.0–1.0) combines bilirubin, GGT, hyaluronic acid, B2-macroglobulin, age, and gender in the following algorithm:

y = exp [–4.185818 – (0.0249 × age) g + (0.7464 × sex) + (1.0039 × B2-macroglobulin) g + (0.0302 × hyaluronic y acid) + (0.0691 × bilirubin) – (0.0012 × GGT)]. Originally described by Adams et al. [32], this scoring algorithm was assessed among 221 patients with CHC (117 in the estimation group and 104 in the validation group). The median biopsy length was 13 mm, and approximately 50% of the total cohort had significant fibrosis. The AUC for predicting significant fibrosis was 0.85 (CI, 0.778–0.926). Using a


cutoff of less than 0.50, the test had a sensitivity for excluding F2-F4 fibrosis of 88% to 95% (NPVs ranged from 95%–98%). When a cutoff of s 0.50 was used, the specificity for predicting F2-F4 fibrosis was 88% to 92% (PPVs ranged from 87%–88%). When a cutoff of greater than 0.84 was used, the specificity for predicting cirrhosis was 94% to 89%. In a separate study of 180 CHC patients, the Hepascore had a lower AUC of 0.79 and a PPV for predicting F2-F4 fibrosis of 77.8 when a cutoff score of s 0.5 was used [33••]. When a cutoff score of less than 0.5 was applied, the NPV for excluding F2-F4 fibrosis was only 63.5%. A similar percentage of patients had F2-F4 fibrosis (~50%) as in the original study cohort. Fibrometer The fibrometer incorporates hyaluronate, prothrombin time, platelets, AST, B2-macroglobulin, urea, and age. This test was estimated in a cohort of 383 patients with either CHC or hepatitis B [34]. The percentage of patients with F2-F4 fibrosis was 55%, and the median liver biopsy length was 18 mm. The overall AUC for predicting significant fibrosis was 0.88 and 0.89 in the estimation and validation cohorts, respectively. The PPV was 86.3% and the NPV was 77.6%. Unlike the other scoring algorithms, this test does not use cut points and has the advantage of lacking indeterminate results [33••].

Comparison Studies Recent interest has focused on assessing the various scoring systems in head-to-head comparison studies. Leroy et al. [33••] compared the overall diagnostic performance of six separate scoring algorithms (APRI, Forns index, FibroTest, Fibrometer, Hepascore, and MP3) among 180 CHC patients. The mean liver biopsy length was 23 mm, and 28% of the study cohort had F2-F4 fibrosis. The best AUC for discriminating F0-F1 versus F2-F4 fibrosis was 0.86 for Fibrometer. Among the others—APRI, MP3, Forns index, FibroTest and Hepascore—the AUCs were 0.81, 0.84, 0.78, 0.84, and 0.79, respectively. Using the cut points from the original studies, the test with the best ability to exclude F2-F4 fibrosis was the MP3 cutoff of less than 0.20 (NPV, 84%), followed by the FibroTest cutoff of less than 0.22 (NPV, 82.5%). For predicting F2-F4 fibrosis, the best test was the MP3 cutoff of greater than 0.50 (PPV, 94.4%), followed by the APRI score of greater than 2.0 (PPV, 90.6%). By applying these stringent cutoff points, approximately 50% of the patients would have been classified correctly with 80% certainty. However, this leaves 20% of the patients incorrectly classified and 50% of patients unclassifiable. Adler et al. [35] compared the AUCs for FibroTest, FIB-4, Forns index, APRI, and FibroIndex from two patient populations, one with 152 CHC patients and the

other with 290 patients with chronic liver disease, the majority of whom had CHC. The prevalence of F2-F4 fibrosis was 77%. The AUCs were found to be 0.794 for FibroTest, 0.786 for FIB-4, 0.75 for Forns index, 0.742 for APRI, and 0.689 for FibroIndex. FibroTest and FIB-4 were shown to have equal diagnostic power. Overall, it seems that when multiple tests are compared within an individual cohort of patients, the scoring algorithms with the best diagnostic utility are the MP3, FibroTest, and FIB-4. However, it should be noted that approximately 50% of patients remain unclassifiable when only one scoring system is used. A unique problem for these noninvasive tests may be the CHC patients with a persistently normal ALT. Sebastiani et al. [36] compared the APRI, Forns index, AST-to-ALT ratio, FibroTest, and FibroIndex in 80 CHC patients with normal ALT and 164 CHC patients with elevated ALT. When compared with respective liver biopsies, all these tests showed poorer performance in the normal-ALT group compared with the elevated-ALT group. Of all studied noninvasive tests, the FibroTest had the best results in the normal-ALT CHC group.

Combining Tests in a Sequential Algorithm Recent interest has focused on combinations of noninvasive tests in an effort to further enhance their overall diagnostic accuracy. Snyder et al. [37] assessed the combination of APRI and FIBROSpect II for predicting significant fibrosis in 93 CHC patients. Significant fibrosis was noted in 53.8% of the study cohort. Using the APRI first, a cutoff value of c 0.42 correctly identified 19 of 20 patients with mild fibrosis (NPV, 95%). Additionally, a cutoff value of s 1.2 correctly identified 31 of 33 patients with significant fibrosis (PPV, 93.6%). A total of 40 patients remained unclassified and thus indeterminate. When these 40 patients were then evaluated with FIBROSpect II using cutoffs of c 25 and s 85, 16 were correctly identified as having either mild or significant fibrosis. Overall, only 26% of patients remained unclassified and thus would require a liver biopsy. The overall AUC for the combined scores was 0.931 (CI, 0.859–0.973). Sebastiani et al. [38•] evaluated the efficacy of different scoring algorithms (FibroTest, APRI, Forns index) used in combination in 290 CHC patients (190 in the estimation group and 100 in the validation group). Among patients with elevated ALT, the best diagnostic algorithm was calculation of the APRI followed by the FibroTest in those left unclassified (51%). Overall, the diagnostic accuracy was 94% and liver biopsies were precluded in 50% of the cases. Similarly, Bourliere et al. [39] evaluated three scoring algorithms (APRI, FibroTest, and Forns index) in 235 CHC patients from the Fibropaca Multicenter Independent Study. Significant fibrosis was present in 42% of


cases, and the mean liver biopsy size was 16 ± 7.5 mm. Overall, the AUCs of APRI, FibroTest, and Forns index were 0.81, 0.71, and 0.76, respectively. The combination of APRI, Forns index, and FibroTest allowed 81.3% of patients to be correctly staged when the respective cutoffs for each test were used. Finally, Leroy et al. [33••] also assessed combination algorithms in a large group of CHC patients. A FibroTest score greater than 0.59 and an APRI score greater than 2.0 predicted the presence of F3-F4 fibrosis with more than 90% certainty. Additionally, F2-F4 fibrosis could be ruled out with 90% certainty if the FibroTest score was less than 0.22 and the APRI score was less than 0.5.

Summary of Noninvasive Serum Markers and Scoring Systems Several scoring systems have been validated in multiple patient populations. Overall, individual scoring algorithms are modest in their ability to distinguish F2-F4 fibrosis from lesser degrees of fibrosis but are better at predicting extensive fibrosis (F3-F4). The major reasons for this discrepancy among scoring algorithms include variations in the prevalence of significant fibrosis, different histologic staging systems used (eg, Ishak, Scheuer, Metavir), and the median liver biopsy length. Combining two noninvasive scoring systems appears to enhance the diagnostic utility of the tests, allowing for correct fibrosis staging in more than 50% of patients. However, these tests still have limitations, including increased inaccuracy in the setting of CHC with a normal ALT and inability to assess for concomitant steatosis, nonalcoholic steatohepatitis, or increased iron overload.

Noninvasive Imaging Studies

57

the summary sensitivity and specificity for detecting F2F4 fibrosis at a threshold of 7.1 to 8.8 kPa to be 63.8% and 86.5%, respectively [19]. Additionally, the summary sensitivity and specificity for detecting F4 fibrosis at a threshold of greater than 12.5 kPa were 85.6% and 93.2%, respectively. Interestingly, when FibroScan and FibroTest were used together, the AUCs were 0.88 for stage F2 or greater, 0.95 for stage F3 or greater, and 0.95 for F4 [41]. Limitations exist for FibroScan, however. Data suggest that obesity, ascites, steatosis, and acute hepatitis may decrease the diagnostic utility of the test.

Magnetic resonance elastography Magnetic resonance (MR) elastography is a modality previously used to evaluate breast tissue. It uses a piston situated under the supine patient that delivers 65-Hz longitudinal mechanical waves to the right liver lobe during the MRI sequence. Ultimately, the phase maps were processed and yielded shear elasticity and shear viscosity maps (both the elasticity and viscosity are increased in the setting of significant fibrosis) [42]. Huwart et al. [43•] published a prospective study involving 96 liver biopsy patients (66 of whom had CHC) and compared the AUC of MR elastography with that of the APRI. The prevalence of significant fibrosis in this study population was 60%. For MR elastography, the AUCs for elasticity and viscosity were 0.999 and 0.863 for significant fibrosis, respectively, and 1.000 and 0.986 for stage F4, respectively. For comparison, the APRI AUC was 0.854 for significant fibrosis and 0.851 for stage F4. The conclusion was that MR elastography is accurate and superior to the APRI in determining significant fibrosis. Although additional studies, such as those in the setting of acute liver injury, are warranted, MR elastography holds promise for its potential to accurately stage fibrosis in CHC patients.

Transient elastography Transient elastography (FibroScan; Echosens, Paris, France) uses ultrasound (5 MHz) and low-frequency (50 Hz) elastic waves with a propagation speed that is correlated to elasticity [40]. Ultimately, the measurement of the transmission speed, in kilopascals, is correlated to hepatic “stiffness.” The more stiff the tissue, the faster the propagation. Castera et al. [41] conducted a prospective study of 183 consecutive CHC patients comparing FibroScan with APRI, FibroTest, and liver biopsy. In this study, the median FibroScan value was 7.4, with a range from 2.4 to 75.4 kPa. The investigators used cutoff values of 7.1 kPa for F2 or greater, 9.5 kPa for F3 or greater, and 12.5 kPa for cirrhosis. The AUCs were similar for FibroScan and FibroTest (0.83, 0.85 for F2 or greater; 0.90, 0.90 for F3 or greater; 0.95, 0.87 for F4). In those with FibroScan values greater than 7.1 kPa, the PPV for significant fibrosis was 95%. The PPV for cirrhosis was 90% with measurements above 12.5 kPa. A recent systematic review of three studies assessing FibroScan found

Conclusions The liver biopsy remains an imperfect gold standard in determining grade of inflammation and stage of fibrosis in the setting of CHC. Subsequently, noninvasive serum and imaging tests have been studied in an effort to accurately stage fibrosis severity and decrease the overall need to perform liver biopsies. Many fibrosis scoring tests have now been tested and validated in multiple patient populations. Additionally, many tests have been compared head to head and in combination. By using selected cutoff points for specific tests, the diagnostic accuracy for excluding or predicting mild fibrosis from significant or extensive fibrosis is enhanced. Testing with a combination of serum scoring algorithms and of serum scoring algorithms and imaging modalities appears to accurately assess fibrosis staging in upward of 50% of patients, but further studies are needed to validate these specific combinations. The major drawback to noninvasive testing


remains the inability to assess for histopathologic factors that may affect disease progression or response to antiviral therapy, such as steatosis, steatohepatitis, and iron overload. Clinicians should be aware of the benefits and limitations of these noninvasive tests, and the decision to use them in lieu of liver biopsy should be made on a case-by-case basis.

12.

13.

14.

Disclaimer The opinions or ascertains herein are the private views of the authors and are not to be construed as official or reflecting the view of the Department of the Army or the Department of Defense.

Disclosures Dr. Aita reports no potential conflict of interest relevant to this article. Dr. Harrison has received grant support from Roche, Schering-Plough, and Valeant; has been an advisor to Roche; and has served on the speakers’ bureaus of Roche, Schering-Plough, BristolMyers Squibb, and Novartis.

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Trocme C, Leroy V, Sturm N, et al.: Longitudinal evaluation of a fibrosis index combining MMP-1 and PIIINP compared to MMP-9, TIMP-1 and hyaluronic acid in patients with chronic hepatitis C treated by interferon-alpha and ribavirin. J Viral Hepat 2006, 13:643–651. 32. Adams LA, Bulsara M, Rossi E, et al.: Hepascore: an accurate validated predictor of liver fibrosis in chronic hepatitis C infection. Clin Chem 2005, 51:1867–1873. 33.•• Leroy V, Hilleret MN, Sturm N, 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. This article provides a nice comparison of the following: MP3, FibroTest, Fibrometer, Hepascore, Forns index, and the APRI. One of the primary conclusions is that these noninvasive serum markers can provide reliable information on hepatic fibrosis in approximately one third of patients, especially when tests are used in combination. 34. Cales P, Oberti F, Michalak S, et al.: A novel panel of blood markers to assess the degree of liver fibrosis. Hepatology 2005, 42:1373–1381. 35. Adler M, Gulbis B, Moreno C: The predictive value of FIB-4 versus FibroTest, APRI, FibroIndex, and Forns index to noninvasively estimate fibrosis in hepatitis C and nonhepatitis C liver diseases. Hepatology 2008, 42:762–763. 36. Sebastiani G, Vario A, Guido M, et al.: Performance of noninvasive markers for liver fibrosis is reduced in chronic hepatitis C with normal transaminases. J Viral Hepat 2008, 15:212–218. 37. Snyder N, Nguyen A, Gajula L, et al.: The APRI may be enhanced by the use of the FIBROSpect II in the estimation of fibrosis in chronic hepatitis C. Clin Chim Acta 2007, 381:119–123. 38.• Sebastiani G, Vario A, Guido M, et al.: Stepwise combination algorithms of non-invasive markers to diagnose significant fibrosis in chronic hepatitis C. J Hepatol 2006, 44:686–693. This study shows how a stepwise, algorithmic approach using noninvasive serum markers can effectively determine the extent of hepatic fibrosis in a proportion of patients with CHC. This principle will likely have implications for future incorporation of noninvasive serum markers into diagnostic algorithms that may reduce the frequency of liver biopsies.

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Bourliere M, Penaranda G, Renou C, 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. 40. Baranova A, Younossi ZM: The future is around the corner: noninvasive diagnosis of progressive nonalcoholic steatohepatitis. Hepatology 2008, 47:373–375. 41. Castera L, Vergniol J, Foucher J, et al.: Prospective comparison of transient elastography, Fibrotest, APRI, and liver biopsy for the assessment of fibrosis in chronic hepatitis C. Gastroenterology 2005, 128:343–350. 42. Huwart L, Peeters F, Sinkus R: Liver fibrosis: non-invasive assessment with MR elastography. NMR Biomed 2006, 19:173–179. 43.• Huwart L, Sempoux C, Salameh N, et al.: Liver fibrosis: noninvasive assessment with MR elastography versus aspartate aminotransferase-to-platelet ratio index. Radiology 2007, 245:458–466. This article provides a nice comparison of MR elastography with the APRI. Although MR elastography should undergo additional scrutiny in hepatic fibrosis screening, this article shows the potential promise that MR elastography holds for fibrosis screening.