hepatic steatosis: Quantification by Proton Density Fat ...

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Purpose:

To determine utility of proton density fat fraction (PDFF) measurements for quantifying the liver fat content in patients with nonalcoholic fatty liver disease (NAFLD), and compare these results with liver biopsy findings.

Materials and Methods:

This retrospective study was approved by the institutional review board with waivers of informed consent. Between June 2010 and April 2011, 86 patients received a diagnosis of NAFLD. Ten patients did not accept liver biopsy and six patients had contraindications for magnetic resonance (MR) imaging. Seventy patients were included in this study. Seventy patients with NAFLD (40 men, 30 women; mean age, 44.7 years; range, 16–69 years) underwent T1independent volumetric multiecho gradient-echo imaging with T2* correction and spectral fat modeling. Median time interval between MR imaging and liver biopsy was 14.5 days (range, 0–259 days). MR examinations were performed with a 1.5-T MR imaging system. Complexbased PDFF measurements were performed by placing regions of interest in Couinaud system segments V–VI and all liver segments from I to VIII. All liver biopsy specimens were retrieved from archives and evaluated by one pathologist for hepatic steatosis according to criteria from a previous study. Pearson correlation coefficient, receiver operating characteristics, and linear regression analyses were used for statistical analyses.

Results:

Mean PDFF calculated with MR imaging was 18.1% 6 9.5 (standard deviation). Close correlation for quantification of hepatic steatosis was observed between PDFF and liver biopsy (r = 0.82). PDFF was effective in discriminating moderate or severe hepatic steatosis from mild or no hepatic steatosis, with area under the curve of 0.95. The correlation between biopsy and PDFF-determined steatosis was less pronounced when fibrosis was present (r = 0.60) than when fibrosis was absent (r = 0.86; P = .02).

Conclusion:

PDFF measurement by MR imaging provided a noninvasive, accurate estimation of the presence and grading of hepatic steatosis in patients with NAFLD. Hepatic fibrosis reduced the correlation between biopsy results and PDFF.

1

From the Department of Radiology, Liver Imaging Team, Hacettepe University, School of Medicine, Sihhiye, Ankara, Turkey 06100 (I.S.I., M.K.); Departments of Gastroenterology (H.A., R.I., G.K., K.B.), Pathology (B.S.), and Biostatistics (A.E.), Faculty of Medicine, Ankara University, Ankara, Turkey; and General Electric Healthcare, Istanbul, Turkey (A.C.). Received June 19, 2012; revision requested August 24; revision received October 2; accepted October 31; final version accepted November 13. M.K. supported by the Turkish Academy of Sciences, in the framework of the Young Scientist Award Program (EA-TUBA-GEBIP/2011). R.I. is an associate member of the TUBA. Address correspondence to M.K. (e-mail: [email protected]).

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Radiology: Volume 267: Number 3—June 2013 n radiology.rsna.org

767

Imaging

Ilkay S. Idilman, MD Hatice Aniktar, MD Ramazan Idilman, MD Gokhan Kabacam, MD Berna Savas, MD Atilla Elhan, PhD Azim Celik, PhD Kadir Bahar, MD Musturay Karcaaltincaba, MD

Original Research n Gastrointestinal

Hepatic Steatosis: Quantification by Proton Density Fat Fraction with MR Imaging versus Liver Biopsy1

GASTROINTESTINAL IMAGING: Hepatic Steatosis: Quantification by Proton Density Fat Fraction with MR Imaging versus Liver Biopsy

H

epatic steatosis is the abnormal and excessive intracellular accumulation of fat, primarily as triglycerides in hepatocytes. Nonalcoholic fatty liver disease (NAFLD) is a spectrum of disorders that range from simple (pure) steatosis to nonalcoholic steatohepatitis (NASH) and cirrhosis (1–4). NAFLD affects approximately 10%–30% of the general population and is a disease of both the adult and pediatric populations (1,2,5–8). NAFLD is a hepatic manifestation of metabolic syndrome, which is associated with obesity, insulin resistance, type 2 diabetes mellitus, hypertension, hyperlipidemia, and cardiovascular disease (1–4,8–11). Previous studies (1–4,7,8,11–13) have demonstrated that approximately 30% of individuals with NAFLD show histologic progression: Of these patients, 15%–20% may develop cirrhosis and 30%–40% may suffer liver-related morbidity and mortality. No specific biochemical or serological tests for the diagnosis of NAFLD that we know of are available at this time. Liver biopsy remains the reference method to accurately diagnose hepatic steatosis. However, among the several limitations of liver biopsy are its invasiveness and potential for bleeding and perforation. In addition, sampling error and interobserver diagnostic variability have also been reported (14,15). Ultrasonography (US) and computed tomography (CT) can be useful in the detection of fatty liver. However, US and CT have

Advances in Knowledge nn Proton density fat fraction (PDFF) calculated by MR imaging provides a noninvasive and accurate estimation of the presence and grading of hepatic steatosis between nonexistent or mild and moderate or severe forms, with 93% sensitivity and 85% specificity. nn The correlation between biopsy and PDFF-determined steatosis was less pronounced when fibrosis was present (r = .60) than when fibrosis was absent (r = 0.86; P = .02). 768

limited ability for quantifying hepatic fat content (16,17). Therefore, new noninvasive diagnostic modalities are needed to detect and quantify hepatic steatosis in the whole liver as an alternative to liver biopsy. Magnetic resonance (MR) imaging modalities, such as MR imaging with in-phase and out-of-phase images (Dixon method) and MR imaging with and without fat saturation, have been used for determination of hepatic fat content (18–20). Magnetic resonance spectroscopy is an accepted reference imaging method for the assessment of hepatic fat content (21–23). However, the acquisition parameters, method of analysis, and location of the volume assessed can affect the accuracy of its evaluation. Proton density fat fraction (PDFF) calculation is a recently described (24– 27) chemical shift–based water and fat separation technique that can be performed by magnitude and complexbased techniques. The complex-based technique uses both magnitude and phase images, and magnitude-based techniques use only magnitude images for PDFF calculation. This is a promising method that can be completed in a breath hold and allows for the simple calculation of fat fraction in any segment of the liver. The advantage of this technique versus older MR imaging techniques (Dixon and fat saturation methods) is that this technique provides correction of factors that influence MR signal intensity, such as T1 bias, T2* decay, spectral complexity of fat, noise bias, and eddy currents. This technique has been shown to provide accurate quantification of hepatic fat content compared with MR spectroscopy (20,24–27). To our knowledge, correlation with liver biopsy results has not been evaluated. The aim of our study was to determine the utility of PDFF measurements to quantify the liver fat content

Implication for Patient Care nn It is possible to accurately quantify liver steatosis by using PDFF in one breath hold by threedimensional sequence that covers the entire liver.

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in patients with NAFLD, and compare these results with liver biopsy findings.

Materials and Methods This retrospective study was approved by the institutional review board with waivers of informed consent. The physician authors (I.S.I, M.K, R.I.) had control of the data and the information submitted for the publication. The employee of GE Healthcare (A.C.) did not have control of any data or information.

Study Population Between June 2010 and April 2011, 70 consecutive patients who were diagnosed with NAFLD in a liver disease outpatient clinic and underwent PDFF measurement to quantify the presence of hepatic steatosis were included in this study. The median time interval between liver biopsy and MR imaging was 14.5 days (range, 0–259 days). Seventy percent of the patients (49 of 70) had MR imaging within 4 weeks and 83% (58 of 70) had it within 3 months of liver biopsy. The diagnosis of NAFLD was based on a combination of biochemical, radiologic, and histologic criteria, and exclusion of other forms of acute and chronic liver diseases (1,2,16,28). Criteria for inclusion were based on the

Published online before print 10.1148/radiol.13121360 Content codes: Radiology 2013; 267:767–775 Abbreviations: BMI = body mass index NAFLD = nonalcoholic fatty liver disease NASH = nonalcoholic steatohepatitis PDFF = proton density fat fraction ROC = receiver operating characteristic Author contributions: Guarantors of integrity of entire study, I.S.I., R.I., K.B., M.K.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; literature research, I.S.I., G.K., M.K.; clinical studies, I.S.I., R.I., G.K., B.S., A.C., K.B., M.K.; experimental studies, I.S.I., B.S., A.C.; statistical analysis, R.I., A.E.; and manuscript editing, I.S.I., R.I., G.K., A.E., A.C., M.K. Conflicts of interest are listed at the end of this article.

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following: patient aged over 16 years; evidence of absent or minimal alcohol consumption (ie, ,20 g of alcohol per day for women and ,30 g of alcohol per day for men); absence of confounding disease, including acute and/or chronic viral hepatitis (hepatitis A, B, or C) (n = 20); and no other forms of liver disease, including autoimmune (n = 10), druginduced (n = 6), and metabolic (n = 8) liver diseases.

Methods Laboratory tests.—Serum alanine aminotransferase, aspartate aminotransferase, gamma glutamyl transpeptidase, alkaline phosphatase, bilirubin, fasting glucose, cholesterol, triglycerides levels, and complete blood cell counts were measured by our central laboratory on a 24-channel automated chemical analyzer by using standard reagents before biopsy and MR imaging. Insulin was measured by radioimmunoassay. For exclusion of other forms of liver disease, we measured serological markers for viral infections (anti–hepatitis A virus immunoglobulin M, hepatitis B surface antigen, anti–hepatitis B surface, hepatitis B e antigen, anti–hepatitis B, anti–hepatitis B core antigen immunoglobulin M, immunoglobulin G, antihepatitis C virus, anticytomegalovirus, antiherpes simplex virus, anti–Epstein-Barr virus, serum iron, ferritin, copper, ceruloplasmin, and alpha-1 antitrypsin levels), and we performed serological studies for antinuclear antibody, antismooth muscle antibody, and antimitochondrial antibodies. Body mass index (BMI) was calculated as body weight in kilograms divided by height in meters (kg/m2). Definitions of obesity were based on criteria from the World Health Organization, and BMI from 25 to 29.9 kg/m2 was considered overweight and BMI of 30 kg/m2 or greater was considered to be obese (29). Insulin resistance was calculated on the basis of fasting plasma glucose and insulin values by using the following homeostasis model assessment and insulin resistance method calculation: plasma glucose (mg/ dL) 3 insulin (µg/mL)/405 (30). Histologic evaluation.—Liver biopsies were percutaneously performed by a gastroenterologist through the right-side

intercostal area of segment V–VI. All liver biopsy specimens were retrieved from the archives of the Department of Pathology at Ankara University. Liver biopsy material was mounted on archival slides that were originally prepared from 10% formalin-fixed paraffin-embedded tissue and stained with hematoxylineosin and Masson trichrome to evaluate fibrosis. Biopsy specimens were evaluated by a pathologist (B.S., 15 years of experience) blinded to the clinical and biochemical data. Biopsies were scored by using the NASH Clinical Research Network NAFLD activity score and fibrosis score (31). Hepatic steatosis was classified from grade 0 to grade 3. Minimal steatosis was classified as less than 5.0% steatosis; mild steatosis, or grade 1, between 5.0% and 33.0%; moderate steatosis, or grade 2, between 33.0% and 66.0%; and severe steatosis, or grade 3, as more than 66.0%. Lobular inflammation was defined according to foci per magnification field 2003, and graded as follows: grade 0, no inflammation; grade 1, less than 2 foci; grade 2, 2–4 foci; and grade 3, more than 4 foci. Portal inflammation was graded as follows: grade 0, none; grade 1, mild; grade 2, moderate; or grade 3, marked. Fibrosis was graded as follows: stage 0, none; stage 1, perisinusoidal or periportal; stage 2, perisinusoidal and portal or periportal; stage 3, bridging fibrosis; or stage 4, cirrhosis. Clinically important fibrosis was defined as fibrosis stage 2–4. NAFLD activity score, with a range of 0 to 8, was calculated based on the grade of steatosis (grades 0–3), lobular inflammation (grades 0–3), and ballooning (grades 0–2)(31). NASH was defined as patients with NAFLD activity score graded 5 or greater, while patients with NAFLD activity score less than 3 were not diagnosed as having NASH (31).

MR Examinations The radiologists (I.S.I., 4 years of experience, and M.K., 14 years of experience) who performed the PDFF measurements were unaware of the biopsy results. An eight-channel phased-array body coil was used for this acquisition. The patients were examined in the supine position. The MR examinations were performed


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on a 1.5-T MR imaging system (Signa HDxt 1.5T; GE Healthcare, Waukesha, Wis). MR imaging.—A three-plane gradient echo localizer sequence was performed at the beginning of each examination. The protocol included an MR-sequence software product (IDEAL-IQ; GE Healthcare), which is a three-dimensional volumetric imaging sequence used to create T2* and triglyceride fat fraction maps from a single breath-hold acquisition. The technique was used to estimate R2* (1/T2*) and PDFF (water-triglyceride fat separation) in the liver in a single simultaneous acquisition. Afterward, the resulting PDFF maps were corrected for T2* effects. Six gradient echoes were applied to reconstruct water and triglyceride fat images, relative triglyceride fat fractions, and R2* maps. Iterative decomposition of water and fat with echo asymmetry and least-squares estimation sequence uses a complex field map to incorporate the T2* and field inhomogeneity effects into the overall multiecho acquisition signal model. Yu et al (32) have shown that by acquiring a six-echo image and by estimating complex field maps by using an iterative least-squares estimation algorithm, it is possible to achieve fat-water separation and T2* estimation in a single breath-hold acquisition. The parameters of this sequence were as follows: repetition time, 12.9 msec; field of view, 35–40 cm; matrix, 224 3 160; bandwidth, 125 kHz; flip angle, 5°; section thickness, 10 mm; and a single three-dimensional image with 22 to 28 sections. We acquired data sets with six different echo times that ranged from 1.6 to 9.8 msec. A two-dimensional self-calibrated parallel imaging technique, autocalibrating reconstruction of Cartesian sampling, was used with an acceleration factor of 2. The sequence was acquired in a breath-hold time (,25 seconds in all patients). The images were processed by using the software provided by the manufacturer (IDEAL-IQ; GE Healthcare) to create water, fat, inphase, out-of-phase, R2*, and fat fraction maps instantaneously. Image processing.—By using a workstation (AW 4.4; GE Healthcare), an elliptic region of interest (ROI) of 4 cm2 769


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Table 1 Characteristics of 70 Patients with NAFLD Parameter Age (y) BMI (kg/m2) Obesity Diabetes mellitus Insulin resistance . 2.7 Hypertension Hyperlipidemia Fasting glucose (mg/dL) Cholesterol (mg/dL) Triglyceride (mg/dL) HDL (mg/dL) Serum AST level (U/L) Serum ALT level (U/L) Serum GGT level (U/L) Serum total bilirubin level (mg/dL) Calculated PDFF (%) Percentage of hepatic steatosis (%)

All Study Patients

Men

Women

P

44.7 6 13.1 (47)* 29.9 6 4.3 (29.1)* 30 of 70 (43)† 11 of 70 (16)† 54 of 70 (77)† 16 of 70 (23)† 43 of 70 (61)† 97.6 6 40.2 (88.5)* 203.8 6 39.0 (200.0)* 166.2 6 70.9 (152.0)* 42.7 6 9.8 (42.0)* 42.7 6 16.3 (38.0)* 69.9 6 34.8 (64.5)* 68.1 6 51.2 (50.5)* 0.78 6 0.32 (0.8)* 18.1 6 9.5 (17.4)* 49.0 6 29.2 (55)*

38.8 6 12.8 (40)* 28.9 6 3.0 (28)* 13 of 40 (33)† 5 of 40 (13)† 32 of 40 (80)† 6 of 40 (15)† 22 of 40 (55)† 91.3 6 35.2 (84)* 197.2 6 41.0 (193.0)* 186.5 6 77.6 (183.0)* 39.5 6 8.6 (37.5)* 40.6 6 15.8 (35.0)* 78.7 6 40.7 (72.5)* 78.0 6 60.8 (51.5)* 0.83 6 0.34 (0.8)* 18.6 6 9.5 (19.5)* 51.2 6 30.2 (65)*

52.5 6 8.8 (54.5)* 31.2 6 5.3 (31.1)* 17 of 30 (57)† 6 of 30 (20)† 22 of 30 (73)† 10 of 30 (33)† 21 of 30 (70)† 106.1 6 45.2 (92)* 211.0 6 35.9 (212.5)* 143.8 6 55.6 (122.5)* 46.4 6 10.0 (44.0)* 45.7 6 16.8 (38.0)* 58.1 6 19.9 (53.5)* 54.9 6 31.1 (49.5)* 0.72 6 0.30 (0.8)* 17.3 6 9.6 (16.2)* 46.2 6 28.0 (50)*

,.001 .043 .02 .511 .511 .71 .241 .126 .16 .016 .009 .196 .007 .043 .187 .554 .487

Note.— Normal fasting glucose level is from 75 to 115 mg/dL; normal cholesterol level is from 120 to 200 mg/dL; normal triglyceride level is from 40 to 165 mg/dL; normal HDL level is from 40 to 60 mg/dL; normal serum AST level is 10 to 37 U/L; normal serum ALT level is 10 to 37 U/L; normal serum GGT level is from 0 to 55 U/L; normal serum total bilirubin level is from 0.2 to 1.3 mg/dL. ALT = alanine aminotransferase, AST = aspartate aminotransferase, GGT = g-glutamyltransferase, HDL = high density lipoprotein. * Data are mean 6 standard deviation, with the median in parentheses. †

Numbers in parentheses are percentages.

was placed on the fat fraction images at Couinaud system segments V and VI. A similar measurement was also performed for other liver segments from I to VIII (a total of 8 segments) by using an ROI of approximately 2 cm2.

Statistical Analyses Student t test was used to assess continuous variables between two groups. Categorical variables were evaluated by x2 test or Fisher exact test, where applicable. The degree of association between continuous and/or ordinal variables was calculated by using the Pearson correlation coefficient. The difference between two correlation coefficients obtained from independent samples was assessed by using the r to z Fisher transformation. The receiver operating characteristic (ROC) curve was used to describe the diagnostic performance of the PDFF calculation. The area under the ROC curve and 95% confidence interval were calculated. Comparison between ROC curves for the groups was tested by using the 770

z test (33). Linear regression analysis was performed to determine the effect of age, sex, obesity, diabetes mellitus, hypertension or hyperlipidemia, percentage of hepatic steatosis, hepatic inflammation, and fibrosis on the quantification of steatotic hepatocytes. ROC analysis was performed to determine the diagnostic accuracy of PDFF calculation. Cut-off ranges were calculated around the optimal cut off to maximize sensitivity and specificity to differentiate moderate or severe steatosis (.33% steatotic hepatocytes) from mild or no hepatic steatosis (,33.0% steatotic hepatocytes). For all tests, a two-tailed P value of less than .05 was considered statistically significant. Statistical analyses were performed by using statistical software (SPSS version 2.12.0; IBM, Armonk, NY).

Results At the time of the imaging, the mean age of the patients was 44.7 years 6 13.1 (standard deviation; range, 16–69 years). Our study was composed of 40 men

(mean age, 38.8 years 6 12.8; range, 16–66 years) and 30 women (52.5 years 6 8.8; range, 35–69 years). The mean BMI was 29.9 kg/m2 (range, 22.7–45.9 kg/m2) and 43% of the patients (30 of 70) were obese. Sixteen percent (11 of 70) of the patients had diabetes mellitus, 77% (54 of 70) had insulin resistance (homeostasis model assessment score of 2.7), 61% (43 of 70) had hyperlipidemia, and 23% (16 of 70) had hypertension. Median serum aspartate aminotransferase level was 38 U/L (range, 20–91 U/L) (0.63 mkat/L; range, 0.33–1.52 mkat/L), serum alanine aminotransferase level was 64.5 U/L (range, 20–202 U/L) (1.08 mkat/L; range, 0.33–3.37 mkat/L), g-glutamyltransferase level was 50.5 (range, 17–296 U/L) (0.84 mkat/L; range, 0.28–4.94 mkat/L), and total bilirubin level was 0.8 mg/dL (range, 0.2–1.8 mg/ dL) (13.68 mmol/L; range, 3.42–30.79 mmol/L) (Table 1). On the liver biopsy examination, hepatic steatosis grades for the biopsy samples were as follows: 27.1% (19 of 70)



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Figure 1

Figure 1: Severe hepatic steatosis without fibrosis (80% steatosis on histologic analysis; PDFF, 24.4%) in a 28-yearold man. Bottom row shows histologic biopsy result from the liver (stained with hematoxylin-eosin a). On PDFF images (top two rows), liver appears more hyperintense on grayscale images and lighter blue on colored images as the degree of steatosis increases. IP, in-phase; OP, out-of-phase; c-PDFF, colored (rainbow) PDFF c-R2∗, colored (hot iron) R2∗ map.

Table 2 Histopathologic Characteristics of 70 Patients with NAFLD Condition Steatosis Grade 0 Grade 1 Grade 2 Grade 3 Inflammation Grade 0 Grade 1 Grade 2 Grade 3 Fibrosis Stage 0 Stage 1 Stage 2 Stage 3

No. of Patients 7 (10.0) 19 (27.1) 11 (15.7) 33 (47.1) 7 (10.0) 52 (74.3) 8 (11.4) 3 (4.3 42 (60.0) 18 (25.7) 6 (8.6) 4 (5.7)

Note.—Numbers in parentheses are percentages.

were grade 1; 15.7% (11 of 70) were grade 2; 47.1% (33 of 70) were grade 3, and 10% (10 of 70) were grade 0. Necroinflammation, either lobular or portal, was graded as follows: 10% of patients (7 of 70) had no necroinflammation; 74.3% (52 of 70) had grade 1 necroinflammation; 11.4% (8 of 70) had grade 2 necroinflammation; and the remaining 4.3% (3 of 70) had grade 3 necroinflammation. Of note, 40% of the samples (28 of 70) showed the presence of fibrosis, and 14.3% (10 of 70) of those samples that showed the presence of fibrosis showed clinically important fibrosis (ie, presence of perisinusoidal and portal or periportal fibrosis, bridging fibrosis, or cirrhosis) (Table 2). Mean PDFF calculated with MR imaging was 18.1% 6 9.5% (median, 17.4%; range, 2.1%–42.8%), and the mean percentage of histologic steatosis


was 49.0% 6 29.2% (median, 55.0%; range, 0%–90.0%) (Fig 1). A close correlation was observed between PDFF and liver biopsy–determined steatosis (r = 0.820) (Fig 2). Good correlation was also found in patients who had intervals between the liver biopsy and PDFF of 1 month (r = 0.841), 3 months (r = 0.816), and more than 3 months (r = 0.836). The correlation of PDFF in mild hepatic steatosis was found to be better than that of moderate or severe steatosis (r = 0.835 and r = 0.402, respectively; P = .003). Mean R2* and T2* values in the liver were 33 sec21 6 4 sec21 (range, 25–43 sec21) and 30 msec 6 3.5 (range, 23–40 msec), respectively. These findings were consistent with absence of iron overload. Mean PDFF in segments I, II, III, IV, V, VI, VII, and VIII were 16.4%, 17.1%, 17.4%, 17.4%, 18.3%, 18.7%, 19.2%, and 19.3%, respectively. There was 771


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Figure 2 Figure 2: Scatterplot shows close correlation between PDFF and liver biopsy results (as percentage of steatotic hepatocytes) for quantifying hepatic steatosis (r = 0.820). Line = line of best fit.

no statistically significant difference among calculated PDFFs with respect to different liver segments (P = .60). Estimation of steatosis with PDFF was underestimated in patients with hepatic fibrosis compared with patients without fibrosis (r = 0.601 vs r = 0.859, respectively; P = .020) (Fig 3, Table 3). The cut-off point of PDFF measurement was 15.03% (area under the curve, 0.95; 95% confidence interval: 0.91, 1.00) to differentiate moderate or severe steatosis from mild or no hepatic steatosis, with a sensitivity of 93.0% and a specificity of 85.0%, and respective positive and negative predicted values of 91.0% and 88.0% (Table 4) (Fig 4). The cut-off point of PDFF value with highest sensitivity (sensitivity, 100%; specificity, 39%) was 9.9% and highest specificity (sensitivity, 64%; specificity, 100%) was 21.7%. The area under the curve for patients with hepatic fibrosis (0.861 [95% confidence interval: 0.71, 1.00]) was slightly lower than the value in patients without fibrosis (0.97 [95% confidence interval: 0.92, 1.00]), but this was not a statistically significant difference (P = .20) (Table 4). 772

Discussion PDFF measurement had good diagnostic accuracy for quantifying steatosis compared with liver biopsy results. We observed close correlation between PDFF and liver biopsy–determined steatosis. Good correlation was also found for the various percentages of hepatic steatosis. PDFF differentiated moderate or severe steatosis from mild or no steatosis, with 93.0% sensitivity and 85.0% specificity. However, the correlation between biopsy results and MR imaging was lower in patients with moderate or severe forms of hepatic steatosis compared with patients with more mild forms of steatosis. It should be kept in mind that MR imaging shows the fraction of protons that are lipid versus water, while pathologic analysis measures the fraction of hepatocytes that show steatosis. Noninvasive monitoring of steatosis with MR imaging can provide a parameter to improve understanding of the disease process and monitor effects of treatment. Several factors, including hepatic fibrosis and hepatic iron content, affect the diagnostic accuracy of MR

imaging for estimation of hepatic steatosis (34–36). In our study, the presence of hepatic fibrosis weakened the correlation between biopsy and PDFF– determined steatosis. These findings confirmed results that were obtained by MR spectroscopy and chemical shift methods in previous studies (34–36). McPherson et al (34) reported that to quantify hepatic steatosis, MR spectroscopy and Dixon in-phase and outof-phase protocols had better accuracy in patients who had mild or no fibrosis compared with patients who had moderate or severe fibrosis with a median time between liver biopsy and resection and MR imaging of 9 days (range, 0–209 days) (34). The reason for the discrepancy between MR imaging techniques and liver biopsy in the presence of fibrosis is unclear. However, in our study, PDFF was measured in each hepatic segment of the liver because of volumetric acquisition of the MR imaging sequence, with no demonstrated difference among the segments. This result suggested that PDFF measurement can assess the presence and severity of hepatic steatosis in all liver segments (37).



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Figure 3 Figure 3: Scatterplot shows effect of hepatic fibrosis on the correlation between PDFF and liver biopsy for quantifying hepatic steatosis with fibrosis () and without fibrosis (). Two lines of best fit are shown: without fibrosis (solid line) and with fibrosis (dotted line).

The R2* value of the liver linearly increased with the iron concentration in the liver (38). The R2* map obtained from the sequence used in our study enabled the estimation of liver iron content, and thereby removed the magnetic susceptibility effect of iron from fat fraction measurements. R2* values were within normal range in our patient cohort. Animal studies, in vitro studies, and a preliminary study yielded promising evidence for quantification of fat in the presence of iron, but the reliability of this technique has to be tested in a larger patient group with iron overload (39–42). MR spectroscopy measurements usually require a physicist and dedicated software for advanced analysis, and MR spectroscopy is not widely used, despite it being a noninvasive technique that is a reference standard. The advantages of PDFF calculation are its ability to be obtained in less than 25 seconds and the feasibility of standardization among different MR imagers (24). The PDFF method is easy to perform and does not require a physicist for calculation of the fat fraction. The fat fraction can also be measured by other methods, such as inphase, out-of-phase, and fat-saturated

Table 3


Linear Regression Analysis of Factors that Influenced Correlation between PDFF Calculation and Liver Biopsy for Quantifying Hepatic Steatosis Parameter Age (y) ,50 (n = 45) 50 (n = 25) Sex Men Women BMI (kg/m2) ,30 (n = 37) 30 (n = 28) Presence of diabetes mellitus No Yes Presence of hypertension No Yes Presence of hyperlipidemia No (n = 27) Yes (n = 43) Percentage of histologic steatosis Mild (n = 26) Moderate and severe (n = 44) Presence of hepatic inflammation No (n = 7) Yes (n = 63) Presence of hepatic fibrosis No (n = 42) Yes (n = 28)

Correlation Coefficient (r)

P Value .631

0.83 0.79 .332 0.85 0.77 .254 0.851 0.744 .317 0.842 0.692 .741 0.818 0.849 .395 0.883 0.824 .003 0.835 0.403 .08 0.962 0.788 .02 0.859 0.601

773


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Table 4 Diagnostic Accuracy of PDFF Measurement for Estimation of Hepatic Fat Content in Patients with NAFLD Patient Cut-off Value Overall, 15.03 Without fibrosis,15.03 With fibrosis, 21.66

AUC

Sensitivity

Specificity

PPV

NPV

0.95 (0.91, 1.00) 0.97 (0.92, 1.00) 0.86 (0.71, 1.00)

0.93 (0.82, 0.98) 0.86 (0.65, 0.95) 0.65 (0.45, 0.8)

0.85 (0.67, 0.94) 0.95 (0.77, 0.99) 1.00 (0.57, 1.00)

0.91 (0.81, 0.96) 0.95 (0.82, 1.00) 1.00 (0.85, 1.00)

0.88 (0.78, 0.94) 0.87 (0.72, 0.95) 0.39 (0.22, 0.59)

Note.—Numbers in parentheses are 95% confidence interval. AUC = area under the curve, NPV = negative predictive value, PPV = positive predictive value.

patients and patients with a shorter and longer interval between MR imaging and biopsy in the subgroup analysis. In conclusion, PDFF provided an accurate estimation of the presence of hepatic steatosis and grading between none or mild and moderate or severe forms of hepatic steatosis in patients with NAFLD. Hepatic fibrosis reduced the correlation between liver biopsy and PDFF-determined steatosis.

Figure 4

Disclosures of Conflicts of Interest: I.S.I. No relevant conflicts of interest to disclose. H.A. No relevant conflicts of interest to disclose. R.I. No relevant conflicts of interest to disclose. G.K. No relevant conflicts of interest to disclose. B.S. No relevant conflicts of interest to disclose. A.E. No relevant conflicts of interest to disclose. A.C. Financial activities related to the present article: none to disclose. Financial activities not related to the present article: employee of GE Healthcare. Other relationships: none to disclose. K.B. No relevant conflicts of interest to disclose. M.K. No relevant conflicts of interest to disclose.

Figure 4: ROC curves for quantifying hepatic steatosis in all patients (solid line), in patients with fibrosis (dotted line), and in patients without fibrosis (dashed line).

images, which do not take all confounding factors (eg, T1 bias, T2* effects, and field inhomogeneity related to the MR signal) into account. These factors may lead to erroneous calculations (20,24,43,44). In our study, the complex-type PDFF was used for estimation of hepatic steatosis. The only practical difference between magnitude and complex-type PDFF calculation is the range, with a 0%–50.0% fat fraction range for the magnitude-based technique and a 0%–100% fat fraction range for the complex–based technique (24). In our study, no patient had more than 50.0% steatosis that was calculated by MR imaging, but most of the patients had more than 50.0% steatosis 774

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