The Combination of MR Elastography and Proton Density Fat Fraction Improves Diagnosis of Nonalcoholic Steatohepatitis

Nonalcoholic fatty liver disease (NAFLD) is the most rapidly growing cause of chronic liver disease worldwide, affecting about 25% of the global adult population.1 NAFLD is a disease spectrum whose mild form, nonalcoholic fatty liver (NAFL), is defined as the presence of hepatic steatosis without any secondary cause of hepatic fat accumulation such as excessive alcohol consumption, long-term use of a steatogenic medication, or other liver disease etiologies.2 NAFLD is associated with metabolic syndrome, obesity, diabetes mellitus, dyslipidemia, and cardiovascular disease.3, 4 Nonalcoholic steatohepatitis (NASH) is a more aggressive form of NAFLD, characterized by the presence of inflammatory features and degenerative hepatocellular changes in addition to steatosis.5, 6 The overall prevalence of NASH in the general population is estimated between 1.5% and 6.45%.1

The dynamic nature of NAFLD has been described in many studies.7, 8 Patients with NAFLD, especially with uncontrolled metabolic disease and diabetes, suffer an increased risk of developing fibrosis with eventual progression to cirrhosis and end-stage liver disease. The presence of inflammation in NASH triggers fibrogenesis and causes progression into higher stages of fibrosis and cirrhosis.9, 10 NASH is also related to increased incidence of hepatocellular carcinoma11, 12 and liver transplantation.13 Higher stages of fibrosis are associated with increased overall and liver-related mortality.14, 15

Liver biopsy has been the reference standard for diagnosing NAFLD, including identifying NASH and staging fibrosis.2 However, biopsy has several limitations such as cost, sampling and inter-observer variability, and risk of discomfort and complications.16, 17 Thus, developing non-invasive imaging and biochemical markers for diagnosing and grading NAFLD has been the subject of extensive research in the last decade.18, 19

MRI techniques for the quantification of liver fat and the measurement of liver stiffness are widely studied.18, 19 Magnetic resonance proton density fat fraction (MR-PDFF) and magnetic resonance elastography (MRE) have high diagnostic accuracy for detecting and grading steatosis20 and staging fibrosis,21, 22 respectively. Both techniques have higher diagnostic performance than non-MRI-based techniques, such as transient elastography (TE) and TE-based controlled attenuation parameter.22-26 However, differentiating NASH from NAFL is still challenging.

The primary aim of this study was to investigate the ability of multiple MRI biomarkers (MRE, PDFF, R2* mapping, T1 mapping, and diffusion-weighted imaging [DWI]), either as single measures or in combination with each other or with biochemical markers, to differentiate between NASH and NAFL, and between lower and higher stages of liver fibrosis in adults with clinically suspected NAFLD. The reliability of a biomarker is not only determined by its diagnostic performance, but also by its repeatability. Hence, a secondary aim was to measure the repeatability of the MRI biomarkers.

Materials and Methods Study Population

After approval from the regional ethical review board, a prospective study was conducted at our hospital between March 2017 and December 2019. Written informed consent was obtained from all study participants. One hundred and thirty-four individuals, recruited from the Department of Gastroenterology and Hepatology and from the Swedish CArdioPulmonary BioImage Study “SCAPIS”,27 were invited to a screening visit, where data on demographics, medical history, and concomitant medication were collected. Blood sampling was also performed at screening visit to measure cytokeratin-18 (CK18) M30 and liver function tests including alanine transaminase (ALT) and aspartate transaminase (AST).

Eligibility included: individuals aged 18–70 with clinically suspected NAFLD and at least one of the following: imaging indicative of NAFLD,19 ALT more than 1.5 × upper limit of normal (upper limit being 1.1 μkat/liter for men and 0.75 μkat/liter for women), CK18 M30 more than 180 U/liter, and/or biopsy showing NAFLD within 3 months prior to screening visit. Individuals with a past or present alcohol consumption of more than 30 g alcohol per day for men and 20 g for women, drug abuse, other liver diseases, corticosteroid or immunosuppressive therapy within 10 weeks, pregnancy/breastfeeding, and/or contraindication for MRI or liver biopsy were excluded. Seventy-five individuals fulfilled the inclusion and exclusion criteria.

Individuals with no available liver biopsy within 3 months underwent liver biopsy 1–4 weeks after the screening visit. Three out of the 75 persons were excluded since the liver biopsy did not show any steatosis. One of the included persons discontinued the study voluntarily before MRI examination. Thus, 71 individuals were referred to MRI. Of these, three were excluded because of claustrophobia. Consequently, the study population consisted of 68 participants.

Thirty participants out of the study population (11 NAFL and 19 NASH determined from liver biopsy) underwent a second MRI in order to assess repeatability. Those participants were selected to represent various histopathological groups, i.e., including participants with both NAFL and NASH and with different stages of fibrosis.

Histopathological Analysis

Biopsies were evaluated by two liver pathologists (AW) with more than 30 years of experience blinded to clinical, biochemical, and radiological data individually and in consensus. The steatosis-activity-fibrosis (SAF) histological scoring system was used,5 grading steatosis 0–3, activity 0–4, and fibrosis 0–4. Activity score was calculated by the summation of hepatocyte ballooning (0–2) and lobular inflammation (0–2), and thus ranging 0–4. All cases with at least grade 1 steatosis were diagnosed as NAFLD independently of other criteria. When each of the three features (steatosis, ballooning, and lobular inflammation) was classified as at least grade 1, then the biopsy was categorized as NASH. For analysis of fibrosis, two groups were formed according to the severity and clinical relevance of the fibrosis, i.e., F0–1 (no or mild fibrosis) and F2–4 (moderate to advanced fibrosis).

Transient Elastography

TE was performed prior to liver biopsy by one of two experienced specialist nurses, blinded to all other data. Examinations were performed using the FibroScan 402 system (Echosens, Paris, France), and either the M probe or the XL probe based on the computer-guided recommendation. Patients were asked to fast for at least 6 hours before the examination. TE was performed with the participant in supine position. The median value of TE-measured liver stiffness (TE-LS) in kilopascals (kPa) of at least 10 valid measurements was calculated. The examination was considered invalid if the interquartile range/median value exceeded 30%.28

Magnetic Resonance Imaging

MRI was performed 4–8 weeks after biopsy to allow for healing. The participants were asked to fast for at least 6 hours before the examination. A 3.0-T scanner (Signa PET/MR, General Electric Healthcare, Waukesha, WI) with a 16-channel body coil was used. The 30 participants in the repeatability group underwent a second MRI within 2–4 weeks of the first scan.

Magnetic Resonance Elastography

MRE was performed as previously described,29 using a commercially available acoustic driver system (Resoundant, Rochester, MN) generating 60-Hz shear waves which were transmitted using a passive driver placed against the abdominal wall anterior to the liver. A spin-echo echo-planar imaging (SE-EPI) pulse sequence with motion-encoding gradients was used.30 The acquisition parameters are listed in the Supplemental Material. Quantitative liver stiffness maps and confidence maps (elastograms) were generated on the scanner.

MR-PDFF and R2* Mapping

PDFF was performed using Iterative Decomposition of water and fat with Echo Asymmetry and Least squares estimation (IDEAL-IQ), a commercially available multi-echo 3D gradient-echo sequence which has the ability to limit the confounding effects of T1 and T2* and implements multi-peak fat model to account for the multiple resonant peaks of triglycerides.31 The acquisition parameters are listed in the Supplemental Material. PDFF maps and R2* maps (relaxation rate = 1/T2*) were generated with IDEAL-IQ.

T1 Mapping

Saturation Method using Adaptive Recovery Times for T1 Mapping (SMART1Map) has been described elsewhere32 as a method for T1 mapping in cardiac applications. It applies a single-point saturation-recovery FIESTA technique with the ability to measure true T1. The acquisition parameters are listed in the Supplemental Material.

Diffusion-Weighted Imaging

DWI was performed using a conventional SE-EPI sequence with b-values of 150 seconds/mm2, 400 seconds/mm2, and 800 seconds/mm2. The acquisition parameters are listed in the Supplemental Material. Apparent diffusion coefficient (ADC) maps were generated automatically.

Liver Volume Measurement

A commercially available 3D gradient-echo T1-weighted sequence with two-point Dixon technique (LAVA-Flex) was used to acquire 32 axial slices through the liver in a single full-expiration breath-hold. SmartPaint software (version 1.0, Centre for Image Analysis, Uppsala University, Uppsala, Sweden) was used for post-processing the generated water-images and measuring liver volume (cm3).

Image Analysis

An image analyst (AH) with 5 years of experience in quantitative liver MRI, blinded to histopathological and biochemical results, performed the quantitative MRI analysis using ImageJ software (version 1.50i, National Institutes of Health, Bethesda, MD). In accordance with the Quantitative Imaging Biomarker Alliance (QIBA) MRE protocol,33 a free-hand region of interest (ROI) was drawn separately on each acquired slice of MRE elastograms excluding large blood vessels, the edge of the liver, fissures, and masked regions on the confidence maps. Slices with less than 500 pixels in the ROI were excluded. The ROI was cloned between the elastograms and the related anatomic/magnitude images to ensure a good anatomic correlation. The mean liver stiffness (kPa) and the ROI size (mm2) were used to calculate the overall mean MRE-measured liver stiffness (MRE-LS) in kPa, weighted by ROI size. A free-hand ROI was drawn separately on each acquired slice of the PDFF, R2*, T1, and ADC maps using the same approach as for MRE. The mean values of all the acquired slices were obtained for PDFF (%), R2* (second−1), and ADC (10−6 mm2/second). The median value was obtained from the T1 maps (single slice) and used to calculate R1 (relaxation rate = 1/T1, second−1).

A second reader (SA), a radiologist with 5 years of experience in general and abdominal radiology, blinded to histopathological and biochemical results and to the first reader's measurements, performed the analysis of MRE separately using the same approach mentioned above in order to evaluate the inter-rater reliability.

Statistical Analysis

All statistical analyses were done using SAS software (version 9.4, SAS Institute Inc., Cary, NC) and IBM SPSS Statistics for Windows (version 27, IBM Corp., Armonk, NY). The study population was initially divided into two groups by diagnosis (NASH/NAFL). For baseline characteristics, independent samples t-test was used to compare continuous variables and Pearson's chi-squared test was used to compare categorical variables between the two groups. Descriptive statistics of the studied biomarkers were summarized as mean, SD, and median, and grouped according to the diagnosis (NASH/NAFL) and the dichotomized fibrosis stages (F0–1/F2–4) from the histopathology analysis. Univariate logistic regression analysis was performed on all the biomarkers as independent variables, first with NASH/NAFL and then with F0–1/F2–4 as the dependent variable. Using logistic regression analysis, the best performing bivariate models were identified. Receiver operating characteristic curves (ROC) were used to determine the diagnostic accuracy of the univariate biomarkers and the bivariate models by calculating the area under the ROC (AUROC) and thus identifying the optimal cutoffs and the corresponding sensitivity and specificity. Spearman's correlation was used to analyze the correlation between the imaging biomarkers and the grades of activity, ballooning, lobular inflammation, and fibrosis. Repeatability of imaging biomarkers was analyzed by intra-individual coefficient of variation (CV) and intraclass correlation coefficient (ICC). ICC was also used to analyze the inter-rater reliability between the two readers who performed MRE analysis. Statistical significance was set at P < 0.05.

Results Baseline Characteristics

The study population consisted of 68 individuals with biopsy-proven NAFLD (40 men, 28 women) with a mean age of 54.5 years and a mean body mass index of 30.8 kg/m2. NASH diagnosis was established in 53 participants and 15 were diagnosed as NAFL based on the histopathological assessment. Baseline characteristics and the distribution of different steatosis grades, activity grades, and fibrosis stages are presented in Table 1. There were no statistically significant differences between the groups, except for the frequency of type 2 diabetes which was significantly higher in the NASH group.

TABLE 1. Baseline Characteristics of 68 Participants With NAFLD Variable Total (N = 68) NASH (N = 53) NAFL (N = 15) P-Value Age (years), mean (SD) 54.5 (13.09) 53.5 (14.11) 58 (7.99) 0.122a Men, N (%) 40 (58.8%) 32 (60.4%) 8 (53.3%) 0.625b Women, N (%) 28 (41.2%) 21 (39.6%) 7 (46.7%) Caucasian race, N (%) 62 (91.2%) 47 (88.7%) 15 (100.0%) 0.172b BMI (kg/m2), mean (SD) 30.8 (3.71) 31.2 (3.81) 29.1 (2.9) 0.051a Essential hypertension, N (%) 35 (51.5%) 29 (54.7%) 6 (40.0%) 0.513b Type 2 diabetes, N (%) 26 (38.2%) 24 (45.3%) 2 (13.3%) 0.037b Steatosis grade 0, N 0 0 0 1, N 22 12 10 2, N 24 22 2 3, N 22 19 3 Activity grade 0, N 2 0 2 1, N 13 0 13 2, N 44 44 0 3, N 8 8 0 4, N 1 1 0 Fibrosis stage 0, N 4 2 2 1, N 35 25 10 2, N 21 18 3 3, N 5 5 0 4, N 3 3 0 NAFLD = nonalcoholic fatty liver disease; NASH = nonalcoholic steatohepatitis; NAFL = nonalcoholic fatty liver; BMI = body mass index.

TE, MRE, PDFF, R2* mapping, R1 mapping, ADC, and liver volume measurement were able to be obtained in 66, 64, 68, 68, 64, 65, and 67 participants, respectively. In the second MRI examination, PDFF, R2* mapping, R1 mapping, ADC, and liver volume measurement were obtained for all the 30 participants, while MRE could be assessed in 29 participants.

The ICC of MRE analysis by two readers (inter-rater reliability) was 0.98.

Differentiation Between NASH and NAFL

Summarized descriptive statistics for imaging and biochemical markers grouped by NASH/NAFL are presented in Table 2. Univariate logistic regression analysis showed significant differences between the groups in TE-LS, MRE-LS, CK18 M30, and ALT.

TABLE 2. Descriptive Statistics and Predictive Properties of Univariate TE, MRI, and Biochemical Predictors of NASH in 68 Participants With NAFLD Summary Statistics by Group Prediction Performance Variable Group N Mean SD Median P-Valuea AUROC Cutoffb Sensitivity Specificity TE, kPa NASH 51 9.45 10.8 6.9 0.009 0.77 >5.3 0.82 0.73 NAFL 15 5.09 1.66 4.9 MRE, kPa NASH 49 3.13 1.57 2.7 0.028 0.74 >2.74 0.49 1.00 NAFL 15 2.42 0.17 2.4 >2.5c 0.65 0.80 PDFF, % NASH 53 18.5 8.43 18 0.07 0.70 >10.4 0.81 0.60 NAFL 15 13.7 9.29 10.2 R2*, second−1 NASH 53 67.8 25.16 61.9 0.388 0.56 >53.4 0.79 0.40 NAFL 15 61.8 12.76 59.4 R1, second−1 NASH 49 1.1 0.2 1.08 0.404 0.53 >1.09 0.45 0.80 NAFL 15 1.06 0.07 1.05 ADC, 10−6 mm2/second NASH 50 914 112.3 915 0.804 0.55 <830 0.24 0.93 NAFL 15 922 106 912 Liver volume, cm3 NASH 52 2050 541.6 1970 0.090 0.66 >1660 0.81 0.53 NAFL 15 1780 452.3 1660 CK18 M30, U/liter NASH 53 257 370.7 145 0.016 0.76 >186 0.47 0.93 NAFL 15 82.5 67.21 59 ALT, μkat/liter NASH 53 1.13 0.76 0.9 0.047 0.70 >0.87 0.55 0.80 NAFL 15 0.69 0.34 0.66 AST, μkat/liter NASH 53 0.8 0.48 0.63 0.076 0.66 >0.63 0.55 0.80 NAFL 15 0.58 0.15 0.56 NAFLD = nonalcoholic fatty liver disease; NASH = nonalcoholic steatohepatitis; NAFL = nonalcoholic fatty liver; AUROC = area under the receiver operating characteristic curve; TE = transient elastography; MRE = magnetic resonance elastography; PDFF = proton density fat fraction; R2* = relaxation rate 1/T2*; R1 = relaxation rate 1/T1; ADC = apparent diffusion coefficient; CK18 = cytokeratin 18; ALT = alanine transaminase; AST = aspartate transaminase.

In bivariate logistic regression analysis, both MRE and PDFF contributed significantly to a bivariate model for diagnosing NASH (AUROC = 0.84) (Table 3 and Fig. 1).

TABLE 3. Diagnostic Performance of Selected Bivariate Models in 68 Participants With NAFLD Prediction Modela Prediction Performance Variables in Model Diagnosis Prediction Rule AUROC Cutoffb Sensitivity Specificity Variable P-Valuec NASH −12.53 + 4.6 × MRE + 0.13 × PDFF 0.84 >1.38 0.74 0.87 MRE, kPa 0.008 PDFF, % 0.009 F2–4 −9.37 + 2.34 × MRE + 4.16 × AST

留言 (0)

沒有登入
gif