Ethics statement

The retrospective research protocol used in this study was approved by the institutional review board (IRB) of Wonkwang University Hospital. Written informed consent was obtained from all study participants for the use of MRI data and electronic health records including pathological information. This study was conducted in accordance with the Helsinki Declaration and Good Clinical Practice.

Subject population

A total of 30 subjects consisting of 11 patients with histopathologically proven-NASH (mean age 39.1 ± 13.8 yrs), 12 patients with simple steatsosis (SS; mean age 41.8 ± 11.2 yrs) and 7 normal controls (NC; mean age 33.3 ± 8.0 yrs) were recruited for this study from January 2012 to December 2017 (Fig. 1 and Table 1). A normal control group was defined as <5% liver fat content on 1H MR spectroscopy (MRS) and NAFLD was defined as >5% liver fat content25. The different histological features of NAFLD were assessed by using NAFLD activity scores (NAS). The NAS was calculated by making the sum of the scores for steatosis, lobular inflammation, and ballooning26. The NAFLD subgroups are defined SS (NAS < 5) and NASH (NAS ≥ 5) in this study.

The inclusion criteria were as follows: (a) subject age was between 18 and 70 years; (b) alcohol consumption for a 2-year period prior to baseline liver histology: men consuming <20 g per day and women <10 g per day; (c) patients without any active malignant tumors, and chronic/acute liver diseases except obesity or type 2 diabetes; (d) negative patients with viral hepatitis B and C markers; (e) patients without any decompensated liver disease (bilirubin <50 μmol/L, albumin >35 g/L, platelet count >100 × 109/L, international normalized ratio <1.3, no ascites); and (f) patients without any contraindications to MRI examination. All the subjects were excluded secondary etiologies for hepatic fat accumulation23.

Reference standard for diagnosing NAFLD

All liver biopsy specimens in NAFLD were obtained by the percutaneous and/or transjugular approaches (n = 23, 100%). The histologic data were assessed according to the diagnostic criteria of the Pathology Committee as follows26: the aggregated NAS [0–8]; the score of each element (steatosis [0–3], lobular inflammation [0–3], ballooning [0–2] and fibrosis scores [0,1,2,3]). NAS system27 was used for pathological determination of NAFLD with fibrosis staging (fibrosis grade: F0–F4). Fibrosis grades in subject population were as follows: F0 (total n = 7 (23.3%); NC = 7), F1 (n = 11 (36.7%); SS = 9 vs. NASH = 2), F2 (n = 9 (30.0%); SS = 3 vs. NASH = 6), F3 (n = 3 (10.0%); SS = 0 vs. NASH = 3), and F4 (n = 0 (0.0%)).

MR imaging acquisition

All MR scans were performed with a 3 T (Tesla) MRI system (Achieva; Philips Medical Systems, Best, The Netherlands) and a 32-channel array coil.

The T1-weighted images (T1WI) were acquired with three-dimensional T1 high-resolution isotropic volume excitation (THRIVE) pulse sequence: TR/TE = 4.2/1.97 msec; field of view (FOV) = 38 × 38 × 14 cm3, matrix size = 512 × 512, slice thickness = 0.74 × 0.74 × 2.0 mm3, number of slices = 70, number of excitation (NEX) = 2 and scan time = 14 sec. The upper abdominal images were obtained in axial plane and the imaging sequence was triggered to expiration within a single breath-hold. Additional T1- and T2-weighted imaging sequences were acquired in axial, coronal and sagittal sectional planes28. Total scan time per subject ranged between 35 and 45 minutes.

Pre-processing and quantification of MR data for LSN assessment

The LSN quantification software was coded by Matlab program (MathWorks, Natick, Massachusetts). Customized semi-automated post-processing program operates on Windows platform (client version: XP or higher; Microsoft, Redmond, WA). Our LSN program was used the MR images of DICOM format to generate a nodularity score using previously described procedure17,18,19. Figure 2 shows the overall flowchart to develop the algorithm for qualitative and quantitative analysis of LSN. The main algorithm for evaluating the LSN was as follows: the bias correction of field uniformity, the liver boundary detection for drawing the liver reference line, the liver segmentation, and LSN measurement using smooth curve-fitting analysis.

Bias correction for field uniformity

The settings for optimal window level were automatically or manually adjusted by using the mean signal intensity within liver parenchyma. In order to deal with intensity inhomogeneities on abdominal MR images, we introduced a simple multiplicative-additive model of intensity inhomogeneity29. From the physics of images obtained from a variety of imaging modalities (MRI and CT), an obtained image (I) can be explained as following Eq. (1)

$$I=bJ+n$$ (1)

Where J is the true image, b is the component that reflects the signal intensity inhomogeneity, and n is additive noise. The value of component b is referred to a value accounting for the field bias. The true image J reflects the intrinsic physical properties on the objects being imaged, thus it is assumed to be piecewise constant. The field bias b is assumed to be slowly varying. The additive noise n can be assumed to be a zero-mean Gaussian noise. This study considers the obtained image I as a function defined on continuous domain, followed by the image processing assumption in a previous study29. This image model can be described the composition of real-world images, in which signal intensity inhomogeneity (b) is attributed to a component of an image29.

Liver boundary detection and liver segmentation for reference line

For the liver boundary detection, this study was used a novel region-based method for liver segmentation as level set method30, which was provided the local clustering criterion function with correction with intensity inhomogeneities (Fig. 3). The boundary line on the selected slice liver was produced after the bias correction (Fig. 4b). The liver surface line for LSN quantification was extracted as a reference line and two radiologists (with greater than 20 years’ experience) finally confirmed the liver surface line (Fig. 4d). The boundary detection and segmentation techniques take maximizing the local intensity clustering property and minimizing energy formulation to determine and exclude any existing signal outliers caused by generated systematic artifacts30.

LSN measurement from segmented liver image

Following pre-processing of MR image data, liver parenchyma within confirmed liver surface line were used for the deterministic curve-fitting analysis. Regions of interest (ROIs) for LSN measurement were selected along the boundary of the liver (Fig. 4d). The user input a ROI range across the datapoints of the liver surface line. After input a ROI range, a smooth curve-fitting line (polynomial line shape) was generated on selected ROI dataset (Fig. 4e). Finally, the difference between the liver surface line and a new curve-fitting line was evaluated on a pixel-by-pixel basis. The difference value was squared and then calculated mean value, variation and standard deviation (SD). The final LSN score in individual subject was arithmetically calculated as the mean LSN from the measurements on ROIs. The program exported the final LSN score and the header information of DICOM file to a database.

LSN analysis in clinical NAFLD patients

The liver MR images in each patient were assessed blindly by two radiologists (HWL, reader A; KHY, reader B) using developed semi-automated LSN software. The radiologists in the liver diagnosis were the experienced abdominal radiologists (>20 years). They had no knowledge of the clinical outcome and access to the readings of the other. To assess the inter-observer variability in the LSN measurements, both radiologists independently assessed the liver images. The overall score of LSN for each patient was calculated as mean score.

Technical details in the LSN measurements are described in a recent paper17. In short, the readers pointed out a ROI along the liver surface line on selected slice image. The differences between the detected liver boundary and curve-fitted polynomial line (one of 2nd, 3rd and 4th-order line shape) on the ROIs was measured on a pixel-by-pixel basis and then averaged for each slice by the program. The overall LSN score was arithmetically calculated as an averaged value of the 4-times measurements. The LSN measurement was well accordant when applied to MRI and CT images. Here, we only used MR image data for LSN quantification for this study. Figure 5 shows the representative images in LSN measurements on the axial MR image.

The MR images were re-analyzed from the same reader after the individual measurements were generated to assure that no sharp turns were falsely provided by the LSN quantification program. At least three and/or four ROI measurements were performed for each subject. A final LSN score was calculated by the program as an averaged value of the individual measurements, with a higher LSN score indicating a higher degree of surface nodularity. The time of processing for quantifying the final LSN score was recorded by the program.

Statistical analysis

The LSN scores in three independent groups and fibrosis grades were compared using the SPSS version 20.0 program (SPSS Inc., Chicago, IL, USA). The variation in LSN scores was analyzed by the Kruskal–Wallis H test for three groups and the Mann–Whitney U-test for intergroup comparisons. Coefficient of variance (CV) was calculated for the variability of LSN measurements23. The mean CV values in each group were 7% for NC, 13% for SS, and 14% for NASH (range: 7–14%; average 11.3%). Intra-rater agreement was performed based on the intraclass correlation coefficient (ICC) between the LSN scores. The ICCs were denoted on the basis of the levels of reliability as follows31: as poor (<0.4), moderate (0.4 to <0.6), good (0.6 to <0.8), and excellent (0.8 to 1.0).

The diagnostic performance of LSN score according to fibrosis grades was evaluated with receiver operating characteristics (ROC) curve analysis including of the area under the ROC curve (AUROC), sensitivity, and specificity. Statistical significance in all tests was set at two-sided p-values less than 0.05.