A Novel Quantitative Ultrasound Technique for Identifying Non-alcoholic Steatohepatitis

Feng Gao; Qiong He; Gang Li; Ou-Yang Huang; Liang-Jie Tang; Xiao-Dong Wang; Giovanni Targher; Christopher D. Byrne; Jian-Wen Luo; Ming-HuaZheng

Disclosures

Liver International. 2022;42(1):80-91.

Abstract and Introduction

Abstract

Background and Aims: There remains a need to develop a non-invasive, accurate and easy-to-use tool to identify patients with non-alcoholic steatohepatitis (NASH). Successful clinical and preclinical applications demonstrate the ability of quantitative ultrasound (QUS) techniques to improve medical diagnostics. We aimed to develop and validate a diagnostic tool, based on QUS analysis, for identifying NASH.

Methods: A total of 259 Chinese individuals with biopsy-proven non-alcoholic fatty liver disease (NAFLD) were enrolled in the study. The histological spectrum of NAFLD was classified according to the NASH clinical research network scoring system. Radiofrequency (RF) data, raw data of iLivTouch, was acquired for further QUS analysis. The least absolute shrinkage and selection operator (LASSO) method was used to select the most useful predictive features.

Results: Eighteen candidate RF parameters were reduced to two significant parameters by shrinking the regression coefficients with the LASSO method. We built a novel QUS score based on these two parameters, and this QUS score showed good discriminatory capacity and calibration for identifying NASH both in the training set (area under the ROC curve [AUROC]: 0.798, 95% confidence interval [CI] 0.731–0.865; Hosmer-Lemeshow test, P = .755) and in the validation set (AUROC: 0.816, 95% CI 0.725–0.906; Hosmer-Lemeshow test, P = .397). Subgroup analysis showed that the QUS score performed well in different subgroups.

Conclusions: The QUS score, which was developed from QUS, provides a novel, non-invasive and practical way for identifying NASH.

Introduction

Non-alcoholic fatty liver disease (NAFLD) is an epidemic and public health threat that affects ~25% of the adult population globally.^[1] NAFLD is classified histologically as simple steatosis (NAFL), non-alcoholic steatohepatitis (NASH) with varying levels of fibrosis and cirrhosis.^[2] In recent years, NASH has become the most rapidly increasing indication for liver transplantation in the United States.^[3] In addition, NASH is closely associated with an increased risk of developing important extra-hepatic complications, such as type 2 diabetes, cardiovascular disease, chronic kidney disease and certain extra-hepatic malignancies.^[4–7] Thus, the correct identification of patients with NASH is clinically relevant for prognosis and therapy decisions.

NASH remains difficult to diagnose despite considerable research in this area in recent years.^[8] Currently, there are no simple, non-invasive tests available for diagnosing NASH, specifically in the absence of, or the presence of mild liver fibrosis. The current expensive 'gold standard' for diagnosing and staging NASH, both in clinical practice and in investigational trials, is liver biopsy, which is an invasive procedure with associated risks such as postprocedural bleeding.^[9] Therefore, there remains a need to develop a non-invasive, accurate and easy-to-use tool to identify patients with NASH.

Conventional medical imaging technologies, including ultrasonography, have continued to improve over the years. The value of quantitative ultrasound (QUS) analysis has been widely recognized, particularly for the diagnosis of liver fibrosis. Vibration-controlled transient elastography (TE), point shear wave elastography (pSWE), two-dimensional shear wave elastography (2D-SWE) and acoustic radiation force impulse elastography are current ultrasound elastography techniques that enable the assessment of liver stiffness in real time through a quantitative electrogram.^[10] These imaging techniques are a significant breakthrough for the diagnosis and staging of liver fibrosis^[11,12] but are of limited value for the diagnosis of NASH in the absence of liver fibrosis.

iLivTouch is based on TE and has been widely used in the clinic because it enables fast and quantitative assessment of liver stiffness and steatosis.^[13] During the assessment with iLivtouch, both the ultrasound attenuation parameter (UAP) and the liver stiffness measurement (LSM) can be easily obtained. These two parameters have proved to be valuable for the measurement of liver fibrosis and steatosis.^[14–16] In addition, radiofrequency (RF) raw data can be also acquired from iLivTouch for further QUS analysis. RF data can provide specific numbers related to tissue features that can increase the specificity of image findings leading to improvements in diagnostic ultrasound. A large number of preclinical and clinical studies have proved that QUS parameters are related to the microstructure changes within tissues.^[17–23]

Therefore, the aim of our study was to develop and test the diagnostic performance of a non-invasive tool based on QUS technology for identifying the presence of NASH in a cohort of Chinese patients with biopsy-confirmed NAFLD.

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Abstract and Introduction
Methods
Results
Discussion
References
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Table 1. Baseline characteristics of participants with NAFLD
	Overall population N = 259	Training set N = 173	Validation set N = 86	P value
Demographics
Age, +years	42.8 ± 13.7	42.5 ± 13.0	43.6 ± 15.2	.540
Men, n (%)	188 (72.6%)	131 (75.7%)	57 (66.3%)	.109
Metabolic factors
BMI, kg/m²	26.7 ± 3.2	26.6 ± 3.1	26.8 ± 3.5	.712
Waist circumference, cm	92.2 ± 8.9	92.4 ± 8.9	91.6 ± 9.0	.489
Central obesity, n (%)	222 (85.7%)	146 (84.3%)	76 (88.4%)	.389
Type 2 diabetes, n (%)	98 (37.8%)	65 (37.6%)	33 (38.4%)	0.901
Hypertension, n (%)	77 (29.7%)	47 (27.2%)	30 (34.9%)	.201
Metabolic syndrome, n (%)	193 (74.5%)	120 (69.4%)	73 (88.4%)	.007
Laboratory parameters
ALT, IU/L	49 (28–90)	48 (27–87)	55 (30–93)	.285
AST, IU/L	34 (24–56)	33 (24–52)	37 (27–60)	.068
γ-GGT, IU/L	53 (30–87)	45 (29–77)	64 (39–109)	.003
Albumin, g/L	4.5 ± 0.5	4.5 ± 0.4	4.5 ± 0.5	.351
Bilirubin, μmol/L	14.2 ± 9.8	13.7 ± 6.4	15.3 ± 14.4	.215
Fasting glucose, mmol/L	5.8 ± 1.7	5.8 ± 1.7	5.9 ± 1.6	.753
Fasting insulin, mIU/L	14.4 (9.0–20.7)	14.4 (9.1–20.8)	14.4 (9.0–20.5)	.855
HbA1c, %	6.5 ± 1.8	6.5 ± 1.7	6.5 ± 1.9	.997
HOMA-IR	3.5 (2.3–5.3)	3.5 (2.3–5.2)	3.6 (2.1–5.4)	.803
Prothrombin time, s	12.9 ± 0.7	12.9 ± 0.6	12.9 ± 0.7	.384
Platelet count, ×10⁹/L	243 ± 69	237.5 ± 66.3	253.3 ± 72.1	.081
TG, mmol/L	2.2 ± 2.1	2.1 ± 2.3	2.3 ± 1.8	.464
TC, mmol/L	5.1 ± 1.1	5.1 ± 1.1	5.1 ± 1.2	.980
HDL-C, mmol/L	1.0 ± 0.2	1.0 ± 0.2	1.0 ± 0.2	.414
LDL-C, mmol/L	3.0 ± 0.9	3.0 ± 0.8	2.9 ± 1.0	.328
CK18-M30, IU/L	156 (79–329)	141 (80–297)	159 (79–371)	.477
iLivTouch parameters
UAP, dB/m²	310 ± 22	311 ± 21	309 ± 25	.489
LSM, kPa	10.5 ± 4.8	9.7 ± 3.7	12.0 ± 6.2	<.001
Liver histology
Length of liver biopsy specimen (cm)	1.5 (1.5–2.0)	1.5 (1.5–2.0)	1.5 (1.5–2.0)	.601
Fibrosis stage, n (%)				.836
F0	53 (20.5%)	35 (20.2%)	18 (20.9%)
F1	138 (53.3%)	93 (53.8%)	45 (52.3%)
F2	49 (18.9%)	32 (18.5%)	17 (19.8%)
F3	13 (5.0%)	10 (5.8%)	3 (3.5%)
F4	6 (2.3%)	3 (1.7%)	3 (3.5%)
Steatosis grade, n (%)				.887
S0	44 (17.0%)	31 (17.9%)	13 (15.1%)
S1	92 (35.5%)	60 (34.7%)	32 (37.2%)
S2	50 (19.3%)	32 (18.5%)	18 (20.9%)
S3	73 (28.2%)	50 (28.19%)	23 (26.7%)
Ballooning grade, n (%)				.363
B0	49 (18.9%)	29 (16.8%)	20 (23.3%)
B1	128 (49.4%)	90 (52.0%)	38 (44.2%)
B2	82 (31.7%)	54 (31.2%)	28 (32.6%)
Lobular inflammation grade, n (%)				.470
L0	28 (10.8%)	20 (11.6%)	8 (9.3%)
L1	174 (67.2%)	119 (68.8%)	55 (64.0%)
L2	56 (21.6%)	33 (19.1%)	23 (26.7%)
L3	1 (0.4%)	1 (0.6%)	0 (0.0%)
NAS score	4 (3–5)	4 (3–5)	4 (3–5)	.740
NASH^a	100 (38.6%)	63 (36.4%)	37 (43.0%)	.304
NASH with fibrosis^b	90 (34.7%)	57 (32.9%)	33 (38.4%)	.388

Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; CK-18, cytokeratin-18 fragments; GGT, γ-glutamyltranspeptidase; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostasis model assessment-insulin resistance; LDL-C, low-density lipoprotein cholesterol; LSM, liver stiffness measurement; NAFLD, non-alcoholic fatty liver disease; NAS, NAFLD activity score; NASH, non-alcoholic steatohepatitis; TC, total cholesterol; TG, triglycerides; UAP, ultrasound attenuation parameter.
^aNASH was defined as the presence of hepatic steatosis, lobular inflammation and ballooning with NAS ≥5.
^bNASH with fibrosis was defined as the presence of NASH and any histological stage of fibrosis (F ≥ 1 fibrosis).

Table 2. AUROC (95% confidence intervals) analyses of QUS score and plasma CK18-M30 levels for the diagnosis of NASH
	QUS score	CK18-M30
Training set (n = 173)
Steatosis	0.907 (0.860–0.954)	0.787 (0.707–0.868)
NASH^a	0.798 (0.731–0.865)	0.697 (0.613–0.782)
NASH with fibrosis^b	0.773 (0.701–0.845)	0.669 (0.580–0.758)
Validation set (n = 86)
Steatosis	0.886 (0.801–0.971)	0.686 (0.550–0.821)
NASH^a	0.816 (0.725–0.906)	0.753 (0.645–0.862)
NASH with fibrosis^b	0.796 (0.703–0.889)	0.737 (0.623–0.852)

Abbreviations: AUROC, area under receiver operating characteristic curve; NAS, NAFLD activity score; NASH, non-alcoholic steatohepatitis; QUS, QUS, quantitative ultrasound.
^aNASH was defined as the presence of steatosis, lobular inflammation and ballooning with NAS ≥5.
^bNASH with fibrosis was defined as the presence of definitive NASH and F ≥ 1 fibrosis.

Table 3. Diagnostic performances of two selected cut-offs of the QUS score for NASH in training and validation data sets
	QUS score
Training data set
Cut-off	6.0	7.5
Sensitivity, % (n/N)	90.5% (57/63)	28.6% (18/63)
Specificity, % (n/N)	55.5% (61/110)	94.5% (104/110)
PPV, % (n/N)	53.8% (57/106)	75.0% (18/24)
NPV, % (n/N)	91.0% (61/67)	70.0% (104/149)
LR+	2.03	5.20
LR−	0.17	0.76
Diagnostic odds ratio	11.94	6.84
Validation data set
Sensitivity, % (n/N)	86.5% (32/37)	37.8% (14/37)
Specificity, % (n/N)	67.3% (33/49)	89.8% (44/49)
PPV, % (n/N)	66.7% (32/48)	73.7% (14/19)
NPV, % (n/N)	86.8% (33/38)	65.7% (44/67)
LR+	2.65	3.71
LR−	0.20	0.69
Diagnostic odds ratio	13.25	5.37

The bold values represent best values/results. Abbreviations: LR−, negative likelihood ratio; LR+, positive likelihood ratio; NASH, non-alcoholic steatohepatitis; NPV, negative predictive value; PPV, positive predictive value; QUS, QUS, quantitative ultrasound.

Table 4. Diagnostic performances of the QUS score for prediction of NASH in specific subgroups
	Percentage	AUROC (95% CI)	Optimal cut-off	Sensitivity	Specificity	PPV	NPV
Sex
Men	72.6% (188/259)	0.81 (0.75–0.87)	6.1	0.90	0.60	0.58	0.91
Women	27.4% (71/259)	0.79 (0.69–0.90)	5.8	0.89	0.65	0.63	0.90
Age thresholds
<60 y	87.6% (227/259)	0.79 (0.73–0.85)	5.9	0.96	0.52	0.58	0.95
≥60 y	12.4% (32/259)	0.86 (0.69–1.00)	6.2	0.86	0.88	0.67	0.96
T2DM
Yes	37.8% (98/259)	0.80 (0.71–0.90)	6.4	0.81	0.76	0.62	0.89
No	62.2% (161/259)	0.80 (0.74–0.87)	5.9	0.97	0.53	0.60	0.96
MetS
Yes	74.5% (193/259)	0.78 (0.72–0.85)	5.9	0.91	0.56	0.59	0.90
No	25.5% (66/259)	0.85 (0.76–0.94)	6.0	0.95	0.65	0.54	0.97
BMI
<25 kg/m²	31.3% (81/259)	0.82 (0.72–0.92)	6.2	0.85	0.75	0.53	0.94
25–30 kg/m²	55.6% (144/259)	0.82 (0.75–0.89)	5.9	1.00	0.56	0.60	1.00
≥30 kg/m²	13.1% (34/259)	0.58 (0.37–0.79)	7.1	0.52	0.91	0.92	0.48
ALT levels
<35 IU/L	33.2% (86/259)	0.79 (0.67–0.90)	5.9	0.87	0.70	0.38	0.96
≥35 IU/L	66.8% (173/259)	0.77 (0.70–0.84)	6.4	0.79	0.65	0.68	0.76

Abbreviations: ALT indicates alanine aminotransferase; AUROC, area under the receiver operating characteristics; BMI, body mass index; CI, confidence intervals; MetS, metabolic syndrome; NASH, nonalcoholic steatohepatitis; NPV, negative predictive value; PPV, positive predictive value; QUS, QUS, quantitative ultrasound; T2DM, type 2 diabetes.