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Evidence Supporting Diagnostic Value of Liver Imaging Reporting and Data System for CT- and MR Imaging-based Diagnosis of Hepatocellular Carcinoma: A Systematic Review and Meta-analysis

Document Type : Systematic Review

Authors

1 Department of Radiology, College of Health and Medical Technology, Al-Ayen University, Thi-Qar, 64001, Iraq

2 Cancer Institute ADIR, Tehran University of Medical Sciences (TUMS), Tehran, Iran

3 Quantitative MR Imaging and Spectroscopy Group (QMISG), Research Centre for Molecular and Cellular Imaging (RCMCI), Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences (TUMS), Tehran, Iran

4 Department of Radiology, Hilla University College, Babylon, Iraq

5 Department of Radiology Technology, Al-Manara College for Medical Sciences, Missan, Iraq

10.31661/jbpe.v0i0.2211-1562

Abstract

Background: Based on the Liver Imaging Data and Reporting System (LI-RADS) guidelines, Hepatocellular Carcinoma (HCC) can be diagnosed using imaging criteria in patients at risk of HCC.
Objective: This study aimed to assess the diagnostic value of LI-RADS in high-risk patients with HCC.
Material and Methods: This systematic review is conducted on international databases, including Google Scholar, Web of Science, PubMed, Embase, PROQUEST, and Cochrane Library, with appropriate keywords. Using the binomial distribution formula, the variance of each study was calculated, and all the data were analyzed using STATA version 16. The pooled sensitivity and specificity were determined using a random-effects meta-analysis approach. Also, we used the chi-squared test and I2 index to calculate heterogeneity among studies, and Funnel plots and Egger tests were used for evaluating publication bias.
Results: The pooled sensitivity was estimated at 0.80 (95% CI: 0.76-0.84). According to different types of Liver Imaging Reporting and Data Systems (LI-RADS), the highest pooled sensitivity was in version 2018 (0.83 (95% CI: 0.79-0.87) (I2: 80.6%, P of chi 2 test for heterogeneity: <0.001 and T2: 0.001). The pooled specificity was estimated as 0.89 (95% CI: 0.87-0.92). According to different types of LI-RADS, the highest pooled specificity was in version 2014 (93.0 (95% CI: 89.0-96.0) (I2: 81.7%, P of chi 2 test for heterogeneity: <0.001 and T2: 0.001).  
Conclusion: LI-RADS can assist radiologists in achieving the required sensitivity and specificity in high-risk patients suspected to have HCC. Therefore, this strategy can serve as an appropriate tool for identifying HCC.

Keywords

Introduction

Hepatocellular carcinoma (HCC) or hepatoma is a common type of cancer starting in the liver [ 1 ], and is also recognized as the fourth greatest cause of cancer-related fatalities and the sixth most prevalent cancer [ 2 ]. The sex incidence ratio of this cancer is 2 to 1, i.e., males to females [ 3 ]. The most significant risk factor for this cancer type, particularly in Western countries, is cirrhosis, a late-stage liver disease. Notwithstanding various etiologies for cirrhosis, chronic hepatitis C, excessive alcohol consumption, and nonalcoholic steatohepatitis are the most frequent reasons for this disease across the globe. Infection with the hepatitis B virus (HBV) is the main cause of HCC and liver cirrhosis worldwide, and due to direct carcinogenic effects, this virus may predispose individuals to develop HCC before cirrhosis [ 4 ]. The five-year survival rate for HCC may reach more than 50% if detected at an initial stage and treated with surgery, percutaneous radiofrequency ablation, or orthotopic liver transplantation [ 5 - 7 ].

Imaging, particularly multiphase contrast-enhanced Computerized Tomography (CT) scan and Magnetic Resonance Imaging (MRI), is crucial for the diagnosis of HCC [ 8 ]. The Liver Imaging Reporting and Data System (LI-RADS) assigns a five-point scale, ranging from LR-1 (definitely benign) to LR-5 (definitely HCC), to a lesion based on its probability of HCC. LI-RADS classification includes both major and auxiliary features. Major features, including arterial hyperenhancement and capsule appearance, are used to classify hepatic lesions. Using auxiliary features, the classification of a lesion is upgraded (not beyond LR-4) or downgraded. The LI-RADS offers a diagnostic algorithm, an illustrated atlas, and a standard terminology to standardize the imaging diagnosis of HCC [ 9 ]. However, LI-RADS does not have the potential for the characterization of HCC from other hepatic cancers. Evaluating the diagnostic value of LI-RADS in high-risk patients suspected to have HCC was the goal of the present investigation.

Material and Methods

Search strategy

In this systematic review, three radiologists independently conducted a meta-analysis search based on Google Scholar, Web of Science, PubMed, Embase, PROQUEST, and Cochrane Library databases to find articles published before February 2021. The following keywords and their combinations, abbreviations, and Mesh-terms were used for the systematic search: “hepatocellular carcinoma (HCC)” OR “Hepatic Cirrhotic masses”, AND “Liver Reporting and Data System”, OR “LI-RADS versions 2014, 2016, 2017, and 2018”, AND “magnetic resonance imaging (MRI)”, AND LI-RADS MRI Sequence”.

Study selection

The following criteria were considered to include studies for this review: (1) English original articles and (2) articles with a minimum sample size of 20 patients and sufficient data for the calculation of false negative and positive, as well as true negative and positive values. Exclusion criteria for this study were: (1) case reports and series, review articles, or meta-analysis, (2) original articles written in other languages than English, (3) articles that did not evaluate sensitivity and specificity, (4) articles with insufficient data for building rows and columns 2×2 contingency, (5) studies with population overlap, and (6) studies discussed cancers other than HCC, e.g., metastatic cancer, epidermoid carcinoma, and cholangiocarcinoma.

Screening and extracting data

The identified articles were independently evaluated by two reviewers considering the inclusion and exclusion criteria. Figure 1 shows the process of selecting articles. The articles were screened out based on the title and abstract and then the full text, respectively. Finally, the extraction of data from studies was conducted by two independent reviewers. The information extracted from studies were first author name, authors’ country, year of publication, study design, gender, age, sample size, pathological complete response, specificity, sensitivity, and accuracy, as well as negative and positive predictive values. In case of any disagreement between two reviewers regarding the extracted data, a third reviewer assessed all discrepant cases.

Figure 1. PRISMA flow diagram illustrating process of selecting articles

Quality assessment for risk of bias

The quality of included studies was assessed by an author using QUADAS criteria, a tool used in systematic reviews for evaluating the risk of bias and assessing the quality of studies of diagnostic accuracy. This tool consists of four key domains, comprising (1) patient selection, (2) index test(s), (3) reference standard, and (4) patient flow and timing of tests [ 10 ].

Risk of bias across studies

To estimate the potential publication bias, we used Begg’s and Egger’s tests.

Statistical analysis

The effect size and the 95% CI were calculated using Stata version 17 (StataCorp, College Station, TX, USA). Also, the publication bias was assessed using Begg’s test. The heterogeneity of each group was measured using the inconsistency index (I2), and an I2 greater than 50%, or a P-value lower than 0.05 is recognized as significant heterogeneity. If the heterogeneity was high, a random-effect model was used to calculate the pooling effect and 95% CI. Otherwise, the fixed effect was used. The diagnostic value of LI-RADS in high-risk patients with HCC was determined by calculating pooled specificity, sensitivity, and accuracy with 95% confidence intervals (CI).

Results

After searching the above-mentioned databases, 35 relevant publications were found using PubMed/Medline and Science Direct databases during 2014-2019. The characteristics of included studies and their sensitivity and specificity estimation are represented in Tables 1 and 2. These investigations (LI-RADS 2014, LI-ADS 2017, and LI-RADS 2018 types of research) were divided into three groups, and seven methodological variables were taken from each group (Tables 1 and 2).

# Author (year) LI-RADS version Strength of magnetic field/vendor (Tesla) NO. of the lesion (no. of HCC) Study type Imaging interpretations
1 A-Hong Ren et al. (2019) 11] LI-RADS v2018 1.5 T GE 3.0 T GE 146 Retrospective 3 Radiologists with 10, 9, and 5 years of experience in abdominal imaging
2 Dong Ik Cha et al. (2020) [ 12 ] LI-RADS v2018 3.0 T Philips Healthcare 122 Prospective Two Radiologists with experience in abdominal imaging
3 Daniel R. Ludwig et al. (2019) [ 13 ] LI-RADS v2018 1.5 T GE 27 Retrospective two fellowships trained abdominal radiologists with 7 and 3 years of post-fellowship experience
4 Mohammad Chaudhry et al. (2019) [ 14 ] LI-RADS v2018 3.0-T GE 53 Retrospective 3 faculty abdominal radiologists (2, 7, and 8 years of post-fellowship experience in abdominal MRI)
5 Andrea S. Kierans et al. (2020) [ 15 ] LI-RADS v2018 LI-RADS v2017 1.5- or 3-T Trio, Siemens Healthcare 40 Retrospective abdominal imaging radiologist with 5 years of post-fellowship experience
6 A.M. DE GAETANO et al. (2019) [ 16 ] LI-RADS v2018 1.5 (GE) Healthcare 17 Retrospective 2 board-certified radiologists with15 & 5 experienced in the interpretation of liver MR imaging.
7 Andrew Chan et al. (2019) [ 17 ] LI-RADS v2018 3.0 T Philips Healthcare 87 Retrospective two radiology residents (AC in 2nd year, MS in 3rd year)
8 Victoria Chernyak et al. (2018) [ 18 ] LI-RADS v2018 1.5- or 3-T Siemens Healthcare 100 Retrospective Radiologists of experience in abdominal imaging
9 Sunyoung Lee et al. (2020) [ 19 ] LI-RADS v2018 3.0-T Siemens 3.0 -T GE 263 Retrospective Two board-certified abdominal radiologists (Two radiologists with 27 and 8 years of experience in liver imaging, respectively)
10 Hanyu Jiang et al. (2019) [ 20 ] LI-RADS v2018 3.0 T Siemens 173 Retrospective two abdominal radiologists (with 10 years and 4 years of experience in liver imaging)
11 Gaurav Khatri et al. (2019) [ 21 ] LI-RADS v2018 1.5T and 3T 93 Prospective Five radiologists
12 Yeun-Yoon Kim et al. (2019) [ 22 ] LI-RADS v2018 1.5-T Philips 3.0-T Siemens 165 Retrospective Two board-certified radiologists with 25 years and 3 years of experience with liver MRI retrospectively and independently analyzed the images.
13 Federica Vernuccio et al. (2019) [ 23 ] LI-RADS v2018 1.5-T GE 3-T Philips 109 Retrospective Two radiologists with expertise in abdominal imaging
14 Jeong Hee Yoon et al. (2018) [ 24 ] LI-RADS v2018 1.5T GE 3T Trio, Siemens 43 Prospective Three fellowship-trained board-certified with 25, 11, 11 years of experience in liver MRI.
15 Paul Smereka et al. (2020) [ 25 ] LI-RADS v2018 1.5-T Siemens 3-T GE 71 Prospective Three fellowship-trained abdominal radiologists (10, 5, and 2 years of experience)
16 Grzegorz Rosiak et al. (2018) [ 26 ] LI-RADS v2017 1.5T Siemens Magnetom Avanto. 70 Prospective Radiologists of experience in abdominal imaging
17 Mohammad Abd Alkhalik Basha et al. (2019) [ 27 ] LI-RADS v2017 1.5-Tesla Philips 67 Prospective two radiologists had more than 10 years of experience in hepatic MRI
18 Ying Ding et al. (2018) [ 28 ] LI-RADS v2017 1.5-T Siemens 145 Prospective Two radiologists with more than 10 years of experience in abdominal MRI
19 Youngwoo Kim et al. (2017) [ 29 ] LI-RADS v2017 1.5T or 3T GE 41 Prospective Two abdominal radiologists.
20 Weimin Liu et al. (2017) [ 30 ] LI-RADS v2017 1.5-T Aera, Siemens 170 Retrospective Two radiologists (with 8 years of experience in abdominal radiology)
21 Tong Zhang et al. (2019) [ 31 ] LI-RADS v2017 3.0 T Siemens 245 Retrospective Two radiologists (with ten years of experience in abdominal radiology)
22 Ji Soo Song et al. (2014) [ 32 ] LI-RADS v2014 1.5-T Siemens 77 Prospective Two abdominal radiologists.
23 Tyler J. Fraum et al. (2018) [ 33 ] LI-RADS v2018 1.5-T Avanto Siemens 3-T Siemens 138 Retrospective Three abdominal radiologists with 10-17 years of post-fellowship experience.
24 Jae Seok Bae et al. (2017) [ 34 ] LI-RADS v2017 1.5-T GE 3-T Siemens 167 Retrospective Two clinically experienced abdominal radiologists both with 10 years of experience in abdominal imaging).
25 Anton S. Becker et al. (2016) [ 35 ] LI-RADS v2014 1.5-T SIEMENS 55 Retrospective Four board-certified radiologists with different experiences in liver imaging.
26 Milena Cerny et al. (2018) [ 36 ] LI-RADS v2017 1.5-T or 3.0-T - Retrospective Two fellowship-trained abdominal radiologists with experience in liver imaging.
27 Ningxin Chen et al. (2016) [ 37 ] LI-RADS v2014 1.5-T GE 3-T GE 111 Retrospective Two fellowship-trained abdominal radiologists
28 Aless andro Furlan et al. (2018) [ 38 ] LI-RADS v2017 3-T GE 71 Retrospective 3 board-certified and fellowship-trained abdominal radiologists
29 Robert M. Hicks et al. (2016) [ 39 ] LI-RADS v2014 3.0T Siemens 68 Prospective Two abdominal radiologists (with three- and ten-years’ experience)
30 Natally Horvat et al. (2017) [ 40 ] LI-RADS v2014 1.5-T 3-T 51 Retrospective Two senior board-certificated abdominal radiologists
31 Ijin Joo et al. (2016) [ 41 ] LI-RADS v2014 1.5-T GE 3.0-T GE 71 Retrospective Three fellowship-trained radiologists
32 Weimin Liu et al. (2017) [ 30 ] LI-RADS v2014 1.5-T GE 151 Retrospective 2 radiologists (with eight years of experience in abdominal radiology)
33 Maxime Ronot et al. (2017) [ 42 ] LI-RADS v2014 1.5 or 3 T magnets in three centers. 341 Retrospective senior abdominal radiologists
34 QI Tang et al. (2018) [ 43 ] LI-RADS v2017 3.0-T Siemens 42 Prospective Two radiologists with 20 and 12years of liver imaging experience randomly.
35 Sunyoung Lee et al. (2020) [ 19 ] LI-RADS v2018 3.0-T systems Trio Tim, Siemens Healthineers 263 Retrospective -
36 A-Hong Ren et al. (2019) [ 11 ] LI-RADS v2017 1.5 T GE 3.0 T GE 146 Retrospective 3 Radiologists 10, 9, and 5 years of experience in abdominal imaging
LI-RADS: Liver Imaging Reporting and Data System, HCC: Hepatocellular Carcinoma, MRI: Magnetic Resonance Imaging, GE: General Electric
 Table 1.The characteristics of studies included in this study
# Author (year) Lesion size (mm) Reference standard Sensitivity (95%CI) Specificity (95%CI)
1 A-Hong Ren et al. (2019)) [ 11 ] <20 Liver biopsy, follow-up 80.8 90.1
2 Dong Ik Cha et al. (2020) [ 12 ] ≤30 Histopathologic 90.3 96
3 Daniel R. Ludwig et al. (2019) [ 13 ] ≤30 Pathologic diagnosis, Biopsy 66.7 98.5
4 Mohammad Chaudhry et al. (2019) [ 14 ] 21 (11–54) Histopathology 87 85
5 Andrea S. Kierans et al. (2020) [ 15 ] 20≥ Histopathology, Biopsy 77.4 91.6
6 A.M. DE GAETANO et al. (2019) [ 16 ] ≤20 Pathologic 94.1 55.7
7 Andrew Chan et al. (2019) [ 17 ] ≤20 Pathologic diagnosis, Biopsy 80.8 88
8 Victoria Chernyak et al. (2018) [ 18 ] ≤30 Pathologic diagnosis 86 75
9 Sunyoung Lee et al. (2020) [ 19 ] ≥10 MRI and pathological diagnosis 67.5 98.1
10 Hanyu Jiang et al. (2019) [ 20 ] ≤50 Histopathologic examination 86 82
11 Gaurav Khatri et al. (2019) [ 21 ] ≤60 Clinical follow-up criteriapathologic diagnosis 92.1 88.6
12 Yeun-Yoon Kim et al. (2019) [ 22 ] 12-115 Histopathological diagnosed 83 89
13 Federica Vernuccio et al. (2019) [ 23 ] 10–20 Pathological diagnosis 84 84
14 Jeong Hee Yoon et al. (2018) [ 24 ] 10–19 Pathologic, imaging follow-up 62.2 97.7
15 Paul Smereka et al. (2020) [ 25 ] ≥20 Pathologically 87.2 96.6
16 Grzegorz Rosiak et al. (2018) [ 26 ] ≥20 Biopsyfollow-up imaging 96 75
17 Alkhalik Basha et al. (2019) [ 27 ] ≥20 Histopathological diagnosed 97.01 88.43
18 Ying Ding et al. (2018) [ 28 ] <10 Histopathological diagnosed 73.8 71
19 Youngwoo Kim et al. (2017) [ 29 ] ≤20 Histopathological diagnosed 82 79
20 Weimin Liu et al. (2017) [ 30 ] ≥20 Surgical pathology, needle biopsy, two years of follow-up 84.8 95.8
21 Tong Zhang et al. (2019) [ 31 ] ≥30 Histological diagnosis 61.2 92.5
22 Ji Soo Song et al. (2018) [ 32 ] 8–72 Histological findings 76.6 89.6
23 Tyler J. Fraum et al. (2018) [ 33 ] ≥20 Pathologic analysis 56.4 81.8
24 Jae Seok Bae et al. (2017) [ 34 ] ≥20 Pathologic analysis 91 90
25 Anton S. Becker et al. (2016) [ 35 ] ≥20 Pathologic analysis 34.5 98
26 Milena Cerny et al. (2018) [ 36 ] ≥20 Surgical resection, biopsy 87.9 87.5
27 Ningxin Chen et al. (2016) [ 37 ] ≥20 Pathologic analysis 84 96
28 Aless andro Furlan et al. (2018) [ 38 ] ≥20 Surgical resection, biopsy 80 87
29 Robert M. Hicks et al. (2016) [ 39 ] ≥20 Pathologic analysis 91 94
30 Natally Horvat et al. (2017) [ 40 ] ≥10 Pathologic analysis 93.3 84.2
31 Ijin Joo et al. (2016) [ 41 ] ≥10 Surgical resection, biopsy 65.2 93.3
32 Maxime Ronot et al. (2017) [ 42 ] ≥20 Pathologic analysis, follow-up 72.5 89.9
33 QI Tang et al. (2018) [ 43 ] ≥10 Surgical resection, Biopsy, follow-up 71.1 55.6
34 Sunyoung Lee et al. (2020) [ 19 ] ≥20 Pathologic analysis 85.6 88.1
35 A-Hong Ren et al. (2019) [ 11 ] ≥50 Biopsy, follow-up 71.2 91.5
CI: Confidence Intervals, MRI: Magnetic Resonance Imaging
 Table 2.The estimated sensitivity and specificity in the studies included in the current meta-analysis

Estimation of pooled specificity

Based on the random-effects model, the pooled specificity was predicted to be 0.89 (95% CI: 0.87-0.92), indicating that this test could detect 0.89 real negative instances. There was substantial variability across studies in terms of pooled specificity (I2: 90.7%; P-value<0.001 for heterogeneity; T2: 0.001). The maximum pooled specificity (93%) was observed in LI-RADS version (v) 2014 (95% CI: 89.0-96.0; I2: 81.7%, P-value<0.001 for heterogeneity; T2: 0.001). Table 3 and Figure 2 provide further details on the pooled estimation of specificity that in lesion size ≤20 was 0.88 in the assessment of specificity for the various subgroups of lesion size (≤20 and >20; 95% CI: 0.85-0.92). There was also high inter-study heterogeneity (I2: 88.3%; P-value=0.001 for heterogeneity; T2: 0.001). Additionally, the combined estimate of specificity for lesions less than 20 was 0.90% (95% CI: 0.87-0.93). Moreover, significant differences were across studies (I2=91.3%; P-value<0.001 for heterogeneity; T2: 0.01; Figure 3).

Types of LI-RADS Number of studies Pooled estimation (95% CI) Pheterogeneity I2 (%) T2
Sensitivity
2014 8 74.0 (60.0- 87.0) <001 95.7 0.04
2017 12 81.0 (75.0-88.0) <001 94.0 0.01
2018 15 83.0 (79.0-87.0) <001 80.6 0.001
Overall 35 80.0 (76.0-84.0) <001 93.1 0.01
Specificity
2014 8 93.0 (89.0-96.0) <001 81.7 0.001
2017 12 85.0 (79.0-91.0) <001 92.5 0.01
2018 15 91.0 (88.0-94.0) <001 88.0 0.001
Overall 35 89.0 (87.0-92.0) <001 90.7 0.001
LI-RADS: Liver Imaging Reporting and Data System, CI: Confidence Intervals
 Table 3.Pooled estimation of sensitivity and specificity according to the types of Liver Imaging Reporting and Data System

Figure 2. The pooled estimation of the sensitivity and specificity of liver imaging reporting and data system (LI-RADS) in the detection of hepatocellular carcinoma according to different versions of LI-RADS. (CI: Confidence Intervals)

Figure 3. The pooled estimation of specificity of imaging reporting and data system in the detection of hepatocellular carcinoma according to the lesion size. (CI: Confidence Intervals)

Estimation of pooled sensitivity

The sensitivity of pooled data for 35 articles was 0.80% (95% CI: 0.76-0.84), suggesting that the test has an accuracy of 0.80% for genuine positive cases. Considerable variability was found across studies in terms of pooled sensitivity (I2: 93.1%; P-value<0.001 for heterogeneity; T2: 0.01). LI-RADS v2018 had the greatest pooled sensitivity (0.83%; 95%: 0.79-0.87; I2: 80.6%; P-value<0.001 for heterogeneity; T2: 0.001). Table 3 and Figure 2 provide further details on the pooled estimation of sensitivity details. The pooled sensitivity estimates in lesion size ≤20 were 0.81 in the calculation of sensitivity for the various subgroups of lesion size (>20; 95% CI: 0.73-0.90; I2: 96.7%; P<0.001 for heterogeneity; T2: 0.02). There was considerable heterogeneity among the studies. Furthermore, the pooled sensitivity estimated in lesions >20 was 0.79% (95% CI: 0.75-0.84). Significant differences were also detected across trials (I2: 88.3%; P-value<0.001 for heterogeneity; T2: 0.01; Figure 4).

Figure 4. The pooled estimation of sensitivity of imaging reporting and data system in the detection of hepatocellular carcinoma according to the lesion size. (CI: Confidence Intervals, REML: Restricted Maximum Likelihood)

Meta-regression

The impact of factors, such as sample size and publication year of various articles on the pooled specificity and sensitivity were evaluated to determine the reason for the heterogeneity across studies. The effect of sample size (P-value=0.49 and P-value=0.72) and study year (P-value=0.80 and P-value=0.17) on estimating the heterogeneity of pooled sensitivity and specificity across studies was statistically insignificant, respectively. The distribution of sensitivity and specificity for various sample sizes is displayed in Figure 5.

Figure 5. The distribution of estimated sensitivity (a) and specificity (b) according to different sample sizes. (CI: Confidence Intervals)

Publication bias

Both sensitivity and specificity showed a strong publication bias in the findings of Begg’s and Egger’s tests, with P-value=0.001 and P<0.001 (Figure 6).

Figure 6. The funnel plot to assess publication bias in estimated sensitivity (a) and specificity (b). (CI: Confidence Intervals)

Discussion

We investigated the value of the LI-RADS in the detection and characterization of HCC. Based on the results, the pooled sensitivity and specificity were estimated as 0.80 (95% CI: 0.76-0.84) and 0.89 (95% CI: 0.87-0.92), suggesting this test can detect 0.80 of true positive cases and 0.89 of true negative cases, respectively. According to different subgroups of lesion size (≤20 and >20), the sensitivity and specificity were 0.81 and 0.79 (95% CI: 0.73-0.90 and 0.75-0.84) and 0.88 and 0.90 (95% CI: 0.85-0.92 and 0.87-0.93), respectively. This finding signifies that the sensitivity of LI-RADS in the detection of low-grade lesions is higher than that of high-grade lesions. MRI-based radiomics analysis, in comparison to other imaging methods, demonstrated a sensitivity of 93.8% in the detection of HCC [ 44 ]. The discrepancy between the estimation rates of sensitivity and specificity in studies can be due to varied sample sizes and diagnostic techniques used on patients with different clinical stages.

Regarding different versions of LI-RADS, the highest pooled sensitivity was in LI-RADS v2018 (0.83%; 95% CI: 0.79-0.87), while that of specificity was detected in LI-RADS v2014 (93.0%; 95% CI: 89.0-96.0). In another study, the LI-RADS version 2018 showed higher sensitivity than LI-RADS v2017 (81% vs. 68%). Also, in low-grade lesions, the sensitivity of LI-RADS v2018 was indicated to be higher than LI-RADS v2017 (76% vs. 11%), but the specificity of LI-RADS v2018 was lower than LI-RADS v2017 (94% vs. 99%) [ 45 ]. The higher sensitivity for LI-RADS v2018 has been reported in another original study.

The estimated sensitivity of LR-5 criteria of LR-TIV v2018 and v2017 was 63.9% and 55.2%, respectively, whereas the specificity for the two versions was the same (97.3%) [ 15 ]. The performance of varying versions of the LI-RADS (v2017 vs v2014) in the detection of HCC assessed in other studies showed a positive predictive value and high specificity for the mentioned versions [ 46 - 48 ]. The results of heterogeneity among included studies can be attributed to different factors, such as clinician skills and the situation of under-study patients. However, it should be regarded that the balance between sensitivity and specificity is very important, especially for diseases with high mortality [ 49 ].

The sensitivity of gadoxetate disodium-enhanced MR imaging in detecting HCC was estimated to be 0.85%, and this rate for multidetector CT was 0.69% [ 50 ]. In a meta-analysis, the pooled sensitivity results of contrast-enhanced CT and gadolinium-enhanced MRI in detecting HCC were 73.6% and 77.5%, respectively, while those of non-contrast-enhanced US were 59.3% [ 51 ]. The sensitivity estimated by the above-mentioned approaches is fewer than that estimated by the current study.

Various appearances of the lesion are based on the kind of lesion, as well as its size and echogenicity. Small, localized HCC, compared to normal liver, looks hypoechoic. Larger lesions are heterogeneous due to fatty change, fibrosis, calcification, and necrosis. Focal fatty sparing-diffuse HCC may have a peripheral halo of hypoechogenicity, and this feature may be challenging to diagnose or separate from underlying cirrhosis.

The main goal of using MRI and CT scan in the medical care of HCC patients is the early diagnosis of the disease and initiation of early intervention. Both diagnostic techniques have high specificity; thus, treatment can begin without the use of additional invasive diagnostic methods, such as biopsy [ 52 , 53 ]. However, due to low cost-effectiveness, MRI and CT scans are not recommended by national clinical practitioners [ 54 ]. Therefore, the LI-RADS, as an advanced and cost-effective method, can be used in the care of patients at risk of or with HCC. Based on previous studies, the sensitivity of LI-RADS US for detecting HCC has a wide range (20.5-94%) [ 51 , 52 , 55 ]. LI-RADS US generally has a lower sensitivity for detecting HCC in patients at high risk of HCC [ 51 ]. The obtained result of the current study has a much higher sensitivity than those of a recent meta-analysis, which reported a sensitivity of 47% for the early detection of HCC [ 51 ]. According to the results of the study by Son et al. (2019) in the surveillance of patients at high risk of HCC, US liver imaging report classification, and US-3 data system showed high specificity but low sensitivity for HCC diagnosis [ 56 ].

The present study had some limitations, as follows: 1) significant heterogeneity between included studies, leading to using the random effects model, 2) the lack of data, resulting in not performing some sub-group analyses regarding the etiology of the chronic liver disease and presence of cirrhosis, and 3) the presence of significant publication bias.

Conclusion

The results of sensitivity and specificity estimated in the current study were acceptable. Therefore, LI-RADS can assist radiologists in achieving the required sensitivity and specificity. The LI-RADS criteria were developed for diagnosing high-specificity progressed HCCs, namely HCCs, that have progressed along the hepatocarcinogenesis pathway to the point where they are malignant, with the potential for vascular invasion and metastasis.

Acknowledgment

Authors would like to thank the cooperation of the Tehran University of Medical Sciences, Tehran, Iran.

Authors’ Contribution

A. Arian and H. Suhail Najm Alareer designed the conception of the study; HJ. Taher and A. Dinar Abdullah conducted a statical analysis; of A. Arian and H. Suhail Najm Alareer performed technical support and conceptual advice. All authors contributed to the drafted manuscript, revised it critically, and approved the final version.

Conflict of Interest

None

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Journal of Biomedical Physics and Engineering
Volume 14, Issue 1
63
February 2024
Pages 5-20
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