Estimating liver cirrhosis severity with extracellular volume fraction by spectral CT

Estimating liver cirrhosis severity with extracellular volume fraction by spectral CT | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Estimating liver cirrhosis severity with extracellular volume fraction by spectral CT Hong Zhang, Ee Hao, Dongqin Xia, Mingyue Ma, Jiayu Wu, Tongchi Liu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5847341/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose To investigate the diagnostic value of spectral CT in calculating extracellular volume fraction (ECV) for assessing the severity of liver cirrhosis. Methods In this retrospective study, 172 patients (127 liver cirrhosis patients and 45 controls),who underwent spectral CT liver enhancement scans, and were categorized based on the Child-Pugh classification. During the delayed phase, ECV values were derived from iodine density map. These ECV values were then compared across the control group and subclassified cirrhosis groups (Child-Pugh classes A, B, and C). Furthermore, a correlation analysis was performed to assess the relationship between ECV values and Child-Pugh scores in liver cirrhosis. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic performance of ECV values and MELD-Na in the Child-Pugh classification of liver cirrhosis. Results The ECV values were 25.49±3.15, 29.73±3.20, 35.64±3.15, and 45.30±5.16 for the control, Child-Pugh A, Child-Pugh B, and Child-Pugh C group, respectively, demonstrating significant intergroup differences (F=184.67 P<0.001). A strong positive correlation was observed between ECV and Child-Pugh liver function classification (r=0.791, P<0.001). The diagnostic performance of ECV for differentiating between Child-Pugh classes A and B (AUC: 0.901), B and C (AUC: 0.966) was higher compared to the MELD-Na score (AUC: 0.772 and 0.868) (P<0.05, respectively). Multivariate analyses showed that ECV was the factor independently associated with cirrhosis (OR=1.610, P<0.001). Conclusion ECV values measured using spectral CT can serve as a noninvasive biomarker for assessing the severity of liver cirrhosis. Spectral CT Extracellular Volume Liver Cirrhosis Classification Evaluation Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Globally, the causes and incidence rates of chronic liver diseases vary. However, as the disease progresses, it leads to liver fibrosis, which gradually evolves into cirrhosis and ultimately hepatocellular carcinoma. In recent years, the incidence rate of cirrhosis has been increasing, posing a serious burden on society and patients [ 1 , 2 ]. Cirrhosis is characterized by severe scarring, structural distortion, and functional failure of liver tissue due to collagen deposition, accompanied by life-threatening complications such as portal hypertension, variceal bleeding, peritonitis, ascites, and hepatic encephalopathy [ 3 ]. Therefore, cirrhosis is currently the 11th most common cause of death worldwide [ 2 ] and the fourth most common cause of death among adults in Central Europe [ 4 , 5 ]. Accurate assessment of the severity of cirrhosis is of great significance for patient treatment, prognosis, and overall mortality risk assessment [ 6 ]. Currently, various scoring methods for evaluating liver fibrosis and cirrhosis have been developed clinically based on laboratory tests [ 7 ]. However, the most widely accepted and effective scoring system in clinical practice is still the Child-Pugh score [ 8 ]. Although imagological examination also plays an important role in the prognosis assessment of cirrhosis patients, most of them are based on morphological studies. Spectral CT, as a quantitative imaging tool, is applied to liver lesions because it can achieve material decomposition, allowing for quantitative measurement of the iodine concentration value of substances, and indirectly calculating the extracellular volume. Cirrhosis patients have increased collagen deposition in the liver due to long-term chronic liver disease, in which expansion in the extracellular space results in an increase in ECV. [ 9 ]. Therefore, based on this pathological basis, it is possible to assess cirrhosis by quantitatively calculating the ECV value using spectral CT. Based on this background, this study explores the diagnostic efficacy of measuring the ECV of liver cells using spectral CT in the assessment of cirrhosis severity. Materials and methods Patients This study is a retrospective study, approved by the hospital ethics management committee, and exempt from obtaining informed consent from enrolled patients. We selected 127 patients who underwent spectral CT abdominal contrast-enhanced scans in our department from October 2023 to January 2025 and were clinically and pathologically diagnosed with cirrhosis. At the same time, 45 normal controls matched in gender and age were selected. General information and laboratory test results of all enrolled cases were collected, including hematocrit, platelet, prothrombin time, bilirubin, albumin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and serum sodium concentration. The control group of cirrhotic patients was divided into three groups based on Child-Pugh severity of cirrhosis: Child-Pugh A, Child-Pugh B, and Child-Pugh C. Case inclusion criteria: 1. Clinically and pathologically diagnosed with cirrhosis; 2. Possess complete general information, imaging, and laboratory examination data. Case exclusion criteria: 1. Presence of liver neoplastic lesions; 2. Previous history of other malignant tumors; 3. Recent history of blood transfusion or liver trauma. CT examination A triphasic contrast-enhanced scan of the liver was conducted using Philips 256-slice spectral CT, employing an automatic tracking method for blood flow detection. The diaphragm-level abdominal aorta was set as the detection plane, with a trigger threshold of 150 HU. Images were acquired at 8 seconds, 40 seconds, and 180 seconds after reaching the threshold, representing the hepatic arterial phase, portal venous phase, and delayed phase, respectively. The contrast agent used was iopromide (370 mg/ml), with a general dose of 70-90 ml (1.2 ml/kg) and an injection rate of 3 ml/s. The scanning parameters were as follows: tube voltage,120 kV; tube current,137 mAs, helical pitch, 0.9, slice thickness,1.5 mm; slice interval,1.5 mm; FOV,320 mm. The spectral image data (spectral-based imaging, SBI) obtained after scanning was uploaded to a workstation. Image post-processing and data measurement SBI data was processed by a Philips IntelliSpace Portal V12.0 workstation. We selected the liver enhancement delay 180s image and utilized the built-in Multiphase Analysis software within the workstation to calculate ECV. After calibration, we avoided the liver vessels and calcifications, and on the liver's largest volume plane, we selected the caudate lobe(SI), upper segment of left lateral lobe(SII), lower segment of left lateral lobe(SIII), left medial lobe(SIV), lower segment of right anterior lobe(SV),lower segment of right posterior lobe(SVI), upper segment of right posterior lobe(SVII) and upper segment of right anterior lobe(SVIII) to draw regions of interest(ROIs) with an area of approximately 100mm 2 for measurement. The average ECV of the eight liver segments was taken as the liver's ECV value, as shown in Figure 1. The measurements were performed by two senior attending physicians, and the average value was taken as the final measurement result. Statistical analysis Data were entered bilaterally into Excel software, and processed using Prism 5.0 (GraphPad Software) and SPSS Statistics (Version 26, IBM). Measurement data were presented as mean±standard deviation, while count data were expressed as percentages (%). All data underwent normality and homogeneity of variance tests. If the data followed a normal distribution, t-tests were used for comparisons between two groups, and one-way ANOVA was applied for comparisons among multiple groups. If the data did not follow a normal distribution, the rank sum test was used. Correlation between data was analyzed using Pearson or Spearman correlation, and ROC curve analysis was used to predict the highest sensitivity and specificity and determine the optimal cutoff value. Logistic regression was used to determine independent factors for the diagnosis of cirrhosis, A p-value < 0.05 was considered statistically significant. Results Cohort characteristics and laboratory test There were 58 cases in the Child-Pugh A group (36 males and 22 females), 47 cases in the Child-Pugh B group (29 males and 18 females), 22 cases in the Child-Pugh C group (14 males and 8 females), and 35 cases in the control group (19 males and 16 females).In the cirrhosis group, there were 65 cases of hepatitis B-related liver disease, 25 cases of hepatitis C-related liver disease, 2 cases of alcoholic liver disease, 6 cases of autoimmune liver disease, 1 case of Budd-Chiari syndrome, and 28 cases of cirrhosis of unknown etiology. PT, INR, bilirubin, AST, ALT, albumin, platelet, albumin-bilirubin (ALBI), and modified end-stage liver disease with the incorporation of sodium (MELD-Na) exhibited statistical differences across all groups (P < 0.001). Detailed data regarding age, gender, laboratory tests, and CT-measured ECV values for different subgroups are outlined in Table 1. Table 1 . Clinical, laboratory, and CT parameters Variable Controls (n = 45) Child-Pugh A (n = 58) Child-Pugh B (n = 47) Child-Pugh C (n = 22) P value Clinical parameters Age, years 57.27 (11.53) 58.17 (11.35) 59.38 (12.85) 57.45 (9.44) 0.833 Sex 0.880 Female, % 14 (31.1) 22 (37.9) 18 (38.3) 8 (36.4) Male, % 31 (68.9) 36 (62.1) 29 (61.7) 14 (63.6) Liver disease type <0.001 Hepatitis B virus — 30 (51.7) 21 (44.7) 14 (63.6) Hepatitis C virus — 12 (20.7) 7 (14.9) 6 (27.3) Autoimmune hepatitis — 2 (3.4) 3 (6.4) 1 (4.5) Budd-Chiari syndrome — 0 (0.0) 1 (2.1) 0 (0.0) Alcoholic hepatitis — 1 (1.7) 1 (2.1) 0 (0.0) Unknown — 13 (22.4) 14 (29.8) 1 (4.5) Laboratory parameters Hematocrit value (%) 40.84 (4.17) 36.64 (7.68) 32.77 (7.93) 30.31 (7.86) <0.001 PT (s) 10.38 (1.34) 12.06 (1.88) 14.00 (1.85) 17.42 (3.14) <0.001 INR 0.88 (0.08) 1.02 (0.14) 1.17 (0.15) 1.48 (0.33) <0.001 Bilirubin (umol/l) 13.38 (5.43) 22.75 (11.50) 31.48 (16.47) 89.37 (106.35) <0.001 Albumin (g/l) 42.82 (3.14) 39.64 (4.62) 33.79 (5.17) 28.58 (3.15) <0.001 AST (U/l) 20.02 (5.75) 40.97 (51.61) 45.57 (36.02) 134.82 (211.67) <0.001 ALT (U/l) 17.87 (8.16) 30.29 (23.24) 33.77 (30.97) 98.59 (149.32) <0.001 Na (mmol/l) 140.20 (1.87) 139.74 (2.44) 138.06 (3.32) 137.55 (5.44) <0.001 Platelet (109/l) 216.91 (59.00) 106.22 (54.42) 99.37 (74.77) 109.14 (91.61) <0.001 Creatinine (umol/l) 62.00 (13.93) 58.71 (13.23) 64.14 (23.24) 73.83 (20.31) 0.008 Child-pugh score 5.00 (0.00) 5.55 (0.50) 7.62 (0.64) 11.32 (1.55) <0.001 MELD_Na score -2.90 (0.30) -2.51 (0.43) -1.92 (0.42) -1.25 (0.41) <0.001 ALBI score -2.94 (0.30) -2.51 (0.43) -1.86 (0.37) -1.08 (0.37) <0.001 CT parameters ECV (%) 25.49 (3.15) 29.73 (3.20) 35.64 (3.15) 45.30 (5.16) <0.001 Continuous variables are expressed as mean ± standard deviation. Categorical variables are expressed as numbers and percentages in parentheses. ECV, extracellular volume fraction; ALBI, albumin-bilirubin; MELD_Na, modified end-stage liver disease with the incorporation of sodium; PT, prothrombin time; INR, international normalized ratio; AST, aspartate aminotransferase; ALT, alanine aminotransferase. Comparison of ECV values among Child-Pugh groups of liver cirrhosis The ECV value of the control group was 25.49 ±3.15, whereas the ECV values for the Child-Pugh A, B, and C groups of cirrhosis were 29.73±3.20,35.64±3.15 and 45.30±5.16, respectively. One-way ANOVA analysis indicated statistically significant differences in ECV among the groups (F=184.67, P <0.001), Figure 2. Correlation between ECV value and Child-Pugh classification of liver cirrhosis A correlation analysis was conducted between the ECV value with the Child-Pugh score. The ECV value demonstrated a significant positive correlation with the Child-Pugh score for cirrhosis (r=0.791, p<0.001). The results indicated that as the severity of cirrhosis increased, the ECV value tended to rise (Fig 3). Diagnostic efficacy of ECV value in assessing the severity of liver cirrhosis The receiver operating characteristic (ROC) curve shows the area under the curve (AUC) of the ECV value for diagnosing the control group and cirrhosis group was 0.911 (95% CI 0.858-0.949). Using a cutoff value of 29.46, the best discrimination between non-cirrhosis and cirrhosis was achieved, with sensitivity and specificity of 76.38% and 95.56%, respectively. The AUC of the ECV value for diagnosing Child-Pugh A and Child-Pugh B stages of cirrhosis was 0.901, which was significantly higher than that of MELD-Na (Z=2.441, p=0.015); The AUC of the ECV value for diagnosing Child-Pugh B and Child-Pugh C stages of cirrhosis was 0.966 (95% CI 0.892 - 0.995), which was significantly higher than that of MELD-Na (Z=2.042, p=0.041). (Fig 4, Table 2). Table 2 . Diagnostic efficacy of ECV, ALBI, and MELD-Na for controls and subclassified cirrhosis groups AUC Cutoff value Sensitivity（%） Specificity（%） PPV（%） NPV（%） Youden Control vs. cirrhosis ECV 0.911 (0.858 to 0.949) 29.46 76.38 (68.0 - 83.5) 95.56 (84.9 - 99.5) 98.0 (92.9 - 99.8) 58.9 (46.8 - 70.3) 0.719 ALBI 0.878 (0.819 to 0.923) -2.65 80.31 (72.3 - 86.8) 88.89 (75.9 - 96.3) 95.3 (89.4 - 98.5) 61.5 (48.6 - 73.3) 0.692 MELD_Na 0.868 (0.810 to 0.911) 6 88.98 (82.2 - 93.8) 71.11 (55.7 - 83.6) 89.7 (83.0 - 94.4) 69.6 (54.2 - 82.3) 0.601 Child-Pugh A vs. B ECV # 0.901 (0.827 to 0.951) 33.72 78.72 (64.3 - 89.3) 87.93 (76.7 - 95.0) 84.1 (69.9 - 93.4) 83.6 (71.9 - 91.8) 0.667 ALBI 0.847 (0.764 to 0.910) -1.87 57.45 (42.2 - 71.7) 98.28 (90.8 - 100.0) 96.4 (81.7 - 99.9) 74.0 (62.8 - 83.4) 0.557 MELD_Na 0.772 (0.680 to 0.848) 8 78.72 (64.3 - 89.3) 62.07 (48.4 - 74.5) 62.7 (49.1 - 75.0) 78.3 (63.6 - 89.1) 0.408 Child-Pugh B vs. C ECV# 0.966 (0.892 to 0.995) 39.53 90.91 (70.8 - 98.9) 95.74 (85.5 - 99.5) 90.9 (70.8 - 98.9) 95.7 (85.5 - 99.5) 0.867 ALBI 0.906 (0.811 to 0.963) -1.5 81.82 (59.7 - 94.8) 89.36 (76.9 - 96.5) 78.3 (56.3 - 92.5) 91.3 (79.2 - 97.6) 0.712 MELD_Na 0.868 (0.764 to 0.937) 12 86.36 (65.1 - 97.1) 70.21 (55.1 - 82.7) 57.6 (39.2 - 74.5) 91.7 (77.5 - 98.2) 0.566 # represents the diagnostic efficacy is statistically significant ( p ＜0.05). ECV, extracellular volume fraction; ALBI, albumin-bilirubin; MELD-Na, model of end-stage liver disease with the incorporation of sodium. Analysis of risk factors for liver cirrhosis As shown in Table 3, multivariate logistic regression analysis showed that ECV was independently associated with liver cirrhosis (OR: 1.610, CI: 1.219-2.125, P<0.001). Table 3 . Logistic regression analysis of liver cirrhosis patients versus controls. Variable OR 95% CI p-value ECV 1.610 1.219-2.125 0.001 Sex 0.642 0.164-2.512 0.524 Age 1.019 0.958-1.085 0.543 prothrombin time 1.588 0.961-2.627 0.071 Bilirubin 1.087 0.943-1.253 0.251 Albumin 1.018 0.832-1.246 0.863 AST 1.164 1.021-1.328 0.024 ALT 0.995 0.911-1.087 0.906 Creatinine 0.975 0.918-1.035 0.407 MELD-Na 1.344 0.655-2.758 0.420 Discussion The extracellular volume fraction (ECV) is the sum of the fractions of the extravascular-extracellular space and intravascular space, reflecting the microvascular density and the degree of matrix fibrosis. Based on this principle, ECV has initially been extensively utilized in the assessment of myocardial fibrosis, particularly in evaluating myocardial fibrosis and myocardial lesions through magnetic resonance T1 mapping [ 10 , 11 ]. Apart from its application in the assessment of myocardial fibrosis [ 12 ], CT-based ECV measurement has also been employed for distinguishing between benign and malignant tumors, assessing tumor staging, evaluating treatment response, and predicting prognosis [ 13 ]. Patients with liver fibrosis and cirrhosis exhibit an increase in collagen deposition within the liver, subsequently enlarging the extracellular space. Given this pathological basis, the degree of liver fibrosis can be indirectly reflected through the quantitative measurement of liver ECV values [ 9 ]. Methods for measuring ECV values via CT include ECV-ΔHU, which relies on CT values, and ECV-Iodine, which utilizes iodine density maps. In the context of liver ECV measurement among patients with liver fibrosis, Yoon [ 14 ] conducted a Spectral CT study and discovered that ECV-Iodine demonstrates superior diagnostic efficacy for liver fibrosis (F ≥ 2) compared to ECV-HU. Similarly, Nagayama Y [ 15 ] employed dual-energy CT and observed that the correlation coefficient of ECV-Iodine is higher than that of ECV-ΔHU within the liver fibrosis group, indicating a higher degree of reliability and credibility for ECV-Iodine in assessing liver fibrosis. Consequently, this study employs Spectral CT to calculate liver ECV values based on iodine density maps. Currently, the majority of domestic and international literature primarily focuses on evaluating the degree of liver fibrosis in the application of ECT, with limited reports on cirrhosis research. However, since the liver fibrosis stage has already progressed in cirrhosis patients, merely assessing the fibrosis stage appears insufficient to draw conclusive insights into the severity of liver function. Recently, scholars have demonstrated that the measurement of ECV values utilizing T1 mapping technology can effectively evaluate the grading of liver function in cirrhosis patients [ 16 , 17 ]. Xu Y [ 18 ] further discovered through dual-energy CT that ECV values hold predictive potential for short-term disease progression in patients with acute decompensated hepatitis B cirrhosis. However, to date, there have been no reported studies on the assessment of cirrhosis severity staging through the measurement of ECV values using the latest generation of Spectral CT. In this study, we aim to explore the feasibility of assessing the severity of cirrhosis by measuring liver ECV values based on Spectral CT. To achieve this, we have included patients with a confirmed diagnosis of cirrhosis as our research subjects and utilized the widely recognized and clinically established Child-Pugh scoring method as the basis for grouping. This study revealed that the ECV values for the normal control group, Child-Pugh A group, Child-Pugh B group, and Child-Pugh C group were 25.49 ± 3.15, 29.73 ± 3.20, 35.64 ± 3.15, and 45.30 ± 5.16 respectively. Notably, there was a statistically significant difference across all groups (F = 184.67, P < 0.001), which aligns closely with the findings of Narine Mesropyan [ 19 ] who examined the correlation between magnetic resonance ECV values and the severity of liver cirrhosis. Furthermore, Wang Yao [ 20 ] and her team observed a significant positive correlation between liver ECV values measured using spectral CT and liver function classification (r = 0.85, P < 0.05). Similarly, our study demonstrated a significant positive correlation between ECV and liver function Child-Pugh classification based on spectral CT measurements (r = 0.791, P < 0.001). This suggests that as the severity of liver cirrhosis progresses, there is a continuous increase in the synthesis and deposition of extracellular matrix proteins, leading to the expansion of the extracellular space and consequently, elevated ECV values. Multivariate analyses showed that ECV was the factor independently associated with cirrhosis(OR = 1.610, P < 0.001), and the analysis of the ROC showed ECV value for diagnosing the control group and cirrhosis group was 0.911 (95% CI 0.858–0.949), suggesting that ECV can be used as a biomarker for early identification of cirrhosis. Our study revealed that the area under the curve (AUC) for ECV in discriminating between Child-Pugh A and B, as well as Child-Pugh B and C, exceeded that of MELD-Na. Notably, CT-based ECV measurement exhibited superior diagnostic efficacy compared to MELD-Na derived from laboratory tests. This superiority can be attributed to several factors. Firstly, MELD-Na incorporates diverse laboratory markers in assessing liver function, which may be influenced by lesions outside the liver and comorbidities unrelated to liver disease, thereby compromising their specificity. Secondly, utilized in calculating ECV are directly sourced from the liver parenchyma and normalized with respect to hematocrit, potentially leading to more pure and accurate predictions. This study exhibits certain limitations. Firstly, being retrospective, inevitably suffers from selection bias. Additionally, the relatively small sample size, particularly the limited number of cases in the Child-Pugh C group, introduces sampling bias and compromises the depth and precision of the research. Secondly, due to practical constraints, we opted to inject the contrast agent 180 seconds after the delay period. However, further investigation is warranted to determine whether a longer delay period would yield different results in ECV value measurement. Lastly, while this study encompassed various types of cirrhosis stemming from diverse etiologies, the number of cases attributed to etiologies other than hepatitis B and hepatitis C was insufficient. Consequently, no further exploration was conducted to assess potential differences in ECV values among cirrhosis cases with different etiologies. Future studies should aim to expand the sample size, particularly for Child-Pugh C cases and non-hepatitis cirrhosis cases. Conclusion In this study, the measurement of ECV values utilizing spectral CT iodine concentration maps revealed significant inter-group disparities between the control group and various Child-Pugh cirrhosis groups. Notably, a strong positive correlation was observed between ECV values and Child-Pugh scores. Furthermore, in the ROC diagnostic performance evaluation, the ECV measurement demonstrated unique advantages. In conclusion, ECV values emerge as promising non-invasive biomarkers for grading the severity of cirrhosis, deserving further clinical exploration and promotion. Declarations Author Contribution All authors had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Software,E.H. and J.W.; Investigation, T.L.; Formal Analysis, J.W. and E.H.; Resources, X.W. and M.M.; Data curation, M.G; Writing - Original Draft, H.Z and D.X; Writing - Review & Editing, X.W.; Funding Acquisition, H.Z and X.W. References Ginès P, Krag A, Abraldes JG, et al. Liver cirrhosis. Lancet. 2021;398(10308):1359-1376. Asrani SK, Devarbhavi H, Eaton J, Kamath PS. Burden of liver diseases in the world. J Hepatol. 2019;70(1):151-171. 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Nagayama Y, Kato Y, Inoue T, et al. Liver fibrosis assessment with multiphasic dual-energy CT: diagnostic performance of iodine uptake parameters. Eur Radiol. 2021 Nov;31(11):8823-8824. Xu H, Li YG, Mu JK, et al. Research on the evaluation of the liver function grading for the patients with hepatitis B cirrhosis using T1 mapping based extracellular volume fraction. Chin J Magn Reson Imaging. 2023;14(05):132-138. Mesropyan N, Kupczyk PA, Dold L, et al. Assessment of liver cirrhosis severity with extracellular volume fraction MRI. Sci Rep. 2022;12(1):9422. Xu Y, Li Y, Li S, et al. Dual-energy CT quantification of extracellular liver volume predicts short-term disease progression in patients with hepatitis B liver cirrhosis-acute decompensation. Insights Imaging. 2023;14(1):51. Mesropyan N, Kupczyk PA, Dold L, et al. Assessment of liver cirrhosis severity with extracellular volume fraction MRI. Sci Rep. 2022;12(1):9422. Wang Y, Ma CF, Wang GH, et al. The application value of spectral CT in liver function classification of liver cirrhosis. J Chin Clin Med Imaging. 2022,33(11):779-783. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5847341","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":405714501,"identity":"13461cf2-12c7-41e0-abc9-7d6cef7c2c06","order_by":0,"name":"Hong Zhang","email":"","orcid":"","institution":"Affiliated Xi’an Central Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Zhang","suffix":""},{"id":405714502,"identity":"10cb0f4d-4a70-45d7-b99a-75abf87c15f8","order_by":1,"name":"Ee Hao","email":"","orcid":"","institution":"The Air Force 986 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ee","middleName":"","lastName":"Hao","suffix":""},{"id":405714503,"identity":"83c5035f-ab82-48bd-8fb0-863aadffa0b2","order_by":2,"name":"Dongqin Xia","email":"","orcid":"","institution":"Affiliated Xi’an Central Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Dongqin","middleName":"","lastName":"Xia","suffix":""},{"id":405714504,"identity":"a06e20df-8e9c-4da9-9451-adde7cd552b6","order_by":3,"name":"Mingyue Ma","email":"","orcid":"","institution":"Affiliated Xi’an Central Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Mingyue","middleName":"","lastName":"Ma","suffix":""},{"id":405714505,"identity":"507955b7-e5db-4b13-bd06-f023452702b1","order_by":4,"name":"Jiayu Wu","email":"","orcid":"","institution":"Affiliated Xi’an Central Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Jiayu","middleName":"","lastName":"Wu","suffix":""},{"id":405714506,"identity":"21fb0026-918f-4c1d-954d-c40cff7e8f04","order_by":5,"name":"Tongchi Liu","email":"","orcid":"","institution":"Affiliated Xi’an Central Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Tongchi","middleName":"","lastName":"Liu","suffix":""},{"id":405714507,"identity":"89a33443-3d92-4665-8c34-74fe1d3d8c4f","order_by":6,"name":"Ming Gao","email":"","orcid":"","institution":"Affiliated Xi’an Central Hospital of Xi’an Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Gao","suffix":""},{"id":405714508,"identity":"fd1cd145-405e-4431-8b8c-9592f52c64fe","order_by":7,"name":"Xiaoping Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYJCCAwwM/3j42RsbH34gQcsBGcmew83GEqRYZGNwI71NgIcYtfL9Zw8eLvh1h0dy5sM2BgkGOzndBgJaDA6cSzg8s+8ZD790YtuDAoZkY7MDhLQw9hgc5u1h5pGcndhuIMFwIHEbIS3yzTwQLQY3D7ZJ8BCjheEYUAvPj8M8BjcYidRicIYv4TBvQxqPZE8iMJANiPALMMQOf+b5Y2PPz3784cMPFXZyBLUwMADjgrENbilB5VAtDH+IUjkKRsEoGAUjFQAABL1FotRpxqEAAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Xi’an Central Hospital of Xi’an Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoping","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-01-17 08:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5847341/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5847341/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74680194,"identity":"5982dd69-d8f2-49f6-bd7b-cbe0c64187e5","added_by":"auto","created_at":"2025-01-24 15:42:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":804276,"visible":true,"origin":"","legend":"\u003cp\u003eECV measurement based on Multiphase Analysis software in Philips IntelliSpace Portal V12.0 workstation. (a)ROIs drawn in the SII,SIV,SVII and SVIII;(b)ROIs drawn in the SI,SIII,SV and SVI.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5847341/v1/b83bde79d9567b5b7d994691.png"},{"id":74680193,"identity":"fe2bcdcb-bdca-45cf-bfb1-987bd4e65b37","added_by":"auto","created_at":"2025-01-24 15:42:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":75971,"visible":true,"origin":"","legend":"\u003cp\u003eOne-way ANOVA test of extracellular volume fraction in the control group and cirrhosis groups\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5847341/v1/bdbfd498216fd7c5dcdd30a6.png"},{"id":74680731,"identity":"719ab2ea-fe8d-4eeb-a57f-33e084a7615e","added_by":"auto","created_at":"2025-01-24 15:50:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70220,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations of ECV with Child-Pugh score in liver cirrhosis\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5847341/v1/6862fef16d900f38bc147cb1.png"},{"id":74680734,"identity":"9ddc0c48-62fc-4601-8d82-9526079a6d07","added_by":"auto","created_at":"2025-01-24 15:50:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":312995,"visible":true,"origin":"","legend":"\u003cp\u003eGraphs show receiver operating characteristic curves of ECV as well as clinical scores of liver disease severity for differentiation between Controls and cirrhosis(a), Child-Pugh A and B classes (b), and Child-Pugh B and C classes (c).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5847341/v1/fa8350d08c962261e305d5d8.png"},{"id":75338445,"identity":"547b46e8-ce80-4ba6-98d9-7a783d9480bd","added_by":"auto","created_at":"2025-02-03 13:53:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1902584,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5847341/v1/8a57d2e7-aba5-43ce-aff4-8691edd8be68.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimating liver cirrhosis severity with extracellular volume fraction by spectral CT","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, the causes and incidence rates of chronic liver diseases vary. However, as the disease progresses, it leads to liver fibrosis, which gradually evolves into cirrhosis and ultimately hepatocellular carcinoma. In recent years, the incidence rate of cirrhosis has been increasing, posing a serious burden on society and patients [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Cirrhosis is characterized by severe scarring, structural distortion, and functional failure of liver tissue due to collagen deposition, accompanied by life-threatening complications such as portal hypertension, variceal bleeding, peritonitis, ascites, and hepatic encephalopathy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, cirrhosis is currently the 11th most common cause of death worldwide [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and the fourth most common cause of death among adults in Central Europe [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Accurate assessment of the severity of cirrhosis is of great significance for patient treatment, prognosis, and overall mortality risk assessment [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, various scoring methods for evaluating liver fibrosis and cirrhosis have been developed clinically based on laboratory tests [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the most widely accepted and effective scoring system in clinical practice is still the Child-Pugh score [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although imagological examination also plays an important role in the prognosis assessment of cirrhosis patients, most of them are based on morphological studies. Spectral CT, as a quantitative imaging tool, is applied to liver lesions because it can achieve material decomposition, allowing for quantitative measurement of the iodine concentration value of substances, and indirectly calculating the extracellular volume. Cirrhosis patients have increased collagen deposition in the liver due to long-term chronic liver disease, in which expansion in the extracellular space results in an increase in ECV. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, based on this pathological basis, it is possible to assess cirrhosis by quantitatively calculating the ECV value using spectral CT. Based on this background, this study explores the diagnostic efficacy of measuring the ECV of liver cells using spectral CT in the assessment of cirrhosis severity.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003ePatients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a retrospective study, approved by the hospital ethics management committee, and exempt from obtaining informed consent from enrolled patients. We selected 127 patients who underwent spectral CT abdominal contrast-enhanced scans in our department from October 2023 to January 2025 and were clinically and pathologically diagnosed with cirrhosis. At the same time, 45 normal controls matched in gender and age were selected. General information and laboratory test results of all enrolled cases were collected, including hematocrit, platelet, prothrombin time, bilirubin, albumin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and serum sodium concentration. The control group of cirrhotic patients was divided into three groups based on Child-Pugh severity of cirrhosis: Child-Pugh A, Child-Pugh B, and Child-Pugh C.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCase inclusion criteria: 1. Clinically and pathologically diagnosed with cirrhosis; 2. Possess complete general information, imaging, and laboratory examination data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCase exclusion criteria: 1. Presence of liver neoplastic lesions; 2. Previous history of other malignant tumors; 3. Recent history of blood transfusion or liver trauma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCT examination\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA triphasic contrast-enhanced scan of the liver was conducted using Philips 256-slice spectral CT, employing an automatic tracking method for blood flow detection. The diaphragm-level abdominal aorta was set as the detection plane, with a trigger threshold of 150 HU. Images were acquired at 8 seconds, 40 seconds, and 180 seconds after reaching the threshold, representing the hepatic arterial phase, portal venous phase, and delayed phase, respectively. The contrast agent used was iopromide (370 mg/ml), with a general dose of 70-90 ml (1.2 ml/kg) and an injection rate of 3 ml/s. The scanning parameters were as follows: tube voltage,120 kV; tube current,137 mAs, helical pitch, 0.9, slice thickness,1.5 mm; slice interval,1.5 mm; FOV,320 mm. The spectral image data (spectral-based imaging, SBI) obtained after scanning was uploaded to a workstation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImage post-processing and data measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSBI data was processed by a Philips IntelliSpace Portal V12.0 workstation. We selected the liver enhancement delay 180s image and utilized the built-in Multiphase Analysis software within the workstation to calculate ECV. After calibration, we avoided the liver vessels and calcifications, and on the liver\u0026apos;s largest volume plane, we selected the caudate lobe(SI), upper segment of left lateral lobe(SII), lower segment of left lateral lobe(SIII), left medial lobe(SIV), lower segment of right anterior lobe(SV),lower segment of right posterior lobe(SVI), upper segment of right posterior lobe(SVII) and upper segment of right anterior lobe(SVIII) to draw regions of interest(ROIs) with an area of approximately 100mm\u003csup\u003e2\u003c/sup\u003e for measurement. The average ECV of the eight liver segments was taken as the liver\u0026apos;s ECV value, as shown in Figure 1. The measurements were performed by two senior attending physicians, and the average value was taken as the final measurement result.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were entered bilaterally into Excel software, and processed using Prism 5.0 (GraphPad Software) and SPSS Statistics (Version 26, IBM). Measurement data were presented as mean\u0026plusmn;standard deviation, while count data were expressed as percentages (%). All data underwent normality and homogeneity of variance tests. If the data followed a normal distribution, t-tests were used for comparisons between two groups, and one-way ANOVA was applied for comparisons among multiple groups. If the data did not follow a normal distribution, the rank sum test was used. Correlation between data was analyzed using Pearson or Spearman correlation, and ROC curve analysis was used to predict the highest sensitivity and specificity and determine the optimal cutoff value. Logistic regression was used to determine independent factors for the diagnosis of cirrhosis, A p-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCohort characteristics and laboratory test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were 58 cases in the Child-Pugh A group (36 males and 22 females), 47 cases in the Child-Pugh B group (29 males and 18 females), 22 cases in the Child-Pugh C group (14 males and 8 females), and 35 cases in the control group (19 males and 16 females).In the cirrhosis group, there were 65 cases of hepatitis B-related liver disease, 25 cases of hepatitis C-related liver disease, 2 cases of alcoholic liver disease, 6 cases of autoimmune liver disease, 1 case of Budd-Chiari syndrome, and 28 cases of cirrhosis of unknown etiology. PT, INR, bilirubin, AST, ALT, albumin, platelet, albumin-bilirubin (ALBI), and modified end-stage liver disease with the incorporation of sodium (MELD-Na) exhibited statistical differences across all groups (P \u0026lt; 0.001). Detailed data regarding age, gender, laboratory tests, and CT-measured ECV values for different subgroups are outlined in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Clinical, laboratory, and CT parameters\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls\u003c/strong\u003e\u003cbr\u003e(n = 45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild-Pugh A\u003cbr\u003e\u0026nbsp; (n = 58)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild-Pugh B\u0026nbsp;\u003cbr\u003e\u0026nbsp;(n = 47)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild-Pugh C\u0026nbsp;\u003cbr\u003e\u0026nbsp;(n = 22)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 18px;\"\u003e\n \u003cp\u003e57.27 (11.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e58.17 (11.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e59.38 (12.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e57.45 (9.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;Female, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e14 (31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e22 (37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e18 (38.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e8 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;Male, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e31 (68.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e36 (62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e29 (61.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e14 (63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiver disease type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003eHepatitis B virus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e30 (51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e21 (44.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e14 (63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003eHepatitis C virus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e12 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e7 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e6 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003eAutoimmune hepatitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e2 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e3 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003eBudd-Chiari syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003eAlcoholic hepatitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e13 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e14 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eHematocrit value (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e40.84 (4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e36.64 (7.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e32.77 (7.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e30.31 (7.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003ePT (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e10.38 (1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e12.06 (1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e14.00 (1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e17.42 (3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.88 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1.02 (0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e1.17 (0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e1.48 (0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eBilirubin (umol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e13.38 (5.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e22.75 (11.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e31.48 (16.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e89.37 (106.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eAlbumin (g/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e42.82 (3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e39.64 (4.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e33.79 (5.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e28.58 (3.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eAST (U/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e20.02 (5.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e40.97 (51.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e45.57 (36.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e134.82 (211.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eALT (U/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e17.87 (8.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e30.29 (23.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e33.77 (30.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e98.59 (149.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eNa (mmol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e140.20 (1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e139.74 (2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e138.06 (3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e137.55 (5.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003ePlatelet\u0026nbsp;(109/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e216.91 (59.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e106.22 (54.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e99.37 (74.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e109.14 (91.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eCreatinine (umol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e62.00 (13.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e58.71 (13.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e64.14 (23.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e73.83 (20.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eChild-pugh score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e5.00 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e5.55 (0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e7.62 (0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e11.32 (1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eMELD_Na score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-2.90 (0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-2.51 (0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-1.92 (0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-1.25 (0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eALBI score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e-2.94 (0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-2.51 (0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e-1.86 (0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e-1.08 (0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCT parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eECV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e25.49 (3.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e29.73 (3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e35.64 (3.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e45.30 (5.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eContinuous variables are expressed as mean \u0026plusmn; standard deviation. Categorical variables are expressed as numbers and percentages in parentheses. ECV, extracellular volume fraction; ALBI, albumin-bilirubin; MELD_Na, modified end-stage liver disease with the incorporation of sodium; PT, prothrombin time; INR, international normalized ratio; AST, aspartate aminotransferase; ALT, alanine aminotransferase.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of ECV values among Child-Pugh groups of liver cirrhosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ECV value of the control group was 25.49 \u0026plusmn;3.15, whereas the ECV values for the Child-Pugh A, B, and C groups of cirrhosis were 29.73\u0026plusmn;3.20,35.64\u0026plusmn;3.15 and 45.30\u0026plusmn;5.16, respectively. One-way ANOVA analysis indicated statistically significant differences in ECV among the groups (F=184.67, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), Figure 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation between ECV value and Child-Pugh classification of liver cirrhosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA correlation analysis was conducted between the ECV value with the Child-Pugh score. The ECV value demonstrated a significant positive correlation with the Child-Pugh score for cirrhosis (r=0.791, p\u0026lt;0.001). The results indicated that as the severity of cirrhosis increased, the ECV value tended to rise (Fig 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic efficacy of ECV value in assessing the severity of liver cirrhosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe receiver operating characteristic (ROC) curve shows the area under the curve (AUC) of the ECV value for diagnosing the control group and cirrhosis group was 0.911 (95% CI 0.858-0.949). Using a cutoff value of 29.46, the best discrimination between non-cirrhosis and cirrhosis was achieved, with sensitivity and specificity of 76.38% and 95.56%, respectively. The AUC of the ECV value for diagnosing Child-Pugh A and Child-Pugh B stages of cirrhosis was 0.901, which was significantly higher than that of MELD-Na (Z=2.441, p=0.015); The AUC of the ECV value for diagnosing Child-Pugh B and Child-Pugh C stages of cirrhosis was 0.966 (95% CI 0.892 - 0.995), which was significantly higher than that of MELD-Na (Z=2.042, p=0.041). (Fig 4, Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Diagnostic efficacy of ECV, ALBI, and MELD-Na for controls and subclassified cirrhosis groups\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"688\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003eCutoff value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003eSensitivity（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003eSpecificity（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003ePPV（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003eNPV（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003eYouden\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 98px;\"\u003e\n \u003cp\u003eControl vs. cirrhosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eECV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.911 (0.858 to 0.949)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e29.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e76.38 (68.0 - 83.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e95.56 (84.9 - 99.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e98.0 (92.9 - 99.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e58.9 (46.8 - 70.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eALBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.878 (0.819 to 0.923)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e80.31 (72.3 - 86.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e88.89 (75.9 - 96.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e95.3 (89.4 - 98.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e61.5 (48.6 - 73.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eMELD_Na\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.868 (0.810 to 0.911)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e88.98 (82.2 - 93.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e71.11 (55.7 - 83.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e89.7 (83.0 - 94.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e69.6 (54.2 - 82.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"bottom\" style=\"width: 688px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 98px;\"\u003e\n \u003cp\u003eChild-Pugh A vs. B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eECV\u003cstrong\u003e#\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.901 (0.827 to 0.951)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e33.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e78.72 (64.3 - 89.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e87.93 (76.7 - 95.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e84.1 (69.9 - 93.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e83.6 (71.9 - 91.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eALBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.847 (0.764 to 0.910)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e57.45 (42.2 - 71.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e98.28 (90.8 - 100.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e96.4 (81.7 - 99.9) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e74.0 (62.8 - 83.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eMELD_Na\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.772 (0.680 to 0.848)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e78.72 (64.3 - 89.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e62.07 (48.4 - 74.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e62.7 (49.1 - 75.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e78.3 (63.6 - 89.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"bottom\" style=\"width: 688px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 98px;\"\u003e\n \u003cp\u003eChild-Pugh B vs. C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eECV#\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.966 (0.892 to 0.995)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e39.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e90.91 (70.8 - 98.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e95.74 (85.5 - 99.5) \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e90.9 (70.8 - 98.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e95.7 (85.5 - 99.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eALBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.906 (0.811 to 0.963)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e81.82 (59.7 - 94.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e89.36 (76.9 - 96.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e78.3 (56.3 - 92.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e91.3 (79.2 - 97.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003eMELD_Na\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.868 (0.764 to 0.937)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 84px;\"\u003e\n \u003cp\u003e86.36 (65.1 - 97.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 87px;\"\u003e\n \u003cp\u003e70.21 (55.1 - 82.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e57.6 (39.2 - 74.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e91.7 (77.5 - 98.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e# represents the diagnostic efficacy is statistically significant (\u003cem\u003ep\u003c/em\u003e＜0.05). ECV, extracellular volume fraction; ALBI, albumin-bilirubin; MELD-Na, model of end-stage liver disease with the incorporation of sodium.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of risk factors for liver cirrhosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 3, multivariate logistic regression analysis showed that ECV was independently associated with liver cirrhosis (OR: 1.610, CI: 1.219-2.125, P\u0026lt;0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. Logistic regression analysis of liver cirrhosis patients versus controls.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eECV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e1.219-2.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.164-2.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.958-1.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eprothrombin time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.961-2.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eBilirubin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.943-1.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.832-1.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e1.021-1.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.911-1.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.918-1.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003eMELD-Na\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e1.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27px;\"\u003e\n \u003cp\u003e0.655-2.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe extracellular volume fraction (ECV) is the sum of the fractions of the extravascular-extracellular space and intravascular space, reflecting the microvascular density and the degree of matrix fibrosis. Based on this principle, ECV has initially been extensively utilized in the assessment of myocardial fibrosis, particularly in evaluating myocardial fibrosis and myocardial lesions through magnetic resonance T1 mapping [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Apart from its application in the assessment of myocardial fibrosis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], CT-based ECV measurement has also been employed for distinguishing between benign and malignant tumors, assessing tumor staging, evaluating treatment response, and predicting prognosis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePatients with liver fibrosis and cirrhosis exhibit an increase in collagen deposition within the liver, subsequently enlarging the extracellular space. Given this pathological basis, the degree of liver fibrosis can be indirectly reflected through the quantitative measurement of liver ECV values [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Methods for measuring ECV values via CT include ECV-ΔHU, which relies on CT values, and ECV-Iodine, which utilizes iodine density maps. In the context of liver ECV measurement among patients with liver fibrosis, Yoon [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] conducted a Spectral CT study and discovered that ECV-Iodine demonstrates superior diagnostic efficacy for liver fibrosis (F\u0026thinsp;\u0026ge;\u0026thinsp;2) compared to ECV-HU. Similarly, Nagayama Y [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] employed dual-energy CT and observed that the correlation coefficient of ECV-Iodine is higher than that of ECV-ΔHU within the liver fibrosis group, indicating a higher degree of reliability and credibility for ECV-Iodine in assessing liver fibrosis. Consequently, this study employs Spectral CT to calculate liver ECV values based on iodine density maps. Currently, the majority of domestic and international literature primarily focuses on evaluating the degree of liver fibrosis in the application of ECT, with limited reports on cirrhosis research. However, since the liver fibrosis stage has already progressed in cirrhosis patients, merely assessing the fibrosis stage appears insufficient to draw conclusive insights into the severity of liver function. Recently, scholars have demonstrated that the measurement of ECV values utilizing T1 mapping technology can effectively evaluate the grading of liver function in cirrhosis patients [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Xu Y [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] further discovered through dual-energy CT that ECV values hold predictive potential for short-term disease progression in patients with acute decompensated hepatitis B cirrhosis. However, to date, there have been no reported studies on the assessment of cirrhosis severity staging through the measurement of ECV values using the latest generation of Spectral CT. In this study, we aim to explore the feasibility of assessing the severity of cirrhosis by measuring liver ECV values based on Spectral CT. To achieve this, we have included patients with a confirmed diagnosis of cirrhosis as our research subjects and utilized the widely recognized and clinically established Child-Pugh scoring method as the basis for grouping.\u003c/p\u003e \u003cp\u003eThis study revealed that the ECV values for the normal control group, Child-Pugh A group, Child-Pugh B group, and Child-Pugh C group were 25.49\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15, 29.73\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20, 35.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15, and 45.30\u0026thinsp;\u0026plusmn;\u0026thinsp;5.16 respectively. Notably, there was a statistically significant difference across all groups (F\u0026thinsp;=\u0026thinsp;184.67, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which aligns closely with the findings of Narine Mesropyan [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] who examined the correlation between magnetic resonance ECV values and the severity of liver cirrhosis. Furthermore, Wang Yao [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and her team observed a significant positive correlation between liver ECV values measured using spectral CT and liver function classification (r\u0026thinsp;=\u0026thinsp;0.85, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, our study demonstrated a significant positive correlation between ECV and liver function Child-Pugh classification based on spectral CT measurements (r\u0026thinsp;=\u0026thinsp;0.791, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This suggests that as the severity of liver cirrhosis progresses, there is a continuous increase in the synthesis and deposition of extracellular matrix proteins, leading to the expansion of the extracellular space and consequently, elevated ECV values. Multivariate analyses showed that ECV was the factor independently associated with cirrhosis(OR\u0026thinsp;=\u0026thinsp;1.610, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the analysis of the ROC showed ECV value for diagnosing the control group and cirrhosis group was 0.911 (95% CI 0.858\u0026ndash;0.949), suggesting that ECV can be used as a biomarker for early identification of cirrhosis. Our study revealed that the area under the curve (AUC) for ECV in discriminating between Child-Pugh A and B, as well as Child-Pugh B and C, exceeded that of MELD-Na. Notably, CT-based ECV measurement exhibited superior diagnostic efficacy compared to MELD-Na derived from laboratory tests. This superiority can be attributed to several factors. Firstly, MELD-Na incorporates diverse laboratory markers in assessing liver function, which may be influenced by lesions outside the liver and comorbidities unrelated to liver disease, thereby compromising their specificity. Secondly, utilized in calculating ECV are directly sourced from the liver parenchyma and normalized with respect to hematocrit, potentially leading to more pure and accurate predictions.\u003c/p\u003e \u003cp\u003eThis study exhibits certain limitations. Firstly, being retrospective, inevitably suffers from selection bias. Additionally, the relatively small sample size, particularly the limited number of cases in the Child-Pugh C group, introduces sampling bias and compromises the depth and precision of the research. Secondly, due to practical constraints, we opted to inject the contrast agent 180 seconds after the delay period. However, further investigation is warranted to determine whether a longer delay period would yield different results in ECV value measurement. Lastly, while this study encompassed various types of cirrhosis stemming from diverse etiologies, the number of cases attributed to etiologies other than hepatitis B and hepatitis C was insufficient. Consequently, no further exploration was conducted to assess potential differences in ECV values among cirrhosis cases with different etiologies. Future studies should aim to expand the sample size, particularly for Child-Pugh C cases and non-hepatitis cirrhosis cases.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, the measurement of ECV values utilizing spectral CT iodine concentration maps revealed significant inter-group disparities between the control group and various Child-Pugh cirrhosis groups. Notably, a strong positive correlation was observed between ECV values and Child-Pugh scores. Furthermore, in the ROC diagnostic performance evaluation, the ECV measurement demonstrated unique advantages. In conclusion, ECV values emerge as promising non-invasive biomarkers for grading the severity of cirrhosis, deserving further clinical exploration and promotion.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Software,E.H. and J.W.; Investigation, T.L.; Formal Analysis, J.W. and E.H.; Resources, X.W. and M.M.; Data curation, M.G; Writing - Original Draft, H.Z and D.X; Writing - Review \u0026amp; Editing, X.W.; Funding Acquisition, H.Z and X.W.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGin\u0026egrave;s P, Krag A, Abraldes JG, et al. Liver cirrhosis. Lancet. 2021;398(10308):1359-1376. \u003c/li\u003e\n\u003cli\u003eAsrani SK, Devarbhavi H, Eaton J, Kamath PS. Burden of liver diseases in the world. J Hepatol. 2019;70(1):151-171.\u003c/li\u003e\n\u003cli\u003eChinese Society of Gastroenterology, Chinese Medical Association. Chinese consensus on the management of liver cirrhosis. J Dig Dis. 2024;25(6):332-352.\u003c/li\u003e\n\u003cli\u003eLlovet JM, Zucman-Rossi J, Pikarsky E, et al. Hepatocellular carcinoma. Nat Rev Dis Primers. 2016;2:16018.\u003c/li\u003e\n\u003cli\u003eD\u0026apos;Amico G, Morabito A, D\u0026apos;Amico M, et al. Clinical states of cirrhosis and competing risks. J Hepatol. 2018;68(3):563-576.\u003c/li\u003e\n\u003cli\u003eQi X, Arora A, Tang S, Mancuso A, Romeiro FG. Prognostic Assessment and Management of Liver Cirrhosis. Biomed Res Int. 2017;2017:5326898.\u003c/li\u003e\n\u003cli\u003eSmith A, Baumgartner K, Bositis C. Cirrhosis: Diagnosis and Management. Am Fam Physician. 2019;100(12):759-770.\u003c/li\u003e\n\u003cli\u003eEl Hajra I, Sanduzzi-Zamparelli M, Sapena V, et al. Outcome of patients with HCC and liver dysfunction under immunotherapy: a systematic review and meta-analysis. Hepatology. 2023;77(4):1139-1149.\u003c/li\u003e\n\u003cli\u003eWiese S, Voiosu A, Hove JD, et al. Fibrogenesis and inflammation contribute to the pathogenesis of cirrhotic cardiomyopathy. Aliment Pharmacol Ther. 2020;52(2):340-350.\u003c/li\u003e\n\u003cli\u003eKitkungvan D, Yang EY, El Tallawi KC, et al. Extracellular Volume in Primary Mitral Regurgitation. JACC Cardiovasc Imaging. 2021;14(6):1146-1160.\u003c/li\u003e\n\u003cli\u003ePorcari A, Masi A, Martinez-Naharro A, et al. Redefining Cardiac Involvement and Targets of Treatment in Systemic Immunoglobulin AL Amyloidosis. JAMA Cardiol. 2024 Aug 21:e242555.\u003c/li\u003e\n\u003cli\u003eKato S, Misumi Y, Horita N, et al. Clinical Utility of Computed Tomography-Derived Myocardial Extracellular Volume Fraction: A Systematic Review and Meta-Analysis. JACC Cardiovasc Imaging. 2024;17(5):516-528. \u003c/li\u003e\n\u003cli\u003eFujita N, Ushijima Y, Itoyama M, et al. Extracellular volume fraction determined by dual-layer spectral detector CT: Possible role in predicting the efficacy of preoperative neoadjuvant chemotherapy in pancreatic ductal adenocarcinoma. Eur J Radiol. 2023;162:110756.\u003c/li\u003e\n\u003cli\u003eYoon JH, Lee JM, Kim JH, et al. Hepatic fibrosis grading with extracellular volume fraction from iodine mapping in spectral liver CT. Eur J Radiol. 2021;137:109604. \u003c/li\u003e\n\u003cli\u003eNagayama Y, Kato Y, Inoue T, et al. Liver fibrosis assessment with multiphasic dual-energy CT: diagnostic performance of iodine uptake parameters. Eur Radiol. 2021 Nov;31(11):8823-8824.\u003c/li\u003e\n\u003cli\u003eXu H, Li YG, Mu JK, et al. Research on the evaluation of the liver function grading for the patients with hepatitis B cirrhosis using T1 mapping based extracellular volume fraction. Chin J Magn Reson Imaging. 2023;14(05):132-138.\u003c/li\u003e\n\u003cli\u003eMesropyan N, Kupczyk PA, Dold L, et al. Assessment of liver cirrhosis severity with extracellular volume fraction MRI. Sci Rep. 2022;12(1):9422.\u003c/li\u003e\n\u003cli\u003eXu Y, Li Y, Li S, et al. Dual-energy CT quantification of extracellular liver volume predicts short-term disease progression in patients with hepatitis B liver cirrhosis-acute decompensation. Insights Imaging. 2023;14(1):51.\u003c/li\u003e\n\u003cli\u003eMesropyan N, Kupczyk PA, Dold L, et al. Assessment of liver cirrhosis severity with extracellular volume fraction MRI. Sci Rep. 2022;12(1):9422.\u003c/li\u003e\n\u003cli\u003eWang Y, Ma CF, Wang GH, et al. The application value of spectral CT in liver function classification of liver cirrhosis. J Chin Clin Med Imaging. 2022,33(11):779-783.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Spectral CT, Extracellular Volume, Liver Cirrhosis, Classification, Evaluation","lastPublishedDoi":"10.21203/rs.3.rs-5847341/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5847341/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose \u003c/strong\u003eTo investigate the diagnostic value of spectral CT in calculating extracellular volume fraction (ECV) for assessing the severity of liver cirrhosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eIn this retrospective study, 172 patients (127 liver cirrhosis patients and 45 controls),who underwent spectral CT liver enhancement scans, and were categorized based on the Child-Pugh classification. During the delayed phase, ECV values were derived from iodine density map. These ECV values were then compared across the control group and subclassified cirrhosis groups (Child-Pugh classes A, B, and C). Furthermore, a correlation analysis was performed to assess the relationship between ECV values and Child-Pugh scores in liver cirrhosis. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic performance of ECV values and MELD-Na in the Child-Pugh classification of liver cirrhosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eThe ECV values were 25.49±3.15, 29.73±3.20, 35.64±3.15, and 45.30±5.16 for the control, Child-Pugh A, Child-Pugh B, and Child-Pugh C group, respectively, demonstrating significant intergroup differences (F=184.67 P\u0026lt;0.001). A strong positive correlation was observed between ECV and Child-Pugh liver function classification (r=0.791, P\u0026lt;0.001). The diagnostic performance of ECV for differentiating between Child-Pugh classes A and B (AUC: 0.901), B and C (AUC: 0.966) was higher compared to the MELD-Na score (AUC: 0.772 and 0.868) (P\u0026lt;0.05, respectively). Multivariate analyses showed that ECV was the factor independently associated with cirrhosis (OR=1.610, P\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eECV values measured using spectral CT can serve as a noninvasive biomarker for assessing the severity of liver cirrhosis.\u003c/p\u003e","manuscriptTitle":"Estimating liver cirrhosis severity with extracellular volume fraction by spectral CT","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-24 15:42:38","doi":"10.21203/rs.3.rs-5847341/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4b719152-2726-4689-bea5-fb5325717f89","owner":[],"postedDate":"January 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T13:53:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-24 15:42:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5847341","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5847341","identity":"rs-5847341","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}