Prognostic Nomograms Based on Microvascular Invasion Grade for Early-stage Hepatocellular Carcinoma Patients After Curative Hepatectomy

Prognostic Nomograms Based on Microvascular Invasion Grade for Early-stage Hepatocellular Carcinoma Patients After Curative Hepatectomy | 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 Prognostic Nomograms Based on Microvascular Invasion Grade for Early-stage Hepatocellular Carcinoma Patients After Curative Hepatectomy Jingpeng Ke, Honghao Ye, Fangzhou Lin, Yingjun Shi, Aoxue Zhong, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1276658/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Background: Hepatocellular carcinoma (HCC) is among the most frequent causes of cancer-related deaths worldwide. Although predictive models for postoperative early recurrence in patients with HCC have been established, this is the first study to develop and evaluate a predictive model based on the microvascular invasion classification for early recurrence and survival after curative hepatectomy in patients with early-stage HCC. Methods: The database of patients with early-stage HCC who underwent curative hepatectomy in the First Affiliated Hospital of Fujian Medical University and the First Affiliated Hospital of Xiamen University was retrospectively reviewed. Kaplan-Meier curves and Cox proportional hazards regression models were used to analyse disease-free survival (DFS) and overall survival (OS). Nomogram models were constructed on the datasets from the First Affiliated Hospital of Fujian Medical University, which were validated using bootstrap resampling with 30% samples as internal validation. Data of patients from the First Affiliated Hospital of Xiamen University were used for external validation. Results: A total of 703 patients with early-stage HCC were included in our study. An eight-factor nomogram for predicting recurrence or metastasis and a six-factor nomogram for predicting survival were created. The concordance indexes were 0.775 (95% confidence interval [CI], 0.720-0.830) for the DFS nomogram and 0.812 for the OS nomogram (95% CI, 0.732-0.892) in the training cohort; 0.865 (95% CI, 0.806-0.924) and 0.839 (95% CI, 0.675-1.00), respectively, in the internal validation cohort; and 0.857 (95% CI, 0.763-0.951) and 0.842 (95% CI, 0.708-0.970), respectively, in the external validation cohort. The calibration curves showed optimal agreement between the predicted and observed DFS and OS rates. The predictive accuracy was significantly better than that of the classic HCC staging systems. Conclusions: This study developed and validated nomograms for predicting recurrence, especially early recurrence, and overall survival in patients with early-stage HCC after curative resection with high predictive accuracy. nomogram microvascular invasion grade early-stage HCC Figures Figure 1 Figure 2 Figure 3 Background Hepatocellular carcinoma (HCC) is among the most frequent causes of cancer-related deaths worldwide [ 1 ] . Despite remarkable improvements in comprehensive HCC treatment, radical surgical resection and liver transplantation are considered the only curative treatments for patients with HCC classified as early-stage (stages 0 and A) according to the Barcelona Clinic Liver Cancer (BCLC) staging system. However, postoperative recurrence and metastasis rates of patients with early HCC vary from 50–70% [ 2 ] , resulting in poor overall survival (OS). Early recurrence after liver resection for HCC is the leading cause of death during the first 2 years [ 3 ] . Therefore, developing a model for predicting postoperative recurrence, especially early recurrence, for patients with early-stage HCC to guide risk stratification and treatment is urgently needed. Microvascular invasion (MVI), a mass of cancer cells in the vascular cavity with adhesion to endothelial cells, and only visible under a microscope [ 4 ] , has been reported by previous studies to be an indicator of early invasive manifestation of HCC. It is a crucial independent predictive factor for early recurrence and poor OS among patients with HCC who underwent hepatectomy or received liver transplantation. Most patients with BCLC early-stage HCC with early recurrence are pathologically verified as MVI positive [ 5 – 7 ] . Moreover, a previous study found that more invading tumor cells and multiple-invaded microvessels might be related to poor survival and recurrence rates [ 4 ] . These findings suggest that the BCLC staging system should reappraise HCC based on the presence or grade of MVI to distinguish the biological behavior of early-stage HCC. MVI is graded according to the number of cancer cells and the distance of MVI to the tumor according to the Standard for Diagnosis and Treatment of Primary Liver Cancer [ 8 ] . Although predictive models for postoperative early recurrence in patients with HCC have been established, a predictive model for patients with early BCLC stage HCC patients according to the MVI grade has not been reported. Therefore, we retrospectively investigated the clinical and histopathological characteristics of patients with early HCC after curative hepatectomy from multiple centres to establish a prognostic nomogram based on MVI grade to predict early recurrence and OS. Methods Patients and study design The database was retrospectively derived from patients with HCC who underwent hepatectomy at the First Affiliated Hospital of Fujian Medical University (FHFU) and the First Affiliated Hospital of Xiamen University (FHXU) from March 2015 to March 2020. The inclusion criteria for patients with HCC patients in this study were: (1) early-stage HCC (BCLC stage 0 or A) diagnosis that was confirmed by postoperative pathology; (2) Child-Pugh A or B liver function before surgery; (3) R0 surgical resection of tumor with curative intent; (4) all patients who survived for at least 30 days after surgery; (5) no preoperative anticancer treatments that could introduce any bias; and (6) clinicopathological data and follow-up information were available. Patients with the following criteria were excluded: (1) recurrent HCC, (2) combined hepatocellular cholangiocarcinoma, (3) previous history of malignancy, and (4) age < 18 years. Nomogram models were constructed on the datasets from the FHFU, which were also validated using bootstrap resampling as internal validation, and the dataset from the FHXU was used for external validation. This study was approved by the Clinical Research Ethics Committee of the two centres. Written informed consent was obtained from all subjects before the operation. All procedures were performed in accordance with the Declaration of Helsinki. Clinical variables Demographic, laboratory, and HCC pathological data were collected. The laboratory tests included various tests for routine blood parameters, full sets of tests for blood clotting, full sets of tests for blood biochemistry, and hepatitis virus markers. Imaging data included, but were not limited to, the number of tumours, presence of satellite nodules, diameter of the largest nodule, tumour capsule, and cirrhosis based on preoperative contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI). The diagnosis and classification of MVI was confirmed according to the Standard for Diagnosis and Treatment of Primary Liver Cancer [ 4 ] . Follow-up During the follow-up, serum alpha-fetoprotein (AFP) levels were measured, and ultrasonography, CT, or MRI of the chest and abdomen was done once every 2 months for the first 2 years after surgery. For patients who were free of cancer recurrence 2 years after surgery, a 6-month interval surveillance was performed. Disease-free survival (DFS) was defined as the duration from the first surgery to the first recurrence, metastasis, or death. OS was defined as the duration from the first surgery to death or the last follow-up. Statistical analysis Continuous variables are expressed as mean ± standard deviation. Chi-squared or Fisher’s exact tests were used to assess differences in categorical variables. The Wilcoxon rank-sum test was used to compare continuous variables between groups. The cut-off values were established using the X-tile software version 3.6.1 (Yale University School of Medicine, New Haven, Connecticut, United States). For DFS and OS curves during follow-up, Kaplan-Meier curves, log-rank Mantel-Cox test, and Cox proportional hazards regression analyses were used. Nomograms were generated using the rms package in R software version 3.5.2 (R Foundation for Statistical Computing, Vienna, Austria) [ 9 ] . The predictive accuracy and discriminative ability of the nomogram were assessed using concordance index (C-index) [ 7 ] and calibration curves. The larger the C-index, the more accurate the prognostic prediction is. A value of P < 0.05 was considered significant. Results Characteristics of patients in study and validation cohorts Overall, 703 patients (490 from FHFU used as training cohort, and 213 from FHXU used as external validation cohort) with BCLC early-stage HCC were included. The baseline characteristics of the two cohorts are shown in Table 1 . The average age of the entire cohort was 53.7 ± 10.9 years, with a male to female ratio of 4.72:1 (580/130). The average follow-up time for all patients was 18.8 ± 10.2 months. The 8-month, 1-, 2-, and 3-year recurrence or metastasis rates were 5.8%, 8.1%, 11.8%, and 12.7%, respectively. The 8-month, 1-, 2-, and 3-year survival rates were 1.7%, 2.4%, 3.9%, and 4.2%, respectively. Among them, M1 was observed in 173 cases (24.6%), while M2 was observed in 102 cases (14.5%). Between-group differences in sex, age, operative time, follow-up time, ASA scores, laboratory, and HCC pathological data were not significant (Table 1 ). The association of MVI grade relative to OS or DFS is shown in Fig. 1 . The Kaplan-Meier curves of OS and DFS showed that the M2 group had significantly poorer outcomes than the M1 and M0 groups (both P < 0.001). Table 1 The basic clinical characteristics of early-stage HCC patients Clinical parameter Total (n = 703) FHFU cohort (n = 490) FHXU cohort (n = 213) Sex, male/female 580/123 404/86 175/38 Age, year 53.7 ± 10.9 53.4 ± 11.1 52.8 ± 10.5 BCLC staging system, (0/A) 45/658 31/459 13/200 WBC, 10 9 /L 5.2 ± 1.6 5.0 ± 1.9 4.9 ± 1.5 PLT, 10 9 /L 160.3 ± 62.5 158.6 ± 58.7 161.6 ± 65.3 Hb, g/L 142.4 ± 14.9 139.6 ± 15.1 141.6 ± 15.5 Hematocrit, % 41.5 ± 3.9 41.3 ± 2.7 40.4 ± 3.3 MCV, fL 90.7 ± 4.8 92.2 ± 5.1 89.9 ± 5.0 MCH, pg 31.1 ± 2.0 31.3 ± 1.9 31.2 ± 1.8 Neutrophil, 10 9 /L 3.1 ± 1.2 3.0 ± 1.1 3.1 ± 1.2 Lymphocyte, 10 9 /L 1.6 ± 0.6 1.6 ± 0.6 1.6 ± 0.6 Monocyte, 10 9 /L 0.4 ± 0.1 0.4 ± 0.1 0.4 ± 0.1 NR, % 58.3 ± 9.3 59.4 ± 9.8 55.4 ± 8.9 LR, % 31.8 ± 8.5 32.3 ± 8.2 32.1 ± 08.6 MR, % 7.0 ± 2.0 7.0 ± 1.9 7.0 ± 2.1 RDW, % 13.2 ± 0.9 13.1 ± 0.8 13.1 ± 0.8 RBC, 10 9 /L 4.6 ± 0.6 4.6 ± 0.6 4.6 ± 0.5 AFP, µg/L 339.1 ± 484.8 319 ± 480 364.7 ± 491.4 ALT, U/L 35.1 ± 28.0 35.3 ± 27 36.4 ± 35.5 HBV DNA level, 10 6 IU/mL 283/420 197/293 86/127 Albumin, g/L 42.2 ± 3.2 42.5 ± 3.1 42.1 ± 3.1 Scr, µmol/L 72.1 ± 15.6 76.9 ± 17.1 77.4 ± 16.5 γ-GGT, U/L 78.3 ± 102.9 107.1 ± 75.0 75.6 ± 97.2 ALP, U/L 86.5 ± 45.9 84.1 ± 37.4 84.4 ± 39.8 TBil, µmol/L 14.7 ± 6.1 13.5 ± 6.0 13.3 ± 6.7 DBil, µmol/L 5.5 ± 3.0 5.5 ± 3.0 5.3 ± 3.1 IBil, µmol/L 9.2 ± 3.8 9.0 ± 3.4 9.7 ± 3.3 TBA, µmol/L 9.9 ± 13.4 8.8 ± 17.1 9.2 ± 15.6 TP, g/L 69.7 ± 5.0 74.9 ± 4.7 75.6 ± 4.8 ALB, g/L 42.2 ± 3.2 42.5 ± 3.1 42.1 ± 3.1 GLB, g/L 27.5 ± 4.2 29.2 ± 5.3 27.7 ± 4.8 ALB/GLB 1.6 ± 0.3 1.6 ± 0.3 1.6 ± 0.3 PAB, mg/L 233.0 ± 71.1 240.2 ± 70.4 235.4 ± 70.3 AFU, g/L 27.5 ± 11.5 27.3 ± 11.3 26.5 ± 9.8 ADA, U/L 6.7 ± 2.2 6.6 ± 2.2 6.6 ± 2.2 LDH, U/L 168.7 ± 63.8 164.6 ± 46.9 168 ± 64.2 Urea, mmol/L 5.5 ± 1.4 5.4 ± 1.4 5.6 ± 1.4 Uric acid, µmol/L 320 ± 78.5 325 ± 75.5 333 ± 79.9 GLU, mmol/L 5.5 ± 1.4 5.4 ± 1.3 5.4 ± 1.4 TCHO, mmol/L 4.2 ± 0.9 4.2 ± 0.9 4.2 ± 0.9 TG, mmol/L 1.2 ± 0.7 1.2 ± 0.7 1.2 ± 0.6 HDL, mmol/L 1.2 ± 0.3 1.2 ± 0.3 1.2 ± 0.3 LDL, mmol/L 2.8 ± 0.8 2.8 ± 0.8 2.6 ± 0.7 Apo-A1, g/L 119.2 ± 30.9 117.7 ± 29.8 121.5 ± 31.0 Apo-B, g/L 85.6 ± 22.0 86.7 ± 22.5 82.3 ± 20.5 Calcium, mmol/L 2.3 ± 0.1 2.3 ± 0.1 2.3 ± 0.1 Phosphorus, mmol/L 1.1 ± 0.2 1.1 ± 0.2 1.1 ± 0.2 Magnesium, mmol/L 0.9 ± 0.1 0.9 ± 0.1 0.9 ± 0.1 Kalium, mmol/L 4.1 ± 0.3 4.1 ± 0.3 4.3 ± 0.3 Natrium, mmol/L 141 ± 2.4 141 ± 2.3 141 ± 2.4 Chlorine, mmol/L 103.0 ± 2.9 103.1 ± 2.7 103.0 ± 3.1 TT, second 20.0 ± 1.5 20.1 ± 1.5 20.0 ± 1.8 FIB, g/L 2.4 ± 0.8 2.4 ± 0.7 2.4 ± 0.8 APTT, second 28.0 ± 4.2 27.8 ± 4.1 28.0 ± 3.7 PT, second 11.7 ± 1.1 11.9 ± 1.0 12.3 ± 1.2 Tumor size, centimiter 5.4 ± 3.6 5.2 ± 3.3 5.4 ± 3.8 Tumor number, single/multiple 670/33 467/23 203/10 Satellite nodules, yes/no 389/314 276/214 113/100 MVI, M0/M1/M2 428/173/102 294/121/75 134/52/27 Tumor capsule, yes/no 328/375 228/262 100/113 Cirrhosis, yes/no 208/495 142/348 66/147 Follow-up time (months) 18.8 ± 10.2 19.0 ± 10.2 18.3 ± 10.3 Recurrence/metastasis rates (%) (8-month/1-year/2-year/3-year) 5.8/8.1/ 11.8/12.7 6.5/9.2/ 11.7/12.5 4.2/5.6/ 9.5/10.4 Survival rate (%) (8-month/1-year/2-year/3-year) 1.7/2.4/ 3.9/4.2 1.9/2.5/ 4.1/4.4 1.8/2.2/ 3.6/4.0 FHFU, the first affiliated hospital of Fujian Medical University; FHXU, the first affiliated hospital of Xiamen University; BCLC staging system, Barcelona Clinic Liver Cancer staging system; WBC, white blood cell; PLT, platelet; Hb, hemoglobin; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; NR, neutrophil ratio; LR, lymphocyte ratio; MR, monocyte ratio; RDW, red blood cell distribution width; RBC, red blood cell; AFP, α-fetoprotein; ALT, alanine aminotransferase; HBV DNA level, hepatitis B virus deoxyribonucleic acid level; Scr, Serum creatinine; γ-GTT, γ-glutamyl transpeptidase; ALP, alkaline phophatase; TBil, total bilirubin; DBil, direct bilirubin; IBil, indirect bilirubin; TBA, total bile acid; TP, total protein; ALB, albumin; GLB, globumin; PAB, prealbumin; AFU, α-fucosidase; ADA, adenosine deaminase; LDH, lactate dehydrogenase; GLU, Glucose; TCHO, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; Apo-A1, apolipoprotein A1; Apo-B, apolipoprotein B; TT, thrombin time; FIB, fibrinogen; APTT, activated partial thromboplastin time; PT, prothrombin time; MVI, microvascular invasion. The univariate analysis results for DFS and OS in the study cohort are shown in Table 2 . Multivariate analysis revealed that eight factors including neutrophils, alkaline phosphatase (ALP), urea, low-density lipoprotein (LDL), apolipoprotein A1 (Apo-A1), thrombin time (TT), tumour size, and MVI grade were independent prognostic factors for DFS, while six factors including TT, MVI grade, mean corpuscular haemoglobin (MCH), monocyte, prealbumin (PAB) and α-fucosidase (AFU) were prognostic factors for OS (Table 2 ). Therefore, these variables were included in the subsequent analysis to establish predictive models. Table 2 Univariate and multivariate of clinical parameters associated with DFS and OS in early-stage HCC patients after R0 resection Clinical parameter DFS OS HR (95% CI) p-value HR (95% CI) p-value Univariate analysis Age, year 0.25(0.08–0.77) 0.015 0.98 (0.95-1) 0.091 Sex, male/female 0.81(0.45–1.46) 0.998 0.88 (0.41–1.9) 0.741 BCLC staging system, (0/A) 3.04(0.75–12.33) 0.122 2.74 (0.68–11.29) 0.995 WBC, ≤ 3.7/>3.7×10 9 /L 2.41(0.98–6.02) 0.055 1.21(1.06–1.5.2) 0.013 PLT, ≤ 160/>160×10 9 /L 1.76(1.12–2.77) 0.018 1.00(1.00–1.00) 0.027 Hb, ≤ 125/>125×10 9 /L 2.05(0.81–5.06) 0.133 0.99(0.97–1.12) 0.312 Hematocrit, ≤ 42.2/>42.2% 1.56(0.67–2.32) 0.073 0.98(0.91–1.12) 0.692 MCV, ≤ 87/>87fL 0.66(0.42–1.03) 0.061 0.95(0.90–1.01) 0.055 MCH, ≤ 31.5/>31.5pg 0.45(0.28–0.72) 1.5×10 9 /L 0.93(0.60–1.45) 0.732 0.86(0.52–1.55) 0.585 Neutrophil, ≤ 3.3/>3.3×10 9 /L 2.02(1.01–3.80) 0.045 1.43(1.14–1.72) 0.002 Monocyte, ≤ 0.5/>0.5×10 9 /L 1.54(0.89–2.60) 0.126 7.51(1.63–15.86) 0.012 LR, ≤ 41.6/>41.6% 0.56(0.33–0.96) 0.036 0.95(0.92–0.99) 0.011 NR, ≤ 47.8/>47.8% 1.60(0.97–2.52) 0.068 1.05(1.00-1.10) 0.033 RDW, ≤ 13.6/>13.6% 1.81(1.12–3.01) 0.016 1.12(0.89–1.56) 0.292 RBC, ≤ 4.5/>4.5×10 9 /L 1.35(0.86–2.63) 0.152 1.22(0.66–2.24) 0.533 AFP, < 400/≥400µg/L 2.01(1.20–3.26) 0.005 1.00(1.00–1.00) < 0.001 ALT, < 61/≥61 U/L 13.12(1.85–5.40) < 0.001 1.00(0.99-1.00) 1.000 HBV DNA, < 50/≥50×10 9 IU/mL 1.74(1.12–2.96) 0.016 1.00(1.00–1.00) 0.971 Albumin, < 50/≥50g/L 0.47(0.23–0.93) 0.031 0.89(0.82–0.97) 0.008 Scr, < 76/≥76µmol/L 0.62(0.34–1.12) 0.123 0.98(0.96–1.02) 0.240 γ-GGT, < 34/≥34U/L 2.10(1.12–3.95) 0.021 1.00(1.00–1.00) 0.541 ALP, < 76/≥76&<117 ≥ 117U/L 4.75(2.61–8.56) < 0.001 1.00(1.00–1.00) 0.182 TBil, < 16.4/≥16.4µmol/L 1.44(0.88–2.12) 0.169 0.97 (0.93–1.24) 0.313 DBil, < 5.6/≥5.6µmol/L 1.52(0.97–2.30) 0.065 0.95 (0.83–1.10) 0.415 IBil, < 6.6/≥6.6µmol/L 1.55(0.83–2.70) 0.178 0.94 (0.86-1.00) 0.182 TBA, < 7.2/≥7.2µmol/L 2.33(1.20–4.32) 0.011 0.95 (0.89–1.01) 0.061 TP, < 77 /≥77g/L 0.26(0.07–1.01) 0.049 1.00 (0.95–1.10) 0.706 ALB, < 37/≥37g/L 1.40(0.23–0.93) 0.031 0.89 (0.82–0.97) 0.008 GLB, < 28.2/≥28.2g/L 1.42(0.88–2.10) 0.168 1.00(0.96–1.06) 0.727 ALB/GLB 200mg/L 0.32(0.14–0.73) 0.007 0.99 (0.99-1.00) 0.0014 AFU, < 38/≥38g/L 4.12(1.62–10.04) 0.003 1.02 (1.00-1.03) 0.032 ADA, < 7/≥7U/L 1.92(1.25–3.04) 0.008 1.12 (0.99–1.32) 0.063 LDH, < 212/≥212U/L 2.02(1.12–3.76) 0.033 1.00 (1.00–1.00) < 0.001 Urea, < 4/≥4&<6.9/≥6.9mmol/L 0.22 (0.08–0.59) 0.003 0.95 (0.76–1.22) 0.623 Uric acid, < 379/≥379µmol/L 1.64(0.99–2.62) 0.053 1.01(0.99–1.03) 0.192 GLU, < 4.9/≥4.9mmol/L 0.60(0.38–0.94) 0.025 0.97(0.78–1.22) 0.821 TCHO, < 3.4/≥3.4mmol/L 1.95(0.74–4.78) 0.184 1.02(0.75–1.40) 0.990 TG, < 1.1/≥1.1mmol/L 0.60(0.37–0.98) 0.043 0.51(0.24–1.18) 0.081 HDL, < 1/≥1mmol/L 0.70(0.43–1.12) 0.156 1.25(0.48–3.05) 0.702 LDL, < 3/≥3mmol/L 2.08(1.22–3.16) 0.005 0.98(0.65–1.52) 0.905 Apo-A1, < 83/≥83g/L 0.45(0.22–0.91) 0.028 1.00(0.99–1.01) 0.760 Apo-B, < 113/≥113g/L 2.01(1.14–3.66) 0.027 1.00(0.99–1.01) 0.708 Calcium, < 2.5/≥2.5mmol/L 1.75(0.24–12.4) 0.590 2.01(0.09–4.01) 0.660 Phosphorus, < 1.1/≥1.1mmol/L 1.72(1.03–3.01) 0.043 10.01(1.82–20.14) 0.008 Magnesium, < 0.8/≥0.8mmol/L 0.70(0.38–1.25) 0.212 2.1(0.02–4.24) 0.751 Kalium, < 4.5/≥4.5mmol/L 1.84(1.01–3.23) 0.044 3.72(1.65–8.62) 0.003 Natrium, < 141/≥141mmol/L 0.57(0.36–0.89) 0.014 0.99(0.87–1.15) 0.860 Chlorine, < 102/≥102mmol/L 0.46(0.29–0.73) < 0.001 0.93(0.84–1.02) 0.151 TT, < 20/≥20second 0.46(0.22–0.95) 0.035 0.75(0.59–0.96) 0.025 FIB, < 2.8/≥2.8g/L 2.10(1.34–3.37) 0.002 1.81(1.40–2.34) < 0.001 APTT, < 25.7/≥25.7second 0.59(0.37–0.92) 0.020 1.01(0.93–1.15) 0.960 PT, < 11.3/≥11.3second 1.52(0.94–2.57) 0.092 1.00(0.76–1.41) 0.933 Tumor size, < 5/≥5&<10/≥10centimiter 3.82(2.12–6.84) < 0.001 1.22 (1.14–1.35) < 0.001 Tumor number, single/multiple 0.62(0.20–12.0) 0.415 1.55(0.55–4.36) 0.408 Satellite nodules, yes/no 1.72(1.13–2.61) 0.022 1.2 (0.69–2.1) 0.525 MVI, M0/M1/M2 2.60(1.51–4.45) < 0.001 2.20(1.61–3.05) < 0.001 Tumor capsule, yes/no 0.87(0.67–1.15) 0.308 0.62(0.20–2.01) 0.415 Cirrhosis, yes/no 0.77(0.46–1.32) 0.313 0.54(0.25–1.15) 0.107 Multivariate analysis Neutrophil 0.34(0.19–0.60) < 0.001 ALP 4.41(2.05–9.62) < 0.001 - - Urea 0.46(0.26–0.80) 0.007 - - LDL 2.15(1.34–3.67) 0.003 - - Apo-A1 0.32(0.16–0.66) 0.002 - - TT 0.34(0.16–0.70) 0.003 0.92(0.87–0.97) 0.003 Tumor size 2.20(1.29–3.82) 0.008 - - MVI grade 2.31(1.25–4.16) 0.009 0.80(0.64–0.99) 0.023 MCH - - 0.67(0.54–0.83) < 0.001 Monocyte - - 4.67(2.37–9.68) < 0.001 PAB - - 0.56(0.38–0.84) 0.005 AFU - - 0.71(0.61–0.82) < 0.001 BCLC staging system, Barcelona Clinic Liver Cancer staging system; WBC, white blood cell; PLT, platelet; Hb, hemoglobin; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; LR, lymphocyte ratio; NR, neutrophil ratio; MR, monocyte ratio; RDW, red blood cell distribution width; MPC, Mean platelet volume; RBC, red blood cell; AFP, α-fetoprotein; ALT, alanine aminotransferase; HBV DNA level, hepatitis B virus deoxyribonucleic acid level; Scr, Serum creatinine; γ-GTT, γ-glutamyl transpeptidase; ALP, alkaline phophatase; TBil, total bilirubin; DBil, direct bilirubin; IBil, indirect bilirubin; TBA, total bile acid; TP, total protein; ALB, albumin; GLB, globumin; PAB, prealbumin; AFU, α-fucosidase; ADA, adenosine deaminase; LDH, lactate dehydrogenase; GLU, Glucose; TCHO, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; Apo-A1, apolipoprotein A1; Apo-B, apolipoprotein B; TT, thrombin time; FIB, fibrinogen; APTT, activated partial thromboplastin time; PT, prothrombin time; MVI, microvascular invasion. Establishment of nomogram model for postoperative early-relapse and evaluation of its discriminability and calibration Based on the independent prognostic factors, nomograms for DFS and OS in the study cohort were established (Fig. 2 ). The results are shown in Table 3 . The C-index of the nomogram for DFS was 0.775 (95% confidence interval [CI], 0.720–0.830). The C-index for OS was 0.812 (95% CI, 0.732–0.892). The validation showed excellent consistency between the observed and predicted 8-month, 1-, 2-and 3-year DFS, and 8-month, 1-, 2- and 3-year OS (Fig. 2 ); with a C-index of 0.865 (95% CI, 0.806–0.924) for DFS and a C-index of 0.839 for OS (95% CI, 0.675-1.00) in the internal validation cohort, and with a C-index of 0.857 (95% CI, 0.763–0.951) for DFS and a C-index of 0.842 (95% CI, 0.708–0.970) for OS in the external validation cohort (Table 3 ). Calibration curves of internal verification and external verification showed good consistency between the observed and predicted events (Fig. 3 ). Taken together, the nomogram models were able to accurately predict postoperative relapse and OS in patients with BCLC early-stage HCC. Table 3 The C-index of the nomograms and classical staging systems Prognostic system Training cohort Internal validation cohort External validation cohort DFS OS DFS OS DFS OS C-index 95%CI C-index 95%CI C-index 95%CI C-index 95%CI C-index 95%CI C-index 95%CI Nomograms 0.775 0.720–0.830 0.812 0.732–0.892 0.865 0.806–0.924 0.839 0.675-1.00 0.957 0.763–0.951 0.842 0.708–0.970 AJCC 0.591 0.558–0.628 0.588 0.546–0.611 0.622 0.581–0.662 0.615 0.572–0.649 0.586 0.544–0.607 0.578 0.533–0.599 BCLC 0.601 0.563–0.648 0.599 0.550–0.641 0.602 0.568–0.651 0.608 0.575–0.655 0.574 0.534–0.622 0.571 0.530–0.619 JIS 0.589 0.543–0.632 0.592 0.548–0.637 0.606 0.552–0.639 0.599 0.554–0.643 0.581 0.535–0.622 0.574 0.528–0.616 HKLC 0.595 0.562–0.629 0.612 0.568–0.632 0.625 0.581–0.649 0.619 0.577–0.638 0.558 0.528–0.580 0.541 0.512–0.568 C-index, concordance index; DFS, disease-free survival; OS, overall survival; CI, confidence interval; AJCC, American Joint Committee on Cancer; BCLC, Barcelona Clinic Liver Cancer staging system; JIS, the Japan Integrated Staging Score; HKLC, the Hong Kong Liver Cancer prognostic classification scheme. Comparison of predictive accuracy between the nomogram models and the classical staging systems The predictive value of the constructed model, in terms of clinical practicability, was compared with that of the 8th edition American Joint Committee on Cancer (AJCC) staging system, the BCLC staging system, the Japan Integrated Staging Score (JIS) and the Hong Kong Liver Cancer prognostic classification scheme (HKLC). The results are shown in Table 3 . In the training cohort, the C-index of the nomogram for DFS and OS was 0.775 and 0.812, respectively, which was significantly higher than the AJCC (DFS: 0.591; OS: 0.588), BCLC (DFS: 0.601; OS: 0.599), JIS (DFS: 0.589; OS: 0.592), and HKLC (DFS: 0.595; OS: 0.612) staging systems. Similarly, in the validation cohort, the C-index of the nomogram for DFS (internal cohort: 0.865; external cohort: 0.857) and OS (internal cohort: 0.839; external cohort: 0.842), was also significantly higher than the AJCC (internal cohort: 0.622, external cohort: 0.586 for DFS; and internal cohort: 0.615, external cohort: 0.578 for OS), BCLC (internal cohort: 0.602, external cohort: 0.574 for DFS; and internal cohort: 0.608, external cohort: 0.571 for OS), JIS (internal cohort: 0.606, external cohort: 0.581 for DFS; and internal cohort: 0.599, external cohort: 0.574 for OS), HKLC (internal cohort: 0.625, external cohort: 0.558 for DFS; and internal cohort: 0.619, external cohort: 0.541 for OS) staging systems. Overall, the nomogram models exhibited superior predictive accuracy to that of these authoritative staging systems for DFS and OS. Discussion Despite patients with BCLC early-stage HCC generally present better prognosis relative to patients with late-stage HCC, a considerable number of patients still suffer from recurrence and metastasis. The presence of MVI is accepted worldwide as one of the most powerful predictors of poor prognosis in patients with early-stage HCC [ 4 , 8 , 9 ] . Furthermore, recent studies have found that the grade of MVI is closely related to postoperative recurrence, especially early recurrence [ 8 , 10 – 13 ] . Of the two most used pathological staging systems for HCC, neither includes MVI as a criterion. A predictive model based on the MVI grading system for recurrence, especially early recurrence in patients with early-stage HCC, has not been reported. Therefore, we established nomograms based on the MVI grading system for recurrence and OS in patients with early-stage HCC after curative sugery, and further validation showed good agreement between the nomogram predictions and actual observations in terms of the predictive probability. In addition, our nomograms had greater predictive performance than the two classical staging systems, the BCLC and AJCC staging systems. The prognosis of patients with HCC is mainly affected by: (1) patient factors, such as immune function, nutritional state, liver function, and status of hepatitis virus infection; (2) tumour factors, such as tumour diameter, MVI classification, and satellite nodules; and (3) factors of treatment, in particularly adjuvant treatment after surgery. In our study, nine of the twelve risk factors associated with recurrence or OS were patient factors, including neutrophil, monocyte, ALP, PAB, MCH, Urea, LDL, Apo-A1, and TT levels, while three factors were tumour-related factors including tumour size, MVI classification, and AFU. These results indicate that the prognosis of HCC is a multifactorial and complex process. As the histopathological types and grades of MVI represent the histopathological changes that occur when a cancer embolus in a vessel evolves to become a satellite lesion or a metastatic site, the histopathological type of MVI can be used as a morphological marker to evaluate the biology and progression of HCC [ 4 , 14 , 15 ] . Whereas, the detectability rate of MVI is low, ranging from 12.4–33.1% in patients with early-stage HCC, and the prognositc value of MVI for patients with early-stage HCC after curative surgery remains disputable [ 16 – 18 ] . In our study, MVI was an independent risk factor related to DFS and OS (Fig. 1 , P < 0.001) with a detection rate of 39.1% (275/703). It is generally known that tumour size is related to patient prognosis; the presence of tumour enlargement predicts poor prognosis in patients with HCC. The cut-off value of tumour size is widely used in different guidelines to predict prognosis as the relationship between tumour size and poor prognosis in patients is not linear. In this study, the cut-off values were set as 5 and 10 cm. Our study identified tumours with a diameter > 10 cm as a significant risk factor for recurrence. Interestingly, although AFP is known as a typical clinical marker for the diagnosis and prognosis of patients with HCC, it was not an independent factor related to prognosis in early-stage HCC after curative hepatectomy in our study. This may be due to the low sensitivity of AFP in predicting the prognosis of early-stage HCC. It has been reported that AFP cannot be detected in 30–35% of patients with primary HCC, while an increased AFP level is also found in those with normal health [ 19 ] . Of note, AFU was a significantly independent factor correlated with OS in early-stage HCC. It is reported that AFU is a specific marker for HCC, which exhibits higher sensitivity and specificity than AFP in diagnosing HCC. In particular, AFU is highly and accurately discriminative of AFP-negative and early-stage HCC. Therefore, dynamic monitoring of AFU is of great significance for the diagnosis and prognosis of early-stage HCC [ 20 ] . Previous studies have reported that immune function and nutritional status are related to the prognosis of patients with HCC [ 21 – 25 ] . In our nomogram models, neutrophil, monocyte, MCH, PAB, and urea (the final product of protein metabolism) are powerful immune and nutritional indices that can be used to predict prognosis. The prognosis of patients with low neutrophil and urea levels (indicating insufficient protein intake) is poor. The tumor microenvironment plays an important role in tumorigenesis. Immune and nutritional status, being part of tumour microcirculation, undoubtedly affects the prognosis of patients with HCC. Increasing evidence shows that basic nutritional status and systemic inflammation are related to the long-term prognosis of cancer patients [ 21 , 26 – 28 ] . Malnutrition and low immune function not only affect the treatment effect in patients with malignant tumours, but also make patients with HCC more prone to relapse and metastasis [ 21 ] . In recent years, metabolic disorders, especially lipid metabolism disorders, have emerged as an important microenvironment for HCC pathogenesis [ 29 , 30 ] . LDL and Apo-A1, as indices of liver lipid metabolism, served as significant predictors for the prognosis of early-stage HCC in this study. It is known that changes in the metabolism of liver lipids are closely related to the occurrence of liver cancer, and in the future, non-alcoholic fatty liver disease may be identified as one of the main causes of primary liver cancer [ 31 ] . Moreover, previous studies have also shown that lipid metabolism disorders can promote tumour cell proliferation by inhibiting the apoptosis of liver cancer cells, resulting in a poor prognosis [ 32 ] . Yet, there is still room for further improvement. First, our model is primarily based on retrospectively collected datasets from two Chinese institutions. Although the models performed well, the inclusion of additional cohorts from other institutions may improve the predictive accuracy of our model. Second, though the sample size in this study is adequate, a larger sample size in conjunction with meaningful information including postoperative adjuvant treatment collected in the future may improve the accuracy of our results. Third, hepatitis B virus (HBV) infection, known to be associated with a poor prognosis of HCC, showed limited prognostic value in our study. This may have been due to some patients receiving non-standardized anti-HBV treatments, which may have affected the statistical results. Conclusions In summary, we developed and validated nomograms for predicting recurrence, especially early recurrence, and OS in patients with early-stage HCC after curative surgery. The predictive performances were superior to the common typical HCC staging systems, and they can provide a reference for clinicians to improve better outcomes in this group of patients. List Of Abbreviations HCC, Hepatocellular carcinoma; BCLC, Barcelona Clinic Liver Cancer; OS, overall survival; MVI, Microvascular invasion; FHFU, the First Affiliated Hospital of Fujian Medical University; FHXU, the First Affiliated Hospital of Xiamen University; CT, computed tomography; MRI, magnetic resonance imaging; AFP, α-fetoprotein; DFS, disease-free survival; C-index, concordance index; ALP, alkaline phosphatase; LDL, low-density lipoprotein; Apo-A1, apolipoprotein A1; TT, thrombin time; MCH, mean corpuscular haemoglobin; PAB, prealbumin; AFU, α-fucosidase; CI, confidence interval; AJCC, American Joint Committee on Cancer staging system; JIS, Japan Integrated Staging Score; HKLC, Hong Kong Liver Cancer prognostic classification scheme; HBV, hepatitis B virus. Declarations Ethics approval and consent to participate This study was approved by the Ethics Review Committee of The First Affiliated Hospital of Fujian Medical University and the Ethics Review Committee of The First Affiliated Hospital of Xiamen University. Written informed consent was obtained from all subjects before the operation. All procedures were performed in accordance with the Declaration of Helsinki. Consent for publication Written informed consent was obtained from every patient with HCC to perform tumour resection for analysis and publication. Availability of data and materials All data generated or analysed during this study are included in this published article. Competing interests The authors declare that they have no competing interests. Funding This work was supported by Startup Fund for scientific research, Fujian Medical University (Grant Number: 2019QH2032) for data collection and analysis. Authors' contributions Conception and design: Ke, J and Chen, H. Development of methodology: Ye, H and Lin, F. Acquisition of Data: Ke, J, Ye, H, Shi, Y, Lin, F and Zhong A. Analysis and interpretation of data: Yang, H, Shi, Y and Zhong A. Writing, review, and/or revision of the manuscript: Ke, J, Ye, H and Chen, H. Study supervision: Chen, H. Conflicts of interest All the authors do not have any possible conflicts of interest. Acknowledgements This study was supported by the Startup Fund for Scientific Research, Fujian Medical University (Grant Number: 2019QH2032). In addition, Hengkai Chen would like to thank his family, especially his wife Dan Lin, children Shuen Chen and Shuhan Chen for providing him with complete spiritual support over the past years. References Siegel RL, Miller KD, Jemal A: Cancer statistics, 2020 . CA Cancer J Clin 2020, 70 (1):7-30. 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Huang PY, Wang CC, Lin CC, Lu SN, Wang JH, Hung CH, Kee KM, Chen CH, Chen KD, Hu TH et al : Predictive Effects of Inflammatory Scores in Patients with BCLC 0-A Hepatocellular Carcinoma after Hepatectomy . J Clin Med 2019, 8 (10). Fan W, Zhang Y, Wang Y, Yao X, Yang J, Li J: Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios as predictors of survival and metastasis for recurrent hepatocellular carcinoma after transarterial chemoembolization . PLoS One 2015, 10 (3):e0119312. Goh BK, Kam JH, Lee SY, Chan CY, Allen JC, Jeyaraj P, Cheow PC, Chow PK, Ooi LL, Chung AY: Significance of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio and prognostic nutrition index as preoperative predictors of early mortality after liver resection for huge (≥10 cm) hepatocellular carcinoma . J Surg Oncol 2016, 113 (6):621-627. Liao W, Zhang J, Zhu Q, Qin L, Yao W, Lei B, Shi W, Yuan S, Tahir SA, Jin J et al : Preoperative Neutrophil-to-Lymphocyte Ratio as a New Prognostic Marker in Hepatocellular Carcinoma after Curative Resection . Transl Oncol 2014, 7 (2):248-255. Lin ZX, Ruan DY, Li Y, Wu DH, Ma XK, Chen J, Chen ZH, Li X, Wang TT, Lin Q et al : Lymphocyte-to-monocyte ratio predicts survival of patients with hepatocellular carcinoma after curative resection . World J Gastroenterol 2015, 21 (38):10898-10906. Galizia G, Auricchio A, de Vita F, Cardella F, Mabilia A, Basile N, Orditura M, Lieto E: Inflammatory and nutritional status is a predictor of long-term outcome in patients undergoing surgery for gastric cancer. Validation of the Naples prognostic score . Ann Ital Chir 2019, 90 :404-416. Kosuga T, Konishi T, Kubota T, Shoda K, Konishi H, Shiozaki A, Okamoto K, Fujiwara H, Kudou M, Arita T et al : Value of Prognostic Nutritional Index as a Predictor of Lymph Node Metastasis in Gastric Cancer . Anticancer Res 2019, 39 (12):6843-6849. Bai X, Feng L: Correlation between Prognostic Nutritional Index, Glasgow Prognostic Score, Systemic Inflammatory Response, and TNM Staging in Colorectal Cancer Patients . Nutr Cancer 2020, 72 (7):1170-1177. Chen K, Ma J, Jia X, Ai W, Ma Z, Pan Q: Advancing the understanding of NAFLD to hepatocellular carcinoma development: From experimental models to humans . Biochim Biophys Acta Rev Cancer 2019, 1871 (1):117-125. Shi C, Xue W, Han B, Yang F, Yin Y, Hu C: Acetaminophen aggravates fat accumulation in NAFLD by inhibiting autophagy via the AMPK/mTOR pathway . Eur J Pharmacol 2019, 850 :15-22. Dou X, Li S, Hu L, Ding L, Ma Y, Ma W, Chai H, Song Z: Glutathione disulfide sensitizes hepatocytes to TNFα-mediated cytotoxicity via IKK-β S-glutathionylation: a potential mechanism underlying non-alcoholic fatty liver disease . Exp Mol Med 2018, 50 (4):1-16. Bai Y, Pei W, Zhang X, Zheng H, Hua C, Min J, Hu L, Du S, Gong Z, Gao J et al : ApoM is an important potential protective factor in the pathogenesis of primary liver cancer . J Cancer 2021, 12 (15):4661-4671. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-1276658","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":87217780,"identity":"74497361-724e-43c0-b781-eda1ff7a7c70","order_by":0,"name":"Jingpeng Ke","email":"","orcid":"","institution":"First Affiliated Hospital of Xiamen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jingpeng","middleName":"","lastName":"Ke","suffix":""},{"id":87217781,"identity":"48a015a1-40b2-4f40-81eb-c70ab9d771a0","order_by":1,"name":"Honghao Ye","email":"","orcid":"","institution":"Fuzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Honghao","middleName":"","lastName":"Ye","suffix":""},{"id":87217782,"identity":"0bcdda00-c338-4c78-8826-c7668304a253","order_by":2,"name":"Fangzhou Lin","email":"","orcid":"","institution":"Fuzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fangzhou","middleName":"","lastName":"Lin","suffix":""},{"id":87217783,"identity":"25555647-b1f8-4612-91eb-e930d38313bc","order_by":3,"name":"Yingjun Shi","email":"","orcid":"","institution":"Fuzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yingjun","middleName":"","lastName":"Shi","suffix":""},{"id":87217784,"identity":"4ee4a1f6-0832-43a1-a79c-9e37ec1dc441","order_by":4,"name":"Aoxue Zhong","email":"","orcid":"","institution":"Fuzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Aoxue","middleName":"","lastName":"Zhong","suffix":""},{"id":87217785,"identity":"ee35d2bc-d929-4df9-b9ae-e14c36174758","order_by":5,"name":"Huang Yang","email":"","orcid":"","institution":"Fujian Agriculture and Forestry University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huang","middleName":"","lastName":"Yang","suffix":""},{"id":87217786,"identity":"7dc24c1d-6c10-4a4c-800c-8dd057d49ce1","order_by":6,"name":"Hengkai Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACNvbm4x8+GPyTY2NmPvggoaKGsBY+nmNpjDMqDhjzs7clGzw4c4ywFjmJHDNmnjMHEiV7zphJPmxhJsJhPAfMHvO23UkwuJFgVpHYwMbA396dQMAvDemGc9ue5QG1pN1I3CHDIHHm7AZCthyQeNvGXAzUcuxG4hk2BgOJXAJaJBIbJHjbmBM33EhsK0hsYyZGSzKbJM+Zw4kzew6zMRCnhecYs+GMijRQIDNLJJw5xkPQL/Lt/R8ffDCwAUYl/8ePPypq5Pjbe/FrwQA8pCkfBaNgFIyCUYAVAACHKU/wcl76vAAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Fujian Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hengkai","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2022-01-19 14:44:16","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-1276658/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-1276658/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":18751007,"identity":"f3252e3b-8f4c-4af2-9b1b-3a0df823bd59","added_by":"auto","created_at":"2022-03-01 21:15:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":315258,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier estimates of the prognosis of patients with early-stage HCC according to microvascular invasion (MVI) grade. (A) The MVI grade satisfactorily determined the disease-free survival (DFS) in the whole cohort; (B) The MVI grade satisfactorily determined the overall survival (OS) in the whole cohort.\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1276658/v2/c9738d69989f2d36ef95e67e.jpg"},{"id":18750685,"identity":"03c7a0e8-f83f-4bb8-b4db-01dbc752c7c1","added_by":"auto","created_at":"2022-03-01 21:12:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":411299,"visible":true,"origin":"","legend":"\u003cp\u003eNomograms for predicting disease-free survival (DFS) and overall survival (OS) in patients with early-stage HCC after curative hepatectomy. (A) DFS; (B) OS. MCH, mean corpuscular hemoglobin; ALP, alkaline phophatase; PAB, prealbumin; AFU, α-fucosidase; Apo-A1, apolipoprotein A1; TT, thrombin time; MVI, microvascular invasion.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1276658/v2/606082595b2b8a8ac3f14cae.jpg"},{"id":18750684,"identity":"61203bc7-15f3-487c-9056-1063df972bf8","added_by":"auto","created_at":"2022-03-01 21:12:34","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":922663,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves for predicting disease-free survival (DFS) and overall survival (OS) using the nomograms. (A) 8-month, 1, 2, and 3-year DFS in the training cohort; (B) 8-month, 1, 2, and 3-year DFS in the internal validation cohort; (C) 8-month, 1, 2, and 3-year DFS in the external validation cohort; (D) 8-month, 1, 2, and 3-year OS in the training cohort; (E) 8-month, 1, 2, and 3-year OS in the internal validation cohort; (A) 8-month, 1, 2, and 3-year OS in the external validation cohort;\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1276658/v2/12bf8f9e04e267ea8878a65b.jpg"},{"id":28083584,"identity":"f045e5e6-b026-411f-800c-cd1f32fbe057","added_by":"auto","created_at":"2022-10-21 10:14:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1550668,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1276658/v2/c6546d08-07b6-4ddc-a8e2-350367b345f4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePrognostic Nomograms Based on Microvascular Invasion Grade for Early-stage Hepatocellular Carcinoma Patients After Curative Hepatectomy\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is among the most frequent causes of cancer-related deaths worldwide \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Despite remarkable improvements in comprehensive HCC treatment, radical surgical resection and liver transplantation are considered the only curative treatments for patients with HCC classified as early-stage (stages 0 and A) according to the Barcelona Clinic Liver Cancer (BCLC) staging system. However, postoperative recurrence and metastasis rates of patients with early HCC vary from 50\u0026ndash;70% \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, resulting in poor overall survival (OS). Early recurrence after liver resection for HCC is the leading cause of death during the first 2 years \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Therefore, developing a model for predicting postoperative recurrence, especially early recurrence, for patients with early-stage HCC to guide risk stratification and treatment is urgently needed.\u003c/p\u003e \u003cp\u003eMicrovascular invasion (MVI), a mass of cancer cells in the vascular cavity with adhesion to endothelial cells, and only visible under a microscope \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, has been reported by previous studies to be an indicator of early invasive manifestation of HCC. It is a crucial independent predictive factor for early recurrence and poor OS among patients with HCC who underwent hepatectomy or received liver transplantation. Most patients with BCLC early-stage HCC with early recurrence are pathologically verified as MVI positive \u003csup\u003e[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Moreover, a previous study found that more invading tumor cells and multiple-invaded microvessels might be related to poor survival and recurrence rates \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. These findings suggest that the BCLC staging system should reappraise HCC based on the presence or grade of MVI to distinguish the biological behavior of early-stage HCC.\u003c/p\u003e \u003cp\u003eMVI is graded according to the number of cancer cells and the distance of MVI to the tumor according to the Standard for Diagnosis and Treatment of Primary Liver Cancer \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Although predictive models for postoperative early recurrence in patients with HCC have been established, a predictive model for patients with early BCLC stage HCC patients according to the MVI grade has not been reported.\u003c/p\u003e \u003cp\u003eTherefore, we retrospectively investigated the clinical and histopathological characteristics of patients with early HCC after curative hepatectomy from multiple centres to establish a prognostic nomogram based on MVI grade to predict early recurrence and OS.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients and study design\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe database was retrospectively derived from patients with HCC who underwent hepatectomy at the First Affiliated Hospital of Fujian Medical University (FHFU) and the First Affiliated Hospital of Xiamen University (FHXU) from March 2015 to March 2020.\u003c/p\u003e\n \u003cp\u003eThe inclusion criteria for patients with HCC patients in this study were: (1) early-stage HCC (BCLC stage 0 or A) diagnosis that was confirmed by postoperative pathology; (2) Child-Pugh A or B liver function before surgery; (3) R0 surgical resection of tumor with curative intent; (4) all patients who survived for at least 30 days after surgery; (5) no preoperative anticancer treatments that could introduce any bias; and (6) clinicopathological data and follow-up information were available. Patients with the following criteria were excluded: (1) recurrent HCC, (2) combined hepatocellular cholangiocarcinoma, (3) previous history of malignancy, and (4) age\u0026thinsp;\u0026lt;\u0026thinsp;18 years.\u003c/p\u003e\n \u003cp\u003eNomogram models were constructed on the datasets from the FHFU, which were also validated using bootstrap resampling as internal validation, and the dataset from the FHXU was used for external validation. This study was approved by the Clinical Research Ethics Committee of the two centres. Written informed consent was obtained from all subjects before the operation. All procedures were performed in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eClinical variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic, laboratory, and HCC pathological data were collected. The laboratory tests included various tests for routine blood parameters, full sets of tests for blood clotting, full sets of tests for blood biochemistry, and hepatitis virus markers. Imaging data included, but were not limited to, the number of tumours, presence of satellite nodules, diameter of the largest nodule, tumour capsule, and cirrhosis based on preoperative contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI). The diagnosis and classification of MVI was confirmed according to the Standard for Diagnosis and Treatment of Primary Liver Cancer \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFollow-up\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the follow-up, serum alpha-fetoprotein (AFP) levels were measured, and ultrasonography, CT, or MRI of the chest and abdomen was done once every 2 months for the first 2 years after surgery. For patients who were free of cancer recurrence 2 years after surgery, a 6-month interval surveillance was performed. Disease-free survival (DFS) was defined as the duration from the first surgery to the first recurrence, metastasis, or death. OS was defined as the duration from the first surgery to death or the last follow-up.\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eContinuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Chi-squared or Fisher\u0026rsquo;s exact tests were used to assess differences in categorical variables. The Wilcoxon rank-sum test was used to compare continuous variables between groups. The cut-off values were established using the X-tile software version 3.6.1 (Yale University School of Medicine, New Haven, Connecticut, United States). For DFS and OS curves during follow-up, Kaplan-Meier curves, log-rank Mantel-Cox test, and Cox proportional hazards regression analyses were used. Nomograms were generated using the rms package in R software version 3.5.2 (R Foundation for Statistical Computing, Vienna, Austria) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The predictive accuracy and discriminative ability of the nomogram were assessed using concordance index (C-index) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e and calibration curves. The larger the C-index, the more accurate the prognostic prediction is. A value of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics of patients in study and validation cohorts\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOverall, 703 patients (490 from FHFU used as training cohort, and 213 from FHXU used as external validation cohort) with BCLC early-stage HCC were included. The baseline characteristics of the two cohorts are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The average age of the entire cohort was 53.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9 years, with a male to female ratio of 4.72:1 (580/130). The average follow-up time for all patients was 18.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2 months. The 8-month, 1-, 2-, and 3-year recurrence or metastasis rates were 5.8%, 8.1%, 11.8%, and 12.7%, respectively. The 8-month, 1-, 2-, and 3-year survival rates were 1.7%, 2.4%, 3.9%, and 4.2%, respectively. Among them, M1 was observed in 173 cases (24.6%), while M2 was observed in 102 cases (14.5%). Between-group differences in sex, age, operative time, follow-up time, ASA scores, laboratory, and HCC pathological data were not significant (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The association of MVI grade relative to OS or DFS is shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The Kaplan-Meier curves of OS and DFS showed that the M2 group had significantly poorer outcomes than the M1 and M0 groups (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe basic clinical characteristics of early-stage HCC patients\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClinical parameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;703)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFHFU cohort\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;490)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFHXU cohort\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;213)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex, male/female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e580/123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e404/86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175/38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCLC staging system, (0/A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45/658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31/459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13/200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160.3\u0026thinsp;\u0026plusmn;\u0026thinsp;62.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158.6\u0026thinsp;\u0026plusmn;\u0026thinsp;58.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161.6\u0026thinsp;\u0026plusmn;\u0026thinsp;65.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHb, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHematocrit, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMCV, fL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMCH, pg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophil, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphocyte, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonocyte, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLR, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.1\u0026thinsp;\u0026plusmn;\u0026thinsp;08.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDW, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBC, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAFP, \u0026micro;g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e339.1\u0026thinsp;\u0026plusmn;\u0026thinsp;484.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e319\u0026thinsp;\u0026plusmn;\u0026thinsp;480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e364.7\u0026thinsp;\u0026plusmn;\u0026thinsp;491.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALT, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.1\u0026thinsp;\u0026plusmn;\u0026thinsp;28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.3\u0026thinsp;\u0026plusmn;\u0026thinsp;27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.4\u0026thinsp;\u0026plusmn;\u0026thinsp;35.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHBV DNA level, \u0026lt;10\u003csup\u003e6\u003c/sup\u003e/\u0026gt;10\u003csup\u003e6\u003c/sup\u003e IU/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e283/420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197/293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86/127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbumin, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScr, \u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.1\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.9\u0026thinsp;\u0026plusmn;\u0026thinsp;17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gamma;-GGT, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.3\u0026thinsp;\u0026plusmn;\u0026thinsp;102.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.1\u0026thinsp;\u0026plusmn;\u0026thinsp;75.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.6\u0026thinsp;\u0026plusmn;\u0026thinsp;97.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALP, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.5\u0026thinsp;\u0026plusmn;\u0026thinsp;45.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.1\u0026thinsp;\u0026plusmn;\u0026thinsp;37.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.4\u0026thinsp;\u0026plusmn;\u0026thinsp;39.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTBil, \u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDBil, \u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIBil, \u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTBA, \u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTP, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALB, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLB, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALB/GLB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAB, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e233.0\u0026thinsp;\u0026plusmn;\u0026thinsp;71.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240.2\u0026thinsp;\u0026plusmn;\u0026thinsp;70.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e235.4\u0026thinsp;\u0026plusmn;\u0026thinsp;70.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAFU, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADA, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDH, U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168.7\u0026thinsp;\u0026plusmn;\u0026thinsp;63.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e164.6\u0026thinsp;\u0026plusmn;\u0026thinsp;46.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168\u0026thinsp;\u0026plusmn;\u0026thinsp;64.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrea, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUric acid, \u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e320\u0026thinsp;\u0026plusmn;\u0026thinsp;78.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e325\u0026thinsp;\u0026plusmn;\u0026thinsp;75.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e333\u0026thinsp;\u0026plusmn;\u0026thinsp;79.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLU, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTCHO, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApo-A1, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.2\u0026thinsp;\u0026plusmn;\u0026thinsp;30.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117.7\u0026thinsp;\u0026plusmn;\u0026thinsp;29.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121.5\u0026thinsp;\u0026plusmn;\u0026thinsp;31.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApo-B, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.6\u0026thinsp;\u0026plusmn;\u0026thinsp;22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.7\u0026thinsp;\u0026plusmn;\u0026thinsp;22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.3\u0026thinsp;\u0026plusmn;\u0026thinsp;20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphorus, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagnesium, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKalium, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatrium, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChlorine, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTT, second\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIB, g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPTT, second\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT, second\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor size, centimiter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor number, single/multiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e670/33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e467/23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203/10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSatellite nodules, yes/no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e389/314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e276/214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113/100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMVI, M0/M1/M2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e428/173/102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e294/121/75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134/52/27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor capsule, yes/no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e328/375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228/262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100/113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCirrhosis, yes/no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e208/495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142/348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66/147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFollow-up time (months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecurrence/metastasis rates (%)\u003c/p\u003e\n \u003cp\u003e(8-month/1-year/2-year/3-year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.8/8.1/\u003c/p\u003e\n \u003cp\u003e11.8/12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5/9.2/\u003c/p\u003e\n \u003cp\u003e11.7/12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2/5.6/\u003c/p\u003e\n \u003cp\u003e9.5/10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurvival rate (%)\u003c/p\u003e\n \u003cp\u003e(8-month/1-year/2-year/3-year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7/2.4/\u003c/p\u003e\n \u003cp\u003e3.9/4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9/2.5/\u003c/p\u003e\n \u003cp\u003e4.1/4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8/2.2/\u003c/p\u003e\n \u003cp\u003e3.6/4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eFHFU, the first affiliated hospital of Fujian Medical University; FHXU, the first affiliated hospital of Xiamen University; BCLC staging system, Barcelona Clinic Liver Cancer staging system; WBC, white blood cell; PLT, platelet; Hb, hemoglobin; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; NR, neutrophil ratio; LR, lymphocyte ratio; MR, monocyte ratio; RDW, red blood cell distribution width; RBC, red blood cell; AFP, \u0026alpha;-fetoprotein; ALT, alanine aminotransferase; HBV DNA level, hepatitis B virus deoxyribonucleic acid level; Scr, Serum creatinine; \u0026gamma;-GTT, \u0026gamma;-glutamyl transpeptidase; ALP, alkaline phophatase; TBil, total bilirubin; DBil, direct bilirubin; IBil, indirect bilirubin; TBA, total bile acid; TP, total protein; ALB, albumin; GLB, globumin; PAB, prealbumin; AFU, \u0026alpha;-fucosidase; ADA, adenosine deaminase; LDH, lactate dehydrogenase; GLU, Glucose; TCHO, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; Apo-A1, apolipoprotein A1; Apo-B, apolipoprotein B; TT, thrombin time; FIB, fibrinogen; APTT, activated partial thromboplastin time; PT, prothrombin time; MVI, microvascular invasion.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThe univariate analysis results for DFS and OS in the study cohort are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Multivariate analysis revealed that eight factors including neutrophils, alkaline phosphatase (ALP), urea, low-density lipoprotein (LDL), apolipoprotein A1 (Apo-A1), thrombin time (TT), tumour size, and MVI grade were independent prognostic factors for DFS, while six factors including TT, MVI grade, mean corpuscular haemoglobin (MCH), monocyte, prealbumin (PAB) and \u0026alpha;-fucosidase (AFU) were prognostic factors for OS (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Therefore, these variables were included in the subsequent analysis to establish predictive models.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate and multivariate of clinical parameters associated with DFS and OS in early-stage HCC patients after R0 resection\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eClinical parameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25(0.08\u0026ndash;0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98 (0.95-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex, male/female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81(0.45\u0026ndash;1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88 (0.41\u0026ndash;1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCLC staging system, (0/A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.04(0.75\u0026ndash;12.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.74 (0.68\u0026ndash;11.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC, \u0026le;\u0026thinsp;3.7/\u0026gt;3.7\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.41(0.98\u0026ndash;6.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21(1.06\u0026ndash;1.5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT, \u0026le;\u0026thinsp;160/\u0026gt;160\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.76(1.12\u0026ndash;2.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(1.00\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHb, \u0026le;\u0026thinsp;125/\u0026gt;125\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.05(0.81\u0026ndash;5.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99(0.97\u0026ndash;1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHematocrit, \u0026le;\u0026thinsp;42.2/\u0026gt;42.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56(0.67\u0026ndash;2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98(0.91\u0026ndash;1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMCV, \u0026le;\u0026thinsp;87/\u0026gt;87fL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66(0.42\u0026ndash;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95(0.90\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMCH, \u0026le;\u0026thinsp;31.5/\u0026gt;31.5pg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45(0.28\u0026ndash;0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85(0.76\u0026ndash;0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphocyte, \u0026le;\u0026thinsp;1.5/\u0026gt;1.5\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93(0.60\u0026ndash;1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86(0.52\u0026ndash;1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophil, \u0026le;\u0026thinsp;3.3/\u0026gt;3.3\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.02(1.01\u0026ndash;3.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43(1.14\u0026ndash;1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonocyte, \u0026le;\u0026thinsp;0.5/\u0026gt;0.5\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54(0.89\u0026ndash;2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.51(1.63\u0026ndash;15.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLR, \u0026le;\u0026thinsp;41.6/\u0026gt;41.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56(0.33\u0026ndash;0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95(0.92\u0026ndash;0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNR, \u0026le;\u0026thinsp;47.8/\u0026gt;47.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.60(0.97\u0026ndash;2.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05(1.00-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRDW, \u0026le;\u0026thinsp;13.6/\u0026gt;13.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81(1.12\u0026ndash;3.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12(0.89\u0026ndash;1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBC, \u0026le;\u0026thinsp;4.5/\u0026gt;4.5\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35(0.86\u0026ndash;2.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22(0.66\u0026ndash;2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAFP, \u0026lt;\u0026thinsp;400/\u0026ge;400\u0026micro;g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01(1.20\u0026ndash;3.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(1.00\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALT, \u0026lt;\u0026thinsp;61/\u0026ge;61 U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.12(1.85\u0026ndash;5.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(0.99-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHBV DNA,\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50/\u0026ge;50\u0026times;10\u003csup\u003e9\u003c/sup\u003e IU/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.74(1.12\u0026ndash;2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(1.00\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbumin, \u0026lt;\u0026thinsp;50/\u0026ge;50g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47(0.23\u0026ndash;0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89(0.82\u0026ndash;0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScr, \u0026lt;\u0026thinsp;76/\u0026ge;76\u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62(0.34\u0026ndash;1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98(0.96\u0026ndash;1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gamma;-GGT, \u0026lt;\u0026thinsp;34/\u0026ge;34U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10(1.12\u0026ndash;3.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(1.00\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.541\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALP, \u0026lt;\u0026thinsp;76/\u0026ge;76\u0026amp;\u0026lt;117\u0026thinsp;\u0026ge;\u0026thinsp;117U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.75(2.61\u0026ndash;8.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(1.00\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTBil, \u0026lt;\u0026thinsp;16.4/\u0026ge;16.4\u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44(0.88\u0026ndash;2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97 (0.93\u0026ndash;1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDBil, \u0026lt;\u0026thinsp;5.6/\u0026ge;5.6\u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52(0.97\u0026ndash;2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95 (0.83\u0026ndash;1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIBil, \u0026lt;\u0026thinsp;6.6/\u0026ge;6.6\u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55(0.83\u0026ndash;2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94 (0.86-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTBA, \u0026lt;\u0026thinsp;7.2/\u0026ge;7.2\u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.33(1.20\u0026ndash;4.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95 (0.89\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTP, \u0026lt;\u0026thinsp;77 /\u0026ge;77g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26(0.07\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (0.95\u0026ndash;1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALB, \u0026lt;\u0026thinsp;37/\u0026ge;37g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40(0.23\u0026ndash;0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89 (0.82\u0026ndash;0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLB, \u0026lt;\u0026thinsp;28.2/\u0026ge;28.2g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42(0.88\u0026ndash;2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(0.96\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALB/GLB\u0026thinsp;\u0026lt;\u0026thinsp;2.2/\u0026ge;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39(0.19\u0026ndash;0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.73 (0.26\u0026ndash;1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAB, \u0026le;\u0026thinsp;200/\u0026gt;200mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32(0.14\u0026ndash;0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99 (0.99-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAFU, \u0026lt;\u0026thinsp;38/\u0026ge;38g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.12(1.62\u0026ndash;10.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02 (1.00-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADA, \u0026lt;\u0026thinsp;7/\u0026ge;7U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.92(1.25\u0026ndash;3.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12 (0.99\u0026ndash;1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDH, \u0026lt;\u0026thinsp;212/\u0026ge;212U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.02(1.12\u0026ndash;3.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrea, \u0026lt;\u0026thinsp;4/\u0026ge;4\u0026amp;\u0026lt;6.9/\u0026ge;6.9mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22 (0.08\u0026ndash;0.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95 (0.76\u0026ndash;1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUric acid, \u0026lt;\u0026thinsp;379/\u0026ge;379\u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64(0.99\u0026ndash;2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01(0.99\u0026ndash;1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLU, \u0026lt;\u0026thinsp;4.9/\u0026ge;4.9mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60(0.38\u0026ndash;0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97(0.78\u0026ndash;1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTCHO, \u0026lt;\u0026thinsp;3.4/\u0026ge;3.4mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95(0.74\u0026ndash;4.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02(0.75\u0026ndash;1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG, \u0026lt;\u0026thinsp;1.1/\u0026ge;1.1mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60(0.37\u0026ndash;0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51(0.24\u0026ndash;1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL, \u0026lt;\u0026thinsp;1/\u0026ge;1mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70(0.43\u0026ndash;1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25(0.48\u0026ndash;3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL, \u0026lt;\u0026thinsp;3/\u0026ge;3mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.08(1.22\u0026ndash;3.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98(0.65\u0026ndash;1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApo-A1, \u0026lt;\u0026thinsp;83/\u0026ge;83g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45(0.22\u0026ndash;0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(0.99\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApo-B, \u0026lt;\u0026thinsp;113/\u0026ge;113g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01(1.14\u0026ndash;3.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(0.99\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium, \u0026lt;\u0026thinsp;2.5/\u0026ge;2.5mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75(0.24\u0026ndash;12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01(0.09\u0026ndash;4.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphorus, \u0026lt;\u0026thinsp;1.1/\u0026ge;1.1mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.72(1.03\u0026ndash;3.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.01(1.82\u0026ndash;20.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMagnesium, \u0026lt;\u0026thinsp;0.8/\u0026ge;0.8mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70(0.38\u0026ndash;1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1(0.02\u0026ndash;4.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKalium, \u0026lt;\u0026thinsp;4.5/\u0026ge;4.5mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.84(1.01\u0026ndash;3.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.72(1.65\u0026ndash;8.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNatrium, \u0026lt;\u0026thinsp;141/\u0026ge;141mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57(0.36\u0026ndash;0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99(0.87\u0026ndash;1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChlorine, \u0026lt;\u0026thinsp;102/\u0026ge;102mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46(0.29\u0026ndash;0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93(0.84\u0026ndash;1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTT, \u0026lt;\u0026thinsp;20/\u0026ge;20second\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46(0.22\u0026ndash;0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75(0.59\u0026ndash;0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIB, \u0026lt;\u0026thinsp;2.8/\u0026ge;2.8g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10(1.34\u0026ndash;3.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81(1.40\u0026ndash;2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPTT, \u0026lt;\u0026thinsp;25.7/\u0026ge;25.7second\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59(0.37\u0026ndash;0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01(0.93\u0026ndash;1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT, \u0026lt;\u0026thinsp;11.3/\u0026ge;11.3second\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52(0.94\u0026ndash;2.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00(0.76\u0026ndash;1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor size,\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5/\u0026ge;5\u0026amp;\u0026lt;10/\u0026ge;10centimiter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.82(2.12\u0026ndash;6.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22 (1.14\u0026ndash;1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor number, single/multiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62(0.20\u0026ndash;12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55(0.55\u0026ndash;4.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSatellite nodules, yes/no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.72(1.13\u0026ndash;2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2 (0.69\u0026ndash;2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMVI, M0/M1/M2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.60(1.51\u0026ndash;4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.20(1.61\u0026ndash;3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor capsule, yes/no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87(0.67\u0026ndash;1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62(0.20\u0026ndash;2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCirrhosis, yes/no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77(0.46\u0026ndash;1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54(0.25\u0026ndash;1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34(0.19\u0026ndash;0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.41(2.05\u0026ndash;9.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46(0.26\u0026ndash;0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.15(1.34\u0026ndash;3.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApo-A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32(0.16\u0026ndash;0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34(0.16\u0026ndash;0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92(0.87\u0026ndash;0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.20(1.29\u0026ndash;3.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMVI grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.31(1.25\u0026ndash;4.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80(0.64\u0026ndash;0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67(0.54\u0026ndash;0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.67(2.37\u0026ndash;9.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56(0.38\u0026ndash;0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAFU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71(0.61\u0026ndash;0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eBCLC staging system, Barcelona Clinic Liver Cancer staging system; WBC, white blood cell; PLT, platelet; Hb, hemoglobin; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin; LR, lymphocyte ratio; NR, neutrophil ratio; MR, monocyte ratio; RDW, red blood cell distribution width; MPC, Mean platelet volume; RBC, red blood cell; AFP, \u0026alpha;-fetoprotein; ALT, alanine aminotransferase; HBV DNA level, hepatitis B virus deoxyribonucleic acid level; Scr, Serum creatinine; \u0026gamma;-GTT, \u0026gamma;-glutamyl transpeptidase; ALP, alkaline phophatase; TBil, total bilirubin; DBil, direct bilirubin; IBil, indirect bilirubin; TBA, total bile acid; TP, total protein; ALB, albumin; GLB, globumin; PAB, prealbumin; AFU, \u0026alpha;-fucosidase; ADA, adenosine deaminase; LDH, lactate dehydrogenase; GLU, Glucose; TCHO, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; Apo-A1, apolipoprotein A1; Apo-B, apolipoprotein B; TT, thrombin time; FIB, fibrinogen; APTT, activated partial thromboplastin time; PT, prothrombin time; MVI, microvascular invasion.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eEstablishment of nomogram model for postoperative early-relapse and evaluation of its discriminability and calibration\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBased on the independent prognostic factors, nomograms for DFS and OS in the study cohort were established (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The results are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The C-index of the nomogram for DFS was 0.775 (95% confidence interval [CI], 0.720\u0026ndash;0.830). The C-index for OS was 0.812 (95% CI, 0.732\u0026ndash;0.892). The validation showed excellent consistency between the observed and predicted 8-month, 1-, 2-and 3-year DFS, and 8-month, 1-, 2- and 3-year OS (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e); with a C-index of 0.865 (95% CI, 0.806\u0026ndash;0.924) for DFS and a C-index of 0.839 for OS (95% CI, 0.675-1.00) in the internal validation cohort, and with a C-index of 0.857 (95% CI, 0.763\u0026ndash;0.951) for DFS and a C-index of 0.842 (95% CI, 0.708\u0026ndash;0.970) for OS in the external validation cohort (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Calibration curves of internal verification and external verification showed good consistency between the observed and predicted events (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Taken together, the nomogram models were able to accurately predict postoperative relapse and OS in patients with BCLC early-stage HCC.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe C-index of the nomograms and classical staging systems\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003ePrognostic system\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eTraining cohort\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eInternal validation cohort\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eExternal validation cohort\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC-index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC-index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC-index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC-index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC-index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC-index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNomograms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.720\u0026ndash;0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.732\u0026ndash;0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.806\u0026ndash;0.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.675-1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.763\u0026ndash;0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.708\u0026ndash;0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAJCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.558\u0026ndash;0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.546\u0026ndash;0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.581\u0026ndash;0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.572\u0026ndash;0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.544\u0026ndash;0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.533\u0026ndash;0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBCLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.563\u0026ndash;0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.550\u0026ndash;0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.568\u0026ndash;0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.575\u0026ndash;0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.534\u0026ndash;0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.530\u0026ndash;0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.543\u0026ndash;0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.548\u0026ndash;0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.552\u0026ndash;0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.554\u0026ndash;0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.535\u0026ndash;0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.528\u0026ndash;0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHKLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.562\u0026ndash;0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.568\u0026ndash;0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.581\u0026ndash;0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.577\u0026ndash;0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.528\u0026ndash;0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.512\u0026ndash;0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003eC-index, concordance index; DFS, disease-free survival; OS, overall survival; CI, confidence interval; AJCC, American Joint Committee on Cancer; BCLC, Barcelona Clinic Liver Cancer staging system; JIS, the Japan Integrated Staging Score; HKLC, the Hong Kong Liver Cancer prognostic classification scheme.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eComparison of predictive accuracy between the nomogram models and the classical staging systems\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe predictive value of the constructed model, in terms of clinical practicability, was compared with that of the 8th edition American Joint Committee on Cancer (AJCC) staging system, the BCLC staging system, the Japan Integrated Staging Score (JIS) and the Hong Kong Liver Cancer prognostic classification scheme (HKLC). The results are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. In the training cohort, the C-index of the nomogram for DFS and OS was 0.775 and 0.812, respectively, which was significantly higher than the AJCC (DFS: 0.591; OS: 0.588), BCLC (DFS: 0.601; OS: 0.599), JIS (DFS: 0.589; OS: 0.592), and HKLC (DFS: 0.595; OS: 0.612) staging systems. Similarly, in the validation cohort, the C-index of the nomogram for DFS (internal cohort: 0.865; external cohort: 0.857) and OS (internal cohort: 0.839; external cohort: 0.842), was also significantly higher than the AJCC (internal cohort: 0.622, external cohort: 0.586 for DFS; and internal cohort: 0.615, external cohort: 0.578 for OS), BCLC (internal cohort: 0.602, external cohort: 0.574 for DFS; and internal cohort: 0.608, external cohort: 0.571 for OS), JIS (internal cohort: 0.606, external cohort: 0.581 for DFS; and internal cohort: 0.599, external cohort: 0.574 for OS), HKLC (internal cohort: 0.625, external cohort: 0.558 for DFS; and internal cohort: 0.619, external cohort: 0.541 for OS) staging systems. Overall, the nomogram models exhibited superior predictive accuracy to that of these authoritative staging systems for DFS and OS.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite patients with BCLC early-stage HCC generally present better prognosis relative to patients with late-stage HCC, a considerable number of patients still suffer from recurrence and metastasis. The presence of MVI is accepted worldwide as one of the most powerful predictors of poor prognosis in patients with early-stage HCC \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Furthermore, recent studies have found that the grade of MVI is closely related to postoperative recurrence, especially early recurrence \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Of the two most used pathological staging systems for HCC, neither includes MVI as a criterion. A predictive model based on the MVI grading system for recurrence, especially early recurrence in patients with early-stage HCC, has not been reported. Therefore, we established nomograms based on the MVI grading system for recurrence and OS in patients with early-stage HCC after curative sugery, and further validation showed good agreement between the nomogram predictions and actual observations in terms of the predictive probability. In addition, our nomograms had greater predictive performance than the two classical staging systems, the BCLC and AJCC staging systems.\u003c/p\u003e \u003cp\u003eThe prognosis of patients with HCC is mainly affected by: (1) patient factors, such as immune function, nutritional state, liver function, and status of hepatitis virus infection; (2) tumour factors, such as tumour diameter, MVI classification, and satellite nodules; and (3) factors of treatment, in particularly adjuvant treatment after surgery. In our study, nine of the twelve risk factors associated with recurrence or OS were patient factors, including neutrophil, monocyte, ALP, PAB, MCH, Urea, LDL, Apo-A1, and TT levels, while three factors were tumour-related factors including tumour size, MVI classification, and AFU. These results indicate that the prognosis of HCC is a multifactorial and complex process.\u003c/p\u003e \u003cp\u003eAs the histopathological types and grades of MVI represent the histopathological changes that occur when a cancer embolus in a vessel evolves to become a satellite lesion or a metastatic site, the histopathological type of MVI can be used as a morphological marker to evaluate the biology and progression of HCC \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Whereas, the detectability rate of MVI is low, ranging from 12.4\u0026ndash;33.1% in patients with early-stage HCC, and the prognositc value of MVI for patients with early-stage HCC after curative surgery remains disputable \u003csup\u003e[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. In our study, MVI was an independent risk factor related to DFS and OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with a detection rate of 39.1% (275/703). It is generally known that tumour size is related to patient prognosis; the presence of tumour enlargement predicts poor prognosis in patients with HCC. The cut-off value of tumour size is widely used in different guidelines to predict prognosis as the relationship between tumour size and poor prognosis in patients is not linear. In this study, the cut-off values were set as 5 and 10 cm. Our study identified tumours with a diameter\u0026thinsp;\u0026gt;\u0026thinsp;10 cm as a significant risk factor for recurrence. Interestingly, although AFP is known as a typical clinical marker for the diagnosis and prognosis of patients with HCC, it was not an independent factor related to prognosis in early-stage HCC after curative hepatectomy in our study. This may be due to the low sensitivity of AFP in predicting the prognosis of early-stage HCC. It has been reported that AFP cannot be detected in 30\u0026ndash;35% of patients with primary HCC, while an increased AFP level is also found in those with normal health \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Of note, AFU was a significantly independent factor correlated with OS in early-stage HCC. It is reported that AFU is a specific marker for HCC, which exhibits higher sensitivity and specificity than AFP in diagnosing HCC. In particular, AFU is highly and accurately discriminative of AFP-negative and early-stage HCC. Therefore, dynamic monitoring of AFU is of great significance for the diagnosis and prognosis of early-stage HCC \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies have reported that immune function and nutritional status are related to the prognosis of patients with HCC \u003csup\u003e[\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. In our nomogram models, neutrophil, monocyte, MCH, PAB, and urea (the final product of protein metabolism) are powerful immune and nutritional indices that can be used to predict prognosis. The prognosis of patients with low neutrophil and urea levels (indicating insufficient protein intake) is poor. The tumor microenvironment plays an important role in tumorigenesis. Immune and nutritional status, being part of tumour microcirculation, undoubtedly affects the prognosis of patients with HCC. Increasing evidence shows that basic nutritional status and systemic inflammation are related to the long-term prognosis of cancer patients \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Malnutrition and low immune function not only affect the treatment effect in patients with malignant tumours, but also make patients with HCC more prone to relapse and metastasis \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn recent years, metabolic disorders, especially lipid metabolism disorders, have emerged as an important microenvironment for HCC pathogenesis \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. LDL and Apo-A1, as indices of liver lipid metabolism, served as significant predictors for the prognosis of early-stage HCC in this study. It is known that changes in the metabolism of liver lipids are closely related to the occurrence of liver cancer, and in the future, non-alcoholic fatty liver disease may be identified as one of the main causes of primary liver cancer \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Moreover, previous studies have also shown that lipid metabolism disorders can promote tumour cell proliferation by inhibiting the apoptosis of liver cancer cells, resulting in a poor prognosis \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eYet, there is still room for further improvement. First, our model is primarily based on retrospectively collected datasets from two Chinese institutions. Although the models performed well, the inclusion of additional cohorts from other institutions may improve the predictive accuracy of our model. Second, though the sample size in this study is adequate, a larger sample size in conjunction with meaningful information including postoperative adjuvant treatment collected in the future may improve the accuracy of our results. Third, hepatitis B virus (HBV) infection, known to be associated with a poor prognosis of HCC, showed limited prognostic value in our study. This may have been due to some patients receiving non-standardized anti-HBV treatments, which may have affected the statistical results.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, we developed and validated nomograms for predicting recurrence, especially early recurrence, and OS in patients with early-stage HCC after curative surgery. The predictive performances were superior to the common typical HCC staging systems, and they can provide a reference for clinicians to improve better outcomes in this group of patients.\u003c/p\u003e"},{"header":"List Of Abbreviations","content":"\u003cp\u003eHCC, Hepatocellular carcinoma; BCLC, Barcelona Clinic Liver Cancer; OS, overall survival; MVI, Microvascular invasion; FHFU, the First Affiliated Hospital of Fujian Medical University; FHXU, the First Affiliated Hospital of Xiamen University; CT, computed tomography; MRI, magnetic resonance imaging; AFP, \u0026alpha;-fetoprotein; DFS, disease-free survival; C-index, concordance index; ALP, alkaline phosphatase; LDL, low-density lipoprotein; Apo-A1, apolipoprotein A1; TT, thrombin time; MCH, mean corpuscular haemoglobin; PAB, prealbumin; AFU, \u0026alpha;-fucosidase; CI, confidence interval; AJCC, American Joint Committee on Cancer staging system; JIS, Japan Integrated Staging Score; HKLC, Hong Kong Liver Cancer prognostic classification scheme; HBV, hepatitis B virus.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Review Committee of The First Affiliated Hospital of Fujian Medical University and the Ethics Review Committee of The First Affiliated Hospital of Xiamen University. Written informed consent was obtained from all subjects before the operation. All procedures were performed in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from every patient with HCC to perform tumour resection for analysis and publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Startup Fund for scientific research, Fujian Medical University (Grant Number: 2019QH2032) for data collection and analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: Ke, J and Chen, H. Development of methodology: Ye, H and Lin, F. Acquisition of Data: Ke, J, Ye, H, Shi, Y, Lin, F and Zhong A. Analysis and interpretation of data: Yang, H, Shi, Y and Zhong A. Writing, review, and/or revision of the manuscript: Ke, J, Ye, H and Chen, H. Study supervision: Chen, H.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors do not have any possible conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Startup Fund for Scientific Research, Fujian Medical University (Grant Number: 2019QH2032). In addition, Hengkai Chen would like to thank his family, especially his wife Dan Lin, children Shuen Chen and Shuhan Chen for providing him with complete spiritual support over the past years.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSiegel RL, Miller KD, Jemal A: \u003cstrong\u003eCancer statistics, 2020\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin\u0026nbsp;\u003c/em\u003e2020, \u003cstrong\u003e70\u003c/strong\u003e(1):7-30.\u003c/li\u003e\n \u003cli\u003eDhir M, Melin AA, Douaiher J, Lin C, Zhen WK, Hussain SM, Geschwind JF, Doyle MB, Abou-Alfa GK, Are C: \u003cstrong\u003eA Review and Update of Treatment Options and Controversies in the Management of Hepatocellular Carcinoma\u003c/strong\u003e. \u003cem\u003eAnn Surg\u0026nbsp;\u003c/em\u003e2016, \u003cstrong\u003e263\u003c/strong\u003e(6):1112-1125.\u003c/li\u003e\n \u003cli\u003ePoon RT, Fan ST, Ng IO, Lo CM, Liu CL, Wong J: \u003cstrong\u003eDifferent risk factors and prognosis 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Although predictive models for postoperative early recurrence in patients with HCC have been established, this is the first study to develop and evaluate a predictive model based on the microvascular invasion classification for early recurrence and survival after curative hepatectomy in patients with early-stage HCC.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe database of patients with early-stage HCC who underwent curative hepatectomy in the First Affiliated Hospital of Fujian Medical University and the First Affiliated Hospital of Xiamen University was retrospectively reviewed. Kaplan-Meier curves and Cox proportional hazards regression models were used to analyse disease-free survival (DFS) and overall survival (OS). Nomogram models were constructed on the datasets from the First Affiliated Hospital of Fujian Medical University, which were validated using bootstrap resampling with 30% samples as internal validation. Data of patients from the First Affiliated Hospital of Xiamen University were used for external validation.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 703 patients with early-stage HCC were included in our study. An eight-factor nomogram for predicting recurrence or metastasis and a six-factor nomogram for predicting survival were created. The concordance indexes were 0.775 (95% confidence interval [CI], 0.720-0.830) for the DFS nomogram and 0.812 for the OS nomogram (95% CI, 0.732-0.892) in the training cohort; 0.865 (95% CI, 0.806-0.924) and 0.839 (95% CI, 0.675-1.00), respectively, in the internal validation cohort; and 0.857 (95% CI, 0.763-0.951) and 0.842 (95% CI, 0.708-0.970), respectively, in the external validation cohort. The calibration curves showed optimal agreement between the predicted and observed DFS and OS rates. The predictive accuracy was significantly better than that of the classic HCC staging systems.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e This study developed and validated nomograms for predicting recurrence, especially early recurrence, and overall survival in patients with early-stage HCC after curative resection with high predictive accuracy.\u003c/p\u003e","manuscriptTitle":"Prognostic Nomograms Based on Microvascular Invasion Grade for Early-stage Hepatocellular Carcinoma Patients After Curative Hepatectomy","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2022-03-01 21:12:32","doi":"10.21203/rs.3.rs-1276658/v2","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}},{"code":1,"date":"2022-01-24 19:29:38","doi":"10.21203/rs.3.rs-1276658/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":"41ce4001-ec50-4412-9ff5-a9dc5ee1725d","owner":[],"postedDate":"March 1st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-10-21T10:14:36+00:00","versionOfRecord":[],"versionCreatedAt":"2022-03-01 21:12:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-1276658","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1276658","identity":"rs-1276658","version":["v2"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}