Novel Nomogram for Predicting Hepatocellular Carcinoma in Hepatitis C virus-associated Cirrhosis Patients after eliminating virus with Direct-acting Antivirals

Novel Nomogram for Predicting Hepatocellular Carcinoma in Hepatitis C virus-associated Cirrhosis Patients after eliminating virus with Direct-acting Antivirals | 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 Article Novel Nomogram for Predicting Hepatocellular Carcinoma in Hepatitis C virus-associated Cirrhosis Patients after eliminating virus with Direct-acting Antivirals xuemei tao, Youfei Zhao, Zeyu Wang, wei lu, Jing Zhang, Yuqiang Mi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3852585/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and aims: Hepatitis C virus (HCV) associated cirrhosis are in high risk of hepatocellular carcinoma (HCC), and this study aimed to explore the risk factors, and establish and validate a novel nomogram. Methods: A total of 309 inpatients with HCV- associated cirrhosis from Tianjin Second People's Hospital were selected as the training cohort, and 363 patients from Beijing You’an Hospital were selected as the validation cohort. Both cohorts received Direct-Acting Antiviral Agents (DAAs) treatment and achieved sustained virological response (SVR). Laboratory parameters were collected at baseline and duration of follow-up. Cox regression analysis was used to explore risk factors of HCC, and a nomogram for prediction was developed and validated. Results: HCC incidence was 5.45 100PY (95% CI, 3.91-7.40) in patients of the training cohort. Age, nonspecific liver nodules, the albumin-Bilirubin (ALBI) score and end of treatment (EOT)-AFP are independent risk factors for HCC by Cox regression analysis. A nomogram was used to predict the 1-year, 3-year and 5-year incidence of HCC, with the areas under receiver operating characteristic curves (AUROCs) of 0.866, 0.813 and 0.764, respectively. The AUROCs in validation cohort at 1, 3, and 5 years were 0.884, 0.783 and 0.692 in this nomogram, respectively. Conclusion: This novel nomogram had a good predictive ability for HCC in patients with HCV-associated cirrhosis after eliminating virus with direct-acting antiviral agents, especially in 3 years. Biological sciences/Cancer Health sciences/Gastroenterology Health sciences/Medical research direct-acting antiviral agents sustained virological response HCV-associated cirrhosis hepatocellular carcinoma nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Hepatitis C virus (HCV) infection is still a global epidemic. An estimated 71 million people were chronically infected with HCV worldwide in 2015, and 399,000 died from cirrhosis or hepatocellular carcinoma (HCC) caused by HCV infection [ 1 ] . China is a region with high incidence of HCV infection. According to data published by Polaris Observatory HCV Collaborators [ 2 ] , an estimated 9,487,000 people were infected with HCV in China in 2020. Chronic HCV infection has been identified as an independent risk factor for HCC development, especially in patients with cirrhosis [ 3 ] . The incidence of HCV-associated HCC is 1–3% after 30 years of infection, mainly in patients with advanced liver fibrosis or cirrhosis, and 2–4% annually once cirrhosis has developed [ 4 ] . At present, Direct-Acting Antiviral Agents (DAAs) is the main treatment for patients with chronic hepatitis C (CHC) and HCV-associated cirrhosis, which have not only changed the scope and spectrum of treatment, but also had high elimination rate of HCV RNA, sufficient safety and few contraindications compared with interferon [ 5 ] . Moreover, through different combined treatment solutions, more than 95% of SVR can be achieved, regardless of HCV genotype or degree of fibrosis [ 6 – 8 ] . Most of studies have shown that there is still a risk of incidence of HCC in CHC patients after achieving sustained virological response (SVR) with DAAs [ 6 , 9 – 12 ] . Therefore, it is important to study the impact of DAAs therapy on the incidence of HCC in the era of DAAs. At present, there are several models established for predicting HCC in CHC, but few study for HCV-cirrhosis (in high risk of HCC), especially achieved SVR after DAAs treatment. In this study, cox regression and nomogram were used to explore the risk factors and prediction models of HCC in HCV- cirrhosis patients achieved SVR with DAAs. MATERIALS AND METHODS 2.1 Included patients A total of 309 HCV-associated cirrhosis patients who completed DAAs treatment in Tianjin Second People's Hospital from January 2014 to April 2020 were enrolled in this study (training cohort). Another 363 DAAs-treated patients with HCV-associated cirrhosis in Beijing You 'an Hospital, Capital Medical University from April 2015 to May 2022 were enrolled as the validation cohort. Inclusion criteria :(1) age ≥ 18, no gender limitation; (2) All patients (both the training cohort and the validation cohort) were anti-HCV and HCV RNA positive before enrollment and acheived SVR with DAA treatment; (3) The criteria of HCV - cirrhosis and HCC are based on the 2018 European Association for the Study of the Liver (EASL) guidelines [ 5 ] , and they were evaluated through imaging or pathological examination; (4) Obtain informed consent from all patients. Exclusion criteria :(1) Patients with prior history of extrahepatic tumors; (2) Patients who have received or were waiting for a liver transplant; (3) Patients with HIV, HAV, HBV, HEV co-infection; (4) alcoholic liver disease, autoimmune hepatitis, drug-induced liver injury, genetic metabolic liver disease and other liver disease patients; (5) Patients without follow-up records or incomplete follow-up data. The Medical Ethics Committee of Tianjin Second People's Hospital approved the study protocol, which conformed to the ethical guidelines of the Declaration of Helsinki amended in 2008. The approval number is the ethical review word [2018]21 of Tianjin Jin Second People's Hospital. Written, informed consent was obtained from each patient. 2.2 Antivirus Solution The 309 DAA patients in training cohort and the 363 DAA patients in validation cohort were treated by experienced clinicians according to the guidelines [ 13 , 14 ] : (1) sofosbuvir 400mg/d + daclatasvir 60mg/d + ribavirin 1000mg/d (12 weeks) regimen; (2) sofosbuvir 400mg/d + Velpatasvir 100mg/d (12 weeks); (3) sofosbuvir 400mg/d + ribavirin 1000mg/d (12 weeks); (4) Ombitasvir 300mg/d + dasabuvir 500mg/d (12 weeks); (5) sofosbuvir 400mg/d + ledipasvir 90mg/d + ribavirin 1000mg/d (12 weeks) regimen; (6) Elbasvir and Grazoprevir 50mg/d (12 weeks); (7) Dasabuvir 60mg + Asunaprevir 100mg (24 weeks) regimen. 2.3 Demographics and laboratory parameters We recorded baseline outcomes for 309 patients at enrollment, including gender, age, weight, height, Body mass index (BMI), HCV genotype, compensatory / decompensated cirrhosis, Child-Pugh score, nonspecific liver nodules, hypertension, diabetes, fatty liver, HCV RNA, Protein Induced by Vitamin K Absence or Antagonist-II (PIVKA-II), carcinoembryonic antigen (CEA), alpha fetoprotein (AFP), serum biochemical indicators: alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), γ-alanine transferase (γ-GT), total bilirubin (TBIL), total protein (TP), albumin (ALB), renal function (BUN), creatinine (CRE), uric acid (UA), glomerular filtration rate (eGFR), blood glucose (GLU), triglyceride (TG), Total cholesterol (CHO), low density lipoprotein (LDL), high density lipoprotein (HDL); coagulation function: prothrombin time (PT), international standardized ratio of prothrombin time (INR); blood routine: red blood cell (RBC), hemoglobin (HGB), white blood cell (WBC), platelet (PLT); liver stiffness measurement (LSM, in kPa) and controlled attenuation parameter (CAP, in dB/m) values were obtained by FibroScan (Echosens, Paris, France). The Fibrosis-4 (FIB-4) index [ 15 ] was calculated as a surrogate marker of liver fibrosis. The score was calculated as follows: Fib-4 index = age (years) × AST (IU/L) / PLT (10 9 /L) × [ALT (IU/L)] 1/2 . The albumin-Bilirubin (ALBI) score [ 16 ] , a simple index reflecting the underlying liver function, was calculated for each patient by the following formula based on the albumin and bilirubin levels: ALBI score = (log10 bilirubin × 0.66) + (albumin × − 0.085); Bilirubin is in µ mol/L and albumin in g/L. According to the grading proposed by Professor Johnson [ 17 ] , ALBI is divided into 1–3 levels: ≤-2.60(score 1), > -2.60 and ≤-1.39(score 2), > -1.39(score 3). The above data were collected again as the patients of training cohort completed antiviral therapy and achieved SVR, and analyzed as factors of end of treatment (EOT). Thereafter, laboratory and demographic parameters were repeated every 6 months until HCC occurrence, death, or the end of follow-up. Laboratory data collected after EOT are denoted as EOT-. 2.4 Follow-up and diagnostic criteria of HCC The end point of this study was the occurrence of HCC or the end of April 2020. All patients received nurse counselling, clinical visit and laboratory assessment (biochemistry, blood routine, HCC-associated tumor markers, etc.) at baseline and every 3–6 months. According to the diagnostic criteria for HCC of EASL guidelines [ 5 ] , patients were screened for HCC by ultrasound (US) or spiral dynamic computed tomography (CT) every 3–6 months. When HCC is suspected, it is further examined by enhanced CT, magnetic resonance imaging (MRI), or liver angiography. If no typical manifestations of HCC are found, the diagnosis should be confirmed by fine needle biopsy and pathological examination. Follow-up period was calculated from the end of DAAs treatment to the diagnosis of HCC, patient death, or the end of follow-up. The date of HCC diagnosis was used as the alternative time of HCC occurrence (Fig. 1 ). 2.5 Definitions of SVR and nonspecific liver nodules SVR definition: according to EASL guidelines [ 14 ] , SVR is defined as 12 weeks after the end of treatment (SVR 12), and HCV RNA is not detected in serum or plasma, evaluated by highly sensitive molecular methods, additionally, the detection limit is 15IU/ml. Nonspecific liver nodules were defined [ 18 ] as ≤ 10 mm or nodules > 10 mm but in which HCC diagnosis was ruled out before starting DAA by contrast enhanced US (CEUS), CT, MRI or biopsy. 2.6 Validation cohort The validation cohort was from You 'an Hospital who were achieved SVR through DAA treatment, then we collected the laboratory and imaging data at baseline and followed up every 3–6 months until HCC detected or the trial terminated. The diagnostic criteria for liver cirrhosis, HCC, SVR and liver nodules were agreed between the two hospitals. 2.7 Statistical Analysis Data conforming to normal distribution were represented by ( ‾x ± s), and HCV RNA was calculated by dennar logarithm. Independent sample t test was used to analyze and compare the training cohort. The skewness distribution of measurement data was represented by M (P25, P75). The comparison between the training and validation cohort was analyzed and compared by Mann-Whitney U test, and the paired samples were analyzed and compared by Wilcoxon signed rank test. The statistical data were expressed as percentages, and analyzed by Chi-square test or Fisher's exact probability method. Kaplan-Meier curve and log-rank test were used to compare the difference in the cumulative incidence of HCC between the two groups. All clinical data were included in binary logistic regression analysis for univariate and multivariate analysis to evaluate the influencing factors of HCC occurrence and obtain a regression equation. Cox proportional risk regression was used to re-evaluate the risk factors for HCC before and after DAAs treatment, and the independent predictors of HCC were obtained by incorporating the indicators with statistical differences in univariate analysis into multivariate analysis. Meanwhile, risk ratio and 95% confidence interval ( CI ) were calculated, and a nomogram model was established to predict HCC. Receiver operating characteristic curve (ROC) curve and area under ROC (AUROC) evaluation model were used. ROC curve and AUC were used to quantify the prediction accuracy of the Nomogram model. The clinical value of the model was evaluated by decision curve analysis (DCA). P ༜0.05 was considered to indicate that all analyses were statistically significant. Incidences, expressed as 100 patient-years (100PY), relative risks ( RR ) and their 95% CI were estimated by means of Poisson regression models, using as offset the logarithm of radiological follow-up. Statistical analyses were performed using SPSS version 26 (SPSS, Inc., Chicago, IL, USA), GraphPad Prism Version 9.0H (GRAPH PAD Software, Inc., La Jolla, CA, USA) and R Version 3.1.2 (R Core Development Team, 2010). RESULTS 3.1 Baseline characteristics of patients A total of 309 patients were included in this study from January 2014 to April 2020 (459 patients were not included according to the exclusion criteria) and completed follow-up. The baseline characteristics of the training cohort are reported in Table 1. The training cohort was followed up for 1-84 months, with a median follow-up of 28 months. The age of patients in the training cohort was (57.02±10.12) years old. Among the patients, 124 were male and 185 were female. In the training cohort, 62 (20.06%) out of the 309 patients had history of diabetes, 63 (20.39%) of fatty liver, and 67 (21.68%) of arterial hypertension. 3.2 Changes of indicators in training cohort before and after DAAs treatment After DAAs treatment, all patients in the training cohort achieved SVR (6 patients who did not achieve SVR were not included in this study), and the changes of liver function, blood lipid, coagulation function, tumor markers, LSM values and other indicators in training cohort before and after DAAs treatment were observed. It was found that AFP, ALT, AST, γ-GT, ALP, TP and LSM in the training cohort were significantly lower than that on the baseline ( P < 0.05), and WBC, RBC and PLT became better after DAAs treatment ( P < 0.05), as shown in Table 2. 3.3 Incidence and cumulative incidence of HCC in training cohort During the study, 41 patients developed HCC within 1-66 months in the training cohort, and HCC incidence was 5.45 100PY (95% CI , 3.91-7.40). The cumulative incidence of HCC at 12, 24, 36 and 48 months was 3.24%, 7.12%, 10.03% and 12.30%, respectively. 3.4 Risk factors of HCC after DAAs treatment For the training cohort, we evaluated some baseline indicators of patients and some indicators after DAAs treatment. Age, liver cirrhosis (compensatory / decompensated), non-specific liver nodules, Child-pugh grade, ALBI, ALB, TBIL, EOT-AFP, EOT-ALT, EOT-AST, EOT-γ-GT, EOT-ABL and EOT-PLT, etc. are risk factors for HCC in univariate analysis. Due to the excessive mixed factors of ALT, AST, and WBC, they were not included in the multivariate analysis. The remaining meaningful indicators of univariate analysis were included in cox regression for multivariate analysis step by step. The results showed that age, non-specific liver nodules, ALBI and EOT-AFP were independent risk factors for HCC, as shown in Table 3. 3.5 Establish a nomogram for prediction of HCC after DAAs treatment By cox regression analysis, we found that age, nonspecific liver nodules, ALBI, and EOT-AFP were independent risk factors for HCC in patients with HCV-associated cirrhosis after DAAs treatment. Based on the hazard ratio of each variable: age (hazard ratio [ HR ] = 1.052; 95% CI , 1.013-1.093; P = 0.009) and non-specific liver nodules ( HR = 3.270; 95% CI , 1.641 to 6.617; P = 0.001), ALBI ( HR = 2.463; 95% CI , 1.584 to 3.830; P = 0.000), EOT-AFP ( HR = 1.031; 95% CI , 1.013 to 1.049; P = 0.001), we developed a nomogram to predict the 1-year, 3-year and 5-year incidence of HCC in training cohort, and then we draw ROCs to observe the accuracy of the model. The AUC values were 0.866, 0.813 and 0.764, respectively, as shown in Figure 2 and Figure 3(A). The calibration chart of HCC incidence in 1, 3, and 5 years predicted by this model was shown in Figure 4(A-C). 3.6 Subgroup analysis of HCC incidence According to the independent risk factors of patients in the training cohort, subgroup analysis was performed on HCC occurrence of patients in the training cohort. Age and non-specific liver nodules were inserted into the subject curve, respectively, and AUROC was 0.650 and 0.587, respectively. The optimal cut-off value of age was 0.266, and the value was 58 years. In log-rank test, the cumulative incidence of HCC in patients aged ≥58 years was significantly higher than that in patients aged < 58 years ( P = 0.001), meanwhile, the cumulative incidence of HCC in patients with non-specific liver nodules was significantly higher than that in patients without non-specific liver nodules ( P = 0.002). The specific cumulative incidence of HCC in the training cohort grouped by age and presence of non-specific liver nodules was shown in Figure 5. HCC incidence in patients with aged ≥58 years was 7.86 100PY (95% CI , 7.86-11.23) and it was 2.97 100PY (95% CI , 1.48-5.32) in patients with aged <58 years (≥58 vs <58: RR 2.65, 95% CI , 1.33-5.28). HCC incidence was 10.15 100PY (95% CI , 5.40-17.36) with non-specific liver nodules while was 4.68 100PY (95% CI , 3.09-6.81) without it (with non-specific liver nodules vs without: RR 2.26, 95% CI , 1.17-5.37). HCC incidence was 2.50 100PY (95% CI , 1.14-4.74) in ALBI score-1 and 8.18 100PY (95% CI , 5.60-11.55) in ALBI 2-3 (ALBI 2-3 vs ALBI 1: RR 3.28, 95% CI , 1.56-6.87) (Table 4). 3.7 External validation of the nomogram prediction A total of 363 patients with HCV-associated cirrhosis after DAA from Beijing You 'an Hospital were selected as the validation cohort. The patients were (61.48±6.17) years old, including 156 males and 207 females. Baseline data for the validation and test cohorts are provided in Table 5. 46 patients out of 363 patients (all achieved SVR) developed HCC in the duration of follow-up in the validation cohort, and HCC incidence were 2.34 100PY (95% CI , 1.71-3.12). The AUCs of Nomogram on predicting HCC in validation cohort at 1-year, 3-year and 5-year were 0.884 (95% CI , 0.851-0.917), 0.783 (95% CI , 0.670-0.896) and 0.692 (95% CI , 0.605-0.779), respectively. This nomogram showed a good prediction, as shown in Figure 3(B). The calibration plots of HCC occurrence at 1, 3, and 5 years were shown in Figure 4(D-F). The DCA showed that the threshold probability interval between 0.05-0.1 of the Nomogram model curve was higher than the two extreme curves [Figure 4(G-I)]. DISCUSSION Many previous studies indicated that cirrhosis was a major risk factor of HCC in CHC patients. A cohort study of veterans from the United States [ 19 ] included more than 45,000 CHC patients determined that having cirrhosis and without obtaining SVR were predictive factors for HCC. Kumada et al. [ 20 ] took patients with CHC-related decompensated cirrhosis as the study subjects, including 364 patients from the UK as the “DAA group” and 249 patients from Japan as the control “non-DAA group”, and cox multivariate analysis suggested that ALB and failure to obtain SVR could increase the risk of HCC after adjusting the baseline characteristics between the two groups with propensity matching score. Degasperi et al. [ 21 ] enrolled 400 HCV associated cirrhosis patients achieving SVR after DAAs treatment that were tested PIVKA-II and AFP at the beginning and ending of DAAs treatment, and the results showed that the 4-year probability of HCC development was 3% in patients with negative for both markers, 18% in patients with positive for both, and 38% in patients with positive for at least one. Thus, PIVKA-II and AFP independently predict HCC in DAAs-treated patients with CHC cirrhosis, while their combination improved risk stratification. Nevertheless, most of the studies came from Europe, America and Japan, few from China. Meanwhile, the occurrence of HCC induced by HCV was on the rise in recent years [ 3 , 5 , 22 – 24 ] . Therefore, a high accuracy prediction model to monitor the occurrence of HCC in chinese patients is urgently needed, especially for HCV associated cirrhosis patients as a high-risk group of HCC. Of course, economy and accessibility of clinical application should be considered, so we established a prediction model based on simple laboratory indicators. In this study, age was brought into the ROCs curve, and the best cut-off value was 58 years old. ASAHINA et al. [ 25 ] also showed that elderly people had a higher risk of HCC. HCC incidence (10.15 100PY) in patients with non-specific liver nodules was more than without it (4.68 100PY and RR 2.26). Similarly, Mariño et al. [ 18 ] found in a large cohort study in Spain that the highest risk of HCC among HCV-treated cirrhotics was associated to the recognition of NCLN at imaging. Even, a multicenter study by Sangiovanni et al. [ 26 ] also showed that non-specific nodules were related to HCC occurrence in patients with HCV-associated cirrhosis. This might be due to the fact that these patients with nonspecific nodules correspond to small low- or high-grade dysplasia or large regenerated nodules in cirrhosis. It was thought that those patients who developed HCC and had nonspecific nodules might belong to a class of patients without adequate screening. The emerging tumors may also be the result of the evolution of dysplastic nodules and of the growth of already existing subclinical dormant clones of transformed hepatocytes. The mechanisms involved in the transition from dysplasia to HCC or the growth of dormant clones are not known but immune surveillance is surely involved. [ 27 – 29 ] Imaging limitations made the specific composition of nonspecific liver nodules unknown in our cohort, but there was no significant difference in nonspecific liver nodules between two groups according to the same criterion and radiologists. Data from Italy [ 30 ] show that evident nodules classified by biopsy as low or high-grade dysplasia may become HCC during follow-up. Neither the training corhorts nor validation cohorts underwent biopsy according to current recommendations to determine the origin of the nodules. Therefore, whether patients with HCV cirrhosis who have nonspecific liver nodules before treatment are suitable for DAAs treatment remains to be debated. ALBI score [ 16 ] is composed of ALB and TBIL, a simple, evidence-based and objective indicator that can reflect the potential liver function of patients at all stages of disease. A large number of studies [ 31 – 33 ] have shown that ALBI score can be used as a noninvasive predictor of HCC or in combination. ALONSO et al. [ 34 ] determined serum ALB in a multicenter cohort study ( HR 0.400; 95% CI 0.174–0.923) could be used as an independent risk factor for HCC development after SVR acquisition in patients with HCV advanced liver fibrosis, and a non-invasive predictive model was proposed. Fan et al. [ 35 ] , in a large, multi-center study, proposed aMAP score, a prediction model for HCC occurrence in patients with hepatitis B and C, also included ALBI score as one of the predictive indicators. In this study, HCC incidence was 2.50 100PY (95% CI , 1.14–4.74) in ALBI score-1 and 8.18 100PY (95% CI , 5.60-11.55) in ALBI 2–3 (ALBI 2–3 vs ALBI 1: RR 3.28, 95% CI , 1.56–6.87). The high RR value suggests that ALBI score may play an important role in predicting the occurrence of HCC in patients with HCV-associated cirrhosis. Therefore, in clinical work, patients with ALBI 2–3 should be followed up more frequently to monitor the changes of patients' condition. AFP, the most frequently detected biomarker for HCC, is characterized by low sensitivity and specificity [ 5 , 36 ] ., and AFP is only expressed in 40% of early HCC. In active chronic liver disease, on the other hand, AFP levels elevated along with transaminases elevated can be explained as hepatocyte regeneration in some conditions. Hence, the performance of AFP alone as a biomarker for HCC is unsatisfactory at present. The results of this study showed that AFP was not an independent risk factor for HCC development before treatment, but was associated with HCC development at EOT, and AFP value decreased after DAAs treatment compared with that before, the difference was statistically significant ( P < 0.001). This suggests that AFP values are highly influenced by HCV activity and decreased significantly from baseline to EOT, along with viral eradication and transaminase normalization, manifesting the specificity of AFP in patients with post-SVR cirrhosis. Degasperi et al. [ 21 ] showed EOT-AFP > 15ng/ml in a cohort study of patients with CHC cirrhosis after DAAs treatment has good predictive accuracy for HCC occurrence. A cohort study [ 37 ] from Japan also showed that EOT-AFP is an important predictor of HCC occurrence, and in its proposed simple scoring system, EOT-AFP ≥ 6ng/ mL was used as an independent predictor of HCC occurrence in HCV-infected patients who achieved SVR after DAAs treatment. Several risk factors (Age, nonspecific liver nodules, the (ALBI) score and EOT-AFP) identified in our study overlap in many of the studies mentioned above, which indicates the reliability of our study. The AASLD Guideline [ 38 ] , the EASL Guideline [ 5 ] , and Guideline for the prevention and treatment of hepatitis C [ 13 ] in China all suggest that patients with HCV-related cirrhosis should be followed up every 6 months, including US and AFP testing. However, the sensitivity and specificity of these two indicators are relatively low, and SVR may change the risk of HCC. Moreover, liver function is severely impaired in patients with cirrhosis, and the incidence of HCC is higher than that of CHC patients. Therefore, more frequent and effective monitoring should be conducted in this group. However, considering the rational use of medical resources and the minimization of invasive damage and economic burden on patients, we screened high-risk HCC groups every 3–6 months by testing several commonly used clinical indicators, and proposed different monitoring strategies for different stratifications of HCC risk. For example, patients at high risk for HCV-related cirrhosis should undergo annual enhenced MRI or CT inspection. Assessment of HCC risk allows providers to individualize patient counseling, potentially improving compliance with monitoring recommendations and participation in care. Using this predictive model, patients with HCV-associated cirrhosis at high risk for HCC can be identified in advance and treated aggressively. It can be found that our nomogram model is especially associated to the increased risk of HCC in the short-term after DAAs treatment reaches SVR, and this model is also more suitable for predicting HCC at 1 and 3 years, which is worthy of our attention. Strengths of this study include that all risk factors were readily available at admission and that the utility of the prediction model was supported by external validation. Despite the important findings of this study, there are also limitations: First, most of the patients enrolled in this study were inpatients, which may bias the selection of research objects and affect the results of the study. Second, the results of this study only reflect the events during the follow-up period of 1–84 months, the follow-up time is not consistent, extending the follow-up time may have different results; Third, the follow-up time of the external validation cohort was different from that of the training group, and some baseline data were different; Moreover, the DAAs treatment regimen in this study is not uniform, so the influence of DAA factors on the results cannot be excluded [ 39 ] . Last, although commonly used indicators are suitable for general clinical development, the accuracy of prediction may be further improved if new tumor early screening markers, such as ctDNA/ glycotomic changes, are included. In conclusion, Based on four conventional clinical and laboratory parameters (age, nonspecific liver nodules, ALBI score, and AFP after treatment) without including viral factors, we propose an accurate, reliable, and simple model for predicting HCC risk, which has clinical application value to improve early HCC surveillance and reduce mortality. The significance of screening for HCC is not only to alert patients with HCV cirrhosis who need intensive follow-up to monitor of HCC, but also to present a new challenge for clinicians: how to optimize antiviral therapy to reduce or prevent the development of HCC, which will be the direction of our research. Declarations ETHICS APPROVAL STATEMENT The Medical Ethics Committee of Tianjin Second People's Hospital approved the study protocol, which conformed to the ethical guidelines of the Declaration of Helsinki amended in 2008. PATIENT CONSENT STATEMENT An opt-out approach was used to obtain informed consent from patients and personal information was protected during data collection. DATA AVAILABILITY STATEMENT Our data are not publicly available due to privacy concerns related to the clinical data of patients. Data requests can be made to the corresponding author, Chief Physician Liang Xu of Tianjin Second People's Hospital, via the following email: [email protected] / [email protected] . COMPETING INTERESTS The authors declare that they have no competing interests. AUTHOR CONTRIBUTION Xu L, Tao XM and Mi YQ designed the study; Zhang J provided experimental data for external validation; Xu L, Tao XM and Zhao YF performed the research; Tao XM and Wang ZY carried out statistical analysis; Tao XM wrote the manuscript; Xu L, Mi YQ and Lu W critical revised of the manuscript. all the authors have read and approved the final revision to be published. FUNDING STATEMENT This work was supported by National Natural Science Foundation of China, No. 62375202. Tianjin Key Medical Discipline (Specialty) Construction Project, No.TJYXZDXK-059B. Tianjin Health Science and Technology Project key discipline special, No.TJWJ2022XK034. Research project in key areas of TCM in 2024，No.2024022. References WHO Guidelines Approved by the Guidelines Review Committee [M]. Guidelines for the Care and Treatment of Persons Diagnosed with Chronic Hepatitis C Virus Infection. Geneva; World Health Organization© World Health Organization 2018. 2018. Global change in hepatitis C virus prevalence and cascade of care between 2015 and 2020: a modelling study [J]. Lancet Gastroenterol Hepatol, 2022, 7(5): 396-415. LI D K, CHUNG R T. Impact of hepatitis C virus eradication on hepatocellular carcinogenesis [J]. Cancer, 2015, 121(17): 2874-82. IRSHAD M, MANKOTIA D S, IRSHAD K. An insight into the diagnosis and pathogenesis of hepatitis C virus infection [J]. 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FIB-4: an inexpensive and accurate marker of fibrosis in HCV infection. comparison with liver biopsy and fibrotest [J]. Hepatology, 2007, 46(1): 32-6. JOHNSON P J, BERHANE S, KAGEBAYASHI C, et al. Assessment of liver function in patients with hepatocellular carcinoma: a new evidence-based approach-the ALBI grade [J]. J Clin Oncol, 2015, 33(6): 550-8. PINATO D J, SHARMA R, ALLARA E, et al. The ALBI grade provides objective hepatic reserve estimation across each BCLC stage of hepatocellular carcinoma [J]. J Hepatol, 2017, 66(2): 338-46. MARIñO Z, DARNELL A, LENS S, et al. Time association between hepatitis C therapy and hepatocellular carcinoma emergence in cirrhosis: Relevance of non-characterized nodules [J]. J Hepatol, 2019, 70(5): 874-84. IOANNOU G N, BESTE L A, GREEN P K, et al. Increased Risk for Hepatocellular Carcinoma Persists Up to 10 Years After HCV Eradication in Patients With Baseline Cirrhosis or High FIB-4 Scores [J]. Gastroenterology, 2019, 157(5): 1264-78.e4. KUMADA T, TOYODA H, YASUDA S, et al. Comparison of the Prognosis of Decompensated Cirrhosis in Patients with and Without Eradication of Hepatitis C Virus [J]. Infect Dis Ther, 2021, 10(2): 1001-13. DEGASPERI E, PERBELLINI R, D'AMBROSIO R, et al. Prothrombin induced by vitamin K absence or antagonist-II and alpha foetoprotein to predict development of hepatocellular carcinoma in Caucasian patients with hepatitis C-related cirrhosis treated with direct-acting antiviral agents [J]. Aliment Pharmacol Ther, 2022, 55(3): 350-9. MOHD HANAFIAH K, GROEGER J, FLAXMAN A D, et al. Global epidemiology of hepatitis C virus infection: new estimates of age-specific antibody to HCV seroprevalence [J]. Hepatology, 2013, 57(4): 1333-42. GOWER E, ESTES C, BLACH S, et al. Global epidemiology and genotype distribution of the hepatitis C virus infection [J]. J Hepatol, 2014, 61(1 Suppl): S45-57. AKINYEMIJU T, ABERA S, AHMED M, et al. 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Pathologic diagnosis of early hepatocellular carcinoma: a report of the international consensus group for hepatocellular neoplasia [J]. Hepatology, 2009, 49(2): 658-64. IAVARONE M, MANINI M A, SANGIOVANNI A, et al. Contrast-enhanced computed tomography and ultrasound-guided liver biopsy to diagnose dysplastic liver nodules in cirrhosis [J]. Dig Liver Dis, 2013, 45(1): 43-9. MAO S, YU X, SHAN Y, et al. Albumin-Bilirubin (ALBI) and Monocyte to Lymphocyte Ratio (MLR)-Based Nomogram Model to Predict Tumor Recurrence of AFP-Negative Hepatocellular Carcinoma [J]. J Hepatocell Carcinoma, 2021, 8: 1355-65. KARIYAMA K, NOUSO K, HIRAOKA A, et al. EZ-ALBI Score for Predicting Hepatocellular Carcinoma Prognosis [J]. Liver Cancer, 2020, 9(6): 734-43. LESCURE C, ESTRADE F, PEDRONO M, et al. ALBI Score Is a Strong Predictor of Toxicity Following SIRT for Hepatocellular Carcinoma [J]. Cancers (Basel), 2021, 13(15). ALONSO LóPEZ S, MANZANO M L, GEA F, et al. A Model Based on Noninvasive Markers Predicts Very Low Hepatocellular Carcinoma Risk After Viral Response in Hepatitis C Virus-Advanced Fibrosis [J]. Hepatology, 2020, 72(6): 1924-34. FAN R, PAPATHEODORIDIS G, SUN J, et al. aMAP risk score predicts hepatocellular carcinoma development in patients with chronic hepatitis [J]. J Hepatol, 2020, 73(6): 1368-78. MARRERO J A, KULIK L M, SIRLIN C B, et al. Diagnosis, Staging, and Management of Hepatocellular Carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases [J]. Hepatology, 2018, 68(2): 723-50. TANI J, MORISHITA A, SAKAMOTO T, et al. Simple scoring system for prediction of hepatocellular carcinoma occurrence after hepatitis C virus eradication by direct-acting antiviral treatment: All Kagawa Liver Disease Group Study [J]. Oncol Lett, 2020, 19(3): 2205-12. Hepatitis C Guidance 2018 Update: AASLD-IDSA Recommendations for Testing, Managing, and Treating Hepatitis C Virus Infection [J]. Clin Infect Dis, 2018, 67(10): 1477-92. DASH S, AYDIN Y, WIDMER K E, et al. Hepatocellular Carcinoma Mechanisms Associated with Chronic HCV Infection and the Impact of Direct-Acting Antiviral Treatment [J]. J Hepatocell Carcinoma, 2020, 7: 45-76. Tables Table 1. Baseline characteristics of the training cohort (n＝309) Variables training cohort （n＝309） Variables training cohort （n＝309） Ages (years) 57.02±10.12 TP (g/L) 72.19±8.23 Gender (M/F) 124/185 ALB (g/L) 38.87±6.07 Cirrhosis (compensate /de) 246/63 TBIL (μmol/L) 18.40 (14.30,26.55) nonspecific liver nodules (Y/N) 250/59 BUN (mmol/L) 4.88±2.05 Child-pugh（A/B/C） 240/60/8 CRE (μmol/L) 56 (47,66) genotype (1a/1b/2a/3a/3b/6a) 1/202/62/12/14/18 UA (mmol/L) 303.52±93.08 lg (HCV RNA) IU/ml 6 (5,6) TG (mmol/L) 1.13±0.57 Along with the disease CHO (mmol/L) 3.96±1.15 Diabetes (with/out) 62/247 HDL (mmol/L) 1.25±1.07 Fatty liver (with/out) 63/246 LDL (mmol/L) 1.96±0.75 Hypertension (with/out) 67/242 GLU (mmol/L) 6.32±1.84 FIB-4 5.28 (3.07,8.54) PT (s) 14.36±2.06 ALBI -2.45±0.59 INR 1.27±1.23 AFP (ng/mL) 9.48 (5.43,18.71) WBC (109/L) 4.27±1.69 PIVKA-II (mAU/mL) 23 (18,32) RBC (1012/L) 4.11±0.73 ALT (U/L) 48 (29,76) HGB (g/L) 128.97±22.92 AST (U/L) 66.87±41.63 PLT (109/L) 105.49±60.52 γ-GT (U/L) 56.00 (32.30,97.00) CAP (dB/m) 230.97±44.22 ALP (U/L) 94.52±41.78 LSM (kPa) 25.56±16.22 Note: HCV DNA was calculated by denary logarithm expressed as lg(HCV DNA); FIB-4: The Fibrosis-4 index; ALBI: The albumin-Bilirubin score; CEA: carcinoembryonic antigen; AFP: alpha fetoprotein; PIVKA-II: Vitamin K Absence or Antagonist-II; ALT: alanine aminotransferase; AST: aspartate aminotransferase; γ-GT: γ-alanine transferase; ALP: alkaline phosphatase; TP: total protein; ALB: albumin; TBIL: total bilirubin; BUN: renal function; CRE: creatinine; UA: uric acid; TG: triglyceride; CHO: total cholesterol; HDL: high density lipoprotein; LDL: low density lipoprotein; GLU: blood glucose; PT: prothrombin time; INR: international standardized ratio of prothrombin time; WBC: white blood cell; RBC: red blood cell; HGB: hemoglobin; PLT: platelet; CAP: controlled attenuation parameter; LSM: liver stiffness measurement. Table 2 Changes of laboratory parameters before and after SVR in the training cohort (n=309) Variables Before DAA streatment after DAAs treatment t/Z/χ 2 P ALT (U/L) 59.21±44.76 22.70±16,77 13.39 0 AST (U/L) 66.87±41.63 28.05±22.32 14.413 0 γ-GT (U/L) 56.00 (32.30,97.00) 28.00 (20.00,43.38) 10.156 0 ALP (U/L) 94.52±41.78 85.92±33.89 2.796 0.005 TP (g/L) 72.19±8.23 73.77±7.72 -2.444 0.015 ALB (g/L) 38.87±6.07 42.97±6.75 -7.937 0 TBIL (μmol/L) 18.40 (14.30,26.55) 17.70 (13.00,24.55) -1.939 0.053 BUN (mmol/L) 4.88±2.05 5.80±3.41 -3.982 0 GLU (mmol/L) 6.32±1.84 6.77±2.27 -2.51 0.012 PT (s) 14.36±2.06 14.37±6.07 -0.015 0.988 INR 1.27±1.23 1.21±0.55 0.522 0.602 WBC (10 9 /L) 4.27±1.69 4.92±2.28 -3.981 0 RBC (10 12 /L) 4.11±0.73 4.25±0.76 -2.509 0.012 HGB (g/L) 128.97±22.92 131.09±24.40 -1.106 0.269 PLT (10 9 /L) 105.49±60.52 120.76±63.87 -3.03 0.003 CEA (ng/mL) 3.06 (2.06,4.61) 3.05 (1.79,4.83) 0.009 0.993 AFP (ng/mL) 9.47 (5.44,18.65) 5.10 (3.33,7.20) 10.612 0 PIVKA-II(mAU/mL) 23 (18,32) 26 (21,38) -2.735 0.006 CAP (dB/m) 230.97±44.22 241.85±53.01 -2.062 0.04 LSM (kPa) 25.56±16.22 19.58±15.63 3.499 0.001 Note: SVR: sustained virologic response; DAA: Direct-Acting Antiviral Agents; HCV DNA was calculated by denary logarithm expressed as lg(HCV DNA); CEA: carcinoembryonic antigen; AFP: alpha fetoprotein; PIVKA-II: Vitamin K Absence or Antagonist-II; ALT: alanine aminotransferase; AST: aspartate aminotransferase; γ-GT: γ-alanine transferase; ALP: alkaline phosphatase; TP: total protein; ALB: albumin; TBIL: total bilirubin; BUN: renal function; GLU: blood glucose; PT: prothrombin time; INR: international standardized ratio of prothrombin time; WBC: white blood cell; RBC: red blood cell; HGB: hemoglobin; PLT: platelet; CAP: controlled attenuation parameter; LSM: liver stiffness measurement. Table 3 Factors related to HCC occurrence in the training cohort (n=309) Variables Univariate Multivariate HR 95%CI P value HR 95%CI P value Ages (years) 1.053 1.017-1.090 0.004 1.052 1.013-1.093 0.009 Gender (M/F) 1.705 0.919-3.164 0.091 Cirrhosis (compensate /de) 0.049 0.248-0.968 0.04 nonspecific liver nodules (Y/N) 0.357 0.185-0.687 0.002 3.27 1.641-6.517 0.001 Child-pugh (A/B/C) 2.14 1.340-3.416 0.001 FIB-4 1.07 1.033-1.109 0 ALBI 2.27 1.552-3.320 0 2.463 1.584-3.830 0 AFP (ng/mL) 0.976 0.948-1.005 0.098 PIVKA-II (mAU/mL) 1 1.000-1.001 0.445 ALT (U/L) 0.993 0.984-1.002 0.128 AST (U/L) 1.002 0.995-1.009 0.538 ALB (g/L) 0.925 0.885-0.966 0 TIBL (μmol/L) 1.018 1.010-1.026 0 EOT AFP (ng/mL) 1.048 1.030-1.065 0 1.031 1.013-1.049 0.001 ALT (U/L) 1.019 1.007-1.030 0.002 AST (U/L) 1.009 1.003-1.015 0.002 γ-GT (U/L) 1.005 1.002-1.008 0.001 ALB (g/L) 0.933 0.899-0.969 0 PLT (10 9 /L) 0.987 0.987-0.994 0 Note: HCC: hepatocellular carcinoma; DAA: Direct-Acting Antiviral Agents; HR: Hazard ratio; CI : confidence interval; FIB-4: The Fibrosis-4 index; ALBI: The albumin-Bilirubin score; AFP: alpha fetoprotein; PIVKA-II: Vitamin K Absence or Antagonist-II; ALT: alanine aminotransferase; AST: aspartate aminotransferase; ALB: albumin; TBIL: total bilirubin; EOT: end of treatment; γ-GT: γ-alanine transferase; PLT: platelet. Table 4. Incidence of HCC according to different subgroups in the training cohort (n=309) Events HCC/HCC at risk Incidence 100 PY (95%CI) Contrast RR univariate (95%CI) All patients 41/309 5.45(3.91-7.40) aged ≥58 years 30/155 7.86(7.86-11.23) ≥58 vs <58 2.65(1.33-5.28) aged <58 years 11/154 2.97(1.48-5.32) with liver nodules 13/59 10.15(5.40-17.36) With vs without 2.26(1.17-5.37) without liver nodules 28/250 4.68(3.09-6.81) ALBI 2-3 32/170 8.18(5.60-11.55) ABLI 2-3 vs 1 3.28(1.56-6.87) ALBI 1 9/139 2.50(1.14-4.74) Note: HCC: hepatocellular carcinoma; PY: patient-years; CI : confidence interval; RR: relative risk; ALBI: The Albumin-Bilirubin score. Table 5. Baseline characteristics between the training cohort (n=309) and the validation cohort (n=363) Variables Training（n＝309） Validation(n＝363) t/Z/χ 2 P Ages (years) 57.02±10.12 61.48±6.17 -6.697 0.000 Gender (M/F) 124/185 156/207 0.556 0.456 Cirrhosis (compensate /de) 246/63 292/71 0.072 0.789 nonspecific liver nodules (Y/N) 250/59 283/80 0.882 0.348 ALBI -2.45±0.59 -2.63±0.49 4.318 0.000 AFP (ng/mL) 9.48 (5.43,18.71) 7.20(4.22,13.02) 7.625 0.006 ALT (U/L) 48 (29,76) 55.8(33.6,89) 5.809 0.016 AST (U/L) 56(38,84) 59(40,88.9) 1.539 0.215 γ-GT (U/L) 56(32.3,97) 45(28,73) 2.639 0.104 CHO (mmol/L) 3.96±1.15 3.77±0.81 2.401 0.017 WBC (10 9 /L) 4.27±1.69 4.74±1.80 -3.458 0.001 HGB (g/L) 128.97±22.92 133.04±21.70 -2.356 0.019 PLT (10 9 /L) 105.49±60.52 118.24±57.64 -2.789 0.005 CAP (dB/m) 230.97±44.22 238.29±40.74 -1.955 0.051 LSM (kPa) 25.56±16.22 21.04±11.74 3.537 0.000 Note: DAA: Direct-Acting Antiviral Agents; ALBI: The albumin-Bilirubin score; AFP: alpha fetoprotein; ALT: alanine aminotransferase; AST: aspartate aminotransferase; γ-GT: γ-alanine transferase; CHO: total cholesterol; WBC: white blood cell; HGB: hemoglobin; PLT: platelet; CAP: controlled attenuation parameter; LSM: liver stiffness measurement Additional Declarations No competing interests reported. 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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-3852585","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":267944491,"identity":"5b05e3f5-0c6a-44a3-916a-2ce17b1659ba","order_by":0,"name":"xuemei tao","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"xuemei","middleName":"","lastName":"tao","suffix":""},{"id":267944492,"identity":"5ce641e4-bed7-4352-a47e-8af4ad1b0bca","order_by":1,"name":"Youfei Zhao","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Youfei","middleName":"","lastName":"Zhao","suffix":""},{"id":267944493,"identity":"c7e3e057-30a2-40e9-ae76-d094a30bb045","order_by":2,"name":"Zeyu Wang","email":"","orcid":"","institution":"Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Zeyu","middleName":"","lastName":"Wang","suffix":""},{"id":267944494,"identity":"1d3c1bec-0e72-48ad-89fd-e98405063952","order_by":3,"name":"wei lu","email":"","orcid":"","institution":"Tianjin Medical University Cancer Institute and Hospital","correspondingAuthor":false,"prefix":"","firstName":"wei","middleName":"","lastName":"lu","suffix":""},{"id":267944495,"identity":"f60d6e7c-b49a-4a4d-acc8-4f30cf0b964b","order_by":4,"name":"Jing Zhang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhang","suffix":""},{"id":267944496,"identity":"38f99534-29b3-48ad-83fa-e1632721c413","order_by":5,"name":"Yuqiang Mi","email":"","orcid":"","institution":"Tianjin Second People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuqiang","middleName":"","lastName":"Mi","suffix":""},{"id":267944497,"identity":"7120c5d2-b519-43c6-9deb-a57af1e40401","order_by":6,"name":"Liang Xu","email":"data:image/png;base64,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","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Liang","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-01-11 06:59:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3852585/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3852585/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49895392,"identity":"034c6d8a-1a08-4dbf-ab1a-38ac6fdef2cb","added_by":"auto","created_at":"2024-01-19 21:40:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":266483,"visible":true,"origin":"","legend":"\u003cp\u003eStudy time points and follow-up of this study. Note: DAA: Direct-Acting Antiviral Agents; SVR: sustained virological response; US: ultrasound; CT: computed tomography; HCC: hepatocellular carcinoma; MRI: magnetic resonance imaging.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3852585/v1/9eb9b95c1cc7f4f426960796.png"},{"id":49894581,"identity":"bd96f5ae-79fe-4c3f-9be8-3577a066a359","added_by":"auto","created_at":"2024-01-19 21:32:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":296766,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram to predict the 1-year, 3-year and 5-year risk of HCC. Note: Nodules: nonspecific liver nodules; ALBI: The albumin-Bilirubin score; AFP: alpha fetoprotein; HCC: hepatocellular carcinoma.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3852585/v1/4281c8dcfd195350190881ac.png"},{"id":49894580,"identity":"298b4350-8a39-4c5e-b1b7-689231dabd59","added_by":"auto","created_at":"2024-01-19 21:32:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":129832,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves of the rosette for predicting HCC in training cohort and validation cohort. A: The ROC curve analysis of the purposed nomogram for predicting HCC occurrence in the training cohort. B: The ROC curve analysis of the purposed nomogram for predicting HCC occurrence in the external validation cohort.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3852585/v1/f0fa8c78aca2c87f334ce38a.png"},{"id":49896096,"identity":"d365cb57-84bf-403a-ae15-90edd87ddeda","added_by":"auto","created_at":"2024-01-19 21:48:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":409104,"visible":true,"origin":"","legend":"\u003cp\u003eA-C: Calibration curves for the occurrence of HCC at 1, 3, and 5 years predicted in the training cohort. D-F: The calibration of the purposed nomogram for predicting HCC occurrence in the external validation cohort. G-I: The results of DCA of the purposed nomogram for predicting HCC in the validation cohort.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3852585/v1/a534a69b8d01972a4d2d177b.png"},{"id":49894583,"identity":"f9e60e85-bd3d-474b-8228-d93509f8cf59","added_by":"auto","created_at":"2024-01-19 21:32:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":233590,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of cumulative occurrence of HCV-associated HCC after DAAs treatment. Figure A: Cumulative incidence of HCC in patients aged ≥58 years and \u0026lt; 58 years in the training cohort. Note: age＝0: the cumulative incidence of HCC in patients aged ≥58 years in the DAA-treated group; age＝1: the cumulative incidence of HCC in patients aged \u0026lt;58 years in the training cohort. Figure B: Cumulative incidence of HCC in the training cohort with/without abnormal liver nodules. livernodules＝0: the cumulative incidence of HCC in patients with abnormal liver nodules in the DAA-treated group; livernodules＝1: the cumulative incidence of HCC in patients without abnormal liver nodules in the training cohort.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3852585/v1/585c6c9af0f35febd4669e53.png"},{"id":50170523,"identity":"a58c10ef-92e5-47a2-a978-c33b48e07021","added_by":"auto","created_at":"2024-01-25 15:37:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1282716,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3852585/v1/105172b8-77eb-40e0-921f-f76cbae59e91.pdf"},{"id":49894585,"identity":"66b92e03-7e89-4c74-ad26-f9c8d24d511c","added_by":"auto","created_at":"2024-01-19 21:32:41","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":931762,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.tif","url":"https://assets-eu.researchsquare.com/files/rs-3852585/v1/f9aa90bcefb3c14c82acda80.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Novel Nomogram for Predicting Hepatocellular Carcinoma in Hepatitis C virus-associated Cirrhosis Patients after eliminating virus with Direct-acting Antivirals","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eHepatitis C virus (HCV) infection is still a global epidemic. An estimated 71\u0026nbsp;million people were chronically infected with HCV worldwide in 2015, and 399,000 died from cirrhosis or hepatocellular carcinoma (HCC) caused by HCV infection \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. China is a region with high incidence of HCV infection. According to data published by Polaris Observatory HCV Collaborators \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, an estimated 9,487,000 people were infected with HCV in China in 2020. Chronic HCV infection has been identified as an independent risk factor for HCC development, especially in patients with cirrhosis \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The incidence of HCV-associated HCC is 1\u0026ndash;3% after 30 years of infection, mainly in patients with advanced liver fibrosis or cirrhosis, and 2\u0026ndash;4% annually once cirrhosis has developed \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. At present, Direct-Acting Antiviral Agents (DAAs) is the main treatment for patients with chronic hepatitis C (CHC) and HCV-associated cirrhosis, which have not only changed the scope and spectrum of treatment, but also had high elimination rate of HCV RNA, sufficient safety and few contraindications compared with interferon \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Moreover, through different combined treatment solutions, more than 95% of SVR can be achieved, regardless of HCV genotype or degree of fibrosis \u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Most of studies have shown that there is still a risk of incidence of HCC in CHC patients after achieving sustained virological response (SVR) with DAAs \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Therefore, it is important to study the impact of DAAs therapy on the incidence of HCC in the era of DAAs. At present, there are several models established for predicting HCC in CHC, but few study for HCV-cirrhosis (in high risk of HCC), especially achieved SVR after DAAs treatment. In this study, cox regression and nomogram were used to explore the risk factors and prediction models of HCC in HCV- cirrhosis patients achieved SVR with DAAs.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Included patients\u003c/h2\u003e \u003cp\u003eA total of 309 HCV-associated cirrhosis patients who completed DAAs treatment in Tianjin Second People's Hospital from January 2014 to April 2020 were enrolled in this study (training cohort). Another 363 DAAs-treated patients with HCV-associated cirrhosis in Beijing You 'an Hospital, Capital Medical University from April 2015 to May 2022 were enrolled as the validation cohort.\u003c/p\u003e \u003cp\u003eInclusion criteria :(1) age\u0026thinsp;\u0026ge;\u0026thinsp;18, no gender limitation; (2) All patients (both the training cohort and the validation cohort) were anti-HCV and HCV RNA positive before enrollment and acheived SVR with DAA treatment; (3) The criteria of HCV - cirrhosis and HCC are based on the 2018 European Association for the Study of the Liver (EASL) guidelines \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, and they were evaluated through imaging or pathological examination; (4) Obtain informed consent from all patients. Exclusion criteria :(1) Patients with prior history of extrahepatic tumors; (2) Patients who have received or were waiting for a liver transplant; (3) Patients with HIV, HAV, HBV, HEV co-infection; (4) alcoholic liver disease, autoimmune hepatitis, drug-induced liver injury, genetic metabolic liver disease and other liver disease patients; (5) Patients without follow-up records or incomplete follow-up data.\u003c/p\u003e \u003cp\u003eThe Medical Ethics Committee of Tianjin Second People's Hospital approved the study protocol, which conformed to the ethical guidelines of the Declaration of Helsinki amended in 2008. The approval number is the ethical review word [2018]21 of Tianjin Jin Second People's Hospital. Written, informed consent was obtained from each patient.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.2 Antivirus Solution\u003c/h3\u003e\n\u003cp\u003eThe 309 DAA patients in training cohort and the 363 DAA patients in validation cohort were treated by experienced clinicians according to the guidelines \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e: (1) sofosbuvir 400mg/d\u0026thinsp;+\u0026thinsp;daclatasvir 60mg/d\u0026thinsp;+\u0026thinsp;ribavirin 1000mg/d (12 weeks) regimen; (2) sofosbuvir 400mg/d\u0026thinsp;+\u0026thinsp;Velpatasvir 100mg/d (12 weeks); (3) sofosbuvir 400mg/d\u0026thinsp;+\u0026thinsp;ribavirin 1000mg/d (12 weeks); (4) Ombitasvir 300mg/d\u0026thinsp;+\u0026thinsp;dasabuvir 500mg/d (12 weeks); (5) sofosbuvir 400mg/d\u0026thinsp;+\u0026thinsp;ledipasvir 90mg/d\u0026thinsp;+\u0026thinsp;ribavirin 1000mg/d (12 weeks) regimen; (6) Elbasvir and Grazoprevir 50mg/d (12 weeks); (7) Dasabuvir 60mg\u0026thinsp;+\u0026thinsp;Asunaprevir 100mg (24 weeks) regimen.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Demographics and laboratory parameters\u003c/h2\u003e \u003cp\u003eWe recorded baseline outcomes for 309 patients at enrollment, including gender, age, weight, height, Body mass index (BMI), HCV genotype, compensatory / decompensated cirrhosis, Child-Pugh score, nonspecific liver nodules, hypertension, diabetes, fatty liver, HCV RNA, Protein Induced by Vitamin K Absence or Antagonist-II (PIVKA-II), carcinoembryonic antigen (CEA), alpha fetoprotein (AFP), serum biochemical indicators: alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), γ-alanine transferase (γ-GT), total bilirubin (TBIL), total protein (TP), albumin (ALB), renal function (BUN), creatinine (CRE), uric acid (UA), glomerular filtration rate (eGFR), blood glucose (GLU), triglyceride (TG), Total cholesterol (CHO), low density lipoprotein (LDL), high density lipoprotein (HDL); coagulation function: prothrombin time (PT), international standardized ratio of prothrombin time (INR); blood routine: red blood cell (RBC), hemoglobin (HGB), white blood cell (WBC), platelet (PLT); liver stiffness measurement (LSM, in kPa) and controlled attenuation parameter (CAP, in dB/m) values were obtained by FibroScan (Echosens, Paris, France).\u003c/p\u003e \u003cp\u003eThe Fibrosis-4 (FIB-4) index \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e was calculated as a surrogate marker of liver fibrosis. The score was calculated as follows: Fib-4 index\u0026thinsp;=\u0026thinsp;age (years) \u0026times; AST (IU/L) / PLT (10\u003csup\u003e9\u003c/sup\u003e/L) \u0026times; [ALT (IU/L)] \u003csup\u003e1/2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe albumin-Bilirubin (ALBI) score \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, a simple index reflecting the underlying liver function, was calculated for each patient by the following formula based on the albumin and bilirubin levels: ALBI score = (log10 bilirubin \u0026times; 0.66) + (albumin \u0026times; \u0026minus; 0.085); Bilirubin is in \u0026micro; mol/L and albumin in g/L.\u003c/p\u003e \u003cp\u003eAccording to the grading proposed by Professor Johnson \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, ALBI is divided into 1\u0026ndash;3 levels: \u0026le;-2.60(score 1), \u0026gt; -2.60 and \u0026le;-1.39(score 2), \u0026gt; -1.39(score 3).\u003c/p\u003e \u003cp\u003eThe above data were collected again as the patients of training cohort completed antiviral therapy and achieved SVR, and analyzed as factors of end of treatment (EOT). Thereafter, laboratory and demographic parameters were repeated every 6 months until HCC occurrence, death, or the end of follow-up. Laboratory data collected after EOT are denoted as EOT-.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Follow-up and diagnostic criteria of HCC\u003c/h2\u003e \u003cp\u003eThe end point of this study was the occurrence of HCC or the end of April 2020. All patients received nurse counselling, clinical visit and laboratory assessment (biochemistry, blood routine, HCC-associated tumor markers, etc.) at baseline and every 3\u0026ndash;6 months. According to the diagnostic criteria for HCC of EASL guidelines \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, patients were screened for HCC by ultrasound (US) or spiral dynamic computed tomography (CT) every 3\u0026ndash;6 months. When HCC is suspected, it is further examined by enhanced CT, magnetic resonance imaging (MRI), or liver angiography. If no typical manifestations of HCC are found, the diagnosis should be confirmed by fine needle biopsy and pathological examination. Follow-up period was calculated from the end of DAAs treatment to the diagnosis of HCC, patient death, or the end of follow-up. The date of HCC diagnosis was used as the alternative time of HCC occurrence (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Definitions of SVR and nonspecific liver nodules\u003c/h2\u003e \u003cp\u003eSVR definition: according to EASL guidelines \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, SVR is defined as 12 weeks after the end of treatment (SVR 12), and HCV RNA is not detected in serum or plasma, evaluated by highly sensitive molecular methods, additionally, the detection limit is 15IU/ml.\u003c/p\u003e \u003cp\u003eNonspecific liver nodules were defined \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e as \u0026le;\u0026thinsp;10 mm or nodules\u0026thinsp;\u0026gt;\u0026thinsp;10 mm but in which HCC diagnosis was ruled out before starting DAA by contrast enhanced US (CEUS), CT, MRI or biopsy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Validation cohort\u003c/h2\u003e \u003cp\u003e The validation cohort was from You 'an Hospital who were achieved SVR through DAA treatment, then we collected the laboratory and imaging data at baseline and followed up every 3\u0026ndash;6 months until HCC detected or the trial terminated. The diagnostic criteria for liver cirrhosis, HCC, SVR and liver nodules were agreed between the two hospitals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical Analysis\u003c/h2\u003e \u003cp\u003eData conforming to normal distribution were represented by (\u003cem\u003e\u0026oline;x\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;s), and HCV RNA was calculated by dennar logarithm. Independent sample \u003cem\u003et\u003c/em\u003e test was used to analyze and compare the training cohort. The skewness distribution of measurement data was represented by M (P25, P75). The comparison between the training and validation cohort was analyzed and compared by Mann-Whitney U test, and the paired samples were analyzed and compared by Wilcoxon signed rank test. The statistical data were expressed as percentages, and analyzed by Chi-square test or Fisher's exact probability method. Kaplan-Meier curve and log-rank test were used to compare the difference in the cumulative incidence of HCC between the two groups. All clinical data were included in binary logistic regression analysis for univariate and multivariate analysis to evaluate the influencing factors of HCC occurrence and obtain a regression equation. Cox proportional risk regression was used to re-evaluate the risk factors for HCC before and after DAAs treatment, and the independent predictors of HCC were obtained by incorporating the indicators with statistical differences in univariate analysis into multivariate analysis. Meanwhile, risk ratio and 95% confidence interval (\u003cem\u003eCI\u003c/em\u003e) were calculated, and a nomogram model was established to predict HCC. Receiver operating characteristic curve (ROC) curve and area under ROC (AUROC) evaluation model were used. ROC curve and AUC were used to quantify the prediction accuracy of the Nomogram model. The clinical value of the model was evaluated by decision curve analysis (DCA). \u003cem\u003eP\u003c/em\u003e༜0.05 was considered to indicate that all analyses were statistically significant. Incidences, expressed as 100 patient-years (100PY), relative risks (\u003cem\u003eRR\u003c/em\u003e) and their 95% \u003cem\u003eCI\u003c/em\u003e were estimated by means of Poisson regression models, using as offset the logarithm of radiological follow-up.\u003c/p\u003e \u003cp\u003eStatistical analyses were performed using SPSS version 26 (SPSS, Inc., Chicago, IL, USA), GraphPad Prism Version 9.0H (GRAPH PAD Software, Inc., La Jolla, CA, USA) and R Version 3.1.2 (R Core Development Team, 2010).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003e3.1 Baseline characteristics of patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 309 patients were included in this study from January 2014 to April 2020 (459 patients were not included according to the exclusion criteria) and completed follow-up. The baseline characteristics of the training cohort are reported in Table 1. The training cohort was followed up for 1-84 months, with a median follow-up of 28 months. The age of patients in the training cohort was (57.02\u0026plusmn;10.12) years old.\u0026nbsp;Among the patients, 124 were male and 185 were female. In the\u0026nbsp;training cohort, 62 (20.06%) out of the 309 patients had history of diabetes, 63 (20.39%) of\u0026nbsp;fatty liver, and 67 (21.68%) of arterial hypertension.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Changes of indicators in training cohort before and after DAAs treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter DAAs treatment, all patients in the training cohort achieved SVR (6 patients who did not achieve SVR were not included in this study), and the changes of liver function, blood lipid, coagulation function, tumor markers, LSM values and other indicators in training cohort before and after DAAs treatment were observed. It was found that AFP, ALT, AST, \u0026gamma;-GT, ALP, TP and LSM in the training cohort were significantly lower than that on the baseline (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05), and WBC, RBC and PLT became better after DAAs treatment (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05), as shown in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Incidence and cumulative incidence of HCC in training cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the study, 41 patients developed HCC within 1-66 months in the training cohort, and HCC incidence was 5.45 100PY (95% \u003cem\u003eCI\u003c/em\u003e, 3.91-7.40). The cumulative incidence of HCC at 12, 24, 36 and 48 months was 3.24%, 7.12%, 10.03% and 12.30%, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Risk factors of HCC after DAAs treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the training cohort, we evaluated some baseline indicators of patients and some indicators after DAAs treatment. Age, liver cirrhosis (compensatory / decompensated), non-specific liver nodules, Child-pugh grade, ALBI, ALB, TBIL, EOT-AFP, EOT-ALT, EOT-AST, EOT-\u0026gamma;-GT, EOT-ABL and EOT-PLT, etc. are risk factors for HCC in univariate analysis. Due to the excessive mixed factors of ALT, AST, and WBC, they were not included in the multivariate analysis. The remaining meaningful indicators of univariate analysis were included in cox regression for multivariate analysis step by step. The results showed that age, non-specific liver nodules, ALBI and EOT-AFP were independent risk factors for HCC, as shown in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Establish a nomogram for prediction of HCC after DAAs treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy cox regression analysis, we found that age, nonspecific liver nodules, ALBI, and EOT-AFP were independent risk factors for HCC in patients with HCV-associated cirrhosis after DAAs treatment. Based on the hazard ratio of each variable: age (hazard ratio [\u003cem\u003eHR\u003c/em\u003e] = 1.052; 95% \u003cem\u003eCI\u003c/em\u003e, 1.013-1.093; \u003cem\u003eP\u003c/em\u003e = 0.009) and non-specific liver nodules (\u003cem\u003eHR\u003c/em\u003e = 3.270; 95% \u003cem\u003eCI\u003c/em\u003e, 1.641 to 6.617; \u003cem\u003eP\u003c/em\u003e = 0.001), ALBI (\u003cem\u003eHR\u003c/em\u003e = 2.463; 95% \u003cem\u003eCI\u003c/em\u003e, 1.584 to 3.830; \u003cem\u003eP\u003c/em\u003e = 0.000), EOT-AFP (\u003cem\u003eHR\u003c/em\u003e = 1.031; 95% \u003cem\u003eCI\u003c/em\u003e, 1.013 to 1.049; \u003cem\u003eP\u003c/em\u003e = 0.001), we developed a nomogram to predict the 1-year, 3-year and 5-year incidence of HCC in training cohort, and then we draw ROCs to observe the accuracy of the model. The AUC values were 0.866, 0.813 and 0.764, respectively, as shown in Figure 2 and Figure 3(A). The calibration chart of HCC incidence in 1, 3, and 5 years predicted by this model was shown in Figure 4(A-C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Subgroup analysis of HCC incidence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the independent risk factors of patients in the training cohort, subgroup analysis was performed on HCC occurrence of patients in the training cohort. Age and non-specific liver nodules were inserted into the subject curve, respectively, and AUROC was 0.650 and 0.587, respectively. The optimal cut-off value of age was 0.266, and the value was 58 years. In log-rank test, the cumulative incidence of HCC in patients aged \u0026ge;58 years was significantly higher than that in patients aged \u0026lt; 58 years (\u003cem\u003eP\u003c/em\u003e = 0.001), meanwhile, the cumulative incidence of HCC in patients with non-specific liver nodules was significantly higher than that in patients without non-specific liver nodules (\u003cem\u003eP\u003c/em\u003e = 0.002). The specific cumulative incidence of HCC in the training cohort grouped by age and presence of non-specific liver nodules was shown in Figure 5. HCC incidence in patients with aged \u0026ge;58 years was 7.86 100PY (95% \u003cem\u003eCI\u003c/em\u003e, 7.86-11.23) and it was 2.97 100PY (95% \u003cem\u003eCI\u003c/em\u003e, 1.48-5.32) in patients with aged \u0026lt;58 years (\u0026ge;58 \u003cem\u003evs\u003c/em\u003e \u0026lt;58: \u003cem\u003eRR\u003c/em\u003e 2.65, 95% \u003cem\u003eCI\u003c/em\u003e, 1.33-5.28). HCC incidence was 10.15 100PY (95% \u003cem\u003eCI\u003c/em\u003e, 5.40-17.36) with non-specific liver nodules while was 4.68 100PY (95% \u003cem\u003eCI\u003c/em\u003e, 3.09-6.81) without it (with non-specific liver nodules \u003cem\u003evs\u003c/em\u003e without: \u003cem\u003eRR\u003c/em\u003e 2.26, 95% \u003cem\u003eCI\u003c/em\u003e, 1.17-5.37). HCC incidence was 2.50 100PY (95% \u003cem\u003eCI\u003c/em\u003e, 1.14-4.74) in ALBI score-1 and 8.18 100PY (95% \u003cem\u003eCI\u003c/em\u003e, 5.60-11.55) in ALBI 2-3 (ALBI 2-3 \u003cem\u003evs\u003c/em\u003e ALBI 1: \u003cem\u003eRR\u003c/em\u003e 3.28, 95% \u003cem\u003eCI\u003c/em\u003e, 1.56-6.87) (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 External validation of the nomogram\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eprediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 363 patients with HCV-associated cirrhosis after DAA from Beijing You \u0026apos;an Hospital were selected as the validation cohort. The patients were (61.48\u0026plusmn;6.17) years old, including 156 males and 207 females. Baseline data for the validation and test cohorts are provided in Table 5. 46 patients out of 363 patients (all achieved SVR) developed HCC in the duration of follow-up in the validation cohort, and HCC incidence were 2.34 100PY (95% \u003cem\u003eCI\u003c/em\u003e, 1.71-3.12).\u003c/p\u003e\n\u003cp\u003eThe AUCs of Nomogram on predicting HCC in validation cohort at 1-year, 3-year and 5-year were 0.884 (95% \u003cem\u003eCI\u003c/em\u003e, 0.851-0.917), 0.783 (95% \u003cem\u003eCI\u003c/em\u003e, 0.670-0.896) and 0.692 (95% \u003cem\u003eCI\u003c/em\u003e, 0.605-0.779), respectively. This nomogram showed a good prediction, as shown in Figure 3(B). The calibration plots of HCC occurrence at 1, 3, and 5 years were shown in Figure 4(D-F). The DCA showed that the threshold probability interval between 0.05-0.1 of the Nomogram model curve was higher than the two extreme curves [Figure 4(G-I)].\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eMany previous studies indicated that cirrhosis was a major risk factor of HCC in CHC patients. A cohort study of veterans from the United States \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e included more than 45,000 CHC patients determined that having cirrhosis and without obtaining SVR were predictive factors for HCC. Kumada et al. \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e took patients with CHC-related decompensated cirrhosis as the study subjects, including 364 patients from the UK as the \u0026ldquo;DAA group\u0026rdquo; and 249 patients from Japan as the control \u0026ldquo;non-DAA group\u0026rdquo;, and cox multivariate analysis suggested that ALB and failure to obtain SVR could increase the risk of HCC after adjusting the baseline characteristics between the two groups with propensity matching score. Degasperi et al. \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e enrolled 400 HCV associated cirrhosis patients achieving SVR after DAAs treatment that were tested PIVKA-II and AFP at the beginning and ending of DAAs treatment, and the results showed that the 4-year probability of HCC development was 3% in patients with negative for both markers, 18% in patients with positive for both, and 38% in patients with positive for at least one. Thus, PIVKA-II and AFP independently predict HCC in DAAs-treated patients with CHC cirrhosis, while their combination improved risk stratification. Nevertheless, most of the studies came from Europe, America and Japan, few from China. Meanwhile, the occurrence of HCC induced by HCV was on the rise in recent years \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Therefore, a high accuracy prediction model to monitor the occurrence of HCC in chinese patients is urgently needed, especially for HCV associated cirrhosis patients as a high-risk group of HCC. Of course, economy and accessibility of clinical application should be considered, so we established a prediction model based on simple laboratory indicators.\u003c/p\u003e \u003cp\u003eIn this study, age was brought into the ROCs curve, and the best cut-off value was 58 years old. ASAHINA et al. \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e also showed that elderly people had a higher risk of HCC. HCC incidence (10.15 100PY) in patients with non-specific liver nodules was more than without it (4.68 100PY and RR 2.26). Similarly, Mari\u0026ntilde;o et al. \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e found in a large cohort study in Spain that the highest risk of HCC among HCV-treated cirrhotics was associated to the recognition of NCLN at imaging. Even, a multicenter study by Sangiovanni et al. \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e also showed that non-specific nodules were related to HCC occurrence in patients with HCV-associated cirrhosis. This might be due to the fact that these patients with nonspecific nodules correspond to small low- or high-grade dysplasia or large regenerated nodules in cirrhosis. It was thought that those patients who developed HCC and had nonspecific nodules might belong to a class of patients without adequate screening. The emerging tumors may also be the result of the evolution of dysplastic nodules and of the growth of already existing subclinical dormant clones of transformed hepatocytes. The mechanisms involved in the transition from dysplasia to HCC or the growth of dormant clones are not known but immune surveillance is surely involved.\u003csup\u003e[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e Imaging limitations made the specific composition of nonspecific liver nodules unknown in our cohort, but there was no significant difference in nonspecific liver nodules between two groups according to the same criterion and radiologists. Data from Italy \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e show that evident nodules classified by biopsy as low or high-grade dysplasia may become HCC during follow-up. Neither the training corhorts nor validation cohorts underwent biopsy according to current recommendations to determine the origin of the nodules. Therefore, whether patients with HCV cirrhosis who have nonspecific liver nodules before treatment are suitable for DAAs treatment remains to be debated.\u003c/p\u003e \u003cp\u003eALBI score \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e is composed of ALB and TBIL, a simple, evidence-based and objective indicator that can reflect the potential liver function of patients at all stages of disease. A large number of studies \u003csup\u003e[\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e have shown that ALBI score can be used as a noninvasive predictor of HCC or in combination. ALONSO et al. \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e determined serum ALB in a multicenter cohort study (\u003cem\u003eHR\u003c/em\u003e 0.400; 95% \u003cem\u003eCI\u003c/em\u003e 0.174\u0026ndash;0.923) could be used as an independent risk factor for HCC development after SVR acquisition in patients with HCV advanced liver fibrosis, and a non-invasive predictive model was proposed. Fan et al. \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e, in a large, multi-center study, proposed aMAP score, a prediction model for HCC occurrence in patients with hepatitis B and C, also included ALBI score as one of the predictive indicators. In this study, HCC incidence was 2.50 100PY (95% \u003cem\u003eCI\u003c/em\u003e, 1.14\u0026ndash;4.74) in ALBI score-1 and 8.18 100PY (95% \u003cem\u003eCI\u003c/em\u003e, 5.60-11.55) in ALBI 2\u0026ndash;3 (ALBI 2\u0026ndash;3 \u003cem\u003evs\u003c/em\u003e ALBI 1: \u003cem\u003eRR\u003c/em\u003e 3.28, 95% \u003cem\u003eCI\u003c/em\u003e, 1.56\u0026ndash;6.87). The high RR value suggests that ALBI score may play an important role in predicting the occurrence of HCC in patients with HCV-associated cirrhosis. Therefore, in clinical work, patients with ALBI 2\u0026ndash;3 should be followed up more frequently to monitor the changes of patients' condition. AFP, the most frequently detected biomarker for HCC, is characterized by low sensitivity and specificity \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e., and AFP is only expressed in 40% of early HCC. In active chronic liver disease, on the other hand, AFP levels elevated along with transaminases elevated can be explained as hepatocyte regeneration in some conditions. Hence, the performance of AFP alone as a biomarker for HCC is unsatisfactory at present. The results of this study showed that AFP was not an independent risk factor for HCC development before treatment, but was associated with HCC development at EOT, and AFP value decreased after DAAs treatment compared with that before, the difference was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This suggests that AFP values are highly influenced by HCV activity and decreased significantly from baseline to EOT, along with viral eradication and transaminase normalization, manifesting the specificity of AFP in patients with post-SVR cirrhosis. Degasperi et al. \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e showed EOT-AFP\u0026thinsp;\u0026gt;\u0026thinsp;15ng/ml in a cohort study of patients with CHC cirrhosis after DAAs treatment has good predictive accuracy for HCC occurrence. A cohort study \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e from Japan also showed that EOT-AFP is an important predictor of HCC occurrence, and in its proposed simple scoring system, EOT-AFP\u0026thinsp;\u0026ge;\u0026thinsp;6ng/ mL was used as an independent predictor of HCC occurrence in HCV-infected patients who achieved SVR after DAAs treatment.\u003c/p\u003e \u003cp\u003eSeveral risk factors (Age, nonspecific liver nodules, the (ALBI) score and EOT-AFP) identified in our study overlap in many of the studies mentioned above, which indicates the reliability of our study. The AASLD Guideline \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e, the EASL Guideline \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, and Guideline for the prevention and treatment of hepatitis C \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e in China all suggest that patients with HCV-related cirrhosis should be followed up every 6 months, including US and AFP testing. However, the sensitivity and specificity of these two indicators are relatively low, and SVR may change the risk of HCC. Moreover, liver function is severely impaired in patients with cirrhosis, and the incidence of HCC is higher than that of CHC patients. Therefore, more frequent and effective monitoring should be conducted in this group. However, considering the rational use of medical resources and the minimization of invasive damage and economic burden on patients, we screened high-risk HCC groups every 3\u0026ndash;6 months by testing several commonly used clinical indicators, and proposed different monitoring strategies for different stratifications of HCC risk. For example, patients at high risk for HCV-related cirrhosis should undergo annual enhenced MRI or CT inspection. Assessment of HCC risk allows providers to individualize patient counseling, potentially improving compliance with monitoring recommendations and participation in care. Using this predictive model, patients with HCV-associated cirrhosis at high risk for HCC can be identified in advance and treated aggressively. It can be found that our nomogram model is especially associated to the increased risk of HCC in the short-term after DAAs treatment reaches SVR, and this model is also more suitable for predicting HCC at 1 and 3 years, which is worthy of our attention. Strengths of this study include that all risk factors were readily available at admission and that the utility of the prediction model was supported by external validation.\u003c/p\u003e \u003cp\u003eDespite the important findings of this study, there are also limitations: First, most of the patients enrolled in this study were inpatients, which may bias the selection of research objects and affect the results of the study. Second, the results of this study only reflect the events during the follow-up period of 1\u0026ndash;84 months, the follow-up time is not consistent, extending the follow-up time may have different results; Third, the follow-up time of the external validation cohort was different from that of the training group, and some baseline data were different; Moreover, the DAAs treatment regimen in this study is not uniform, so the influence of DAA factors on the results cannot be excluded \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Last, although commonly used indicators are suitable for general clinical development, the accuracy of prediction may be further improved if new tumor early screening markers, such as ctDNA/ glycotomic changes, are included.\u003c/p\u003e \u003cp\u003eIn conclusion, Based on four conventional clinical and laboratory parameters (age, nonspecific liver nodules, ALBI score, and AFP after treatment) without including viral factors, we propose an accurate, reliable, and simple model for predicting HCC risk, which has clinical application value to improve early HCC surveillance and reduce mortality. The significance of screening for HCC is not only to alert patients with HCV cirrhosis who need intensive follow-up to monitor of HCC, but also to present a new challenge for clinicians: how to optimize antiviral therapy to reduce or prevent the development of HCC, which will be the direction of our research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Medical Ethics Committee of Tianjin Second People\u0026apos;s Hospital approved the study protocol, which conformed to the ethical guidelines of the Declaration of Helsinki amended in 2008. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePATIENT CONSENT STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn opt-out approach was used to obtain informed consent from patients and personal information was protected during data collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur data are not publicly available due to privacy concerns related to the clinical data of patients. Data requests can be made to the corresponding author, Chief Physician Liang Xu of Tianjin Second People\u0026apos;s Hospital, via the following email: [email protected]/[email protected].\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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXu L, Tao XM and Mi YQ designed the study; Zhang J provided experimental data for external validation; Xu L, Tao XM and Zhao YF performed the research; Tao XM and Wang ZY carried out statistical analysis; Tao XM wrote the manuscript; Xu L, Mi YQ and Lu W critical revised of the manuscript. all the authors have read and approved the final revision to be published. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Natural Science Foundation of China, No. 62375202.\u003c/p\u003e\n\u003cp\u003eTianjin Key Medical Discipline (Specialty) Construction Project, No.TJYXZDXK-059B.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Tianjin Health Science and Technology Project key discipline special, No.TJWJ2022XK034.\u003c/p\u003e\n\u003cp\u003eResearch project in key areas of TCM in 2024，No.2024022.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO Guidelines Approved by the Guidelines Review Committee [M]. 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Undefined/non-malignant hepatic nodules are associated with early occurrence of HCC in DAA-treated patients with HCV-related cirrhosis [J]. J Hepatol, 2020, 73(3): 593-602.\u003c/li\u003e\n\u003cli\u003eFAN Y, MAO R, YANG J. NF-\u0026kappa;B and STAT3 signaling pathways collaboratively link inflammation to cancer [J]. Protein Cell, 2013, 4(3): 176-85.\u003c/li\u003e\n\u003cli\u003eHOENICKE L, ZENDER L. Immune surveillance of senescent cells--biological significance in cancer- and non-cancer pathologies [J]. Carcinogenesis, 2012, 33(6): 1123-6.\u003c/li\u003e\n\u003cli\u003ePathologic diagnosis of early hepatocellular carcinoma: a report of the international consensus group for hepatocellular neoplasia [J]. Hepatology, 2009, 49(2): 658-64.\u003c/li\u003e\n\u003cli\u003eIAVARONE M, MANINI M A, SANGIOVANNI A, et al. Contrast-enhanced computed tomography and ultrasound-guided liver biopsy to diagnose dysplastic liver nodules in cirrhosis [J]. Dig Liver Dis, 2013, 45(1): 43-9.\u003c/li\u003e\n\u003cli\u003eMAO S, YU X, SHAN Y, et al. Albumin-Bilirubin (ALBI) and Monocyte to Lymphocyte Ratio (MLR)-Based Nomogram Model to Predict Tumor Recurrence of AFP-Negative Hepatocellular Carcinoma [J]. J Hepatocell Carcinoma, 2021, 8: 1355-65.\u003c/li\u003e\n\u003cli\u003eKARIYAMA K, NOUSO K, HIRAOKA A, et al. EZ-ALBI Score for Predicting Hepatocellular Carcinoma Prognosis [J]. Liver Cancer, 2020, 9(6): 734-43.\u003c/li\u003e\n\u003cli\u003eLESCURE C, ESTRADE F, PEDRONO M, et al. ALBI Score Is a Strong Predictor of Toxicity Following SIRT for Hepatocellular Carcinoma [J]. Cancers (Basel), 2021, 13(15).\u003c/li\u003e\n\u003cli\u003eALONSO L\u0026oacute;PEZ S, MANZANO M L, GEA F, et al. A Model Based on Noninvasive Markers Predicts Very Low Hepatocellular Carcinoma Risk After Viral Response in Hepatitis C Virus-Advanced Fibrosis [J]. Hepatology, 2020, 72(6): 1924-34.\u003c/li\u003e\n\u003cli\u003eFAN R, PAPATHEODORIDIS G, SUN J, et al. aMAP risk score predicts hepatocellular carcinoma development in patients with chronic hepatitis [J]. J Hepatol, 2020, 73(6): 1368-78.\u003c/li\u003e\n\u003cli\u003eMARRERO J A, KULIK L M, SIRLIN C B, et al. Diagnosis, Staging, and Management of Hepatocellular Carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases [J]. Hepatology, 2018, 68(2): 723-50.\u003c/li\u003e\n\u003cli\u003eTANI J, MORISHITA A, SAKAMOTO T, et al. Simple scoring system for prediction of hepatocellular carcinoma occurrence after hepatitis C virus eradication by direct-acting antiviral treatment: All Kagawa Liver Disease Group Study [J]. Oncol Lett, 2020, 19(3): 2205-12.\u003c/li\u003e\n\u003cli\u003eHepatitis C Guidance 2018 Update: AASLD-IDSA Recommendations for Testing, Managing, and Treating Hepatitis C Virus Infection [J]. Clin Infect Dis, 2018, 67(10): 1477-92.\u003c/li\u003e\n\u003cli\u003eDASH S, AYDIN Y, WIDMER K E, et al. Hepatocellular Carcinoma Mechanisms Associated with Chronic HCV Infection and the Impact of Direct-Acting Antiviral Treatment [J]. J Hepatocell Carcinoma, 2020, 7: 45-76.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Baseline characteristics of the training cohort (n＝309)\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.035830618892508%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2442996742671%\" valign=\"top\"\u003e\n \u003cp\u003etraining cohort\u003c/p\u003e\n \u003cp\u003e（n＝309）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.941368078175895%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.778501628664497%\" valign=\"top\"\u003e\n \u003cp\u003etraining cohort\u003c/p\u003e\n \u003cp\u003e（n＝309）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.035830618892508%\" valign=\"top\"\u003e\n \u003cp\u003eAges (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2442996742671%\" valign=\"top\"\u003e\n \u003cp\u003e57.02\u0026plusmn;10.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.941368078175895%\" valign=\"top\"\u003e\n \u003cp\u003eTP\u0026nbsp;(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.778501628664497%\" valign=\"top\"\u003e\n \u003cp\u003e72.19\u0026plusmn;8.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.035830618892508%\" valign=\"top\"\u003e\n \u003cp\u003eGender (M/F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2442996742671%\" valign=\"top\"\u003e\n \u003cp\u003e124/185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.941368078175895%\" valign=\"top\"\u003e\n \u003cp\u003eALB\u0026nbsp;(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.778501628664497%\" valign=\"top\"\u003e\n \u003cp\u003e38.87\u0026plusmn;6.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.035830618892508%\" valign=\"top\"\u003e\n \u003cp\u003eCirrhosis (compensate /de)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2442996742671%\" valign=\"top\"\u003e\n \u003cp\u003e246/63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.941368078175895%\" valign=\"top\"\u003e\n \u003cp\u003eTBIL\u0026nbsp;(\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.778501628664497%\" valign=\"top\"\u003e\n \u003cp\u003e18.40 (14.30,26.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.035830618892508%\" valign=\"top\"\u003e\n \u003cp\u003enonspecific liver nodules (Y/N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2442996742671%\" valign=\"top\"\u003e\n \u003cp\u003e250/59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.941368078175895%\" valign=\"top\"\u003e\n \u003cp\u003eBUN\u0026nbsp;(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.778501628664497%\" valign=\"top\"\u003e\n \u003cp\u003e4.88\u0026plusmn;2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.035830618892508%\" valign=\"top\"\u003e\n \u003cp\u003eChild-pugh（A/B/C）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2442996742671%\" valign=\"top\"\u003e\n \u003cp\u003e240/60/8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.941368078175895%\" valign=\"top\"\u003e\n \u003cp\u003eCRE\u0026nbsp;(\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.778501628664497%\" valign=\"top\"\u003e\n \u003cp\u003e56 (47,66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.035830618892508%\" valign=\"top\"\u003e\n \u003cp\u003egenotype (1a/1b/2a/3a/3b/6a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2442996742671%\" valign=\"top\"\u003e\n \u003cp\u003e1/202/62/12/14/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.941368078175895%\" valign=\"top\"\u003e\n \u003cp\u003eUA\u0026nbsp;(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.778501628664497%\" valign=\"top\"\u003e\n \u003cp\u003e303.52\u0026plusmn;93.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.035830618892508%\" valign=\"top\"\u003e\n \u003cp\u003elg (HCV RNA) IU/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2442996742671%\" valign=\"top\"\u003e\n \u003cp\u003e6 (5,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.941368078175895%\" valign=\"top\"\u003e\n \u003cp\u003eTG\u0026nbsp;(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.778501628664497%\" valign=\"top\"\u003e\n \u003cp\u003e1.13\u0026plusmn;0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"null\"\u003eAlong with the disease\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"null\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"null\"\u003eCHO (mmol/L)\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"null\"\u003e3.96\u0026plusmn;1.15\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.035830618892508%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes (with/out)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2442996742671%\" valign=\"top\"\u003e\n \u003cp\u003e62/247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.941368078175895%\" valign=\"top\"\u003e\n \u003cp\u003eHDL\u0026nbsp;(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.778501628664497%\" valign=\"top\"\u003e\n \u003cp\u003e1.25\u0026plusmn;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.035830618892508%\" valign=\"top\"\u003e\n \u003cp\u003eFatty liver (with/out)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2442996742671%\" valign=\"top\"\u003e\n \u003cp\u003e63/246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.941368078175895%\" valign=\"top\"\u003e\n \u003cp\u003eLDL\u0026nbsp;(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.778501628664497%\" valign=\"top\"\u003e\n \u003cp\u003e1.96\u0026plusmn;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.035830618892508%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension (with/out)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2442996742671%\" valign=\"top\"\u003e\n \u003cp\u003e67/242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.941368078175895%\" valign=\"top\"\u003e\n \u003cp\u003eGLU\u0026nbsp;(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.778501628664497%\" valign=\"top\"\u003e\n \u003cp\u003e6.32\u0026plusmn;1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.035830618892508%\" valign=\"top\"\u003e\n \u003cp\u003eFIB-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2442996742671%\" valign=\"top\"\u003e\n \u003cp\u003e5.28 (3.07,8.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.941368078175895%\" valign=\"top\"\u003e\n \u003cp\u003ePT (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.778501628664497%\" valign=\"top\"\u003e\n \u003cp\u003e14.36\u0026plusmn;2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.035830618892508%\" valign=\"top\"\u003e\n \u003cp\u003eALBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2442996742671%\" valign=\"top\"\u003e\n \u003cp\u003e-2.45\u0026plusmn;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.941368078175895%\" valign=\"top\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.778501628664497%\" valign=\"top\"\u003e\n \u003cp\u003e1.27\u0026plusmn;1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.035830618892508%\" valign=\"top\"\u003e\n \u003cp\u003eAFP (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2442996742671%\" valign=\"top\"\u003e\n \u003cp\u003e9.48 (5.43,18.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.941368078175895%\" valign=\"top\"\u003e\n \u003cp\u003eWBC\u0026nbsp;(109/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.778501628664497%\" valign=\"top\"\u003e\n \u003cp\u003e4.27\u0026plusmn;1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.035830618892508%\" valign=\"top\"\u003e\n \u003cp\u003ePIVKA-II (mAU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2442996742671%\" valign=\"top\"\u003e\n \u003cp\u003e23 (18,32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.941368078175895%\" valign=\"top\"\u003e\n \u003cp\u003eRBC\u0026nbsp;(1012/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.778501628664497%\" valign=\"top\"\u003e\n \u003cp\u003e4.11\u0026plusmn;0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.035830618892508%\" valign=\"top\"\u003e\n \u003cp\u003eALT\u0026nbsp;(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2442996742671%\" valign=\"top\"\u003e\n \u003cp\u003e48 (29,76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.941368078175895%\" valign=\"top\"\u003e\n \u003cp\u003eHGB\u0026nbsp;(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.778501628664497%\" valign=\"top\"\u003e\n \u003cp\u003e128.97\u0026plusmn;22.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.035830618892508%\" valign=\"top\"\u003e\n \u003cp\u003eAST\u0026nbsp;(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2442996742671%\" valign=\"top\"\u003e\n \u003cp\u003e66.87\u0026plusmn;41.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.941368078175895%\" valign=\"top\"\u003e\n \u003cp\u003ePLT\u0026nbsp;(109/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.778501628664497%\" valign=\"top\"\u003e\n \u003cp\u003e105.49\u0026plusmn;60.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.035830618892508%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gamma;-GT\u0026nbsp;(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2442996742671%\" valign=\"top\"\u003e\n \u003cp\u003e56.00 (32.30,97.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.941368078175895%\" valign=\"top\"\u003e\n \u003cp\u003eCAP (dB/m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.778501628664497%\" valign=\"top\"\u003e\n \u003cp\u003e230.97\u0026plusmn;44.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.035830618892508%\" valign=\"top\"\u003e\n \u003cp\u003eALP\u0026nbsp;(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.2442996742671%\" valign=\"top\"\u003e\n \u003cp\u003e94.52\u0026plusmn;41.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.941368078175895%\" valign=\"top\"\u003e\n \u003cp\u003eLSM (kPa)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.778501628664497%\" valign=\"top\"\u003e\n \u003cp\u003e25.56\u0026plusmn;16.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: HCV DNA was calculated by denary logarithm expressed as lg(HCV DNA); FIB-4: The Fibrosis-4 index; ALBI: The albumin-Bilirubin score; CEA: carcinoembryonic antigen; AFP: alpha fetoprotein; PIVKA-II: Vitamin K Absence or Antagonist-II; ALT: alanine aminotransferase; AST: aspartate aminotransferase; \u0026gamma;-GT: \u0026gamma;-alanine transferase; ALP: alkaline phosphatase; TP: total protein; ALB: albumin; TBIL: total bilirubin; BUN: renal function; CRE: creatinine; UA: uric acid; TG: triglyceride; CHO: total cholesterol; HDL: high density lipoprotein; LDL: low density lipoprotein; GLU: blood glucose; PT: prothrombin time; INR: international standardized ratio of prothrombin time; WBC: white blood cell; RBC: red blood cell; HGB: hemoglobin; PLT: platelet; CAP: controlled attenuation parameter; LSM: liver stiffness measurement.\u003c/p\u003e\n\u003cp\u003eTable 2 Changes of laboratory parameters before and after SVR in the training cohort (n=309)\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003eBefore DAA streatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003eafter DAAs treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003et/Z/\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e59.21\u0026plusmn;44.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e22.70\u0026plusmn;16,77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e13.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e66.87\u0026plusmn;41.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e28.05\u0026plusmn;22.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e14.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gamma;-GT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e56.00 (32.30,97.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e28.00 (20.00,43.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e10.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003eALP (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e94.52\u0026plusmn;41.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e85.92\u0026plusmn;33.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e2.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003eTP (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e72.19\u0026plusmn;8.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e73.77\u0026plusmn;7.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e-2.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003eALB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e38.87\u0026plusmn;6.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e42.97\u0026plusmn;6.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e-7.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003eTBIL (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e18.40 (14.30,26.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e17.70 (13.00,24.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e-1.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003eBUN (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e4.88\u0026plusmn;2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e5.80\u0026plusmn;3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e-3.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003eGLU (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e6.32\u0026plusmn;1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e6.77\u0026plusmn;2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e-2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003ePT (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e14.36\u0026plusmn;2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e14.37\u0026plusmn;6.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e1.27\u0026plusmn;1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e1.21\u0026plusmn;0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003eWBC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e4.27\u0026plusmn;1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e4.92\u0026plusmn;2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e-3.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003eRBC (10\u003csup\u003e12\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e4.11\u0026plusmn;0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e4.25\u0026plusmn;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e-2.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003eHGB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e128.97\u0026plusmn;22.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e131.09\u0026plusmn;24.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e-1.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003ePLT (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e105.49\u0026plusmn;60.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e120.76\u0026plusmn;63.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e-3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003eCEA (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e3.06 (2.06,4.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e3.05 (1.79,4.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003eAFP (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e9.47 (5.44,18.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e5.10 (3.33,7.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e10.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003ePIVKA-II(mAU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e23 (18,32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e26 (21,38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e-2.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003eCAP (dB/m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e230.97\u0026plusmn;44.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e241.85\u0026plusmn;53.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e-2.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003eLSM (kPa)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.14569536423841%\" valign=\"top\"\u003e\n \u003cp\u003e25.56\u0026plusmn;16.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.655629139072847%\" valign=\"top\"\u003e\n \u003cp\u003e19.58\u0026plusmn;15.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.264900662251655%\" valign=\"top\"\u003e\n \u003cp\u003e3.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.278145695364238%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: SVR: sustained virologic response; DAA: Direct-Acting Antiviral Agents; HCV DNA was calculated by denary logarithm expressed as lg(HCV DNA); CEA: carcinoembryonic antigen; AFP: alpha fetoprotein; PIVKA-II: Vitamin K Absence or Antagonist-II; ALT: alanine aminotransferase; AST: aspartate aminotransferase; \u0026gamma;-GT: \u0026gamma;-alanine transferase; ALP: alkaline phosphatase; TP: total protein; ALB: albumin; TBIL: total bilirubin; BUN: renal function; GLU: blood glucose; PT: prothrombin time; INR: international standardized ratio of prothrombin time; WBC: white blood cell; RBC: red blood cell; HGB: hemoglobin; PLT: platelet; CAP: controlled attenuation parameter; LSM: liver stiffness measurement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 Factors related to HCC occurrence in the training cohort (n=309)\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"561\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.54188948306595%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eUnivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.54188948306595%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMultivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.951219512195122%\" valign=\"top\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.097560975609756%\" valign=\"top\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.951219512195122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.951219512195122%\" valign=\"top\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.097560975609756%\" valign=\"top\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.951219512195122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eAges (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e1.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e1.017-1.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e1.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e1.013-1.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eGender (M/F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e1.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e0.919-3.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eCirrhosis (compensate /de)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e0.248-0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003enonspecific liver nodules (Y/N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e0.185-0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e1.641-6.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eChild-pugh (A/B/C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e1.340-3.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eFIB-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e1.033-1.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eALBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e1.552-3.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e2.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e1.584-3.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eAFP (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e0.948-1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003ePIVKA-II (mAU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e1.000-1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e0.984-1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e0.995-1.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eALB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e0.885-0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eTIBL (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e1.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e1.010-1.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEOT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eAFP (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e1.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e1.030-1.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e1.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e1.013-1.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e1.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e1.007-1.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e1.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e1.003-1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gamma;-GT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e1.002-1.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003eALB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e0.899-0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.916221033868094%\" valign=\"top\"\u003e\n \u003cp\u003ePLT (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\n \u003cp\u003e0.987-0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.073083778966133%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.73440285204991%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: HCC: hepatocellular carcinoma; DAA: Direct-Acting Antiviral Agents; HR: Hazard ratio; \u003cem\u003eCI\u003c/em\u003e: confidence interval; FIB-4: The Fibrosis-4 index; ALBI: The albumin-Bilirubin score; AFP: alpha fetoprotein; PIVKA-II: Vitamin K Absence or Antagonist-II; ALT: alanine aminotransferase; AST: aspartate aminotransferase; ALB: albumin; TBIL: total bilirubin; EOT: end of treatment; \u0026gamma;-GT: \u0026gamma;-alanine transferase; PLT: platelet.\u003c/p\u003e\n\u003cp\u003eTable 4. Incidence of HCC according to different subgroups in the training cohort (n=309)\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.83974358974359%\" valign=\"top\"\u003e\n \u003cp\u003eEvents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003eHCC/HCC at risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.115384615384617%\" valign=\"top\"\u003e\n \u003cp\u003eIncidence 100 PY (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eContrast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eRR univariate (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.83974358974359%\" valign=\"top\"\u003e\n \u003cp\u003eAll patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e41/309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.115384615384617%\" valign=\"top\"\u003e\n \u003cp\u003e5.45(3.91-7.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.83974358974359%\" valign=\"top\"\u003e\n \u003cp\u003eaged\u0026nbsp;\u0026ge;58 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e30/155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.115384615384617%\" valign=\"top\"\u003e\n \u003cp\u003e7.86(7.86-11.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;58 \u003cem\u003evs\u003c/em\u003e \u0026lt;58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\" valign=\"top\"\u003e\n \u003cp\u003e2.65(1.33-5.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.83974358974359%\" valign=\"top\"\u003e\n \u003cp\u003eaged \u0026lt;58 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e11/154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.115384615384617%\" valign=\"top\"\u003e\n \u003cp\u003e2.97(1.48-5.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.83974358974359%\" valign=\"top\"\u003e\n \u003cp\u003ewith liver nodules\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e13/59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.115384615384617%\" valign=\"top\"\u003e\n \u003cp\u003e10.15(5.40-17.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eWith \u003cem\u003evs\u003c/em\u003e without\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\" valign=\"top\"\u003e\n \u003cp\u003e2.26(1.17-5.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.83974358974359%\" valign=\"top\"\u003e\n \u003cp\u003ewithout liver nodules\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e28/250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.115384615384617%\" valign=\"top\"\u003e\n \u003cp\u003e4.68(3.09-6.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.83974358974359%\" valign=\"top\"\u003e\n \u003cp\u003eALBI 2-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e32/170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.115384615384617%\" valign=\"top\"\u003e\n \u003cp\u003e8.18(5.60-11.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\" valign=\"top\"\u003e\n \u003cp\u003eABLI 2-3 \u003cem\u003evs\u003c/em\u003e 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\" valign=\"top\"\u003e\n \u003cp\u003e3.28(1.56-6.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.83974358974359%\" valign=\"top\"\u003e\n \u003cp\u003eALBI 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e9/139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.115384615384617%\" valign=\"top\"\u003e\n \u003cp\u003e2.50(1.14-4.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.71153846153846%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: HCC: hepatocellular carcinoma; PY: patient-years; \u003cem\u003eCI\u003c/em\u003e:\u0026nbsp;confidence interval;\u0026nbsp;RR: relative risk;\u0026nbsp;ALBI: The Albumin-Bilirubin score.\u003c/p\u003e\n\u003cp\u003eTable 5. Baseline characteristics between the training cohort (n=309) and the validation cohort (n=363)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\" valign=\"top\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.452554744525546%\" valign=\"top\"\u003e\n \u003cp\u003eTraining（n＝309）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.26277372262774%\" valign=\"top\"\u003e\n \u003cp\u003eValidation(n＝363)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.861313868613138%\" valign=\"top\"\u003e\n \u003cp\u003et/Z/\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\" valign=\"top\"\u003e\n \u003cp\u003eAges (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.452554744525546%\" valign=\"top\"\u003e\n \u003cp\u003e57.02\u0026plusmn;10.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.26277372262774%\" valign=\"top\"\u003e\n \u003cp\u003e61.48\u0026plusmn;6.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.861313868613138%\" valign=\"top\"\u003e\n \u003cp\u003e-6.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\" valign=\"top\"\u003e\n \u003cp\u003eGender (M/F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.452554744525546%\" valign=\"top\"\u003e\n \u003cp\u003e124/185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.26277372262774%\" valign=\"top\"\u003e\n \u003cp\u003e156/207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.861313868613138%\" valign=\"top\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e0.456\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\" valign=\"top\"\u003e\n \u003cp\u003eCirrhosis (compensate /de)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.452554744525546%\" valign=\"top\"\u003e\n \u003cp\u003e246/63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.26277372262774%\" valign=\"top\"\u003e\n \u003cp\u003e292/71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.861313868613138%\" valign=\"top\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e0.789\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\" valign=\"top\"\u003e\n \u003cp\u003enonspecific liver nodules (Y/N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.452554744525546%\" valign=\"top\"\u003e\n \u003cp\u003e250/59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.26277372262774%\" valign=\"top\"\u003e\n \u003cp\u003e283/80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.861313868613138%\" valign=\"top\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e0.348\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\" valign=\"top\"\u003e\n \u003cp\u003eALBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.452554744525546%\" valign=\"top\"\u003e\n \u003cp\u003e-2.45\u0026plusmn;0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.26277372262774%\" valign=\"top\"\u003e\n \u003cp\u003e-2.63\u0026plusmn;0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.861313868613138%\" valign=\"top\"\u003e\n \u003cp\u003e4.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\" valign=\"top\"\u003e\n \u003cp\u003eAFP (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.452554744525546%\" valign=\"top\"\u003e\n \u003cp\u003e9.48 (5.43,18.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.26277372262774%\" valign=\"top\"\u003e\n \u003cp\u003e7.20(4.22,13.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.861313868613138%\" valign=\"top\"\u003e\n \u003cp\u003e7.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\" valign=\"top\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.452554744525546%\" valign=\"top\"\u003e\n \u003cp\u003e48 (29,76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.26277372262774%\" valign=\"top\"\u003e\n \u003cp\u003e55.8(33.6,89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.861313868613138%\" valign=\"top\"\u003e\n \u003cp\u003e5.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e0.016\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\" valign=\"top\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.452554744525546%\" valign=\"top\"\u003e\n \u003cp\u003e56(38,84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.26277372262774%\" valign=\"top\"\u003e\n \u003cp\u003e59(40,88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.861313868613138%\" valign=\"top\"\u003e\n \u003cp\u003e1.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e0.215\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gamma;-GT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.452554744525546%\" valign=\"top\"\u003e\n \u003cp\u003e56(32.3,97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.26277372262774%\" valign=\"top\"\u003e\n \u003cp\u003e45(28,73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.861313868613138%\" valign=\"top\"\u003e\n \u003cp\u003e2.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e0.104\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\" valign=\"top\"\u003e\n \u003cp\u003eCHO (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.452554744525546%\" valign=\"top\"\u003e\n \u003cp\u003e3.96\u0026plusmn;1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.26277372262774%\" valign=\"top\"\u003e\n \u003cp\u003e3.77\u0026plusmn;0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.861313868613138%\" valign=\"top\"\u003e\n \u003cp\u003e2.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e0.017\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\" valign=\"top\"\u003e\n \u003cp\u003eWBC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.452554744525546%\" valign=\"top\"\u003e\n \u003cp\u003e4.27\u0026plusmn;1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.26277372262774%\" valign=\"top\"\u003e\n \u003cp\u003e4.74\u0026plusmn;1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.861313868613138%\" valign=\"top\"\u003e\n \u003cp\u003e-3.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\" valign=\"top\"\u003e\n \u003cp\u003eHGB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.452554744525546%\" valign=\"top\"\u003e\n \u003cp\u003e128.97\u0026plusmn;22.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.26277372262774%\" valign=\"top\"\u003e\n \u003cp\u003e133.04\u0026plusmn;21.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.861313868613138%\" valign=\"top\"\u003e\n \u003cp\u003e-2.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e0.019\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\" valign=\"top\"\u003e\n \u003cp\u003ePLT (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.452554744525546%\" valign=\"top\"\u003e\n \u003cp\u003e105.49\u0026plusmn;60.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.26277372262774%\" valign=\"top\"\u003e\n \u003cp\u003e118.24\u0026plusmn;57.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.861313868613138%\" valign=\"top\"\u003e\n \u003cp\u003e-2.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\" valign=\"top\"\u003e\n \u003cp\u003eCAP (dB/m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.452554744525546%\" valign=\"top\"\u003e\n \u003cp\u003e230.97\u0026plusmn;44.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.26277372262774%\" valign=\"top\"\u003e\n \u003cp\u003e238.29\u0026plusmn;40.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.861313868613138%\" valign=\"top\"\u003e\n \u003cp\u003e-1.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e0.051\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\" valign=\"top\"\u003e\n \u003cp\u003eLSM (kPa)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.452554744525546%\" valign=\"top\"\u003e\n \u003cp\u003e25.56\u0026plusmn;16.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.26277372262774%\" valign=\"top\"\u003e\n \u003cp\u003e21.04\u0026plusmn;11.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.861313868613138%\" valign=\"top\"\u003e\n \u003cp\u003e3.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.576642335766424%\" valign=\"top\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: DAA: Direct-Acting Antiviral Agents; ALBI: The albumin-Bilirubin score; AFP: alpha fetoprotein; ALT: alanine aminotransferase; AST: aspartate aminotransferase; \u0026gamma;-GT: \u0026gamma;-alanine transferase; CHO: total cholesterol; WBC: white blood cell; HGB: hemoglobin; PLT: platelet; CAP: controlled attenuation parameter; LSM: liver stiffness measurement\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"direct-acting antiviral agents, sustained virological response, HCV-associated cirrhosis, hepatocellular carcinoma, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-3852585/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3852585/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and aims:\u003c/strong\u003eHepatitis C virus (HCV) associated cirrhosis are in high risk of hepatocellular carcinoma (HCC), and this study aimed to explore the risk factors, and establish and validate a novel nomogram.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A total of 309 inpatients with HCV- associated cirrhosis from Tianjin Second People's Hospital were selected as the training cohort, and 363 patients from Beijing You’an Hospital were selected as the validation cohort. Both cohorts received Direct-Acting Antiviral Agents (DAAs) treatment and achieved sustained virological response (SVR). Laboratory parameters were collected at baseline and duration of follow-up. Cox regression analysis was used to explore risk factors of HCC, and a nomogram for prediction was developed and validated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e HCC incidence was 5.45 100PY (95% CI, 3.91-7.40) in patients of the training cohort. Age, nonspecific liver nodules, the albumin-Bilirubin (ALBI) score and end of treatment (EOT)-AFP are independent risk factors for HCC by Cox regression analysis. A nomogram was used to predict the 1-year, 3-year and 5-year incidence of HCC, with the areas under receiver operating characteristic curves (AUROCs) of 0.866, 0.813 and 0.764, respectively. The AUROCs in validation cohort at 1, 3, and 5 years were 0.884, 0.783 and 0.692 in this nomogram, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This novel nomogram had a good predictive ability for HCC in patients with HCV-associated cirrhosis after eliminating virus with direct-acting antiviral agents, especially in 3 years.\u003c/p\u003e","manuscriptTitle":"Novel Nomogram for Predicting Hepatocellular Carcinoma in Hepatitis C virus-associated Cirrhosis Patients after eliminating virus with Direct-acting Antivirals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-19 21:32:36","doi":"10.21203/rs.3.rs-3852585/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":"eff2a8ca-7748-4421-b849-7b2433749f1c","owner":[],"postedDate":"January 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28234653,"name":"Biological sciences/Cancer"},{"id":28234654,"name":"Health sciences/Gastroenterology"},{"id":28234655,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2024-01-25T15:29:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-19 21:32:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3852585","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3852585","identity":"rs-3852585","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}