The Rising Threat of Liver Cancer in Patients with Cirrhosis: Are Indeterminate Liver Nodules Cause for Concern? Real-world, long-term follow-up data

The Rising Threat of Liver Cancer in Patients with Cirrhosis: Are Indeterminate Liver Nodules Cause for Concern? 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Real-world, long-term follow-up data Yousef Yahia, Ma'mon Qasem, Shahem Abbarh, Husam Saffo, Ibrahim M. Obeidat, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4676169/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Oct, 2024 Read the published version in Journal of Gastrointestinal Cancer → Version 1 posted 7 You are reading this latest preprint version Abstract Background: Several studies have shown a higher risk of liver cancer from indeterminate liver nodules, but the exact occurrence and predictors of liver cancer in this group are still unclear. Our aim is to study the development of liver cancer in this population and identify any potential risk factors. Methods: This retrospective study evaluated cirrhotic patients with indeterminate liver nodules from 2013 to 2023.Data from electronic patient records was analyzed to assess the association between HCC and baseline factors.Subgroup exploratory analysis compared characteristics of patients with de novo HCC and those with nodule transformation HCC. Results: Out of 116 patients with liver nodules, 19 (16%) developed HCC in up to 7.5-year follow-up. Univariate Cox regression analysis showed a significant association between HCC incidence and smoking [hazard ratio (HR) 2.60, 95% Confidence Interval [CI] 1.01-6.74), nodule diameter exceeding 2cm (HR 5.41, 95% CI 1.45-20.18), and baseline LI-RADS score 3 (HR 3.78, 95% CI 1.36-19.52). Multivariate Cox regression analysis revealed significant independent associations with nodule diameters 1 cm to <2cm (adjusted HR 3.35, 95% CI 1.06-10.60) and greater than 2cm (adjusted HR 5.85, 95% CI 1.10-31.16), as well as with LI-RADS 3 lesions (adjusted HR 3.75, 95% CI 1.16-12.11) with adjusting other potential predictors and covariates. Conclusion: Our findings show a higher incidence of HCC in patients with indeterminate liver nodules, increasing over time and reaching 30% at seven years. Nodules larger than 1-2 cm or LI-RADS 3 lesions pose increased risk for HCC. Enhanced surveillance is necessary given the lack of clear management guidelines. Hepatocellular carcinoma Liver cirrhosis Liver nodules Natural History Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction HCC is the fifth most common type of cancer worldwide[ 1 ] and is characterized by its rapid growth[ 2 , 3 ]. It is also the leading cause of death among individuals with liver cirrhosis[ 4 ].Certain population studies suggest that HCC may become the third most common cause of cancer-related deaths in the USA[ 5 , 6 ]. In the Gulf region, which is home to diverse expatriate communities, the age-standardized incidence rate (ASR) for HCC is 4.7 per 100,000 individuals, with a mortality rate of 4.5, as reported by the World Health Organization (WHO)[ 7 ]. In 2017, Qatar reported the highest liver cancer mortality rate among Gulf region countries[ 8 ].Moreover, the incidence of HCC increases with age, with estimates indicating an annual rise of 1–8%[ 9 ]. The pathophysiology of HCC remains incompletely understood, but it is believed to entail a multi-step progression resulting in both micro and macroscopic alterations. These alterations give rise to preneoplastic nodules that require prompt and precise management. Additionally, HCC can develop independently of pre-existing indeterminate nodules, in a process known as de novo hepatocarcinogenesis[ 10 , 11 ].However, the extent to which indeterminate nodules alone increase the risk of HCC, either by originating from these nodules or arising de novo, has not been adequately studied. In patients with cirrhosis, both American and European guidelines recommend screening for HCC every six months using abdominal ultrasound, as early detection can significantly improve outcomes[ 1 , 12 ].If suspicious lesions are found during the ultrasound, further evaluation should be conducted using contrast-enhanced abdominal computed tomography (CT) scan or magnetic resonance imaging (MRI) of the liver. The increased surveillance for HCC in clinical settings has led to a higher detection rate of indeterminate liver nodules. These liver nodules or lesions do not display the typical radiological features of HCC, such as arterial enhancement, venous phase washout, and delayed phase washout, nor do they exhibit any suspicious features that would warrant a liver biopsy and are usually labeled as Liver Imaging Reporting and Data System (LI-RADS) 2–3 lesions. Consequently, these indeterminant nodules present a challenge for clinicians as they lack the characteristic features of benign liver lesions, and, simultaneously, there are no clear guidelines for managing them[ 13 ].Performing a liver biopsy for every indeterminate liver nodule is impractical due to the invasive nature of the procedure and the potential risks outweighing the benefits, particularly with the increasing number of nodules detected through radiological modalities. Although these nodules may not be cancerous, previous studies have indicated their potential to transform into HCC in the future[ 14 , 15 ]. However, limited data is available on managing these nodules when found in cirrhotic patients during routine surveillance. Close observation has been the most common approach used, but there is still controversy over the imaging modality and the follow-up duration. For example, the American Association for the Study of Liver Disease (AASLD) practice guidance, published in 2018, suggests various options for indeterminate nodules, including follow-up imaging, imaging with a different modality or contrast agent, or biopsy. Nevertheless, the guidance does not recommend one option over the others[ 12 ]. It is difficult to assess the risk of HCC in patients with indeterminate liver lesions due to limited available data on the natural history of these lesions. The incidence of HCC in these patients varies widely, ranging from 14–65%[ 14 – 16 ].In the past, research studies have typically overlooked cases of de novo hepatocellular carcinoma (HCC), focusing mainly on nodular outcomes without considering the potential occurrence of de novo HCC. To address this gap, we conducted an observational study to determine the overall incidence of HCC within this specific patient group, encompassing both de novo HCC and HCC arising from existing nodules. This approach could provide a new framework for exploring the impact of undetermined nodules on the surrounding liver tissue, thereby paving the way for further research. Additionally, the study aimed to identify potential predictors and radiological factors that contribute to the occurrence of HCC and to compare these findings with previous studies on HCC incidence in the general cirrhotic population and in cirrhotic patients with indeterminate liver nodules. Method Study Population and Design: We conducted a retrospective study at a single tertiary center, observing patients with cirrhosis who had at least one liver nodule that could not be definitively diagnosed as HCC between January 2013 and December 2023. These nodules are typically detected during routine chronic liver disease management at our hepatology clinic. Such nodules must not exhibit the characteristic radiological features of HCC, such as arterial enhancement, venous phase washout, delayed phase washout, or any suspicious features that necessitate a liver biopsy. Our center routinely performs liver MRI for any indeterminate liver nodules and classifies the lesions based on LI-RADS terminology. Therefore, we identified our study population by searching the electronic medical records, including MRI reports, for specific terms such as "liver nodule," "hepatic nodule," "regenerative nodule," "dysplastic nodule," "indeterminate nodule," "LI-RADS-3," and "LI-RADS-2." The LI-RADS classification system is utilized for categorizing liver lesions in high-risk patients with liver cirrhosis. A LI-RADS-5 classification confirms HCC diagnosis without needing further histological diagnosis, whereas LI-RADS-1 indicates a confirmed benign lesion that does not require additional evaluation. (More details about the LI-RADS system can be found in the Supplementary Appendix). The start date was the date of the first MRI scan that reported an indeterminate lesion. Patients are usually monitored with MRI scans every 6 to 12 months. MRI images with these nodules are reviewed from the first MRI until the end of the study, lost to follow-up, or HCC development. Baseline characteristics of the patients (age, sex, etiology of liver disease, liver function tests, bilirubin, alpha-fetoprotein (AFP), Child-Pugh score, Model of End-stage Liver disease (MELD) score) and radiological features were collected from the electronic medical records. Experienced radiologists perform a comprehensive review of subsequent MRI scans to assess the progression of indeterminate lesions, track the transformation rate to HCC, and assign LI-RADS scores to the lesions. The diagnosis of HCC is determined based on the LI-RADS scoring system or, when necessary, histological diagnosis. Inclusion criteria included individuals aged 18 and older who have been diagnosed with cirrhosis and have at least one hepatic nodule detected by MRI. These nodules should not display definitive HCC (LI-RADS-5) or probable HCC (LI-RADS-4) features that require liver biopsy and pose a high risk of HCC. Additionally, the patient should have at least one stable MRI liver finding within 6–12 months from the initial MRI with the nodules. Exclusion criteria for the study included the absence of cirrhosis, definite HCC (LI-RADS-5) or probable HCC (LI-RADS-4) at baseline, and benign features that do not warrant further evaluation (LI-RADS-1), such as hemangioma. (Detailed eligibility criteria can be found in the Supplementary Appendix.) Statistical Consideration and Data Analysis: For continuous variables, mean (SD) and median (IQR) were used for normally and non-normally distributed data, respectively. Categorical variables are displayed as frequencies and percentages. The study's primary outcome was time to HCC occurrence, and this was estimated using Kaplan-Meier curves. This was measured from the first MRI scan with liver nodules until the patient developed HCC, either de novo or on top of the previous nodule. In the case of censoring, the time was calculated until the last MRI scan before the end of the study. Associations between two or more qualitative data variables were assessed using Chi-square (χ2) test or Fisher Exact test as appropriate. Quantitative data between the two independent groups (HCC and non-HCC) were analyzed using unpaired t or Mann Whitney U test as appropriate. The evaluation of potential risk factors and predictors associated with HCC was studied using Cox regression models followed by Log-rank test to compare the incidence of HCC across various potential subgroups. The impact of clinical, biochemical, and radiological parameters on HCC was examined. Univariate Cox regression model was used to calculate hazard ratios (HR) and their 95% confidence intervals (CI) and to estimate the risk association between each covariate and HCC. After identifying the covariates significantly (considering both statistical and clinical significance) associated with HCC incidence, they were incorporated into a multivariate Cox regression model to establish independent associations. Furthermore, an exploratory statistical analysis was conducted for HCC patients, comparing the potential factors and radiological characteristics of the de novo HCC group and HCC on top of the nodule group. A two-sided P-value < 0.05 was considered as statistically significant. All Statistical analyses were done using the statistical packages SPSS version 29.0 (Armonk, NY: IBM Corp) and Epi Info 2000 (Centers for Disease Control and Prevention, Atlanta, GA). All methods followed the Declaration of Helsinki and the hospital's guidelines and regulations. The ethical committee waived the need for consent, as only anonymous data without patients' identifiers were provided to the research team. The study was approved by the ethics and research committee at the Medical Research Center of Hamad Medical Corporation, Doha, Qatar, with the approval number MRC-01-24-085. Results We identified 116 patients who satisfied the inclusion criteria and presented with liver nodules. Among these individuals, 19 patients (16%) developed HCC; 6 had de novo HCC, and 13 had HCC originating from an indeterminate lesion. Table 1 presents the baseline demographic information, liver disease characteristics, and radiological features of the lesions at the time of the initial liver nodule detection. Most of the cohort were males (82%), and all HCC cases occurred in males (100%). The average age of the entire cohort was 50.17 (± 10.6) years. The primary ethnicity in both groups was Middle Eastern, followed by Asians. The cohort mainly consisted of non-smokers (73%) who had no history of alcohol consumption (87.9%), a pattern that was also observed in the group diagnosed with HCC. In the cohort, hepatitis C virus (HCV) infection was the main cause of liver cirrhosis, accounting for 50.9% of cases. It was even more prevalent in the HCC group, representing 87.9% of cases. No cases of HCC were attributed to metabolic-associated liver disease (MASLD). Child-Pugh grade A was the most common in both non-HCC and HCC groups. The majority of nodules (74.1%) were less than 1 cm in diameter, including 47% in the HCC group. The LI-RADS 2 score was present in 56% of the total cohort, 61.9% in the non-HCC group, and 26.3% in the HCC group. In the Kaplan–Meier curve analysis, the cumulative incidence of HCC at 12, 36, 60, and 84 months was 3.7%, 10%, 20%, and 30%, respectively, as illustrated in Fig. 1 . The log-rank test results from Kaplan–Meier estimates indicated significant differences in incidence of HCC based on smoking status (non-smoker vs smoker), sex (male vs female), alcohol intake (history of alcohol intake - regardless of the amount vs no alcohol intake), size of the largest nodule at the time of diagnosis (less than 2cm or above), and T2-signal by MRI (hyperintense or iso-hypointense) (Table 2, Figs. 2 – 5 ). The univariate Cox regression analysis revealed a statistically significant association between the occurrence of HCC and smoking (hazard ratio [HR] 2.60, 95% Confidence Interval [CI] 1.01–6.74). Additionally, there was a notable association with baseline nodule diameter, remarkably when the size exceeded 2cm (HR 5.41, 95% CI 1.45–20.18) and baseline LI-RADS 3 (HR 3.78, 95% CI 1.36–19.52) (Table 3). The multivariate Cox analysis revealed significant independent associations with nodule diameters greater than 1cm (HR 3.35, 95% CI 1.06–10.60) and 2cm (HR 5.85, 95% CI 1.10-31.16), as well as with LI-RADS 3 lesions (HR 3.75, 95% CI 1.16–12.11) (Table 4). Subgroup exploratory analysis was conducted for the HCC group to descriptively compare the clinical, biochemical, and radiological features of patients with de novo HCC and patients with HCC arising from pre-existing nodules at the time of nodule diagnosis and HCC diagnosis (Table 5). The two subgroups had similar demographic features and blood test results, except for a higher platelet count in the de novo HCC group. The baseline radiological features showed differences, with more smaller nodules(< 1cm) and LI-RADS2 predominance in the de novo HCC group. At the HCC time, there were no significant deviations from the initial baseline blood test results except for AFP levels in the de novo HCC group, which had a median of 50 (range 3.7–158) compared to a median of 9.5 (range 4.5–113) in the other group. Both groups showed a consistent increase in the size of the primary nodule at the time of HCC diagnosis, with 50% of the nodules in the de novo cohort being 2cm or larger, compared to 62% in the other group. Discussion This is a real-world data study; our primary objective was to evaluate the risk of HCC in patients with indeterminate liver nodules. Our findings revealed that 16% of the patients developed HCC during the study period, with cumulative incidence rates at 1, 3, 5, and 7 years of 3.7%, 10%, 20%, and 30%, respectively. Comparing our results with previous studies that evaluated the cumulative incidence of HCC in cirrhotic patients, we observed that our cohort exhibited a higher cumulative incidence of HCC over the years. For instance, a Swedish nationwide retrospective study found a cumulative incidence of HCC in cirrhosis of 8.3% at 5 years[ 17 ]. Ioannou et al. found a 4.7% HCC incidence over 3.6 years[ 9 ],while Paranaguá Vezozzo DC et al. reported a 14.3% cumulative incidence over 5 years[ 18 ]. We also compared our results with studies that assessed the risk of HCC transformation from preexisting indeterminate nodules. Cococcia et al., in a retrospective study of 109 patients with indeterminate nodules, found that HCC developed in one-fifth of the cases over 4.6 years, which closely aligns with our own[ 14 ].Another study indicated that the 3-year rate of nodule transformation to HCC was 15.7%[ 15 ]. Kobayashi et al. reported cumulative HCC development rates of 3.3%, 9.7%, and 12.4% in the first, third, and fifth years, respectively, for regenerative nodules[ 19 ]. Beal et al. followed patients with indeterminate lesions and reported an HCC incidence of 21% in 4 years, consistent with our findings[ 20 ]. Although there is some variation in HCC incidence among these studies, the overall trend indicates a progressive increase in HCC incidence over time, particularly during the five-year period, with a more significant rise in patients with these nodules. Several theories have been proposed to explain the rising incidence of hepatocellular carcinoma (HCC) in the context of liver cirrhosis over time. These include the potential influence of chronic hepatic inflammation, promoted hepatocyte DNA synthesis, and the accrual of genomic alterations over time[ 21 ]. In our cohort, nearly 88% of HCC cases were attributed to HCV-related cirrhosis, consistent with findings from other studies[ 14 , 15 , 22 ]. The increased incidence of HCC in individuals with HCV may stem from the direct carcinogenic effects of specific HCV viral proteins involved in various oncogenic processes. Additionally, differences in the pathogenesis of necroinflammation between HCV and other etiologies, such as MASLD and alcohol-related liver disease, may lead to varying risks of HCC development[ 14 , 22 ]. Our study revealed a higher incidence of HCC in males compared to females, likely influenced by demographic, behavioral, and environmental factors, consistent with findings from other epidemiological studies[ 14 , 23 ]. In our univariate analysis, we found associations between smoking, alcohol intake, and male gender as predictors of HCC, consistent with previous studies[ 14 , 23 , 24 ]. However, these associations did not remain significant in the multi-regression model. Additionally, our multivariate regression analysis revealed that nodule size, especially nodules larger than 1-2cm and LI-RADS-3 nodules, were independently associated with HCC, the same findings seen in previous studies. For instance, Khalili et al. reported a malignancy rate of 14%-23% for indeterminate 1-2-cm nodules[ 16 ].Similarly, Cococcia et al. found a higher incidence of HCC in patients with lesions ≥ 1 cm. However, the author did not find a significant association between lesion size and HCC in regression analysis, possibly due to the small sample size[ 14 ]. It is believed that the higher risk of hepatocellular carcinoma (HCC) associated with larger nodular size (especially > 1cm) may be attributed to a potentially higher growth rate and a shorter tumor volume-doubling time, which are linked to the progression of nodules to hypervascular HCC[ 25 ]. In a meta-analysis of 12 studies evaluating LI-RADS-3 lesions, the risk of developing HCC ranged from 15.8–44.4%[ 26 ]. The presence of hyperintense T2 lesions may potentially indicate the development of HCC. However, our multi-regression model analysis did not reveal any significant correlations. These results are consistent with those reported by Rimola et al[ 27 ]. Our research found that Child-Pugh grade, MELD score, and platelet count level were not predictors for HCC transformation despite their previously established correlation in studies[ 14 , 15 ]. Our analysis indicated that the majority of cases at baseline and at HCC time were classified as Child-Pugh grade A or B rather than C, highlighting the limitations of relying on Child-Pugh grade for HCC prediction. These findings align with a retrospective study of HCC patients, where 78.7% of the patients were identified as Child-Pugh A or B[ 28 ]. During the follow-up period, two cases exhibited nodule disappearance. Additionally, there were five mortality cases during the study period, with three attributed to complications related to liver disease. In the subgroup exploratory analysis for the HCC group, the study compared the characteristics between patients with denovo HCC and those with nodule transformation HCC. It's important to note that the study was purely descriptive and did not conduct any inferential analysis. Therefore, the findings may not be generalizable. However, the primary aim was to identify distinct features between both groups, as detailed in the results section. It's worth mentioning that denovo HCC occurred in 32% of all HCC cases, aligning with findings from Arvind et al. These findings indicate the possibility of inflammatory and covertly carcinogenic conditions that could increase the vulnerability of the entire liver to the development of HCC[ 29 ]. In previous studies on indeterminate nodules, denovo cases were typically excluded as the focus was solely on the nodules' outcome. In our study, however, we sought to assess the overall incidence of HCC in these populations. This approach makes our results more applicable, as patients and clinicians are concerned with the overall incidence of HCC, irrespective of whether the tumor originated from previous nodules. Our study has several strengths. These include a relatively large sample size, precise criteria for nodule diagnoses, the use of multiple MRI keywords during patient search to reduce selection bias, a lengthy follow-up period (with the majority of cases being followed up for 3–5 years and almost 25% observed for up to 7.5 years), a diverse patient population in terms of ethnicity at our center, and comprehensive radiological descriptions of the nodules provided by a specialized radiologist. However, the study has some limitations, mainly due to its retrospective design, which introduces potential biases such as observer bias and hidden unmeasured confounders. The data collection process relied on records, occasionally leading to incomplete information. Further controlled prospective studies over an extended period are advisable to comprehensively establish the risk of HCC in patients with indeterminate liver nodules and to explore the potential for a carcinogenic microenvironment that may increase the risk of metachronous HCC outside the nodules. Embracing this comprehensive approach will enhance our understanding of the potential relationship and enable more accurate risk assessment. Conclusion The incidence of hepatocellular carcinoma is significantly high in patients with indeterminate liver nodules and tends to increase over time, with a cumulative incidence of 30% at seven years. Patients with nodules larger than 1–2 cm or LI-RADS 3 lesions are at a heightened risk of developing HCC. Additionally, the incidence of HCC is higher in HCV-related cirrhosis. These findings emphasize the need for increased surveillance in this population, particularly given the lack of established consensus on management. Consequently, there is a clear need for prospective randomized studies to determine the most appropriate approach for this patient group. Declarations Conflict of interest disclosure: All Authors have no conflicts of interest to declare. Funding statement: None Author Contribution In determining authorship, we referred to the guidelines of the International Committee of Medical Journal Editors (ICMJE). Each author provided sufficient contribution to the concept of the work (Yousef Yahia, Ma'mon Qasem, Shahem Abbarh, Husam Saffo, Ibrahim M. Obeidat, Haidar Hussein Barjas, Mohanad Mohammed Faisal, Malik Halabiya, Prem Chandra, Moutaz Derbala), data acquisition (Yousef Yahia, Ma'mon Qasem, Shahem Abbarh, Husam Saffo, Ibrahim M. Obeidat, Haidar Hussein Barjas, Mohanad Mohammed Faisal, Malik Halabiya), analysis (Yousef Yahia, Prem Chandra, Moutaz Derbala), interpretation (Yousef Yahia, Ma'mon Qasem, Prem Chandra, Moutaz Derbala).All authors worked on drafting the manuscript and/or revising it critically for important intellectual content; all authors approved the version to be published. Acknowledgment: Open Access Fee for the publication of this article will be provided by Qatar National Library. Data availability statement: Data can be obtained from the corresponding author upon request. References EASL Clinical Practice Guidelines. Management of hepatocellular carcinoma, (in eng). J Hepatol. Jul 2018;69(1):182–236. 10.1016/j.jhep.2018.03.019 . Kanwal F, Singal AG. Surveillance for Hepatocellular Carcinoma: Current Best Practice and Future Direction, (in eng), Gastroenterology , vol. 157, no. 1, pp. 54–64, Jul 2019, 10.1053/j.gastro.2019.02.049 . Sangiovanni A et al. The natural history of compensated cirrhosis due to hepatitis C virus: A 17-year cohort study of 214 patients, (in eng), Hepatology , vol. 43, no. 6, pp. 1303-10, Jun 2006, 10.1002/hep.21176 . Llovet JM et al. Trial Design and Endpoints in Hepatocellular Carcinoma: AASLD Consensus Conference, (in eng), Hepatology , vol. 73 Suppl 1, pp. 158–191, Jan 2021, 10.1002/hep.31327 . El-Serag HB, Kanwal F. Epidemiology of hepatocellular carcinoma in the United States: Where are we? Where do we go? Hepatology , vol. 60, no. 5, pp. 1767–1775, 2014, 10.1002/hep.27222 . Welzel TM, et al. Population-Attributable Fractions of Risk Factors for Hepatocellular Carcinoma in the United States. Am J Gastroenterol. 2013;108:1314–21. 10.1038/ajg.2013.160 . Parkin DM, Bray F, Ferlay J, Pisani P. Global Cancer Statistics, 2002, CA: A Cancer Journal for Clinicians , vol. 55, no. 2, pp. 74–108, 2005, 10.3322/canjclin.55.2.74 . Sharafi H, Alavian S. The Rising Threat of Hepatocellular Carcinoma in the Middle East and North Africa Region: Results From Global Burden of Disease Study 2017. Clin Liver Disease. 2019;14(01):219–23. 10.1002/cld.890 . Ioannou GN, Splan MF, Weiss NS, McDonald GB, Beretta L, Lee SP. Incidence and Predictors of Hepatocellular Carcinoma in Patients With Cirrhosis. Clin Gastroenterol Hepatol. 2007;5(8):938–45. 10.1016/j.cgh.2007.02.039 . Borzio M, Paladino F, Francica G. Liver carcinogenesis: diagnostic and clinical aspects of preneoplastic nodules, Hepatoma Research , vol. 2019, 05/14 2019, 10.20517/2394-5079.2019.11 . Sakamoto M, Hirohashi S, Shimosato Y. Early stages of multistep hepatocarcinogenesis: Adenomatous hyperplasia and early hepatocellular carcinoma. Hum Pathol. 1991;22(2):172–8. 10.1016/0046-8177(91)90039-r . Marrero JA et al. Diagnosis, Staging, and Management of Hepatocellular Carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases, Hepatology , vol. 68, no. 2, pp. 723–750, 2018, 10.1002/hep.29913 . Chernyak V et al. Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients, Radiology , vol. 289, no. 3, pp. 816–830, 2018, 10.1148/radiol.2018181494 . Cococcia S, et al. The fate of indeterminate liver lesions: What proportion are precursors of hepatocellular carcinoma? BMC Gastroenterol. 2022;22(1). 10.1186/s12876-022-02135-x . Gazelakis K, et al. Liver disease severity predicts carcinogenesis of dysplastic liver nodules in cirrhosis. Sci Rep. 2021;11(1). 10.1038/s41598-021-00474-5 . Khalili K, Kyoung Kim T, Jang H-J, Kochak Yazdi L, Guindi M, Sherman M. Indeterminate 1-2-cm nodules found on hepatocellular carcinoma surveillance: Biopsy for all, some, or none? Hepatology , vol. 54, no. 6, pp. 2048–2054, 2011, 10.1002/hep.24638 . Bengtsson B, Widman L, Wahlin S, Stål P, Björkström NK, Hagström H. The risk of hepatocellular carcinoma in cirrhosis differs by etiology, age and sex: A Swedish nationwide population-based cohort study. United Eur Gastroenterol J. 2022;10(5):465–76. 10.1002/ueg2.12238 . Paranaguá-Vezozzo DC et al. Epidemiology of HCC in Brazil: incidence and risk factors in a ten-year cohort, (in eng), Ann Hepatol , vol. 13, no. 4, pp. 386 – 93, Jul-Aug 2014. Kobayashi M et al. Dysplastic nodules frequently develop into hepatocellular carcinoma in patients with chronic viral hepatitis and cirrhosis, Cancer , vol. 106, no. 3, pp. 636–647, 2006, 10.1002/cncr.21607 . Beal EW, et al. An indeterminate nodule in the cirrhotic liver discovered by surveillance imaging is a prelude to malignancy. J Surg Oncol. 2014;110(8):967–9. 10.1002/jso.23765 . Tarao K, et al. Real impact of liver cirrhosis on the development of hepatocellular carcinoma in various liver diseases—meta-analytic assessment. Cancer Med. 2019;8(3):1054–65. 10.1002/cam4.1998 . Mariño Z, et al. Time association between hepatitis C therapy and hepatocellular carcinoma emergence in cirrhosis: Relevance of non-characterized nodules. J Hepatol. 2019;70(5):874–84. 10.1016/j.jhep.2019.01.005 . Wu EM, et al. Gender differences in hepatocellular cancer: disparities in nonalcoholic fatty liver disease/steatohepatitis and liver transplantation. Hepatoma Res. 2018;4(10):66. 10.20517/2394-5079.2018.87 . Petrick JL, et al. Tobacco, alcohol use and risk of hepatocellular carcinoma and intrahepatic cholangiocarcinoma: The Liver Cancer Pooling Project. Br J Cancer. 2018;118(7):1005–12. 10.1038/s41416-018-0007-z . Brandi N, Renzulli M. Liver Lesions at Risk of Transformation into Hepatocellular Carcinoma in Cirrhotic Patients: Hepatobiliary Phase Hypointense Nodules without Arterial Phase Hyperenhancement. J Clin Translational Hepatol. 2024;12(1):100–12. 10.14218/jcth.2023.00130 . Kanneganti M, et al. Clinical outcomes of patients with Liver Imaging Reporting and Data System 3 or Liver Imaging Reporting and Data System 4 observations in patients with cirrhosis: A systematic review. Liver Transpl. 2022;28(12):1865–75. 10.1002/lt.26562 . Rimola J, et al. Non-invasive diagnosis of hepatocellular carcinoma ⩽2cm in cirrhosis. Diagnostic accuracy assessing fat, capsule and signal intensity at dynamic MRI. J Hepatol. 2012;56(6):1317–23. 10.1016/j.jhep.2012.01.004 . Aly A, et al. Clinical Outcomes By Child-Pugh Class in Patients With Advanced Hepatocellular Carcinoma in a Community Oncology Setting. Hepatic Oncol. 2023;10(1). 10.2217/hep-2023-0002 . Arvind A, et al. Risk of Hepatocellular Carcinoma in Patients With Indeterminate (LI-RADS 3) Liver Observations. Clin Gastroenterol Hepatol. 2023;21(4):1091–3. 10.1016/j.cgh.2021.11.042 . Tables Table 1 Demographic and clinical characteristics of included patients Characteristics All patients (n=116) No HCC Transformation (n=97) HCC Transformation (n=19) P -value Age, mean (SD) 50.17 (± 10.6) 49.4 (± 11.0) 54 (± 6.7) 0.019 Sex: male, n (%) 96 (82.8) 77 (79) 19 (100) 0.041 Ethnicity, n (%) 0.32 Middle East 84 (72.4) 68 (70) 16 (84.2) Asian 28 (24.1) 26 (27) 2 (10.5) Others 4 (3.4) 3 (3) 1 (5.3) BMI, mean (SD) 29 (± 4.8) 29 (± 4.8) 29 (± 5.3) 0.066 Diabetes Mellitus: yes, n (%) 56 (48.3) 46 (47) 10 (52) 0.612 Smoking status, n (%) 0.069 Non-smoker 85 (73.3) 74 (76.3) 11 (57.9) Smoker 24 (20.7) 17 (17.5) 7 (36.8) Ex-smoker 0 (0.0) 0 (0.0) 0 (0.0) Missing 7 (6.0) 6 (6.2) 1 (5.3) Alcohol Intake, n (%) 0.068 No 102 (87.9) 87 (90) 15 (78.9) Yes 11 (9.5) 7 (7) 4 (21.1) Missing 3 (2.6) 3 (3) 0 (0.0) Etiology of Cirrhosis, n (%) 0.079 Alcohol 3 (2.6) 3 (3.1) 0 (0.0) NASH 17 (14.7) 17 (17.5) 0 (0.0) NASH and Alcohol 3 (2.6) 2 (2.1) 1 (5.3) HCV 59 (50.9) 44 (45.4) 15 (78.9) HCV and Alcohol 1 (0.9) 0 (0.0) 1 (5.3) HBV 15 (12.9) 13 (13.4) 2 (10.5) HBV and HCV 1 (0.9) 1 (1.0) 0 (0.0) Autoimmune 2 (1.7) 2 (2.1) 0 (0.0) PSC/PBC 3 (2.6) 3 (3.1) 0 (0.0) Cryptogenic 10 (8.6) 10 (10.3) 0 (0.0) Others 2 (1.7) 2 (2.1) 0 (0.0) Family History of HCC, n (%) 0.429 No 104 (89.7) 86 (89) 18 (94.7) Yes 3 (2.6) 3 (3) 0 (0.0) Missing 9 (7.8) 8 (8) 1 (5.3) Ascites, n (%) 0.629 No 93 (80.2) 77 (79) 16 (84) yes 23 (19.8) 20 (21) 3 (16) Child-Pugh Grade, n (%) 0.538 A 87 (75.0) 72 (74.2) 15 (78.9) B 23 (19.8) 19 (19.6) 4 (21.1) C 6 (5.2) 6 (6.2) 0 (0.0) Ongoing Liver Injury, n (%) 0.990 Yes 57 (49.1) 48 (49.5) 9 (47.4) No 57 (49.1) 48 (49.5) 9 (47.4) Missing 2 (1.7) 1 (1.0) 1 (5.3) ALT (unit/l), Median (IQR) 42.5 (23-64) 43 (23-65) 37 (21-77) 0.923 AST (unit/l), Median (IQR) 42.5 (26-70) 43 (26-71) 42 (26-70) 0.932 ALP (unit/l), Median (IQR) 93 (71-133) 93 (71-134) 93 (79-141) 0.961 Albumin (gram/l), mean (SD) 35 (± 6.80) 36 (± 7.0) 34 (± 6.16) 0.314 INR, mean (SD) 1.2 (± 0.24) 1.2 (± 0.24) 1.2 (± 0.15) 0.908 Platelet count (× 109/l), Median (IQR) 113 (77-146) 117 (84-151) 82 (58-138) 0.143 MELD score, Median (IQR) 8 (7-11) 8 (7-11) 8 (7-11) 0.873 AFP, Median (IQR) 5 (3-8) 5 (2.8-7) 7.5 (3.2-14) 0.064 Bilirubin (umol/l), median (IQR) 17 (12-26) 17 (12-27) 16 (13-26) 0.829 Radiological Features of Nodules Size of the Largest Nodule, n (%) 0.007 =20mm 6 (5.2) 3 (3.1) 3 (15.8) LIRADS, n (%) 0.004 2 65 (56.0) 60 (61.9) 5 (26.3) 3 51 (44.0) 37 (38.1) 14 (73.7) T2 signal, n (%) 0.005 Hypointense 26 (22.4) 26 (26.8) 0 (0) Iso-intense 84 (72.4) 68 (70.1) 16 (84.2) Hyperintense 6 (5.2) 3 (3.1) 3 (15.8) T1 signal, n (%) 0.092 Hypointense 0 (0) 0 (0) 0 (0) Iso-intense 36 (31.0) 27 (27.8) 9 (47.4) Hyperintense 80 (69.0) 70 (72.2) 10 (52.6) Arterial Enhancement: yes, n (%) 38 (32.8) 31 (32) 7 (36.8) 0.678 Delayed Washout: yes, n (%) 5 (4.3) 3 (3.1) 2 (10.5) 0.145 Table 2 Log rank test results from Kaplan–Meier estimates of survival Covariate Reference Log Rank P- value Smoking Non-smoker versus smoker 4.191 0.041 Sex (Male) Male versus female 5.067 0.024 DM 0.755 0.385 Alcohol intake History of alcohol intake versus no alcohol intake 3.987 0.046 Child Pugh Grade 0.601 0.741 Ongoing Liver Injury 0.063 0.802 Largest Nodule Diameter Equal or Above 2cm versus less than 2cm 9.203 0.010 LI-RADS LIRADS-3 versus LIRADS-2 7.516 0.006 T2 Signal (Hyperintense) Hyperintense versus iso- or hypo-intense 9.941 0.007 T1 Signal Hyperintense versus iso- or hypo-intense 2.558 0.110 Arterial Enhancement Enhancement versus none 0.023 0.878 Delayed Washout Presence versus absence of delayed washout 5.077 0.024 Table 3 Univariate Cox regression analysis Covariates Continuous/Categorical Reference Hazard Ratio (HR) 95% CI P- value Data Available (N) Age at Diagnosis Continuous 1.037 0.987-1.091 0.152 109 Sex Categorical Female 29.179 0.254-3353.285 0.163 109 BMI Continuous 1.001 0.899-1.113 0.991 90 DM Categorical Non-DM 1.506 0.594-3.819 0.388 108 Smoking Categorical Non-smoker 2.603 1.006-6.736 0.049 102 Alcohol Categorical No hx of alcohol intake 2.929 0.969-8.851 0.057 106 ALT Continuous 1.006 0.993-1.020 0.370 109 AST Continuous 1.003 0.990-1.016 0.649 109 ALP Continuous 0.999 0.991-1.006 0.717 109 Albumin Continuous 0.940 0.871-1.014 0.110 109 Bilirubin Continuous 1.003 0.987-1.019 0.751 109 Platelet Count Continuous 0.994 0.984-1.004 0.232 107 MELD score Continuous 1.043 0.935-1.163 0.454 108 AFP Continuous 1.004 0.992-1.015 0.532 107 Largest Nodule Size Categorical < 10mm 2.754 1.024-7.412 0.045 109 5.405 1.448-20.177 0.012 LIRADS Categorical 2 3.784 1.361-10.521 0.011 109 Table 4 Multivariate Cox regression analysis Covariates Hazard ratio (HR) 95% CI P- value Smoking 1.216 0.393-3.767 0.735 Alcohol 3.288 0.75-14.417 0.114 Nodule size (10-19 mm) 3.347 1.057-10.603 0.040 Nodule size (≥20 mm) 5.854 1.10-31.159 0.038 LIRADS (3) 3.753 1.163-12.109 0.027 Table 5 Demographic and clinical characteristics of included patients Characteristics of HCC patients Denovo HCC (n=6) HCC from Preexisting Nodule (n=13) Age, mean (SD) 56 (± 5.24) 53 (± 7.33) Smoking status, n (%) Non-smoker 3 (50.0) 8 (61.5) Smoker 3 (50.0) 4 (30.8) Etiology of Cirrhosis, n (%) Alcohol 0 (0) 0 (0) NASH 0 (0) 0 (0) NASH and Alcohol 1 (16.7) 0 (0) HCV 5 (83.3) 10 (76.9) HCV and Alcohol 0 (0) 1 (7.7) HBV 0 (0) 2 (15.4) HBV and HCV 0 (0) 0 (0) Autoimmune 1 (0) 0 (0) PSC/PBC 2 (0) 0 (0) Cryptogenic 3 (0) 0 (0) Others 4 (0) 0 (0) Child-Pugh Grade, n (%) A 4 (66.7) 11 (84.6) B 2 (33.3) 2 (15.4) Baseline Results: ALT (unit/l), Median (IQR) 34 (25-51) 48 (21-82) AST (unit/l), Median (IQR) 46 (41-55) 42 (24-79) ALP (unit/l), Median (IQR) 80 (70-81) 105 (90-148) Albumin (gram/l), mean (SD) 32 (± 7.6) 35 (± 5.0) Bilirubin (umol/l), median (IQR) 29 (26-64) 13 (11.5-17.5) Platelet count (× 109/l),Median (IQR) 138 (51-159) 81 (63-124) MELD score, Median (IQR) 10.5 (10-13) 7 (7-9) AFP, Median (IQR) 6 (3.2-7.6) 9 (3.5-16.5) Radiological Features of Nodules Size of the Largest Nodule, n (%) =20mm 0 (0) 3 (23.1) LIRADS, n (%) 2 4 (66.7) 1 (7.7) 3 2 (33.3) 12 (92.3) T2 signal, n (%) Hypointense 0 (0) 0 (0) Iso-intense 6 (100) 10 (76.9) Hyperintense 0 (0) 3 (23.1) T1 signal, n (%) Hypointense 0 (0) Iso-intense 2 (33.3) 7 (53.8) Hyperintense 4 (66.7) 6 (46.2) Arterial Enhancement: yes, n (%) 1 (16.7) 6 (46.2) Delayed Washout: yes, n (%) 0 (0) 2 (15.4) Capsular Enhancement: yes, n(%) 0 (0) 0 (0) HCC time (event) Results: ALT (unit/l), Median (IQR) 30 (24-56) 25 (18.5-40) AST (unit/l), Median (IQR) 38 (30-70) 28 (21-45.5) ALP (unit/l), Median (IQR) 105 (86-119) 90 (60-119) Albumin (gram/l), mean (SD) 29 (± 5.8) 37 (± 6.5) Bilirubin (umol/l), median (IQR) 25 (23-30) 14 (11.5-21) Platelet count (× 109/l), Median (IQR) 97 (72-100) 118 (88-155) MELD score, Median (IQR) 10.5 (10-11) 8 (7-11) AFP, Median (IQR) 50 (3.7-158) 9.5 (4.5-113) Child-Pugh Grade, n (%) A 4 (66.7) 10 (76.9) B 2 (33.3) 2 (15.4) C 0 (0) 1 (7.7) Radiological Features of Nodules at HCC time Size of the Largest Nodule, n (%) =20mm 3 (50) 8 (62) Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 16 Oct, 2024 Read the published version in Journal of Gastrointestinal Cancer → Version 1 posted Editorial decision: Revision requested 06 Aug, 2024 Reviews received at journal 02 Aug, 2024 Reviewers agreed at journal 14 Jul, 2024 Reviewers invited by journal 06 Jul, 2024 Editor assigned by journal 06 Jul, 2024 Submission checks completed at journal 03 Jul, 2024 First submitted to journal 02 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4676169","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":331455470,"identity":"26780b17-5a23-48f0-9af1-52db42024b5e","order_by":0,"name":"Yousef Yahia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYHACNoYEBpsEEEuCFC1ppGphYDhMghaDG8nPHjyoOZ/HP+3wwxsMZXbEaEkzN0g4drtY4naasQXDuWRitCSYSSSw3U5suA1kMLYxE6Ml/ZtEwr9zifNvAxmMbfXEaMkxk0hsO5C44XYOyJbDhLVInnlTJpHYl5y48XZOsUXCueOEtfAdT98m+eObXeK82+kbb3woqyasReFCAhIvgY2wDgb5/gPIXGK0jIJRMApGwYgDAIj7QHZJWuVRAAAAAElFTkSuQmCC","orcid":"","institution":"Hamad Medical Corporation","correspondingAuthor":true,"prefix":"","firstName":"Yousef","middleName":"","lastName":"Yahia","suffix":""},{"id":331455471,"identity":"53728fcf-8d15-4c08-8cbf-d71568c9789f","order_by":1,"name":"Ma'mon Qasem","email":"","orcid":"","institution":"Hamad Medical Corporation","correspondingAuthor":false,"prefix":"","firstName":"Ma'mon","middleName":"","lastName":"Qasem","suffix":""},{"id":331455472,"identity":"83bfaa3e-8055-4d07-8868-d5ee1120b89c","order_by":2,"name":"Shahem Abbarh","email":"","orcid":"","institution":"Hamad Medical Corporation","correspondingAuthor":false,"prefix":"","firstName":"Shahem","middleName":"","lastName":"Abbarh","suffix":""},{"id":331455473,"identity":"8d324e77-f66b-441e-981b-bcac87eec812","order_by":3,"name":"Husam Saffo","email":"","orcid":"","institution":"Hamad Medical Corporation","correspondingAuthor":false,"prefix":"","firstName":"Husam","middleName":"","lastName":"Saffo","suffix":""},{"id":331455474,"identity":"9e9a2b2e-4d46-42ea-b799-3cd99e335614","order_by":4,"name":"Ibrahim M. Obeidat","email":"","orcid":"","institution":"Hamad Medical Corporation","correspondingAuthor":false,"prefix":"","firstName":"Ibrahim","middleName":"M.","lastName":"Obeidat","suffix":""},{"id":331455475,"identity":"185a4875-b265-4190-8fab-a69f6b155332","order_by":5,"name":"Haidar Hussein Barjas","email":"","orcid":"","institution":"Hamad Medical Corporation","correspondingAuthor":false,"prefix":"","firstName":"Haidar","middleName":"Hussein","lastName":"Barjas","suffix":""},{"id":331455476,"identity":"108d6b87-6db7-4708-85de-a0b12ef50117","order_by":6,"name":"Mohanad Mohammed Faisal","email":"","orcid":"","institution":"Hamad Medical Corporation","correspondingAuthor":false,"prefix":"","firstName":"Mohanad","middleName":"Mohammed","lastName":"Faisal","suffix":""},{"id":331455477,"identity":"4977c229-ec62-4aaa-a2f7-2f6da3f475c0","order_by":7,"name":"Malik Halabiya","email":"","orcid":"","institution":"Hamad Medical Corporation","correspondingAuthor":false,"prefix":"","firstName":"Malik","middleName":"","lastName":"Halabiya","suffix":""},{"id":331455479,"identity":"b0bfda76-39d3-42fc-97f7-871b43412747","order_by":8,"name":"Prem Chandra","email":"","orcid":"","institution":"Hamad Medical Corporation","correspondingAuthor":false,"prefix":"","firstName":"Prem","middleName":"","lastName":"Chandra","suffix":""},{"id":331455480,"identity":"aa3e4bee-95e1-4aa4-a7af-77b1879bf30c","order_by":9,"name":"Moutaz Derbala","email":"","orcid":"","institution":"Hamad Medical Corporation","correspondingAuthor":false,"prefix":"","firstName":"Moutaz","middleName":"","lastName":"Derbala","suffix":""}],"badges":[],"createdAt":"2024-07-02 19:21:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4676169/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4676169/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12029-024-01122-7","type":"published","date":"2024-10-16T15:58:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61185521,"identity":"bfbd3f28-d644-4228-87fa-f780ab9b8c21","added_by":"auto","created_at":"2024-07-26 17:27:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":18923,"visible":true,"origin":"","legend":"\u003cp\u003eThe cumulative incidence of HCC over follow up duration the cumulative incidence of HCC at 12, 36, 60, and 84 months was 3.7%, 10%, 20%, and 30%, respectively\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4676169/v1/3ece3864c7ad88372fa5e004.png"},{"id":61184946,"identity":"c02d01dd-7067-4294-86b7-3f3dd6f95538","added_by":"auto","created_at":"2024-07-26 17:19:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24200,"visible":true,"origin":"","legend":"\u003cp\u003eThe log-rank test results from Kaplan–Meier estimates showed significantly higher cumulative incidence of HCC among smokers compared to non-smokers.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4676169/v1/16f3fcad7eedf6ddb2613017.png"},{"id":61184948,"identity":"8c0e004a-bfd6-42cb-b33f-6423a13c0f1e","added_by":"auto","created_at":"2024-07-26 17:19:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":24311,"visible":true,"origin":"","legend":"\u003cp\u003eThe log-rank test results from Kaplan–Meier estimates showed significantly higher cumulative incidence of HCC among alcohol intake than non-alcohol intake groups.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4676169/v1/ab6ade9d5b1e6897d65d6b2a.png"},{"id":61184949,"identity":"92d4b24f-68c3-4657-9bb8-6c418690c084","added_by":"auto","created_at":"2024-07-26 17:19:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":27225,"visible":true,"origin":"","legend":"\u003cp\u003eThe log-rank test results from Kaplan–Meier estimates showed significantly higher cumulative incidence of HCC in patients with liver nodule size 1 to 2 cm and \u0026gt; 2cm compared to nodule size \u0026lt;1 cm.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4676169/v1/e922e4a19f571327f4515193.png"},{"id":61184951,"identity":"e2061715-bce7-4ab5-82aa-1c2ca79c6415","added_by":"auto","created_at":"2024-07-26 17:19:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":24393,"visible":true,"origin":"","legend":"\u003cp\u003eThe log-rank test results from Kaplan–Meier estimates depicts significantly higher cumulative incidence of HCC in patients with LI-RADS-3 compared to LI-RADS-2.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4676169/v1/4ebc758503da08308323f10c.png"},{"id":67149091,"identity":"b04dd6db-7822-467a-bccf-a341950aef5a","added_by":"auto","created_at":"2024-10-21 16:11:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1336113,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4676169/v1/3afb62de-a732-4e6c-90dd-679f1271a43f.pdf"},{"id":61184950,"identity":"015219b9-b44a-41d8-9a21-913fa2bc2a45","added_by":"auto","created_at":"2024-07-26 17:19:05","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":16639,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4676169/v1/abc43695bfa958fd3973463c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Rising Threat of Liver Cancer in Patients with Cirrhosis: Are Indeterminate Liver Nodules Cause for Concern? Real-world, long-term follow-up data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHCC is the fifth most common type of cancer worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and is characterized by its rapid growth[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It is also the leading cause of death among individuals with liver cirrhosis[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].Certain population studies suggest that HCC may become the third most common cause of cancer-related deaths in the USA[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In the Gulf region, which is home to diverse expatriate communities, the age-standardized incidence rate (ASR) for HCC is 4.7 per 100,000 individuals, with a mortality rate of 4.5, as reported by the World Health Organization (WHO)[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In 2017, Qatar reported the highest liver cancer mortality rate among Gulf region countries[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].Moreover, the incidence of HCC increases with age, with estimates indicating an annual rise of 1\u0026ndash;8%[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe pathophysiology of HCC remains incompletely understood, but it is believed to entail a multi-step progression resulting in both micro and macroscopic alterations. These alterations give rise to preneoplastic nodules that require prompt and precise management. Additionally, HCC can develop independently of pre-existing indeterminate nodules, in a process known as de novo hepatocarcinogenesis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].However, the extent to which indeterminate nodules alone increase the risk of HCC, either by originating from these nodules or arising de novo, has not been adequately studied.\u003c/p\u003e \u003cp\u003eIn patients with cirrhosis, both American and European guidelines recommend screening for HCC every six months using abdominal ultrasound, as early detection can significantly improve outcomes[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].If suspicious lesions are found during the ultrasound, further evaluation should be conducted using contrast-enhanced abdominal computed tomography (CT) scan or magnetic resonance imaging (MRI) of the liver. The increased surveillance for HCC in clinical settings has led to a higher detection rate of indeterminate liver nodules. These liver nodules or lesions do not display the typical radiological features of HCC, such as arterial enhancement, venous phase washout, and delayed phase washout, nor do they exhibit any suspicious features that would warrant a liver biopsy and are usually labeled as Liver Imaging Reporting and Data System (LI-RADS) 2\u0026ndash;3 lesions.\u003c/p\u003e \u003cp\u003eConsequently, these indeterminant nodules present a challenge for clinicians as they lack the characteristic features of benign liver lesions, and, simultaneously, there are no clear guidelines for managing them[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].Performing a liver biopsy for every indeterminate liver nodule is impractical due to the invasive nature of the procedure and the potential risks outweighing the benefits, particularly with the increasing number of nodules detected through radiological modalities. Although these nodules may not be cancerous, previous studies have indicated their potential to transform into HCC in the future[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, limited data is available on managing these nodules when found in cirrhotic patients during routine surveillance. Close observation has been the most common approach used, but there is still controversy over the imaging modality and the follow-up duration. For example, the American Association for the Study of Liver Disease (AASLD) practice guidance, published in 2018, suggests various options for indeterminate nodules, including follow-up imaging, imaging with a different modality or contrast agent, or biopsy. Nevertheless, the guidance does not recommend one option over the others[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is difficult to assess the risk of HCC in patients with indeterminate liver lesions due to limited available data on the natural history of these lesions. The incidence of HCC in these patients varies widely, ranging from 14\u0026ndash;65%[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].In the past, research studies have typically overlooked cases of de novo hepatocellular carcinoma (HCC), focusing mainly on nodular outcomes without considering the potential occurrence of de novo HCC. To address this gap, we conducted an observational study to determine the overall incidence of HCC within this specific patient group, encompassing both de novo HCC and HCC arising from existing nodules. This approach could provide a new framework for exploring the impact of undetermined nodules on the surrounding liver tissue, thereby paving the way for further research. Additionally, the study aimed to identify potential predictors and radiological factors that contribute to the occurrence of HCC and to compare these findings with previous studies on HCC incidence in the general cirrhotic population and in cirrhotic patients with indeterminate liver nodules.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population and Design:\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective study at a single tertiary center, observing patients with cirrhosis who had at least one liver nodule that could not be definitively diagnosed as HCC between January 2013 and December 2023. These nodules are typically detected during routine chronic liver disease management at our hepatology clinic. Such nodules must not exhibit the characteristic radiological features of HCC, such as arterial enhancement, venous phase washout, delayed phase washout, or any suspicious features that necessitate a liver biopsy.\u003c/p\u003e \u003cp\u003eOur center routinely performs liver MRI for any indeterminate liver nodules and classifies the lesions based on LI-RADS terminology. Therefore, we identified our study population by searching the electronic medical records, including MRI reports, for specific terms such as \"liver nodule,\" \"hepatic nodule,\" \"regenerative nodule,\" \"dysplastic nodule,\" \"indeterminate nodule,\" \"LI-RADS-3,\" and \"LI-RADS-2.\"\u003c/p\u003e \u003cp\u003eThe LI-RADS classification system is utilized for categorizing liver lesions in high-risk patients with liver cirrhosis. A LI-RADS-5 classification confirms HCC diagnosis without needing further histological diagnosis, whereas LI-RADS-1 indicates a confirmed benign lesion that does not require additional evaluation. (More details about the LI-RADS system can be found in the Supplementary Appendix).\u003c/p\u003e \u003cp\u003eThe start date was the date of the first MRI scan that reported an indeterminate lesion. Patients are usually monitored with MRI scans every 6 to 12 months. MRI images with these nodules are reviewed from the first MRI until the end of the study, lost to follow-up, or HCC development. Baseline characteristics of the patients (age, sex, etiology of liver disease, liver function tests, bilirubin, alpha-fetoprotein (AFP), Child-Pugh score, Model of End-stage Liver disease (MELD) score) and radiological features were collected from the electronic medical records.\u003c/p\u003e \u003cp\u003eExperienced radiologists perform a comprehensive review of subsequent MRI scans to assess the progression of indeterminate lesions, track the transformation rate to HCC, and assign LI-RADS scores to the lesions. The diagnosis of HCC is determined based on the LI-RADS scoring system or, when necessary, histological diagnosis.\u003c/p\u003e \u003cp\u003eInclusion criteria included individuals aged 18 and older who have been diagnosed with cirrhosis and have at least one hepatic nodule detected by MRI. These nodules should not display definitive HCC (LI-RADS-5) or probable HCC (LI-RADS-4) features that require liver biopsy and pose a high risk of HCC. Additionally, the patient should have at least one stable MRI liver finding within 6\u0026ndash;12 months from the initial MRI with the nodules.\u003c/p\u003e \u003cp\u003eExclusion criteria for the study included the absence of cirrhosis, definite HCC (LI-RADS-5) or probable HCC (LI-RADS-4) at baseline, and benign features that do not warrant further evaluation (LI-RADS-1), such as hemangioma. (Detailed eligibility criteria can be found in the Supplementary Appendix.)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Consideration and Data Analysis:\u003c/h2\u003e \u003cp\u003eFor continuous variables, mean (SD) and median (IQR) were used for normally and non-normally distributed data, respectively. Categorical variables are displayed as frequencies and percentages. The study's primary outcome was time to HCC occurrence, and this was estimated using Kaplan-Meier curves. This was measured from the first MRI scan with liver nodules until the patient developed HCC, either de novo or on top of the previous nodule. In the case of censoring, the time was calculated until the last MRI scan before the end of the study. Associations between two or more qualitative data variables were assessed using Chi-square (χ2) test or Fisher Exact test as appropriate. Quantitative data between the two independent groups (HCC and non-HCC) were analyzed using unpaired t or Mann Whitney U test as appropriate. The evaluation of potential risk factors and predictors associated with HCC was studied using Cox regression models followed by Log-rank test to compare the incidence of HCC across various potential subgroups. The impact of clinical, biochemical, and radiological parameters on HCC was examined. Univariate Cox regression model was used to calculate hazard ratios (HR) and their 95% confidence intervals (CI) and to estimate the risk association between each covariate and HCC. After identifying the covariates significantly (considering both statistical and clinical significance) associated with HCC incidence, they were incorporated into a multivariate Cox regression model to establish independent associations. Furthermore, an exploratory statistical analysis was conducted for HCC patients, comparing the potential factors and radiological characteristics of the de novo HCC group and HCC on top of the nodule group.\u003c/p\u003e \u003cp\u003eA two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as statistically significant. All Statistical analyses were done using the statistical packages SPSS version 29.0 (Armonk, NY: IBM Corp) and Epi Info 2000 (Centers for Disease Control and Prevention, Atlanta, GA).\u003c/p\u003e \u003cp\u003e All methods followed the Declaration of Helsinki and the hospital's guidelines and regulations. The ethical committee waived the need for consent, as only anonymous data without patients' identifiers were provided to the research team. The study was approved by the ethics and research committee at the Medical Research Center of Hamad Medical Corporation, Doha, Qatar, with the approval number MRC-01-24-085.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eWe identified 116 patients who satisfied the inclusion criteria and presented with liver nodules. Among these individuals, 19 patients (16%) developed HCC; 6 had de novo HCC, and 13 had HCC originating from an indeterminate lesion. Table\u0026nbsp;1 presents the baseline demographic information, liver disease characteristics, and radiological features of the lesions at the time of the initial liver nodule detection.\u003c/p\u003e \u003cp\u003eMost of the cohort were males (82%), and all HCC cases occurred in males (100%). The average age of the entire cohort was 50.17 (\u0026plusmn;\u0026thinsp;10.6) years. The primary ethnicity in both groups was Middle Eastern, followed by Asians. The cohort mainly consisted of non-smokers (73%) who had no history of alcohol consumption (87.9%), a pattern that was also observed in the group diagnosed with HCC.\u003c/p\u003e \u003cp\u003eIn the cohort, hepatitis C virus (HCV) infection was the main cause of liver cirrhosis, accounting for 50.9% of cases. It was even more prevalent in the HCC group, representing 87.9% of cases. No cases of HCC were attributed to metabolic-associated liver disease (MASLD). Child-Pugh grade A was the most common in both non-HCC and HCC groups.\u003c/p\u003e \u003cp\u003eThe majority of nodules (74.1%) were less than 1 cm in diameter, including 47% in the HCC group. The LI-RADS 2 score was present in 56% of the total cohort, 61.9% in the non-HCC group, and 26.3% in the HCC group.\u003c/p\u003e \u003cp\u003eIn the Kaplan\u0026ndash;Meier curve analysis, the cumulative incidence of HCC at 12, 36, 60, and 84 months was 3.7%, 10%, 20%, and 30%, respectively, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe log-rank test results from Kaplan\u0026ndash;Meier estimates indicated significant differences in incidence of HCC based on smoking status (non-smoker vs smoker), sex (male vs female), alcohol intake (history of alcohol intake - regardless of the amount vs no alcohol intake), size of the largest nodule at the time of diagnosis (less than 2cm or above), and T2-signal by MRI (hyperintense or iso-hypointense) (Table\u0026nbsp;2, Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe univariate Cox regression analysis revealed a statistically significant association between the occurrence of HCC and smoking (hazard ratio [HR] 2.60, 95% Confidence Interval [CI] 1.01\u0026ndash;6.74). Additionally, there was a notable association with baseline nodule diameter, remarkably when the size exceeded 2cm (HR 5.41, 95% CI 1.45\u0026ndash;20.18) and baseline LI-RADS 3 (HR 3.78, 95% CI 1.36\u0026ndash;19.52) (Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eThe multivariate Cox analysis revealed significant independent associations with nodule diameters greater than 1cm (HR 3.35, 95% CI 1.06\u0026ndash;10.60) and 2cm (HR 5.85, 95% CI 1.10-31.16), as well as with LI-RADS 3 lesions (HR 3.75, 95% CI 1.16\u0026ndash;12.11) (Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eSubgroup exploratory analysis was conducted for the HCC group to descriptively compare the clinical, biochemical, and radiological features of patients with de novo HCC and patients with HCC arising from pre-existing nodules at the time of nodule diagnosis and HCC diagnosis (Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eThe two subgroups had similar demographic features and blood test results, except for a higher platelet count in the de novo HCC group. The baseline radiological features showed differences, with more smaller nodules(\u0026lt;\u0026thinsp;1cm) and LI-RADS2 predominance in the de novo HCC group.\u003c/p\u003e \u003cp\u003eAt the HCC time, there were no significant deviations from the initial baseline blood test results except for AFP levels in the de novo HCC group, which had a median of 50 (range 3.7\u0026ndash;158) compared to a median of 9.5 (range 4.5\u0026ndash;113) in the other group. Both groups showed a consistent increase in the size of the primary nodule at the time of HCC diagnosis, with 50% of the nodules in the de novo cohort being 2cm or larger, compared to 62% in the other group.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is a real-world data study; our primary objective was to evaluate the risk of HCC in patients with indeterminate liver nodules. Our findings revealed that 16% of the patients developed HCC during the study period, with cumulative incidence rates at 1, 3, 5, and 7 years of 3.7%, 10%, 20%, and 30%, respectively.\u003c/p\u003e \u003cp\u003eComparing our results with previous studies that evaluated the cumulative incidence of HCC in cirrhotic patients, we observed that our cohort exhibited a higher cumulative incidence of HCC over the years. For instance, a Swedish nationwide retrospective study found a cumulative incidence of HCC in cirrhosis of 8.3% at 5 years[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Ioannou et al. found a 4.7% HCC incidence over 3.6 years[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e],while Paranagu\u0026aacute; Vezozzo DC et al. reported a 14.3% cumulative incidence over 5 years[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe also compared our results with studies that assessed the risk of HCC transformation from preexisting indeterminate nodules. Cococcia et al., in a retrospective study of 109 patients with indeterminate nodules, found that HCC developed in one-fifth of the cases over 4.6 years, which closely aligns with our own[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].Another study indicated that the 3-year rate of nodule transformation to HCC was 15.7%[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKobayashi et al. reported cumulative HCC development rates of 3.3%, 9.7%, and 12.4% in the first, third, and fifth years, respectively, for regenerative nodules[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Beal et al. followed patients with indeterminate lesions and reported an HCC incidence of 21% in 4 years, consistent with our findings[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough there is some variation in HCC incidence among these studies, the overall trend indicates a progressive increase in HCC incidence over time, particularly during the five-year period, with a more significant rise in patients with these nodules.\u003c/p\u003e \u003cp\u003eSeveral theories have been proposed to explain the rising incidence of hepatocellular carcinoma (HCC) in the context of liver cirrhosis over time. These include the potential influence of chronic hepatic inflammation, promoted hepatocyte DNA synthesis, and the accrual of genomic alterations over time[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our cohort, nearly 88% of HCC cases were attributed to HCV-related cirrhosis, consistent with findings from other studies[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe increased incidence of HCC in individuals with HCV may stem from the direct carcinogenic effects of specific HCV viral proteins involved in various oncogenic processes. Additionally, differences in the pathogenesis of necroinflammation between HCV and other etiologies, such as MASLD and alcohol-related liver disease, may lead to varying risks of HCC development[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study revealed a higher incidence of HCC in males compared to females, likely influenced by demographic, behavioral, and environmental factors, consistent with findings from other epidemiological studies[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our univariate analysis, we found associations between smoking, alcohol intake, and male gender as predictors of HCC, consistent with previous studies[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, these associations did not remain significant in the multi-regression model.\u003c/p\u003e \u003cp\u003eAdditionally, our multivariate regression analysis revealed that nodule size, especially nodules larger than 1-2cm and LI-RADS-3 nodules, were independently associated with HCC, the same findings seen in previous studies. For instance, Khalili et al. reported a malignancy rate of 14%-23% for indeterminate 1-2-cm nodules[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].Similarly, Cococcia et al. found a higher incidence of HCC in patients with lesions\u0026thinsp;\u0026ge;\u0026thinsp;1 cm. However, the author did not find a significant association between lesion size and HCC in regression analysis, possibly due to the small sample size[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is believed that the higher risk of hepatocellular carcinoma (HCC) associated with larger nodular size (especially\u0026thinsp;\u0026gt;\u0026thinsp;1cm) may be attributed to a potentially higher growth rate and a shorter tumor volume-doubling time, which are linked to the progression of nodules to hypervascular HCC[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn a meta-analysis of 12 studies evaluating LI-RADS-3 lesions, the risk of developing HCC ranged from 15.8\u0026ndash;44.4%[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe presence of hyperintense T2 lesions may potentially indicate the development of HCC. However, our multi-regression model analysis did not reveal any significant correlations. These results are consistent with those reported by Rimola et al[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur research found that Child-Pugh grade, MELD score, and platelet count level were not predictors for HCC transformation despite their previously established correlation in studies[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Our analysis indicated that the majority of cases at baseline and at HCC time were classified as Child-Pugh grade A or B rather than C, highlighting the limitations of relying on Child-Pugh grade for HCC prediction. These findings align with a retrospective study of HCC patients, where 78.7% of the patients were identified as Child-Pugh A or B[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDuring the follow-up period, two cases exhibited nodule disappearance. Additionally, there were five mortality cases during the study period, with three attributed to complications related to liver disease.\u003c/p\u003e \u003cp\u003eIn the subgroup exploratory analysis for the HCC group, the study compared the characteristics between patients with denovo HCC and those with nodule transformation HCC. It's important to note that the study was purely descriptive and did not conduct any inferential analysis. Therefore, the findings may not be generalizable. However, the primary aim was to identify distinct features between both groups, as detailed in the results section. It's worth mentioning that denovo HCC occurred in 32% of all HCC cases, aligning with findings from Arvind et al. These findings indicate the possibility of inflammatory and covertly carcinogenic conditions that could increase the vulnerability of the entire liver to the development of HCC[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn previous studies on indeterminate nodules, denovo cases were typically excluded as the focus was solely on the nodules' outcome. In our study, however, we sought to assess the overall incidence of HCC in these populations. This approach makes our results more applicable, as patients and clinicians are concerned with the overall incidence of HCC, irrespective of whether the tumor originated from previous nodules.\u003c/p\u003e \u003cp\u003eOur study has several strengths. These include a relatively large sample size, precise criteria for nodule diagnoses, the use of multiple MRI keywords during patient search to reduce selection bias, a lengthy follow-up period (with the majority of cases being followed up for 3\u0026ndash;5 years and almost 25% observed for up to 7.5 years), a diverse patient population in terms of ethnicity at our center, and comprehensive radiological descriptions of the nodules provided by a specialized radiologist.\u003c/p\u003e \u003cp\u003eHowever, the study has some limitations, mainly due to its retrospective design, which introduces potential biases such as observer bias and hidden unmeasured confounders. The data collection process relied on records, occasionally leading to incomplete information.\u003c/p\u003e \u003cp\u003eFurther controlled prospective studies over an extended period are advisable to comprehensively establish the risk of HCC in patients with indeterminate liver nodules and to explore the potential for a carcinogenic microenvironment that may increase the risk of metachronous HCC outside the nodules. Embracing this comprehensive approach will enhance our understanding of the potential relationship and enable more accurate risk assessment.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe incidence of hepatocellular carcinoma is significantly high in patients with indeterminate liver nodules and tends to increase over time, with a cumulative incidence of 30% at seven years. Patients with nodules larger than 1\u0026ndash;2 cm or LI-RADS 3 lesions are at a heightened risk of developing HCC. Additionally, the incidence of HCC is higher in HCV-related cirrhosis. These findings emphasize the need for increased surveillance in this population, particularly given the lack of established consensus on management. Consequently, there is a clear need for prospective randomized studies to determine the most appropriate approach for this patient group.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest disclosure:\u003c/h2\u003e \u003cp\u003eAll Authors have no conflicts of interest to declare.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding statement:\u003c/h2\u003e \u003cp\u003eNone\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eIn determining authorship, we referred to the guidelines of the International Committee of Medical Journal Editors (ICMJE). Each author provided sufficient contribution to the concept of the work (Yousef Yahia, Ma'mon Qasem, Shahem Abbarh, Husam Saffo, Ibrahim M. Obeidat, Haidar Hussein Barjas, Mohanad Mohammed Faisal, Malik Halabiya, Prem Chandra, Moutaz Derbala), data acquisition (Yousef Yahia, Ma'mon Qasem, Shahem Abbarh, Husam Saffo, Ibrahim M. Obeidat, Haidar Hussein Barjas, Mohanad Mohammed Faisal, Malik Halabiya), analysis (Yousef Yahia, Prem Chandra, Moutaz Derbala), interpretation (Yousef Yahia, Ma'mon Qasem, Prem Chandra, Moutaz Derbala).All authors worked on drafting the manuscript and/or revising it critically for important intellectual content; all authors approved the version to be published.\u003c/p\u003e\u003ch2\u003eAcknowledgment:\u003c/h2\u003e \u003cp\u003eOpen Access Fee for the publication of this article will be provided by Qatar National Library.\u003c/p\u003e\u003ch2\u003eData availability statement:\u003c/h2\u003e \u003cp\u003e Data can be obtained from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEASL Clinical Practice Guidelines. Management of hepatocellular carcinoma, (in eng). J Hepatol. Jul 2018;69(1):182\u0026ndash;236. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhep.2018.03.019\u003c/span\u003e\u003cspan address=\"10.1016/j.jhep.2018.03.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanwal F, Singal AG. Surveillance for Hepatocellular Carcinoma: Current Best Practice and Future Direction, (in eng), \u003cem\u003eGastroenterology\u003c/em\u003e, vol. 157, no. 1, pp. 54\u0026ndash;64, Jul 2019, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/j.gastro.2019.02.049\u003c/span\u003e\u003cspan address=\"10.1053/j.gastro.2019.02.049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSangiovanni A et al. The natural history of compensated cirrhosis due to hepatitis C virus: A 17-year cohort study of 214 patients, (in eng), \u003cem\u003eHepatology\u003c/em\u003e, vol. 43, no. 6, pp. 1303-10, Jun 2006, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hep.21176\u003c/span\u003e\u003cspan address=\"10.1002/hep.21176\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLlovet JM et al. Trial Design and Endpoints in Hepatocellular Carcinoma: AASLD Consensus Conference, (in eng), \u003cem\u003eHepatology\u003c/em\u003e, vol. 73 Suppl 1, pp. 158\u0026ndash;191, Jan 2021, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hep.31327\u003c/span\u003e\u003cspan address=\"10.1002/hep.31327\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl-Serag HB, Kanwal F. Epidemiology of hepatocellular carcinoma in the United States: Where are we? Where do we go? \u003cem\u003eHepatology\u003c/em\u003e, vol. 60, no. 5, pp. 1767\u0026ndash;1775, 2014, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hep.27222\u003c/span\u003e\u003cspan address=\"10.1002/hep.27222\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWelzel TM, et al. Population-Attributable Fractions of Risk Factors for Hepatocellular Carcinoma in the United States. Am J Gastroenterol. 2013;108:1314\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ajg.2013.160\u003c/span\u003e\u003cspan address=\"10.1038/ajg.2013.160\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParkin DM, Bray F, Ferlay J, Pisani P. Global Cancer Statistics, 2002, \u003cem\u003eCA: A Cancer Journal for Clinicians\u003c/em\u003e, vol. 55, no. 2, pp. 74\u0026ndash;108, 2005, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/canjclin.55.2.74\u003c/span\u003e\u003cspan address=\"10.3322/canjclin.55.2.74\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharafi H, Alavian S. The Rising Threat of Hepatocellular Carcinoma in the Middle East and North Africa Region: Results From Global Burden of Disease Study 2017. Clin Liver Disease. 2019;14(01):219\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cld.890\u003c/span\u003e\u003cspan address=\"10.1002/cld.890\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIoannou GN, Splan MF, Weiss NS, McDonald GB, Beretta L, Lee SP. Incidence and Predictors of Hepatocellular Carcinoma in Patients With Cirrhosis. Clin Gastroenterol Hepatol. 2007;5(8):938\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cgh.2007.02.039\u003c/span\u003e\u003cspan address=\"10.1016/j.cgh.2007.02.039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorzio M, Paladino F, Francica G. Liver carcinogenesis: diagnostic and clinical aspects of preneoplastic nodules, \u003cem\u003eHepatoma Research\u003c/em\u003e, vol. 2019, 05/14 2019, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.20517/2394-5079.2019.11\u003c/span\u003e\u003cspan address=\"10.20517/2394-5079.2019.11\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakamoto M, Hirohashi S, Shimosato Y. Early stages of multistep hepatocarcinogenesis: Adenomatous hyperplasia and early hepatocellular carcinoma. Hum Pathol. 1991;22(2):172\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/0046-8177(91)90039-r\u003c/span\u003e\u003cspan address=\"10.1016/0046-8177(91)90039-r\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarrero JA et al. Diagnosis, Staging, and Management of Hepatocellular Carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases, \u003cem\u003eHepatology\u003c/em\u003e, vol. 68, no. 2, pp. 723\u0026ndash;750, 2018, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hep.29913\u003c/span\u003e\u003cspan address=\"10.1002/hep.29913\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChernyak V et al. Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients, \u003cem\u003eRadiology\u003c/em\u003e, vol. 289, no. 3, pp. 816\u0026ndash;830, 2018, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1148/radiol.2018181494\u003c/span\u003e\u003cspan address=\"10.1148/radiol.2018181494\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCococcia S, et al. The fate of indeterminate liver lesions: What proportion are precursors of hepatocellular carcinoma? BMC Gastroenterol. 2022;22(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12876-022-02135-x\u003c/span\u003e\u003cspan address=\"10.1186/s12876-022-02135-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGazelakis K, et al. Liver disease severity predicts carcinogenesis of dysplastic liver nodules in cirrhosis. Sci Rep. 2021;11(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-021-00474-5\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-00474-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhalili K, Kyoung Kim T, Jang H-J, Kochak Yazdi L, Guindi M, Sherman M. Indeterminate 1-2-cm nodules found on hepatocellular carcinoma surveillance: Biopsy for all, some, or none? \u003cem\u003eHepatology\u003c/em\u003e, vol. 54, no. 6, pp. 2048\u0026ndash;2054, 2011, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hep.24638\u003c/span\u003e\u003cspan address=\"10.1002/hep.24638\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBengtsson B, Widman L, Wahlin S, St\u0026aring;l P, Bj\u0026ouml;rkstr\u0026ouml;m NK, Hagstr\u0026ouml;m H. The risk of hepatocellular carcinoma in cirrhosis differs by etiology, age and sex: A Swedish nationwide population-based cohort study. United Eur Gastroenterol J. 2022;10(5):465\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ueg2.12238\u003c/span\u003e\u003cspan address=\"10.1002/ueg2.12238\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParanagu\u0026aacute;-Vezozzo DC et al. Epidemiology of HCC in Brazil: incidence and risk factors in a ten-year cohort, (in eng), \u003cem\u003eAnn Hepatol\u003c/em\u003e, vol. 13, no. 4, pp. 386\u0026thinsp;\u0026ndash;\u0026thinsp;93, Jul-Aug 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKobayashi M et al. Dysplastic nodules frequently develop into hepatocellular carcinoma in patients with chronic viral hepatitis and cirrhosis, \u003cem\u003eCancer\u003c/em\u003e, vol. 106, no. 3, pp. 636\u0026ndash;647, 2006, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cncr.21607\u003c/span\u003e\u003cspan address=\"10.1002/cncr.21607\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeal EW, et al. An indeterminate nodule in the cirrhotic liver discovered by surveillance imaging is a prelude to malignancy. J Surg Oncol. 2014;110(8):967\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jso.23765\u003c/span\u003e\u003cspan address=\"10.1002/jso.23765\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTarao K, et al. Real impact of liver cirrhosis on the development of hepatocellular carcinoma in various liver diseases\u0026mdash;meta-analytic assessment. Cancer Med. 2019;8(3):1054\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cam4.1998\u003c/span\u003e\u003cspan address=\"10.1002/cam4.1998\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMari\u0026ntilde;o Z, et al. Time association between hepatitis C therapy and hepatocellular carcinoma emergence in cirrhosis: Relevance of non-characterized nodules. J Hepatol. 2019;70(5):874\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhep.2019.01.005\u003c/span\u003e\u003cspan address=\"10.1016/j.jhep.2019.01.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu EM, et al. Gender differences in hepatocellular cancer: disparities in nonalcoholic fatty liver disease/steatohepatitis and liver transplantation. Hepatoma Res. 2018;4(10):66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.20517/2394-5079.2018.87\u003c/span\u003e\u003cspan address=\"10.20517/2394-5079.2018.87\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetrick JL, et al. Tobacco, alcohol use and risk of hepatocellular carcinoma and intrahepatic cholangiocarcinoma: The Liver Cancer Pooling Project. Br J Cancer. 2018;118(7):1005\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41416-018-0007-z\u003c/span\u003e\u003cspan address=\"10.1038/s41416-018-0007-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrandi N, Renzulli M. Liver Lesions at Risk of Transformation into Hepatocellular Carcinoma in Cirrhotic Patients: Hepatobiliary Phase Hypointense Nodules without Arterial Phase Hyperenhancement. J Clin Translational Hepatol. 2024;12(1):100\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.14218/jcth.2023.00130\u003c/span\u003e\u003cspan address=\"10.14218/jcth.2023.00130\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanneganti M, et al. Clinical outcomes of patients with Liver Imaging Reporting and Data System 3 or Liver Imaging Reporting and Data System 4 observations in patients with cirrhosis: A systematic review. Liver Transpl. 2022;28(12):1865\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/lt.26562\u003c/span\u003e\u003cspan address=\"10.1002/lt.26562\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRimola J, et al. Non-invasive diagnosis of hepatocellular carcinoma ⩽2cm in cirrhosis. Diagnostic accuracy assessing fat, capsule and signal intensity at dynamic MRI. J Hepatol. 2012;56(6):1317\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhep.2012.01.004\u003c/span\u003e\u003cspan address=\"10.1016/j.jhep.2012.01.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAly A, et al. Clinical Outcomes By Child-Pugh Class in Patients With Advanced Hepatocellular Carcinoma in a Community Oncology Setting. Hepatic Oncol. 2023;10(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2217/hep-2023-0002\u003c/span\u003e\u003cspan address=\"10.2217/hep-2023-0002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArvind A, et al. Risk of Hepatocellular Carcinoma in Patients With Indeterminate (LI-RADS 3) Liver Observations. Clin Gastroenterol Hepatol. 2023;21(4):1091\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cgh.2021.11.042\u003c/span\u003e\u003cspan address=\"10.1016/j.cgh.2021.11.042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eDemographic and clinical characteristics of included patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"737\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll patients (n=116)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo HCC Transformation (n=97)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHCC Transformation (n=19)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e-value\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e50.17 (\u0026plusmn; 10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e49.4 (\u0026plusmn; 11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e54 (\u0026plusmn; 6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex: male, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e96 (82.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e77 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e19 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eMiddle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e84 (72.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e68 (70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e16 (84.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e28 (24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e26 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e2 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e4 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e3 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e1 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI, mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e29 (\u0026plusmn; 4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e29 (\u0026plusmn; 4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e29 (\u0026plusmn; 5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes Mellitus: yes, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e56 (48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e46 (47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e10 (52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status, n (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eNon-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e85 (73.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e74 (76.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e11 (57.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eSmoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e24 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e17 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e7 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eEx-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e7 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e6 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e1 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol Intake, n (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e102 (87.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e87 (90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e15 (78.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e11 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e7 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e4 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e3 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e3 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEtiology of Cirrhosis, n (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eAlcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e3 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e3 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eNASH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e17 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e17 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eNASH and Alcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e3 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e2 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e1 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eHCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e59 (50.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e44 (45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e15 (78.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eHCV and Alcohol\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e1 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e1 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eHBV\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e15 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e13 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e2 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eHBV and HCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e1 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e1 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eAutoimmune \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e2 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e2 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003ePSC/PBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e3 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e3 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eCryptogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e10 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e10 (10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e2 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e2 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily History of HCC, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e104 (89.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e86 (89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e18 (94.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e3 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e3 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e9 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e8 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e1 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAscites, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e93 (80.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e77 (79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e16 (84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e23 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e20 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e3 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild-Pugh Grade, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e87 (75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e72 (74.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e15 (78.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e23 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e19 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e4 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e6 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e6 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOngoing Liver Injury, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e57 (49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e48 (49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e9 (47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e57 (49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e48 (49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e9 (47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e2 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e1 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e1 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eALT (unit/l), Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e42.5 (23-64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e43 (23-65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e37 (21-77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.923\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAST (unit/l), Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e42.5 (26-70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e43 (26-71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e42 (26-70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eALP (unit/l), Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e93 (71-133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e93 (71-134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e93 (79-141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlbumin (gram/l), mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e35 (\u0026plusmn; 6.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e36 (\u0026plusmn; 7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e34 (\u0026plusmn; 6.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eINR, mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e1.2 (\u0026plusmn; 0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e1.2 (\u0026plusmn; 0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e1.2 (\u0026plusmn; 0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet count (\u0026times; 109/l), Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e113 (77-146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e117 (84-151)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e82 (58-138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMELD score, Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e8 (7-11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e8 (7-11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e8 (7-11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFP, Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e5 (3-8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e5 (2.8-7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e7.5 (3.2-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBilirubin (umol/l), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e17 (12-26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e17 (12-27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e16 (13-26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiological Features of Nodules\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSize of the Largest Nodule, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;10mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e86 (74.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e77 (79.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e9 (47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e10-19mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e24 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e17 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e7 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;=20mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e6 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e3 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e3 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLIRADS, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e65 (56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e60 (61.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e5 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e51 (44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e37 (38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e14 (73.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2 signal, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eHypointense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e26 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e26 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eIso-intense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e84 (72.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e68 (70.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e16 (84.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eHyperintense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e6 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e3 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e3 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1 signal, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eHypointense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eIso-intense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e36 (31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e27 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e9 (47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003eHyperintense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e80 (69.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e70 (72.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e10 (52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eArterial Enhancement: yes, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e38 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e31 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e7 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68478260869565%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelayed Washout: yes, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.673913043478262%\" valign=\"top\"\u003e\n \u003cp\u003e5 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e3 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.66304347826087%\" valign=\"top\"\u003e\n \u003cp\u003e2 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.315217391304348%\" valign=\"top\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eLog rank test results from Kaplan\u0026ndash;Meier estimates of survival\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"696\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.89928057553957%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCovariate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.805755395683455%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.93525179856115%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog Rank\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.359712230215827%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.89928057553957%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.805755395683455%\" valign=\"top\"\u003e\n \u003cp\u003eNon-smoker versus smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.93525179856115%\" valign=\"top\"\u003e\n \u003cp\u003e4.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.359712230215827%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.89928057553957%\" valign=\"top\"\u003e\n \u003cp\u003eSex (Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.805755395683455%\" valign=\"top\"\u003e\n \u003cp\u003eMale versus female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.93525179856115%\" valign=\"top\"\u003e\n \u003cp\u003e5.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.359712230215827%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.89928057553957%\" valign=\"top\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.805755395683455%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.93525179856115%\" valign=\"top\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.359712230215827%\" valign=\"top\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.89928057553957%\" valign=\"top\"\u003e\n \u003cp\u003eAlcohol intake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.805755395683455%\" valign=\"top\"\u003e\n \u003cp\u003eHistory of alcohol intake versus no alcohol intake\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.93525179856115%\" valign=\"top\"\u003e\n \u003cp\u003e3.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.359712230215827%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.046\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.89928057553957%\" valign=\"top\"\u003e\n \u003cp\u003eChild Pugh Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.805755395683455%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.93525179856115%\" valign=\"top\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.359712230215827%\" valign=\"top\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.89928057553957%\" valign=\"top\"\u003e\n \u003cp\u003eOngoing Liver Injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.805755395683455%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.93525179856115%\" valign=\"top\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.359712230215827%\" valign=\"top\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.89928057553957%\" valign=\"top\"\u003e\n \u003cp\u003eLargest Nodule Diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.805755395683455%\" valign=\"top\"\u003e\n \u003cp\u003eEqual or Above 2cm versus less than 2cm\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.93525179856115%\" valign=\"top\"\u003e\n \u003cp\u003e9.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.359712230215827%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.89928057553957%\" valign=\"top\"\u003e\n \u003cp\u003eLI-RADS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.805755395683455%\" valign=\"top\"\u003e\n \u003cp\u003eLIRADS-3 versus LIRADS-2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.93525179856115%\" valign=\"top\"\u003e\n \u003cp\u003e7.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.359712230215827%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.89928057553957%\" valign=\"top\"\u003e\n \u003cp\u003eT2 Signal (Hyperintense)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.805755395683455%\" valign=\"top\"\u003e\n \u003cp\u003eHyperintense versus iso- or hypo-intense\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.93525179856115%\" valign=\"top\"\u003e\n \u003cp\u003e9.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.359712230215827%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.89928057553957%\" valign=\"top\"\u003e\n \u003cp\u003eT1 Signal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.805755395683455%\" valign=\"top\"\u003e\n \u003cp\u003eHyperintense versus iso- or hypo-intense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.93525179856115%\" valign=\"top\"\u003e\n \u003cp\u003e2.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.359712230215827%\" valign=\"top\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.89928057553957%\" valign=\"top\"\u003e\n \u003cp\u003eArterial Enhancement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.805755395683455%\" valign=\"top\"\u003e\n \u003cp\u003eEnhancement versus none\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.93525179856115%\" valign=\"top\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.359712230215827%\" valign=\"top\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.89928057553957%\" valign=\"top\"\u003e\n \u003cp\u003eDelayed Washout\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.805755395683455%\" valign=\"top\"\u003e\n \u003cp\u003ePresence versus absence of delayed washout\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.93525179856115%\" valign=\"top\"\u003e\n \u003cp\u003e5.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.359712230215827%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eUnivariate Cox regression analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"781\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.6696542893726%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCovariates\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.998719590268887%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eContinuous/Categorical\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.108834827144687%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.060179257362355%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard Ratio (HR)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.364916773367478%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9385403329065305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eData Available (N)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.6696542893726%\" valign=\"top\"\u003e\n \u003cp\u003eAge at Diagnosis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.998719590268887%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.108834827144687%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.060179257362355%\" valign=\"top\"\u003e\n \u003cp\u003e1.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.364916773367478%\" valign=\"top\"\u003e\n \u003cp\u003e0.987-1.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9385403329065305%\" valign=\"top\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.6696542893726%\" valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.998719590268887%\" valign=\"top\"\u003e\n \u003cp\u003eCategorical\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.108834827144687%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.060179257362355%\" valign=\"top\"\u003e\n \u003cp\u003e29.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.364916773367478%\" valign=\"top\"\u003e\n \u003cp\u003e0.254-3353.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9385403329065305%\" valign=\"top\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.6696542893726%\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.998719590268887%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.108834827144687%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.060179257362355%\" valign=\"top\"\u003e\n \u003cp\u003e1.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.364916773367478%\" valign=\"top\"\u003e\n \u003cp\u003e0.899-1.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9385403329065305%\" valign=\"top\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.6696542893726%\" valign=\"top\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.998719590268887%\" valign=\"top\"\u003e\n \u003cp\u003eCategorical\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.108834827144687%\" valign=\"top\"\u003e\n \u003cp\u003eNon-DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.060179257362355%\" valign=\"top\"\u003e\n \u003cp\u003e1.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.364916773367478%\" valign=\"top\"\u003e\n \u003cp\u003e0.594-3.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9385403329065305%\" valign=\"top\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.6696542893726%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.998719590268887%\" valign=\"top\"\u003e\n \u003cp\u003eCategorical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.108834827144687%\" valign=\"top\"\u003e\n \u003cp\u003eNon-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.060179257362355%\" valign=\"top\"\u003e\n \u003cp\u003e2.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.364916773367478%\" valign=\"top\"\u003e\n \u003cp\u003e1.006-6.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9385403329065305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.6696542893726%\" valign=\"top\"\u003e\n \u003cp\u003eAlcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.998719590268887%\" valign=\"top\"\u003e\n \u003cp\u003eCategorical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.108834827144687%\" valign=\"top\"\u003e\n \u003cp\u003eNo hx of alcohol intake\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.060179257362355%\" valign=\"top\"\u003e\n \u003cp\u003e2.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.364916773367478%\" valign=\"top\"\u003e\n \u003cp\u003e0.969-8.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9385403329065305%\" valign=\"top\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.6696542893726%\" valign=\"top\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.998719590268887%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.108834827144687%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.060179257362355%\" valign=\"top\"\u003e\n \u003cp\u003e1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.364916773367478%\" valign=\"top\"\u003e\n \u003cp\u003e0.993-1.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9385403329065305%\" valign=\"top\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.6696542893726%\" valign=\"top\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.998719590268887%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.108834827144687%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.060179257362355%\" valign=\"top\"\u003e\n \u003cp\u003e1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.364916773367478%\" valign=\"top\"\u003e\n \u003cp\u003e0.990-1.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9385403329065305%\" valign=\"top\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.6696542893726%\" valign=\"top\"\u003e\n \u003cp\u003eALP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.998719590268887%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.108834827144687%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.060179257362355%\" valign=\"top\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.364916773367478%\" valign=\"top\"\u003e\n \u003cp\u003e0.991-1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9385403329065305%\" valign=\"top\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.6696542893726%\" valign=\"top\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.998719590268887%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.108834827144687%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.060179257362355%\" valign=\"top\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.364916773367478%\" valign=\"top\"\u003e\n \u003cp\u003e0.871-1.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9385403329065305%\" valign=\"top\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.6696542893726%\" valign=\"top\"\u003e\n \u003cp\u003eBilirubin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.998719590268887%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.108834827144687%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.060179257362355%\" valign=\"top\"\u003e\n \u003cp\u003e1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.364916773367478%\" valign=\"top\"\u003e\n \u003cp\u003e0.987-1.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9385403329065305%\" valign=\"top\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.6696542893726%\" valign=\"top\"\u003e\n \u003cp\u003ePlatelet Count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.998719590268887%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.108834827144687%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.060179257362355%\" valign=\"top\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.364916773367478%\" valign=\"top\"\u003e\n \u003cp\u003e0.984-1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9385403329065305%\" valign=\"top\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.6696542893726%\" valign=\"top\"\u003e\n \u003cp\u003eMELD score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.998719590268887%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.108834827144687%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.060179257362355%\" valign=\"top\"\u003e\n \u003cp\u003e1.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.364916773367478%\" valign=\"top\"\u003e\n \u003cp\u003e0.935-1.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9385403329065305%\" valign=\"top\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.6696542893726%\" valign=\"top\"\u003e\n \u003cp\u003eAFP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.998719590268887%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.108834827144687%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.060179257362355%\" valign=\"top\"\u003e\n \u003cp\u003e1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.364916773367478%\" valign=\"top\"\u003e\n \u003cp\u003e0.992-1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9385403329065305%\" valign=\"top\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.6696542893726%\" valign=\"top\"\u003e\n \u003cp\u003eLargest Nodule Size\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.998719590268887%\" valign=\"top\"\u003e\n \u003cp\u003eCategorical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.108834827144687%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 10mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.060179257362355%\" valign=\"top\"\u003e\n \u003cp\u003e2.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.364916773367478%\" valign=\"top\"\u003e\n \u003cp\u003e1.024-7.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9385403329065305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.6696542893726%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.998719590268887%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.108834827144687%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.060179257362355%\" valign=\"top\"\u003e\n \u003cp\u003e5.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.364916773367478%\" valign=\"top\"\u003e\n \u003cp\u003e1.448-20.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9385403329065305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.6696542893726%\" valign=\"top\"\u003e\n \u003cp\u003eLIRADS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.998719590268887%\" valign=\"top\"\u003e\n \u003cp\u003eCategorical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.108834827144687%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.060179257362355%\" valign=\"top\"\u003e\n \u003cp\u003e3.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.364916773367478%\" valign=\"top\"\u003e\n \u003cp\u003e1.361-10.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.9385403329065305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\" valign=\"top\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eMultivariate Cox regression analysis\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"762\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.53280839895013%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.540682414698164%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard ratio (HR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.540682414698164%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.385826771653543%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.53280839895013%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.540682414698164%\" valign=\"top\"\u003e\n \u003cp\u003e1.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.540682414698164%\" valign=\"top\"\u003e\n \u003cp\u003e0.393-3.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.385826771653543%\" valign=\"top\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.53280839895013%\" valign=\"top\"\u003e\n \u003cp\u003eAlcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.540682414698164%\" valign=\"top\"\u003e\n \u003cp\u003e3.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.540682414698164%\" valign=\"top\"\u003e\n \u003cp\u003e0.75-14.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.385826771653543%\" valign=\"top\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.53280839895013%\" valign=\"top\"\u003e\n \u003cp\u003eNodule size (10-19 mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.540682414698164%\" valign=\"top\"\u003e\n \u003cp\u003e3.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.540682414698164%\" valign=\"top\"\u003e\n \u003cp\u003e1.057-10.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.385826771653543%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.53280839895013%\" valign=\"top\"\u003e\n \u003cp\u003eNodule size (\u0026ge;20 mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.540682414698164%\" valign=\"top\"\u003e\n \u003cp\u003e5.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.540682414698164%\" valign=\"top\"\u003e\n \u003cp\u003e1.10-31.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.385826771653543%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.53280839895013%\" valign=\"top\"\u003e\n \u003cp\u003eLIRADS (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.540682414698164%\" valign=\"top\"\u003e\n \u003cp\u003e3.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.540682414698164%\" valign=\"top\"\u003e\n \u003cp\u003e1.163-12.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.385826771653543%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u0026nbsp;\u003c/strong\u003eDemographic and clinical characteristics of included patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"692\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics of HCC patients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDenovo HCC (n=6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHCC from Preexisting Nodule (n=13)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56 (\u0026plusmn; 5.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53 (\u0026plusmn; 7.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status, n (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNon-smoker\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (61.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSmoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEtiology of Cirrhosis, n (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlcohol\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNASH\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNASH and Alcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHCV\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (76.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHCV and Alcohol\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHBV\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHBV and HCV\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAutoimmune \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePSC/PBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCryptogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOthers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild-Pugh Grade, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 (84.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline Results:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eALT (unit/l), Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34 (25-51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48 (21-82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAST (unit/l), Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46 (41-55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42 (24-79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eALP (unit/l), Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e80 (70-81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e105 (90-148)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlbumin (gram/l), mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 (\u0026plusmn; 7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35 (\u0026plusmn; 5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBilirubin (umol/l), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29 (26-64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (11.5-17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet count (\u0026times; 109/l),Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e138 (51-159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81 (63-124)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMELD score, Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.5 (10-13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (7-9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFP, Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (3.2-7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 (3.5-16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiological Features of Nodules\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSize of the Largest Nodule, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;10mm\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10-19mm\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;=20mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLIRADS, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12 (92.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2 signal, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypointense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIso-intense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (76.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHyperintense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1 signal, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypointense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIso-intense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7 (53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHyperintense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eArterial Enhancement: yes, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelayed Washout: yes, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCapsular Enhancement: yes, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHCC time (event) Results:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eALT (unit/l), Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30 (24-56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (18.5-40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAST (unit/l), Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38 (30-70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28 (21-45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eALP (unit/l), Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e105 (86-119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90 (60-119)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlbumin (gram/l), mean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29 (\u0026plusmn; 5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37 (\u0026plusmn; 6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBilirubin (umol/l), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (23-30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14 (11.5-21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatelet count (\u0026times; 109/l), Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e97 (72-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e118 (88-155)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMELD score, Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.5 (10-11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (7-11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFP, Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50 (3.7-158)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.5 (4.5-113)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild-Pugh Grade, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10 (76.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiological Features of Nodules at HCC time\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSize of the Largest Nodule, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;10mm\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10-19mm\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5 (38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;=20mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8 (62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-gastrointestinal-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijgc","sideBox":"Learn more about [Journal of Gastrointestinal Cancer](https://www.springer.com/journal/12029)","snPcode":"12029","submissionUrl":"https://submission.nature.com/new-submission/12029/3","title":"Journal of Gastrointestinal Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Hepatocellular carcinoma, Liver cirrhosis, Liver nodules, Natural History","lastPublishedDoi":"10.21203/rs.3.rs-4676169/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4676169/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral studies have shown a higher risk of liver cancer from indeterminate liver nodules, but the exact occurrence and predictors of liver cancer in this group are still unclear. Our aim is to study the development of liver cancer in this population and identify any potential risk factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study evaluated cirrhotic patients with indeterminate liver nodules from 2013 to 2023.Data from electronic patient records was analyzed to assess the association between HCC and baseline factors.Subgroup exploratory analysis compared characteristics of patients with de novo HCC and those with nodule transformation HCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of 116 patients with liver nodules, 19 (16%) developed HCC in up to 7.5-year follow-up. Univariate Cox regression analysis showed a significant association between HCC incidence and smoking [hazard ratio (HR) 2.60, 95% Confidence Interval [CI] 1.01-6.74), nodule diameter exceeding 2cm (HR 5.41, 95% CI 1.45-20.18), and baseline LI-RADS score 3 (HR 3.78, 95% CI 1.36-19.52). Multivariate Cox regression analysis revealed significant independent associations with nodule diameters 1 cm to \u0026lt;2cm (adjusted HR 3.35, 95% CI 1.06-10.60) and greater than 2cm (adjusted HR 5.85, 95% CI 1.10-31.16), as well as with LI-RADS 3 lesions (adjusted HR 3.75, 95% CI 1.16-12.11) with adjusting other potential predictors and covariates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings show a higher incidence of HCC in patients with indeterminate liver nodules, increasing over time and reaching 30% at seven years. Nodules larger than 1-2 cm or LI-RADS 3 lesions pose increased risk for HCC. Enhanced surveillance is necessary given the lack of clear management guidelines.\u003c/p\u003e","manuscriptTitle":"The Rising Threat of Liver Cancer in Patients with Cirrhosis: Are Indeterminate Liver Nodules Cause for Concern? Real-world, long-term follow-up data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-26 17:19:01","doi":"10.21203/rs.3.rs-4676169/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-06T12:33:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-02T14:56:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75531954927022774791063478373028762524","date":"2024-07-14T13:23:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-06T11:48:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-06T11:45:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-03T04:38:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Gastrointestinal Cancer","date":"2024-07-02T19:19:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-gastrointestinal-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijgc","sideBox":"Learn more about [Journal of Gastrointestinal Cancer](https://www.springer.com/journal/12029)","snPcode":"12029","submissionUrl":"https://submission.nature.com/new-submission/12029/3","title":"Journal of Gastrointestinal Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"02690f28-3fc4-412a-b286-57d5cb39b80c","owner":[],"postedDate":"July 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-21T16:04:37+00:00","versionOfRecord":{"articleIdentity":"rs-4676169","link":"https://doi.org/10.1007/s12029-024-01122-7","journal":{"identity":"journal-of-gastrointestinal-cancer","isVorOnly":false,"title":"Journal of Gastrointestinal Cancer"},"publishedOn":"2024-10-16 15:58:01","publishedOnDateReadable":"October 16th, 2024"},"versionCreatedAt":"2024-07-26 17:19:01","video":"","vorDoi":"10.1007/s12029-024-01122-7","vorDoiUrl":"https://doi.org/10.1007/s12029-024-01122-7","workflowStages":[]},"version":"v1","identity":"rs-4676169","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4676169","identity":"rs-4676169","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}