Longitudinal surveillance of three biomarkers to predict recurrence of hepatocellular carcinoma after radical resection

Longitudinal surveillance of three biomarkers to predict recurrence of hepatocellular carcinoma after radical resection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Longitudinal surveillance of three biomarkers to predict recurrence of hepatocellular carcinoma after radical resection Jingshu Tong, Yong Yang, Changjiang Lu, Xi Yu, Shuqi Mao, Caide Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1919497/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Radical resection is a curative treatment for patients with hepatocellular carcinoma (HCC), but the incidence of recurrence remains high. We aimed to explore the performance of predicting HCC recurrence by longitudinal surveillance of the protein induced by vitamin K absence (PIVKA-II), alpha-fetoprotein (AFP), and lectin-reactive AFP (AFP-L3) during postoperative follow-up. Methods: Patients who underwent radical resection for HCC at the Ningbo Medical Centre Lihuili Hospital between January 2015 and December 2020 were included. All enrolled patients regularly monitor PIVKA-Ⅱ, AFP, AFP-L3 every 3 months during postoperative follow-up. The surveillance performance of PIVKA-Ⅱ, AFP, AFP-L3 during follow-up for the prediction of HCC recurrence was compared in patients. The Generalized Estimation Equation (GEE) was used to analyze the trends of the tumor biomarkers and interactions with time. Area under the receiver operator characteristic (AUROC) curves, the optimal cut-off value, the sensitivity and specificity were calculated to evaluate the performance of the three biomarkers. The recurrence-free survival (RFS) of patients with any of the elevated biomarkers was analyzed by Kaplan-Meier curves and the log-rank test. Results: The GEE analysis indicated that PIVKA-II, AFP, AFP-L3 in the recurrence patients were higher than the no recurrence patients during follow-up, PIVKA-Ⅱ and AFP showed increasing trends from 6 months before recurrence. In predicting recurrence, the AUROCs for PIVKA-Ⅱ, AFP, AFP-L3 and their combination were 0.885, 0.754, 0.781 and 0.885 respectively, the optimal cut-off value for PIVKA-Ⅱ, AFP, AFP-L3 was 29.5 mAU/ml, 10.7 ng/L, 1.5 % respectively. The sensitivity in predicting recurrence for PIVKA-Ⅱ, AFP, AFP-L3 and combination were 75.0%, 54.7%, 57.8% and 79.7% respectively. The RFS of patients with any of the biomarkers elevated during the follow-up was significantly shorter than that without elevated biomarkers ( χ 2 =62.125, P <0.001). Conclusion: Longitudinal surveillance of PIVKA-II, AFP and AFP-L3 can effectively predict recurrence of HCC after operation. Hepatocellular carcinoma Surveillance Tumor biomarkers Recurrence Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Globally, hepatocellular carcinoma (HCC) has been the 5th most common malignancy and the 3th leading cause of cancer mortality(1). Although radical resection has been accepted as a curative treatment for patients with HCC, the recurrence rate of HCC reaches more than 75% at 5 years after resection, the long-term outcome remains unsatisfactory because of the high incidence of postoperative recurrence(2). The high rate of recurrence highlights the importance of surveillance to detect recurrence. Current guidelines recommend regular imaging study (US, CT or MRI) with alpha-fetoprotein (AFP) for the detection of recurrence in postoperative patients, however, there is no satisfactory methods or tools for surveillance recurrence currently. Three tumor biomarkers specific for HCC are used clinically: a protein induced by vitamin K absence (PIVKA-II), AFP, and lectin-reactive AFP (AFP-L3). Previous studies have reported the clinical utility of the three biomarkers for diagnosis of HCC, for evaluation of disease progression, and even for predicting a patient's prognosis(2). In this study, we used longitudinal surveillance of PIVKA-II, AFP, AFP-L3 during postoperative follow-up, which has not been reported in previous studies, aimed to explore the performance of predicting recurrence. Patients And Methods Patient Selection The study cohort consisted of 384 patients who underwent hepatectomy and had histologically confirmed HCC in the Department of Hepatopancreatobiliary Surgery, Ningbo Medical Centre Lihuili Hospital, between January 2015 and December 2020. According to the exclusion criteria, 188 patients were enrolled for further analysis. The exclusion was as follows: (1) patients did not regularly monitor PIVKA-Ⅱ, AFP, and AFP-L3 every 3 months after the operation, (2) complications with other malignancies, (3) a positive final resection margin, and (4) long-term using of vitamin K antagonist (Figure 1). The study was approved by the ethics committee of Ningbo Medical Center Lihuili Hospital (Approval number: KY2020PJ125), Science and Technology program of Zhejiang Health (Approval number: 2021KY1035). Follow-up Patients were followed up until June 2021, ensuring all 188 patients were followed up for more than 6 months, the median follow-up time was 19 months (6-73 months). Patients regularly monitor PIVKA-Ⅱ, AFP, AFP-L3 every 3 months after the operation, and had imaging study (US, CT or MRI) every 3-6 months. When an elevation of tumor biomarkers was detected, additional imaging studies were performed to check for recurrence. If the presence of recurrence was confirmed, patients underwent treatment for recurrent HCC based on treatment guidelines. Follow-up was performed in the outpatient clinic or via phone call. Timeline The diagnosis of postoperative recurrence was based on imaging studies and rarely tissue confirmation. “month 0”, “month -3”, and “month -6” were defined as the points of recurrence (0 month), and at 3 and 6 months before the recurence of HCC. In the patients without recurrence, “month 0”, “month -3”, and “month -6” were defined as the points of end of follow-up, and at 3 and 6 months before the before the end of follow-up (Figure 1). Measurement of Serum Biomarkers Measurements of PIVKA-Ⅱ, AFP, AFP-L3 values were performed with a microchip capillary electrophoresis and liquid-phase binding assay on the μ TASWakoi30 auto-analyzer (Wako Pure Chemical Industries, Ltd., Osaka, Japan)(5). Serum biomarkers were measured at the first follow-up, between 2-3 months after the operation. A previous study reported that half-lives of PIVKA-Ⅱ is 60 h, the half-life of AFP and AFP-L3 is 96 h(6), so the value of postoperative biomarkers was not influenced by preoperative biomarkers elevations. Statistical Analysis Categorical variables are presented as absolute counts and percentages and were compared by using the Chi-square test. Quantitative variables are presented as the median with 25th quantile and 75th quantile, and compared by using the Mann-Whitney U test. The Generalized Estimation Equation (GEE) model was used to analyze the statistical significance of a time effect on PIVKA-Ⅱ, AFP, AFP-L3. The area under the receiver operator characteristic (AUROC) curves were calculated to evaluate three performance of biomarkers for predicting recurrence. The optimal cut-off value enabling maximization of the sum of the sensitivity and specificity was calculated. The sensitivity and specificity and their 95% confidence intervals (CIs) were calculated for each biomarker and their combination under the optimal cut-off values. The recurrence-free survival (RFS) of patients with any of the elevated biomarkers was analyzed by Kaplan-Meier curves and the log-rank test. The difference was considered significant when the P value <0.05. Statistical analysis was performed with SPSS 23.0 statistical software (SPSS Inc., Chicago, IL) and SAS 9.4 statistical software (SAS Inc., North Carolina, IL). Results Cohort characteristic Between January 2015 and September 2020, a total of 384 patients underwent hepatectomy and had histologically confirmed HCC at the Affiliated Lihuili Hospital of Ningbo University. Excluded from this cohort were 177 patients without regularly monitoring the three biomarkers every 3 months after the operation, 12 patients with other malignancies, 3 patients with a positive resection margin, and 4 patients with long-term use of vitamin K antagonists (Figure 1). After exclusion, all 188 patients with more than 6 months of follow-up were eligible for further analysis. The clinicopathological characteristics of these patients are summarized, there were significant differences in maximum tumor size, tumor number, portal vein tumor thrombus, microvascular invasion, tumor capsule, and TNM stage between the recurrence and no recurrence patients (Table 1). TABLE 1: Clinicopathological characteristics of the recurrence patients and no recurrence patients Characteristics Recurrence (n=69) No recurrence (n=119) χ 2 /Z P Age (years,%) ≥60 <60 33(47.8) 36(52.2) 59(49.6) 60(50.4) 0.054 0.817 Sex (%) Male Female 57(82.6) 12(17.4) 89(74.8) 30(25.2) 1.539 0.215 HBsAg (%) Positive Negative 56(81.2) 13(18.8) 92(77.3) 27(22.7) 0.386 0.534 ALB (g/L, %) ≥40 <40 41(59.4) 28(40.6) 75(63.0) 44(37.0) 0.240 0.624 ALT (U/L, %) ≥40 <40 17(24.6) 52(75.4) 33(27.7) 86(72.3) 0.214 0.644 Maximum tumor size (cm, %) ≥6 <6 25(36.2) 44(63.8) 25(21.0) 94(79.0) 5.185 0.023 Number of tumors (%) Single Multiple 47(68.1) 22(31.9) 97(81.5) 22(18.5) 4.373 0.037 Differentiation (%) Poor Well-moderate 32(46.4) 37(53.6) 42(35.3) 77(64.7) 2.248 0.134 Portal vein tumor thrombosis (%) Present Absent 12(17.4) 57(82.6) 6(5.0) 113(95.0) 7.693 0.006 Microvascular invasion (%) Present Absent 39(56.5) 30(43.5) 46(38.7) 73(61.3) 5.628 0.018 Perineural invasion (%) Present Absent 2(2.9) 67(97.1) 2(1.7) 117(98.3) 0.311 0.577 Tumor capsule (%) Present Absent 48(69.6) 21(30.4) 99(83.2) 20(16.8) 4.757 0.029 Child-Pugh class (%) A B 67(97.1) 2(2.9) 117(98.3) 2(1.7) 0.311 0.577 TNM stage (%) III-IV I-II 45(65.2) 24(34.8) 103(86.6) 16(13.4) 11.872 0.001 PIVKA-Ⅱ (mAU/ml) Month -6 Month -3 Month 0 23(14,110) 59(20,286) 161(27,666) 14(12,19) 15(12,18) 15(12,19) 4.677 8.324 8.318 <0.001 <0.001 <0.001 AFP (ng/L) Month -6 Month -3 Month 0 6.2(2.4,26.2) 9.2(2.7,68.3) 14.2(2.5,164.7) 2.8(1.8,4.8) 2.9(1.7,4.8) 2.6(1.5,5.1) 4.885 5.347 5.455 <0.001 <0.001 <0.001 AFP-L3 (%) Month -6 Month -3 Month 0 0.5(0.5,10.4) 1.7(0.5,34.5) 2.4(0.5,39.1) 0.5(0.5,0.5) 0.5(0.5,0.5) 0.5(0.5,0.5) 3.348 6.912 8.111 0.001 <0.001 <0.001 ALB: albumin; ALT: Alanine transaminase. Data are presented as number (percentages). PIVKA-II, AFP, AFP-L3 are presented as median (25th quantile, 75th quantile). Trends in PIVKA-Ⅱ, AFP and AFP-L3 In the GEE analysis, PIVKA-II, AFP, AFP-L3 in the recurrence patients were significantly higher than the no recurrence patients from month -6 to month 0 ( P ≤0.001, Table 1). PIVKA-Ⅱ and AFP showed increasing trends from month -6 to month 0 in the recurrence patients, and there were significant differences compared with the trends in the no recurrence patients ( P =0.001, P <0.001 respectively), but AFP-L3 had no such difference ( P =0.39, Figure 2, Table 2). These indicate that PIVKA-Ⅱ and AFP effects are different according to the time period. TABLE 2: Comparison the trends of three biomarkers between recurrence and no recurrence patients Estimate Standard error Z P PIVKA-Ⅱ Group(mAU/ml) 1735.3 797.6 2.18 0.030 Group * time (month -6 vs -3) 964.9 642.3 1.50 0.133 Group * time (month -6 vs 0) 3713.0 1779.7 2.09 0.04 AFP Group (ng/L) 206.7 60.6 3.41 <0.001 Group * time (month -6 vs -3) 118.5 47.7 2.49 0.012 Group * time (month -6 vs 0) 368.6 110.9 3.32 <0.001 AFP-L3 Group (%) 9.8 3.5 16.68 0.005 Group * time (month -6 vs -3) 7.5 4.8 1.55 0.121 Group * time (month -6 vs 0) 4.7 5.5 0.86 0.39 Performance to predict recurrence In the performance of predict recurrence, the AUROCs for PIVKA-Ⅱ, AFP, AFP-L3 at month 0 were 0.885, 0.754, 0.781 respectively; The AUROCs for PIVKA-Ⅱ, AFP, AFP-L3 at month -3 were 0.871, 0.748, 0.744 respectively; The AUROCs for PIVKA-Ⅱ, AFP, AFP-L3 at month -6 were 0.718, 0.708, 0.603 respectively. The combination of the three biomarkers can improve the performance to predict recurrence, the AUROCs at month -6, month -3, month 0 was 0.786, 0.895, and 0.885 respectively (Figure 3, Table 3). TABLE 3: AUROC for PIVKA- Ⅱ, AFP, AFP-L3 and combinations in predicting recurrence Month from recurrence Month -6 AUROC (95%CI) Month -3 AUROC (95%CI) Month 0 AUROC (95%CI) PIVKA-Ⅱ 0.718(0.633-0.803) 0.871(0.813-0.930) 0.885(0.827-0.943) AFP 0.708(0.621-0.795) 0.748(0.669-0.827) 0.754(0.675-0.833) AFP-L3 0.603(0.512-0.693) 0.744(0.664-0.825) 0.781(0.703-0.858) PIVKA-Ⅱ+AFP 0.789(0.714-0.864) 0.886(0.830-0.942) 0.884(0.823-0.944) PIVKA-Ⅱ+AFPL3 0.733(0.649-0.817) 0.891(0.836-0.946) 0.888(0.831-0.946) AFP+AFPL3 0.701(0.615-0.786) 0.772(0.694-0.850) 0.782(0.705-0.860) PIVKA-Ⅱ+AFP+AFPL3 0.786(0.711-0.862) 0.895(0.840-0.950) 0.885(0.824-0.944) Optimal cut-off value and the performance At month 0, the optimal cut-off value to predict recurrence for PIVKA-Ⅱ, AFP, AFP-L3 were 29.5mAU/ml, 10.7ng/L, 1.5% respectively. With the optimal cut-off value, the sensitivity in predicting recurrence for PIVKA-Ⅱ, AFP, AFP-L3 at month -6 were 42.2%, 37.5%, 34.4% respectively; the sensitivity at month -3 were 68.8%, 50%, 51.6% respectively; the sensitivity at month 0 were 75.0%, 54.7%, 57.8% respectively. The combination of the three biomarkers can improve the performance, and the sensitivities at month -6, month -3, month 0 were 60.9%, 79.7%, 79.7% respectively (Table 4). TABLE 4: Performance of three biomarkers and combinations in predicting recurrence Month from recurrence Sensitivity(%) Specificity(%) PIVKA-Ⅱ≥29.5mAU/ml Month 0 75.0% 94.7% Month -3 68.8% 96.8% Month -6 42.2% 91.6% AFP≥10.7ng/L Month 0 54.7% 96.8% Month -3 50% 96.8% Month -6 37.5% 95.8% AFPL3≥1.5% Month 0 57.8% 96.8% Month -3 51.6% 92.6% Month -6 34.4% 86.3% PIVKA-Ⅱ≥29.5mAU/ml +AFP≥10.6ng/L Month 0 79.7% 94.5% Month -3 76.8% 94.7% Month -6 54.7% 91.6% PIVKA-Ⅱ≥29.5mAU/ml +AFPL3≥1.5% Month 0 78.3% 93.6% Month -3 76.8% 90.4% Month -6 51.6% 83.2% AFP≥10.7ng/L +AFPL3≥1.5% Month 0 65.2% 96.4% Month -3 58.0% 91.2% Month -6 43.8% 84.1% PIVKA-Ⅱ>29.5mAU/ml+AFP>10.7ng/L +AFPL3>1.5% Month 0 79.7% 93.6% Month -3 79.7% 89.5% Month -6 60.9% 83.2% Elevated biomarkers correlate to recurrence The median RFS of all patients was 22 months (n=188, 40 months, 95% CI 26.539-53.461), and the median RFS of patients with any biomarkers elevated above the optimal cut-off value during the follow-up (n=82, 19 months, 95% CI 14.757-23.243) was significantly shorter than that of patients without elevated biomarker (n=106, 58 months, 95% CI 51.575-65.022) (χ 2 =62.125, P <0.001, Figure 4). Discussion In the present study, we explored the performance of tumor biomarkers (PIVKA-Ⅱ, AFP, AFP-L3) surveillance in predicting HCC recurrence. The study demonstrated that PIVKA-Ⅱ, AFP, and AFP-L3 began to elevate from 6 months before recurrence, and showed increasing trends in quite a few patients with HCC recurrence. The GEE model was used to analyze the increasing trends of the three biomarkers, the GEE is a model to estimate the coefficient parameters of the generalized linear model for longitudinally measured outcomes, which does not require a high variance and can be used to evaluate the interaction mode of variables and time between individuals(7). PIVKA-Ⅱ and AFP showed increased trends in recurrence patients, and there was significant difference compared with the trends in the no recurrence. A large sample prospective study used the same method, by longitudinal surveillance of HCC biomarkers in HBV patients to early detect HCC, found that biomarkers elevated from 12 months before diagnosis of HCC(8), the results were similar to our study prediction of recurrence, the biomarkers began to elevate from 6 months before recurrence. Previous evidence has shown that HCC biomarkers can activate a series of signaling pathways such as tyrosine kinase protein1 (JAK1), kinase insert domain receptor (KDR) and epidermal growth factor receptor (EGFR), to promote the proliferation of tumor cells, which may be the reason for the biomarkers can detecting HCC and predicting recurrence(9, 10). We determined the optimal cut-off value to predict recurrence were PIVKA-Ⅱ≥ 29.5mAU/ml, AFP≥ 10.7ng/L, and AFP-L3≥ 1.5%, enabling maximization of the sum of the sensitivity and specificity. This implied that patients with any biomarkers elevated above the optimal cut-off value after the operation, additional imaging studies were recommended to perform. The optimal cut-off values in this study were slightly lower than the optimal cut-off values for HCC preliminary diagnosis(11), but consensus regarding the optimal cut-off values for HCC recurrence has not been determined(12), and considering the unfavorable consequences of delayed the recurrence diagnosis, so a strict criteria was acceptable. With the optimal cut-off value, the sensitivity of predicting recurrence by combination at month -6, month -3, month 0 were 60.9%, 79.7%, 79.7% respectively, compared with detection PIVKA-Ⅱ, AFP, AFP-L3 alone, the triple biomarkers combination showed better sensitivity in predicting recurrence. Despite the sacrifice of specificity, considering the unfavorable consequences of delayed diagnosis of recurrence, sensitivity is taken a higher priority than specificity, so the biomarkers combination might be a cost-effective strategy for the surveillance of recurrence. The biomarkers can more sensitively detect the tiny recurrence focal than imaging studies, maybe the reason for the biomarkers can predict recurrence. A prospective study showed that AFP increased beginning at 6 months before HCC diagnosis(13), another study also reported that AFP and PIVKA-Ⅱ displayed an increasing trend before HCC diagnosis in HCV-infected patients(14). Therefore in the present study, the increasing biomarkers from 6 month before recurrence may indicate that some cases of recurrence might have been diagnosed earlier than they were. Concerns about the performance of AFP-L3, we calculated the optimal cut-off value for AFP-L3 was 1.5%, despite strict, which can effectively improve the sensitivity and has a great advantage in patients with low AFP, and has been proved in the previous study(15). The innovation of our study is that we analyzed the biomarkers by using longitudinal surveillance from postoperative patients during follow-up, which has not been used in analyzing biomarkers before. The previous studies, mainly concentrated on the biomarkers at preoperative or a specific postoperative time to estimate the outcome of HCC patients: single test preoperative biomarkers(16, 17), number of elevated biomarkers one month after operation(18), and half-life of postoperative biomarker(19). The high incidence of recurrence is still a major concern for the long-term survival of HCC patients after operation, but there is no effective methodology for postoperative recurrence surveillance. The present study by longitudinal surveillance after the operation, for more effectively and sensitively to screen the patients with high risk and even predict the time of recurrence. Several limitations in the study are worthy of mentioning. First, we excluded the patients without regularly monitoring the biomarkers after the operation, the stringent exclusion criteria lead to a large of the number of exclusion cases, which may cause selection bias. Second, although all 188 patients were ensured to follow-up for more than 6 months, there was still the probability of potential undiagnosed tumor recurrence in no recurrence patients. The relevant results still need to be further confirmed by large samples and prospective studies, and further studies to investigate the mechanisms behind the elevation rule of biomarkers before recurrence. Conclusion Longitudinal surveillance of PIVKA-II, AFP, and AFP-L3 after radical resection, shows an effective performance in predicting recurrence. The surveillance may have a role in predicting recurrence and could be used as a supplementary test in conjunction with the imaging study. Abbreviations HCC: hepatocellular carcinoma; PIVKA-II: protein induced by vitamin K absence; AFP: alpha-fetoprotein; AFP-L3: lectin-reactive AFP; GEE: Generalized Estimation Equation; AUROC: Area under the receiver operator characteristic; RFS: recurrence-free survival; CIs: confidence intervals; ALB: albumin; ALT: Alanine transaminase; JAK1: tyrosine kinase protein1; KDR: kinase insert domain receptor; EGFR: epidermal growth factor receptor. Declarations Acknowledgments This study was supported by the Ningbo medical and health brand discipline (PPXK 2018-03), Science and Technology program of Zhejiang Health (2021KY1035) and the authors would like to thank the hospital staff, and Jiang Wei. Authors’ Contributions All authors made substantial contributions to conception and design. JT and CL proposed and designed the study; YY and CL collected the data; JT and SM analyzed the data, YX and JT interpreted the results, CL and SM and drafted the article. All authors read and approved the final manuscript. Funding None. Availability of data and materials Data are available on request through contacting authors by [email protected] . Ethical Approval and consent to participate The study was approved by the ethics committee of Ningbo Medical Center Lihuili Hospital (Ethics Committee approval number: KY2020PJ125). Written informed consent was obtained from all the participants included in the study. The study was conducted according to the principles of the Declaration of Helsinki. Consent for publication Not applicable Competing interests The authors declare that there is no conflict of interest regarding the publication of this paper. References Bertuccio P, Turati F, Carioli G, Rodriguez T, La Vecchia C, Malvezzi M, et al. 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The Half-Life of Serum Des-Gamma-Carboxy Prothrombin Is a Prognostic Index of Survival and Recurrence After Liver Resection for Hepatocellular Carcinoma. ANN SURG ONCOL. [Journal Article]. 2016 2016-12-01;23(Suppl 5):921-8. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1919497","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":126404149,"identity":"df3a67c4-84a9-490c-8d16-1931a5141cf0","order_by":0,"name":"Jingshu Tong","email":"","orcid":"","institution":"Ningbo Medical Center Lihuili Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jingshu","middleName":"","lastName":"Tong","suffix":""},{"id":126404150,"identity":"bae1c1a8-639a-4d83-a850-27bbbed5aec4","order_by":1,"name":"Yong Yang","email":"","orcid":"","institution":"Ningbo Medical Center Lihuili Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Yang","suffix":""},{"id":126404151,"identity":"89dd8d2a-dd29-4a95-b06d-0111c63f86b5","order_by":2,"name":"Changjiang Lu","email":"","orcid":"","institution":"Ningbo Medical Center Lihuili Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Changjiang","middleName":"","lastName":"Lu","suffix":""},{"id":126404152,"identity":"c4b006f1-38e6-46df-9af9-f19c0ed7d8f5","order_by":3,"name":"Xi Yu","email":"","orcid":"","institution":"Ningbo Medical Center Lihuili Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Yu","suffix":""},{"id":126404153,"identity":"8f9aaada-67e2-4610-8992-77e1b02bf1a8","order_by":4,"name":"Shuqi Mao","email":"","orcid":"","institution":"Ningbo Medical Center Lihuili Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shuqi","middleName":"","lastName":"Mao","suffix":""},{"id":126404154,"identity":"dfd562b6-c9d6-48fb-89cf-22952dce04fe","order_by":5,"name":"Caide Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYBACPhCRwMYgxyABYrERoYUNqsWYRC1AMrGBeC0SyQ8/PCizSd8u3WPA8KHsMAP/7AZCWtKMJRLOpeXunHPGgHHGucMMEncOENKSwyCR2HY4d8ONHANm3rbDDAYSCQS1MP9IbPufbgDS8pdILWxAWw4kgLUwEqWF55mZRcK5ZMMNd44VHOw5l84jcYOAFn725Mc3f5TZyRvcbt744EeZtRz/DAJaUMABIOYhQf0oGAWjYBSMAlwAAGUYPrI9N3qqAAAAAElFTkSuQmCC","orcid":"","institution":"Ningbo Medical Center Lihuili Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Caide","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2022-08-02 01:59:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1919497/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1919497/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":24934007,"identity":"ba291426-1dd4-4e49-9751-4814db276712","added_by":"auto","created_at":"2022-08-08 17:17:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113377,"visible":true,"origin":"","legend":"\u003cp\u003eExclusion criteria and the Timeline\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1919497/v1/100701bcfea94afd7c91764a.png"},{"id":24934004,"identity":"8db179d4-26e6-4f10-a06d-61b1446f7838","added_by":"auto","created_at":"2022-08-08 17:17:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":96745,"visible":true,"origin":"","legend":"\u003cp\u003eTrend in PIVKA-Ⅱ, AFP, AFP-L3 in recurrence and no recurrence patients\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1919497/v1/93efd32d18a0a63eedfe9b35.png"},{"id":24934005,"identity":"da381206-2a70-4d37-bbe9-09d5f6f56e93","added_by":"auto","created_at":"2022-08-08 17:17:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":58874,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of PIVKA-Ⅱ, AFP, AFP-L3 and combinations in predicting\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1919497/v1/9eec3d22e6e66af8fa5cc7fd.png"},{"id":24934625,"identity":"31e4e8df-3c3e-4e4e-8722-9773eaf656e4","added_by":"auto","created_at":"2022-08-08 17:22:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":39285,"visible":true,"origin":"","legend":"\u003cp\u003eRFS curve for patients with any biomarkers elevated\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-1919497/v1/a0fbb8cc0e6a87ccf7bdd0e1.png"},{"id":33683970,"identity":"7aac31cf-fd87-4983-bd77-d11406d43c84","added_by":"auto","created_at":"2023-03-02 12:29:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":605763,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1919497/v1/cffa0072-4674-4e2c-b657-d90d4f58acaa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Longitudinal surveillance of three biomarkers to predict recurrence of hepatocellular carcinoma after radical resection","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, hepatocellular carcinoma (HCC) has been the 5th most common malignancy and the 3th leading cause of cancer mortality(1). Although radical resection has been accepted as a curative treatment for patients with HCC, the recurrence rate of HCC reaches more than 75% at 5 years after resection, the long-term outcome remains unsatisfactory because of the high incidence of postoperative recurrence(2).\u003c/p\u003e\n\u003cp\u003eThe high rate of recurrence highlights the importance of surveillance to detect recurrence. Current guidelines recommend regular imaging study (US, CT or MRI) with alpha-fetoprotein (AFP) for the detection of recurrence in postoperative patients, however, there is no satisfactory methods or tools for surveillance recurrence currently.\u003c/p\u003e\n\u003cp\u003eThree tumor biomarkers specific for HCC are used clinically: a protein induced by vitamin K absence (PIVKA-II), AFP, and lectin-reactive AFP (AFP-L3). Previous studies have reported the clinical utility of the three biomarkers for diagnosis of HCC, for evaluation of disease progression, and even for predicting a patient\u0026apos;s prognosis(2). In this study, we used longitudinal surveillance of PIVKA-II, AFP, AFP-L3 during postoperative follow-up, which has not been reported in previous studies, aimed to explore the performance of predicting recurrence.\u003c/p\u003e"},{"header":"Patients And Methods","content":"\u003cp\u003e\u003cstrong\u003ePatient Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study cohort consisted of 384 patients who underwent hepatectomy and had histologically confirmed HCC in the Department of Hepatopancreatobiliary Surgery, Ningbo Medical Centre Lihuili Hospital, between January 2015 and December 2020. According to the exclusion criteria, 188 patients were enrolled for further analysis. The exclusion was as follows: (1) patients did not regularly monitor PIVKA-Ⅱ, AFP, and AFP-L3 every 3 months after the operation, (2) complications with other malignancies, (3) a positive final resection margin, and (4) long-term using of vitamin K antagonist (Figure 1). The study was approved by the ethics committee of Ningbo Medical Center Lihuili Hospital (Approval number: KY2020PJ125), Science and Technology program of Zhejiang Health (Approval number: 2021KY1035).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFollow-up\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePatients were followed up until June 2021, ensuring all 188 patients were followed up for more than 6 months, the median follow-up time was 19 months (6-73 months). Patients regularly monitor PIVKA-Ⅱ, AFP, AFP-L3 every 3 months after the operation, and had imaging study (US, CT or MRI) every 3-6 months. When an elevation of tumor biomarkers was detected, additional imaging studies were performed to check for recurrence. If the presence of recurrence was confirmed, patients underwent treatment for recurrent HCC based on treatment guidelines. Follow-up was performed in the outpatient clinic or via phone call.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTimeline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe diagnosis of postoperative recurrence was based on imaging studies and rarely tissue confirmation. “month 0”, “month -3”, and “month -6” were defined as the points of recurrence (0 month), and at 3 and 6 months before the recurence of HCC. In the patients without recurrence, “month 0”, “month -3”, and “month -6” were defined as the points of end of follow-up, and at 3 and 6 months before the before the end of follow-up (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of Serum Biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMeasurements of PIVKA-Ⅱ, AFP, AFP-L3 values were performed with a microchip capillary electrophoresis and liquid-phase binding assay on the μ TASWakoi30 auto-analyzer (Wako Pure Chemical Industries, Ltd., Osaka, Japan)(5). Serum biomarkers were measured at the first follow-up, between 2-3 months after the operation. A previous study reported that half-lives of PIVKA-Ⅱ is 60 h, the half-life of AFP and AFP-L3 is 96 h(6), so the value of postoperative biomarkers was not influenced by preoperative biomarkers elevations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCategorical variables are presented as absolute counts and percentages and were compared by using the Chi-square test. Quantitative variables are presented as the median with 25th quantile and 75th quantile, and compared by using the Mann-Whitney U test. The Generalized Estimation Equation (GEE) model was used to analyze the statistical significance of a time effect on PIVKA-Ⅱ, AFP, AFP-L3. The area under the receiver operator characteristic (AUROC) curves were calculated to evaluate three performance of biomarkers for predicting recurrence. The optimal cut-off value enabling maximization of the sum of the sensitivity and specificity was calculated. The sensitivity and specificity and their 95% confidence intervals (CIs) were calculated for each biomarker and their combination under the optimal cut-off values. The recurrence-free survival (RFS) of patients with any of the elevated biomarkers was analyzed by Kaplan-Meier curves and the log-rank test. The difference was considered significant when the \u003cem\u003eP\u003c/em\u003e value \u0026lt;0.05. Statistical analysis was performed with SPSS 23.0 statistical software (SPSS Inc., Chicago, IL) and SAS 9.4 statistical software (SAS Inc., North Carolina, IL).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCohort characteristic\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween January 2015 and September 2020, a total of 384 patients underwent hepatectomy and had histologically confirmed HCC at the Affiliated Lihuili Hospital of Ningbo University. Excluded from this cohort were 177 patients without regularly monitoring the three biomarkers every 3 months after the operation, 12 patients with other malignancies, 3 patients with a positive resection margin, and 4 patients with long-term use of vitamin K antagonists (Figure 1). After exclusion, all 188 patients with more than 6 months of follow-up were eligible for further analysis. The\u0026nbsp;clinicopathological characteristics of these patients are summarized,\u0026nbsp;there were significant differences in\u0026nbsp;maximum tumor size, tumor number, portal vein tumor thrombus, microvascular invasion,\u0026nbsp;tumor capsule,\u0026nbsp;and\u0026nbsp;TNM stage\u0026nbsp;between the recurrence and no recurrence patients\u0026nbsp;(Table 1).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTABLE 1:\u0026nbsp;\u003c/strong\u003eClinicopathological characteristics of the recurrence patients and no recurrence patients\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.68261562998405%\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.25518341307815%\"\u003e\n \u003cp\u003eRecurrence\u003c/p\u003e\n \u003cp\u003e(n=69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.138755980861244%\"\u003e\n \u003cp\u003eNo recurrence\u003c/p\u003e\n \u003cp\u003e(n=119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.802232854864434%\"\u003e\n \u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e/Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.68261562998405%\"\u003e\n \u003cp\u003eAge (years,%)\u003c/p\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003cp\u003e\u0026lt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.25518341307815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e33(47.8)\u003c/p\u003e\n \u003cp\u003e36(52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.138755980861244%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e59(49.6)\u003c/p\u003e\n \u003cp\u003e60(50.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.802232854864434%\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.68261562998405%\"\u003e\n \u003cp\u003eSex (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Male\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.25518341307815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e57(82.6)\u003c/p\u003e\n \u003cp\u003e12(17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.138755980861244%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e89(74.8)\u003c/p\u003e\n \u003cp\u003e30(25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.802232854864434%\"\u003e\n \u003cp\u003e1.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.68261562998405%\"\u003e\n \u003cp\u003eHBsAg (%)\u003c/p\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003cp\u003eNegative\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.25518341307815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e56(81.2)\u003c/p\u003e\n \u003cp\u003e13(18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.138755980861244%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e92(77.3)\u003c/p\u003e\n \u003cp\u003e27(22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.802232854864434%\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.68261562998405%\"\u003e\n \u003cp\u003eALB (g/L, %)\u003c/p\u003e\n \u003cp\u003e\u0026ge;40\u003c/p\u003e\n \u003cp\u003e\u0026lt;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.25518341307815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e41(59.4)\u003c/p\u003e\n \u003cp\u003e28(40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.138755980861244%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e75(63.0)\u003c/p\u003e\n \u003cp\u003e44(37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.802232854864434%\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.68261562998405%\"\u003e\n \u003cp\u003eALT (U/L, %)\u003c/p\u003e\n \u003cp\u003e\u0026ge;40\u003c/p\u003e\n \u003cp\u003e\u0026lt;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.25518341307815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17(24.6)\u003c/p\u003e\n \u003cp\u003e52(75.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.138755980861244%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e33(27.7)\u003c/p\u003e\n \u003cp\u003e86(72.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.802232854864434%\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.68261562998405%\"\u003e\n \u003cp\u003eMaximum tumor size (cm, %)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge;6\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.25518341307815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25(36.2)\u003c/p\u003e\n \u003cp\u003e44(63.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.138755980861244%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25(21.0)\u003c/p\u003e\n \u003cp\u003e94(79.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.802232854864434%\"\u003e\n \u003cp\u003e5.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.68261562998405%\"\u003e\n \u003cp\u003eNumber of tumors (%)\u003c/p\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003cp\u003eMultiple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.25518341307815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e47(68.1)\u003c/p\u003e\n \u003cp\u003e22(31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.138755980861244%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e97(81.5)\u003c/p\u003e\n \u003cp\u003e22(18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.802232854864434%\"\u003e\n \u003cp\u003e4.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.68261562998405%\"\u003e\n \u003cp\u003eDifferentiation (%)\u003c/p\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003cp\u003eWell-moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.25518341307815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e32(46.4)\u003c/p\u003e\n \u003cp\u003e37(53.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.138755980861244%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e42(35.3)\u003c/p\u003e\n \u003cp\u003e77(64.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.802232854864434%\"\u003e\n \u003cp\u003e2.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.68261562998405%\"\u003e\n \u003cp\u003ePortal vein tumor thrombosis (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Present\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Absent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.25518341307815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12(17.4)\u003c/p\u003e\n \u003cp\u003e57(82.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.138755980861244%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6(5.0)\u003c/p\u003e\n \u003cp\u003e113(95.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.802232854864434%\"\u003e\n \u003cp\u003e7.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.68261562998405%\"\u003e\n \u003cp\u003eMicrovascular invasion (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Present\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Absent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.25518341307815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e39(56.5)\u003c/p\u003e\n \u003cp\u003e30(43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.138755980861244%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e46(38.7)\u003c/p\u003e\n \u003cp\u003e73(61.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.802232854864434%\"\u003e\n \u003cp\u003e5.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.68261562998405%\"\u003e\n \u003cp\u003ePerineural invasion (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Present\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Absent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.25518341307815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2(2.9)\u003c/p\u003e\n \u003cp\u003e67(97.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.138755980861244%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2(1.7)\u003c/p\u003e\n \u003cp\u003e117(98.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.802232854864434%\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.68261562998405%\"\u003e\n \u003cp\u003eTumor capsule (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Present\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Absent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.25518341307815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e48(69.6)\u003c/p\u003e\n \u003cp\u003e21(30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.138755980861244%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e99(83.2)\u003c/p\u003e\n \u003cp\u003e20(16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.802232854864434%\"\u003e\n \u003cp\u003e4.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.68261562998405%\"\u003e\n \u003cp\u003eChild-Pugh class (%)\u003c/p\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.25518341307815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e67(97.1)\u003c/p\u003e\n \u003cp\u003e2(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.138755980861244%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e117(98.3)\u003c/p\u003e\n \u003cp\u003e2(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.802232854864434%\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.68261562998405%\"\u003e\n \u003cp\u003eTNM stage (%)\u003c/p\u003e\n \u003cp\u003eIII-IV\u003c/p\u003e\n \u003cp\u003eI-II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.25518341307815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45(65.2)\u003c/p\u003e\n \u003cp\u003e24(34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.138755980861244%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e103(86.6)\u003c/p\u003e\n \u003cp\u003e16(13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.802232854864434%\"\u003e\n \u003cp\u003e11.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.68261562998405%\"\u003e\n \u003cp\u003ePIVKA-Ⅱ (mAU/ml)\u003c/p\u003e\n \u003cp\u003eMonth \u0026nbsp;-6\u003c/p\u003e\n \u003cp\u003eMonth \u0026nbsp;-3\u003c/p\u003e\n \u003cp\u003eMonth \u0026nbsp;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.25518341307815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e23(14,110)\u003c/p\u003e\n \u003cp\u003e59(20,286)\u003c/p\u003e\n \u003cp\u003e161(27,666)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.138755980861244%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e14(12,19)\u003c/p\u003e\n \u003cp\u003e15(12,18)\u003c/p\u003e\n \u003cp\u003e15(12,19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.802232854864434%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.677\u003c/p\u003e\n \u003cp\u003e8.324\u003c/p\u003e\n \u003cp\u003e8.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.68261562998405%\"\u003e\n \u003cp\u003eAFP (ng/L)\u003c/p\u003e\n \u003cp\u003eMonth \u0026nbsp;-6\u003c/p\u003e\n \u003cp\u003eMonth \u0026nbsp;-3\u003c/p\u003e\n \u003cp\u003eMonth \u0026nbsp;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.25518341307815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6.2(2.4,26.2)\u003c/p\u003e\n \u003cp\u003e9.2(2.7,68.3)\u003c/p\u003e\n \u003cp\u003e14.2(2.5,164.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.138755980861244%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.8(1.8,4.8)\u003c/p\u003e\n \u003cp\u003e2.9(1.7,4.8)\u003c/p\u003e\n \u003cp\u003e2.6(1.5,5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.802232854864434%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.885\u003c/p\u003e\n \u003cp\u003e5.347\u003c/p\u003e\n \u003cp\u003e5.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.68261562998405%\"\u003e\n \u003cp\u003eAFP-L3 (%)\u003c/p\u003e\n \u003cp\u003eMonth \u0026nbsp;-6\u003c/p\u003e\n \u003cp\u003eMonth \u0026nbsp;-3\u003c/p\u003e\n \u003cp\u003eMonth \u0026nbsp;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.25518341307815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.5(0.5,10.4)\u003c/p\u003e\n \u003cp\u003e1.7(0.5,34.5)\u003c/p\u003e\n \u003cp\u003e2.4(0.5,39.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.138755980861244%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.5(0.5,0.5)\u003c/p\u003e\n \u003cp\u003e0.5(0.5,0.5)\u003c/p\u003e\n \u003cp\u003e0.5(0.5,0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.802232854864434%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.348\u003c/p\u003e\n \u003cp\u003e6.912\u003c/p\u003e\n \u003cp\u003e8.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.121212121212121%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eALB: albumin; ALT: Alanine transaminase.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData are presented as number (percentages). PIVKA-II, AFP, AFP-L3 are presented as median (25th quantile, 75th quantile).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrends in PIVKA-Ⅱ, AFP and AFP-L3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the GEE analysis,\u0026nbsp;PIVKA-II, AFP, AFP-L3 in the recurrence patients were significantly higher than the no recurrence patients from month -6 to month 0 (\u003cem\u003eP\u003c/em\u003e\u0026le;0.001, Table 1). PIVKA-Ⅱ and AFP showed increasing trends from month -6 to month 0 in the recurrence patients, and there were significant differences compared with the trends in the no recurrence patients (\u003cem\u003eP\u003c/em\u003e=0.001, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001 respectively), but AFP-L3 had no such difference (\u003cem\u003eP\u003c/em\u003e=0.39, Figure 2, Table 2). These indicate that PIVKA-Ⅱ and AFP effects are different according to the time period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 2:\u0026nbsp;\u003c/strong\u003eComparison the trends of three biomarkers between recurrence and no recurrence patients\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.61687170474517%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003ePIVKA-Ⅱ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.61687170474517%\"\u003e\n \u003cp\u003eGroup(mAU/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003e1735.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003e797.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.61687170474517%\"\u003e\n \u003cp\u003eGroup * time\u003c/p\u003e\n \u003cp\u003e(month -6 vs -3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003e964.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003e642.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.61687170474517%\"\u003e\n \u003cp\u003eGroup * time\u003c/p\u003e\n \u003cp\u003e(month -6 vs 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003e3713.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003e1779.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003eAFP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.61687170474517%\"\u003e\n \u003cp\u003eGroup (ng/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003e206.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003e60.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.61687170474517%\"\u003e\n \u003cp\u003eGroup * time\u003c/p\u003e\n \u003cp\u003e(month -6 vs -3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003e118.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003e47.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.61687170474517%\"\u003e\n \u003cp\u003eGroup * time\u003c/p\u003e\n \u003cp\u003e(month -6 vs 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003e368.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003e110.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003eAFP-L3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.61687170474517%\"\u003e\n \u003cp\u003eGroup (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e16.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.61687170474517%\"\u003e\n \u003cp\u003eGroup * time\u003c/p\u003e\n \u003cp\u003e(month -6 vs -3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.61687170474517%\"\u003e\n \u003cp\u003eGroup * time\u003c/p\u003e\n \u003cp\u003e(month -6 vs 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.68365553602812%\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.507908611599298%\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003ePerformance to predict recurrence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the performance of predict recurrence, the AUROCs for PIVKA-Ⅱ, AFP, AFP-L3 at month 0 were 0.885, 0.754, 0.781 respectively; The AUROCs for PIVKA-Ⅱ, AFP, AFP-L3 at month -3 were 0.871, 0.748, 0.744 respectively; The AUROCs for PIVKA-Ⅱ, AFP, AFP-L3 at month -6 were 0.718, 0.708, 0.603 respectively. The combination of the three biomarkers can improve the performance to predict recurrence, the AUROCs at month -6, month -3, month 0 was 0.786, 0.895, and 0.885 respectively (Figure 3, Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 3:\u0026nbsp;\u003c/strong\u003eAUROC for PIVKA- Ⅱ, AFP, AFP-L3 and combinations in predicting \u0026nbsp;recurrence\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.61904761904762%\"\u003e\n \u003cp\u003eMonth from recurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003eMonth -6 AUROC (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003eMonth -3 AUROC (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003eMonth 0 AUROC (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.61904761904762%\"\u003e\n \u003cp\u003ePIVKA-Ⅱ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.718(0.633-0.803)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.871(0.813-0.930)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.885(0.827-0.943)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.61904761904762%\"\u003e\n \u003cp\u003eAFP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.708(0.621-0.795)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.748(0.669-0.827)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.754(0.675-0.833)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.61904761904762%\"\u003e\n \u003cp\u003eAFP-L3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.603(0.512-0.693)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.744(0.664-0.825)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.781(0.703-0.858)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.61904761904762%\"\u003e\n \u003cp\u003ePIVKA-Ⅱ+AFP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.789(0.714-0.864)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.886(0.830-0.942)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.884(0.823-0.944)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.61904761904762%\"\u003e\n \u003cp\u003ePIVKA-Ⅱ+AFPL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.733(0.649-0.817)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.891(0.836-0.946)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.888(0.831-0.946)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.61904761904762%\"\u003e\n \u003cp\u003eAFP+AFPL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.701(0.615-0.786)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.772(0.694-0.850)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.782(0.705-0.860)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.61904761904762%\"\u003e\n \u003cp\u003ePIVKA-Ⅱ+AFP+AFPL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.786(0.711-0.862)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.895(0.840-0.950)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.126984126984127%\"\u003e\n \u003cp\u003e0.885(0.824-0.944)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eOptimal cut-off value and the performance\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAt month 0, the optimal cut-off value to predict recurrence for PIVKA-Ⅱ, AFP, AFP-L3 were 29.5mAU/ml, 10.7ng/L, 1.5% respectively. With the optimal cut-off value, the sensitivity in predicting recurrence for PIVKA-Ⅱ, AFP, AFP-L3 at month -6 were 42.2%, 37.5%, 34.4% respectively; the sensitivity at month -3 were 68.8%, 50%, 51.6% respectively; the sensitivity at month 0 were 75.0%, 54.7%, 57.8% respectively. The combination of the three biomarkers can improve the performance, and the sensitivities at month -6, month -3, month 0 were 60.9%, 79.7%, 79.7% respectively (Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4:\u003c/strong\u003e Performance of three biomarkers and combinations in predicting recurrence\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth from recurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003eSensitivity(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003eSpecificity(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003ePIVKA-Ⅱ\u0026ge;29.5mAU/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e75.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e94.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth -3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e68.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e96.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth -6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e42.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e91.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eAFP\u0026ge;10.7ng/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e54.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e96.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth -3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e96.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth -6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e37.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e95.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eAFPL3\u0026ge;1.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e57.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e96.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth -3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e51.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e92.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth -6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e34.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e86.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003ePIVKA-Ⅱ\u0026ge;29.5mAU/ml +AFP\u0026ge;10.6ng/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e79.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e94.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth -3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e76.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e94.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth -6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e54.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e91.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003ePIVKA-Ⅱ\u0026ge;29.5mAU/ml +AFPL3\u0026ge;1.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e78.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e93.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth -3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e76.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e90.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth -6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e51.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e83.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eAFP\u0026ge;10.7ng/L +AFPL3\u0026ge;1.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e65.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e96.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth -3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e58.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e91.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth -6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e43.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e84.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003ePIVKA-Ⅱ\u0026gt;29.5mAU/ml+AFP\u0026gt;10.7ng/L\u003c/p\u003e\n \u003cp\u003e+AFPL3\u0026gt;1.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e79.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e93.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth -3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e79.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e89.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"58.30508474576271%\"\u003e\n \u003cp\u003eMonth -6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.52542372881356%\"\u003e\n \u003cp\u003e60.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.16949152542373%\"\u003e\n \u003cp\u003e83.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eElevated biomarkers correlate to recurrence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe median RFS of all patients was 22 months (n=188, 40 months, 95% CI 26.539-53.461), and the median RFS of patients with any biomarkers elevated above the optimal cut-off value during the follow-up (n=82, 19 months, 95% CI 14.757-23.243) was significantly shorter than that of patients without elevated biomarker (n=106, 58 months, 95% CI 51.575-65.022) (\u0026chi;\u003csup\u003e2\u003c/sup\u003e=62.125, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, Figure 4).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we explored the performance of\u0026nbsp;tumor biomarkers (PIVKA-Ⅱ, AFP, AFP-L3)\u0026nbsp;surveillance in predicting HCC recurrence. The study demonstrated that PIVKA-Ⅱ, AFP, and AFP-L3 began to elevate from 6 months before recurrence, and showed increasing trends in quite a few patients with HCC recurrence. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe GEE model was used to analyze the increasing trends of the three biomarkers, the GEE is a model to estimate the coefficient parameters of the generalized linear model for longitudinally measured outcomes, which does not require a high variance and can be used to evaluate the interaction mode of variables and time between individuals(7). PIVKA-Ⅱ and AFP showed increased trends in recurrence patients, and there was significant difference compared with the trends in the no recurrence. A large sample prospective study used the same method, by longitudinal\u0026nbsp;surveillance\u0026nbsp;of HCC biomarkers in HBV patients to early detect HCC, found that\u0026nbsp;biomarkers elevated from 12 months before diagnosis of HCC(8), the results were similar to our study prediction of recurrence, the\u0026nbsp;biomarkers began to elevate from 6 months before recurrence.\u0026nbsp;Previous evidence has shown that HCC biomarkers can activate a series of signaling pathways such as tyrosine\u0026nbsp;kinase protein1 (JAK1), kinase insert domain receptor (KDR) and epidermal growth factor receptor (EGFR), to promote the proliferation of tumor cells, which may be the reason for the biomarkers can detecting HCC and predicting recurrence(9, 10).\u003c/p\u003e\n\u003cp\u003eWe\u0026nbsp;determined\u0026nbsp;the optimal cut-off value to predict recurrence were PIVKA-Ⅱ\u0026ge; 29.5mAU/ml, AFP\u0026ge; 10.7ng/L, and AFP-L3\u0026ge; 1.5%,\u0026nbsp;enabling maximization of the sum of the sensitivity and specificity. This implied that patients with any biomarkers elevated above the optimal cut-off value after the operation, additional imaging studies were\u0026nbsp;recommended to\u0026nbsp;perform. The\u0026nbsp;optimal cut-off values in this study were slightly lower than the optimal cut-off values for HCC preliminary diagnosis(11), but consensus regarding the optimal cut-off values for HCC recurrence has not been determined(12), and considering the unfavorable consequences of delayed the recurrence diagnosis, so a strict criteria was acceptable. With the\u0026nbsp;optimal cut-off value, the sensitivity\u0026nbsp;of predicting recurrence by combination\u0026nbsp;at month -6, month -3, month 0 were 60.9%, 79.7%, 79.7% respectively, compared with detection PIVKA-Ⅱ, AFP, AFP-L3 alone, the triple biomarkers combination showed better sensitivity in predicting recurrence. Despite the sacrifice of\u0026nbsp;specificity, considering the unfavorable consequences of delayed diagnosis of recurrence, sensitivity is taken a higher priority than specificity, so the biomarkers combination might be a cost-effective strategy for the surveillance of recurrence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe biomarkers can more sensitively detect the tiny recurrence focal than\u0026nbsp;imaging studies, maybe the reason for the biomarkers can predict recurrence. A prospective study showed that AFP increased beginning at 6 months before HCC diagnosis(13), another study also reported that AFP and\u0026nbsp;PIVKA-Ⅱ displayed an increasing trend before HCC diagnosis in HCV-infected patients(14). Therefore in the present study, the increasing biomarkers from 6 month before recurrence may indicate that some cases of recurrence might have been diagnosed earlier than they were.\u0026nbsp;Concerns about the performance of AFP-L3, we calculated the optimal cut-off value for AFP-L3 was 1.5%, despite strict, which can effectively improve the sensitivity and has a great advantage in patients with low AFP, and has been proved in the previous study(15).\u003c/p\u003e\n\u003cp\u003eThe innovation of our study is that we analyzed the biomarkers by using longitudinal surveillance from postoperative patients during follow-up, which has not been used in analyzing biomarkers before. The previous studies, mainly concentrated on the biomarkers at preoperative or a specific postoperative time to estimate the outcome of HCC patients: single test preoperative biomarkers(16, 17), number of elevated biomarkers one month after operation(18), and half-life of postoperative biomarker(19).\u0026nbsp;The high incidence of recurrence is still a major concern for the long-term survival of HCC patients after operation, but there is no effective methodology for postoperative recurrence surveillance.\u0026nbsp;The present study by longitudinal surveillance after the operation, for more effectively and sensitively to screen the patients with high risk and even predict the time of recurrence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral limitations in the study are worthy of mentioning. First, we excluded the patients without regularly monitoring the biomarkers after the operation, the stringent exclusion criteria lead to a large of the number of exclusion cases, which may cause selection bias. Second, although all 188 patients were ensured to follow-up for more than 6 months, there was still the probability of potential undiagnosed tumor recurrence in no recurrence patients. The relevant results still need to be further confirmed by large samples and prospective studies, and further studies to investigate the mechanisms behind the elevation rule of biomarkers before recurrence.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eLongitudinal surveillance of PIVKA-II, AFP, and AFP-L3 after radical resection, shows an effective performance in predicting recurrence. The surveillance may have a role in predicting recurrence and could be used as a supplementary test in conjunction with the imaging study. \u003c/p\u003e\n"},{"header":"Abbreviations","content":"\u003cp\u003eHCC: hepatocellular carcinoma; PIVKA-II: protein induced by vitamin K absence; AFP: alpha-fetoprotein; AFP-L3: lectin-reactive AFP; GEE: Generalized Estimation Equation; AUROC: Area under the receiver operator characteristic; RFS: recurrence-free survival; CIs: confidence intervals; ALB: albumin; ALT: Alanine transaminase; JAK1: tyrosine kinase protein1; KDR: kinase insert domain receptor; EGFR: epidermal growth factor receptor.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Ningbo medical and health brand discipline (PPXK 2018-03), Science and Technology program of Zhejiang Health (2021KY1035) and the authors would like to thank the hospital staff, and Jiang Wei.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors made substantial contributions to conception and design. JT and CL proposed and designed the study; YY and CL collected the data; JT and SM analyzed the data, YX and JT interpreted the results, CL and SM and drafted the article.\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available on request through contacting authors by [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the ethics committee of Ningbo Medical Center Lihuili Hospital (Ethics Committee approval number:\u0026nbsp;KY2020PJ125). Written informed consent was obtained from all the participants included in the study. The study was conducted according to the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest regarding the publication of this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBertuccio P, Turati F, Carioli G, Rodriguez T, La Vecchia C, Malvezzi M, et al. Global trends and predictions in hepatocellular carcinoma mortality. J HEPATOL. [Journal Article; Research Support, Non-U.S. Gov\u0026apos;t]. 2017 2017-08-01;67(2):302-9.\u003c/li\u003e\n \u003cli\u003eShinkawa H, Tanaka S, Takemura S, Amano R, Kimura K, Kinoshita M, et al. Nomograms predicting extra- and early intrahepatic recurrence after hepatic resection of hepatocellular carcinoma. SURGERY. [Journal Article; Research Support, Non-U.S. Gov\u0026apos;t]. 2021 2021-04-01;169(4):922-8.\u003c/li\u003e\n \u003cli\u003eTsukuma H, Hiyama T, Tanaka S, Nakao M, Yabuuchi T, Kitamura T, et al. Risk factors for hepatocellular carcinoma among patients with chronic liver disease. N Engl J Med. [Journal Article; Research Support, Non-U.S. Gov\u0026apos;t]. 1993 1993-06-24;328(25):1797-801.\u003c/li\u003e\n \u003cli\u003eFujiyama S, Tanaka M, Maeda S, Ashihara H, Hirata R, Tomita K. Tumor markers in early diagnosis, follow-up and management of patients with hepatocellular carcinoma. Oncology. [Journal Article; Review]. 2002 2002-01-20;62 Suppl 1:57-63.\u003c/li\u003e\n \u003cli\u003eKagebayashi C, Yamaguchi I, Akinaga A, Kitano H, Yokoyama K, Satomura M, et al. Automated immunoassay system for AFP-L3% using on-chip electrokinetic reaction and separation by affinity electrophoresis. ANAL BIOCHEM. [Journal Article]. 2009 2009-05-15;388(2):306-11.\u003c/li\u003e\n \u003cli\u003eFurukawa M, Nakanishi T, Okuda H, Ishida S, Obata H. Changes of plasma des-gamma-carboxy prothrombin levels in patients with hepatocellular carcinoma in response to vitamin K. CANCER-AM CANCER SOC. [Journal Article; Research Support, Non-U.S. Gov\u0026apos;t]. 1992 1992-01-01;69(1):31-8.\u003c/li\u003e\n \u003cli\u003eHubbard AE, Ahern J, Fleischer NL, Van der Laan M, Lippman SA, Jewell N, et al. To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health. EPIDEMIOLOGY. [Journal Article]. 2010 2010-07-01;21(4):467-74.\u003c/li\u003e\n \u003cli\u003eChoi J, Kim GA, Han S, Lee W, Chun S, Lim YS. Longitudinal Assessment of Three Serum Biomarkers to Detect Very Early-Stage Hepatocellular Carcinoma. HEPATOLOGY. [Journal Article; Research Support, Non-U.S. Gov\u0026apos;t]. 2019 2019-05-01;69(5):1983-94.\u003c/li\u003e\n \u003cli\u003eSuzuki M, Shiraha H, Fujikawa T, Takaoka N, Ueda N, Nakanishi Y, et al. Des-gamma-carboxy prothrombin is a potential autologous growth factor for hepatocellular carcinoma. J BIOL CHEM. [Journal Article]. 2005 2005-02-25;280(8):6409-15.\u003c/li\u003e\n \u003cli\u003eBasilico C, Arnesano A, Galluzzo M, Comoglio PM, Michieli P. A high affinity hepatocyte growth factor-binding site in the immunoglobulin-like region of Met. J BIOL CHEM. [Journal Article; Research Support, Non-U.S. Gov\u0026apos;t]. 2008 2008-07-25;283(30):21267-77.\u003c/li\u003e\n \u003cli\u003eLim TS, Kim DY, Han KH, Kim HS, Shin SH, Jung KS, et al. Combined use of AFP, PIVKA-II, and AFP-L3 as tumor markers enhances diagnostic accuracy for hepatocellular carcinoma in cirrhotic patients. Scand J Gastroenterol. 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Combined measurement of preoperative alpha-fetoprotein and des-gamma-carboxy prothrombin predicts recurrence after curative resection in patients with hepatitis-B-related hepatocellular carcinoma. INT J CANCER. [Journal Article; Research Support, Non-U.S. Gov\u0026apos;t]. 2012 2012-11-15;131(10):2332-41.\u003c/li\u003e\n \u003cli\u003eToyoda H, Kumada T, Tada T, Niinomi T, Ito T, Kaneoka Y, et al. Prognostic significance of a combination of pre- and post-treatment tumor markers for hepatocellular carcinoma curatively treated with hepatectomy. J HEPATOL. [Journal Article]. 2012 2012-12-01;57(6):1251-7.\u003c/li\u003e\n \u003cli\u003eOkamura Y, Sugiura T, Ito T, Yamamoto Y, Ashida R, Uesaka K. The Half-Life of Serum Des-Gamma-Carboxy Prothrombin Is a Prognostic Index of Survival and Recurrence After Liver Resection for Hepatocellular Carcinoma. ANN SURG ONCOL. [Journal Article]. 2016 2016-12-01;23(Suppl 5):921-8.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hepatocellular carcinoma, Surveillance, Tumor biomarkers, Recurrence","lastPublishedDoi":"10.21203/rs.3.rs-1919497/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1919497/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003cem\u003e \u003c/em\u003e\u003c/strong\u003eRadical resection is a curative treatment for patients with hepatocellular carcinoma (HCC), but the incidence of recurrence remains high. We aimed to explore the performance of predicting HCC recurrence by longitudinal surveillance of the protein induced by vitamin K absence (PIVKA-II), alpha-fetoprotein (AFP), and lectin-reactive AFP (AFP-L3) during postoperative follow-up. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003cem\u003e \u003c/em\u003ePatients who underwent radical resection for HCC at the Ningbo Medical Centre Lihuili Hospital between January 2015 and December 2020 were included. All enrolled patients regularly monitor PIVKA-Ⅱ, AFP, AFP-L3 every 3 months during postoperative follow-up. The surveillance performance of PIVKA-Ⅱ, AFP, AFP-L3 during follow-up for the prediction of HCC recurrence was compared in patients. The Generalized Estimation Equation (GEE) was used to analyze the trends of the tumor biomarkers and interactions with time. Area under the receiver operator characteristic (AUROC) curves, the optimal cut-off value, the sensitivity and specificity were calculated to evaluate the performance of the three biomarkers. The recurrence-free survival (RFS) of patients with any of the elevated biomarkers was analyzed by Kaplan-Meier curves and the log-rank test. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The GEE analysis indicated that PIVKA-II, AFP, AFP-L3 in the recurrence patients were higher than the no recurrence patients during follow-up, PIVKA-Ⅱ and AFP showed increasing trends from 6 months before recurrence. In predicting recurrence, the AUROCs for PIVKA-Ⅱ, AFP, AFP-L3 and their combination were 0.885, 0.754, 0.781 and 0.885 respectively, the optimal cut-off value for PIVKA-Ⅱ, AFP, AFP-L3 was 29.5 mAU/ml, 10.7 ng/L, 1.5 % respectively. The sensitivity in predicting recurrence for PIVKA-Ⅱ, AFP, AFP-L3 and combination were 75.0%, 54.7%, 57.8% and 79.7% respectively. The RFS of patients with any of the biomarkers elevated during the follow-up was significantly shorter than that without elevated biomarkers (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e=62.125, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003cem\u003e \u003c/em\u003e\u003c/strong\u003eLongitudinal surveillance of PIVKA-II, AFP and AFP-L3 can effectively predict recurrence of HCC after operation.\u003c/p\u003e","manuscriptTitle":"Longitudinal surveillance of three biomarkers to predict recurrence of hepatocellular carcinoma after radical resection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-08 17:17:17","doi":"10.21203/rs.3.rs-1919497/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e647144f-1498-43a7-bbea-c9b5e48c01cb","owner":[],"postedDate":"August 8th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-03-02T12:29:26+00:00","versionOfRecord":[],"versionCreatedAt":"2022-08-08 17:17:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1919497","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1919497","identity":"rs-1919497","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}