Efficacy and safety of transarterial chemoembolization combined with first-line tyrosine kinase inhibitors and programmed death-1 inhibitors for progressed hepatocellular carcinoma

Efficacy and safety of transarterial chemoembolization combined with first-line tyrosine kinase inhibitors and programmed death-1 inhibitors for progressed hepatocellular carcinoma | 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 Efficacy and safety of transarterial chemoembolization combined with first-line tyrosine kinase inhibitors and programmed death-1 inhibitors for progressed hepatocellular carcinoma Long-Wang Lin, Hang Xie, Kun Ke, Le-Ye Yan, Rong Chen, Jun-Qing Lin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2694765/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 : After the failure of transarterial chemoembolization (TACE) combined with first-line tyrosine kinase inhibitor therapy, the subsequent therapy for progressed hepatocellular carcinoma (HCC) patients is still controversial. This study was performed to evaluate the safety and efficacy of the subsequent combination of PD-1 inhibitors (TACE combined with first-line tyrosine kinase plus PD-1 inhibitors) relative to switching to the subsequent regorafenib (TACE plus regorafenib). Methods : The data of HCC patients who suffered from the failure of TACE combined with first-line tyrosine kinase inhibitor therapy from July 2019 to August 2022 were assessed in this single-center retrospective study. Primary study outcomes included progression-free survival (PFS) and overall survival (OS), while the secondary outcomes were treatment-related adverse events, disease control rate (DCR), and objective response rate (ORR). Results : We enrolled a final total of 113 patients, including 73 patients who received TACE combined with first-line tyrosine kinase and PD-1 inhibitors (Group 1) and 40 patients who received TACE plus regorafenib (Group 2). The OS in Group 1 (15.0; 95% confidence interval [CI], 9.8–20.1 months) was significantly higher compared to Group 2 (9.0; 95% CI, 6.6–11.3 months) ( P = 0.016). The PFS in Group 1 (11.0; 95% CI, 8.4–13.5 months) was also significantly higher compared to Group 2 (6.0; 95% CI, 4.6–7.3 months) ( P = 0.010). No significant between-group differences in the ORR ( P = 0.562) and DCR ( P = 0.202) were found; however, the percentage of patients with proteinuria in Group 1 was significantly lower compared to Group 2 (2.73% vs. 20.00%, P = 0.006). Conclusions : The subsequent combining PD-1 inhibitors after the failure of TACE plus first-line tyrosine kinase inhibitor (TACE combined with first-line tyrosine kinase plus PD-1 inhibitors) may be associated with improved OS and PFS compared with switching to the subsequent regorafenib (TACE plus regorafenib). first-line tyrosine kinase inhibitors regorafenib PD-1 inhibitors transcatheter arterial chemoembolization unresectable hepatocellular carcinoma Figures Figure 1 Figure 2 Figure 3 Introduction Hepatocellular carcinoma (HCC) is the sixth-most common malignancy, as well as the third-leading cause of cancer death, accounting for 8.3% of all cancer deaths worldwide [1]. Further, the majority of new patients with HCC are diagnosed with advanced stages, which renders them ineligible for curative resection [2]. Patients with HCC are usually treated with transarterial chemoembolization (TACE) and systemic therapy [3]. Based on the Barcelona Clinic Liver Cancer (BCLC) staging system, for HCC patients in BCLC stage B, TACE is the standard of care [4]; while for HCC patients with BCLC stage C, systemic treatment incluing tyrosine kinase inhibitors (TKI) is the standard of care [5]. Although TACE and systemic treatment provide a survival benefit, TACE alone frequently results in incomplete tumor necrosis, eventually becoming less effective [6]. Meanwhile, systemic treatment easily results in an intolerance to the medication or a failed treatment response among patients with HCC [7]. In consideration of these limitations of TACE and systemic treatment alone, TACE combined with systemic treatment has become an important therapy for patients with unresectable HCC (uHCC) [8, 9]. Many studies have suggested that this combination treatment for HCC leads to improved outcomes; for example, a study revealed that the prognosis of patients treated with TACE combined with systemic treatment (namely, sorafenib/lenvatinib) was better than patients who were administered repeated TACE alone [9]. Unfortunately, among patients receiving first-line TKI in combination with TACE, resistance to first-line TKI will inevitably appear after a period of progression-free survival (PFS), resulting in a poor prognosis [10]. Currently, patients with progressive HCC do not yet have a universally accepted treatment [11]. However, several other treatment options are available [12-14]. For example, the approval of regorafenib was based on the efficacy data in which regorafenib monotherapy was demonstrated to have a survival benefit, extending the overall survival (OS) to 10.3 months [15]. This action is attributed to the fact that compared with first-line TKI such as sorafenib, the molecular target of regorafenib is unique, and its pharmacological activity is stronger; it can more efficiently impede protein kinase activity required for tumor immunity [16-18]. Additionally, for uHCC patients after TACE plus first-line TKI therapy failed, TACE plus regorafenib has been approved for sequential treatment. As another possible second-line choice of therapy for uHCC patients after failure of TACE combined with first-line TKI, immunotherapy with programmed death-1 (PD-1) inhibitors has been shown to be associated with a survival benefit [19]. Specifically, for these HCC patients, the data from phase III trials have shown that as second-line therapy, PD-1 inhibitors prolong the OS to 13.9 months and the PFS to three months [20]. It is known that PD-1 inhibitors can modulate the tumor immune response and enhance immunity; thus, they can bring a survival benefit to uHCC patients during second-line therapy [21]. However, for these patients, the survival obtained by PD-1 inhibitor monotherapy is still unsatisfactory [19]. Thereby, PD-1 inhibitors in combination with other treatments (such as TACE and TKI) are being considered for these uHCC patients during second-line therapy; for instance, a recent study suggested that, as the second-line therapy for uHCC patients, PD-1 inhibitors in combination with first-line TKI (e.g., sorafenib) can prolong the survival time (i.e., the median OS can reach 14.1 months and the median PFS can reach 5.3 months) [22]. In the above contexts, it is hypothesized that for HCC patients with disease progression during TACE combined with first-line TKI, the subsequent combination of PD-1 inhibitors (TACE combined with first-line TKI and PD-1 inhibitors) may bring a new idea to the second-line therapy. In fact, researchers have documented that, for HCC patients with disease progression during first-line therapy, TACE combined with first-line TKI plus PD-1 inhibitors carries the potential to further improve the efficacy, leading to a significant increase in tumor response and survival benefit [23-25]. Therefore, it is believed that subsequent combining PD-1 inhibitors for HCC patients with disease progression during TACE combined with first-line TKI might result in synergistic anti-tumor activity, leading to improved clinical outcomes. Thus, the purpose of this study was to examine the effectiveness of the subsequent combination of PD-1 inhibitors for these HCC patients, by comparing their outcomes with switching to second‐line TKI therapy (such as regorafenib). Materials And Methods Patients This retrospective study initially evaluated 513 HCC patients from July 2019 to August 2022. The screening process is summarized in Figure 1. Finally, 113 HCC patients with disease progression during TACE combined with first-line TKI were enrolled in this study, stratified as 73 patients who were administered the subsequent treatment of TACE combined with first-line TKI plus PD-1 inhibitors (Group 1) and 40 patients who were administered the subsequent treatment of TACE plus regorafenib (Group 2). The inclusion criteria for enrollment were: (1) unresectable HCC, (2) status after the failure of TACE combined with first‐line TKI therapy, (3) receiving subsequent therapy (TACE plus regorafenib or TACE combined with first-line TKI and PD-1 inhibitors), (4) Child–Pugh class A or B, (5) Eastern Cooperative Oncology Group performance status (ECOG-PS) score ≤ 1 point, and (6) Barcelona Clinic Liver Cancer (BCLC) stage B or C. Meanwhile, the exclusion criteria were: (1) patients with upper gastrointestinal bleeding caused by portal hypertension within six months; (2) patients with tumors at multiple sites; (3) patients with immunotherapy alone; (4) patients with sorafenib or lenvatinib treatment alone; (5) patients with incomplete data; (5) patients with ECOG-PS score > 1 point; (6) patients with TACE treatment alone; (7) patients with BCLC A; (8) patients with other subsequent treatments such as traditional Chinese medicine treatment, radiation therapy, or nilotinib alone, TACE plus nilotinib, and TACE plus PD-1 inhibitor treatment; (9) patients refusing subsequent treatment; and (10) contraindications for TACE, regorafenib, or PD-1 inhibitor treatment. We recorded patient demographic profiles, biochemistry data, and tumor characteristics at baseline and the point of disease progression. The data of interest were: age, sex, etiology of cirrhosis, Child–Pugh score, cirrhotic level, BCLC stage, extrahepatic metastasis, albumin–bilirubin (ALBI) grade, protein induced by vitamin K absence or antagonist II (PIVKA-II), alpha-fetoprotein (AFP), carbohydrate antigen 199 (CA 19-9), arteriovenous fistula (AVF), extrahepatic collateral arteries (ECAs), γ-glutamyl transferase (GGT), aspartate aminotransferase (AST), prothrombin time (PT), alanine aminotransferase (ALT), alkaline phosphatase (ALP). The ALBI grade was calculated and liver cirrhosis was defined as previously described. [26, 27] At baseline, a hepatitis marker and viral load were measured in all patients, and patients with antiviral indications were treated with antiviral drugs. Patients received sofosbuvir therapy for hepatitis C virus infection and entecavir for hepatitis B virus infection. Treatment options After TACE plus first-line TKI therapy failed, the subsequent therapy, including the combination of PD-1 inhibitor or switching to regorafenib, was determined in accordance with the patient’s disease condition and wishes. TACE plus regorafenib treatment The TACE procedure was performed by experienced physicians. TACE was conducted within 7 days of diagnosis. TACE was repeated when a stable disease (SD) or partial treatment response (PR) was identified. After TACE treatment approximately every 6–8 weeks, follow-up imaging examinations were performed. One week after TACE, regorafenib (40 mg/pill; Bayer HealthCare AG, Leverkusen, Germany) was administered orally at a dosage of 160 mg daily. Regorafenib was administered for three weeks and was stopped for one week. Each four-week period comprised a treatment cycle. In the event of grade 3 or 4 treatment-related adverse events (TRAE), the dosage of regorafenib was decreased to 80 mg daily. If the TARE did not disappear or decrease within the week after dose adjustment, the patient was counseled to discontinue regorafenib therapy until their symptoms had alleviated or resolved. When the toxicity was below the baseline level (according to the discretion of the investigator), the dosage was recovered to 160 mg daily. TACE combined with first-line TKI plus PD-1 inhibitor therapy The same TACE procedure was conducted, as we described above. Then, three to five days following the first TACE treatment, first-line TKI (sorafenib/lenvatinib) and PD-1 inhibitors were administered simultaneously. Lenvatinib (4 mg/pill) was administered orally at a dosage of 12 mg (if body weight was over 60 kg) or 8 mg (if body weight was below 60 kg) daily. The sorafenib (200 mg/pill) was administered orally at a dosage of 400 mg daily. For PD-1 inhibitor therapy, camrelizumab (200 mg/bottle) or sintilimab (100 mg/bottle) was administered intravenously at a dosage of 200 mg every 3 weeks. In the event of grade 3 or 4 TRAE, the lenvatinib dose was reduced to a dosage of either 8 mg (if body weight was over 60 kg) or 4 mg (if body weight was below 60 kg) daily, and the sorafenib dose was reduced to 200 mg daily until the TRAE was alleviated or eliminated. Corticosteroids were considered if a severe immune-related TRAE associated with PD-1 inhibitor therapy occurred. After adjustment, when grade 3 or 4 TRAE persisted, discontinue sorafenib, lenvatinib, and PD-1 inhibitors; when toxicity had diminished and the patient was capable of tolerating the treatment, the dose could be recommenced. (according to the discretion of the investigator). Treatment evaluation and follow-up The primary study outcomes were OS and PFS. For this study, OS was defined as the interval of time from the start of the subsequent therapy to death, while PFS was defined as the interval of time from the start of the subsequent therapy to the first documentation of PD or death. Treatment response was categorized according to the Modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria [28] and included complete response (CR), PR, SD, and progression of disease (PD). The ORR was defined as the sum of CR and PR, while the DCR was defined as the sum of CR, PR, and SD. Patients were followed up every six to eight weeks by imaging examination (computed tomography or magnetic resonance imaging) to monitor disease status. TRAEs were assessed using the Common Terminology Criteria for Adverse Events version 5.0. Statistical analysis All of the statistical analyses were conducted with SPSS software version 25.0 (IBM Corporation, Armonk, NY, USA). Continuous variables are displayed as mean ± standard deviation (SD) values and categorical variables are expressed using numbers and percentages ( n , (%)). Continuous variables were compared with an independent-samples t test and categorical variables were compared utilizing the chi-squared test. The survival curve analysis was conducted using the Kaplan–Meier method, and differences were evaluated using the log-rank test. Cox proportional-hazards modeling was used for univariate and multivariate analyses for OS and PFS. Univariate Cox proportional hazards models were performed for each variable; then, variables with P <0.05 were included in multivariate analyses to determine their value as independent predictors of OS and PFS. P < 0.05 noted a statistically significant difference. Results Baseline characteristics Table 1 shows the characteristics of the included 113 patients. no significant difference between the two groups was found with regard to age ( P = 0.316), sex ( P = 0.129), etiology ( P = 0.769), Child–Pugh score ( P = 0.255), cirrhosis ( P = 0.803), BCLC stage C ( P = 0.368), ALBI grade ( P = 0.073), tumor number ( P = 0.946), largest tumor size ( P = 0.131), AFP level ( P = 0.070), PIVKA-II ( P = 0.122), vascular invasion ( P = 0.525), extrahepatic metastasis ( P = 0.782), APFs ( P = 0.160), ECAs ( P = 0.112), PT ( P = 0.056), ALT ( P = 0.330), AST ( P = 0.363), ALP ( P = 0.167), GGT ( P = 0.301), and CA 19-9 ( P = 0.173). The median duration of follow-up was 28 months (range: 8–42). The median treatment duration in the TACE+sorafenib/lenvatinib+PD-1 inhibitor was 8.6 months (Range: 3.6–19.7) compared with 8.0 months (range: 3.3–18.2 months) in the TACE+regorafenib group. Additionally, the median number of TACE treatments in each patient was 6 (range: 4–13) in the TACE+sorafenib/lenvatinib+PD-1 inhibitor group compared with 5 (range: 3–11) in the TACE+regorafenib group. Two types of PD-1 inhibitors including sintilimab (n = 28, 38.4%) and camrelizumab (n = 45, 61.6%) were applied; while two types of first-line TKI including sorafenib (n = 23, 31.5%) and lenvatinib (n = 50, 68.5%) were applied. The PD-1 inhibitor was injected in a mean of 10 cycles, ranging from 2 to 18. After the study deadline, in the TACE+sorafenib/lenvatinib+PD-1 inhibitors group, 15 patients continued to receive their original regimen, while 24 patients were treated with third-line therapy, including regorafenib plus PD-1 inhibitors (n=4), regorafenib plus TACE plus PD-1 inhibitors (n=8), the best supportive treatment (n=6), and radiotherapy (n=6); in the TACE+regorafenib group, 4 patients continued to receive the original regimen, while 12 patients were treated with third-line therapy, including TACE combined with PD-1 inhibitors plus regorafenib (n=6), HAIC plus regorafenib (n=3), the best supportive treatment (n=2), and radiotherapy (n=1). OS and PFS There were 59 patients total who died during the follow-up period, including 35 (47.94%) in Group 1 and 24 (60.00%) in Group 2. The median OS in Group 1 (15.0; 95% confidence interval [CI], 9.8–20.1 months) was significantly higher than that in Group 2 (9.0; 95% CI, 6.6–11.3 months) ( P = 0.016) (Figure 2). Tumor progression was observed in 42 patients overall, including 22 patients (32.87%) in Group 1 and 18 patients (45.00%) in Group 2. The median PFS in Group 1 (11.0; 95% CI, 8.4-13.5 months) was significantly higher compared to that in Group 2 (6.0; 95% CI, 4.6-7.3 months) ( P = 0.010) (Figure 3). Treatment response According to the mRECIST criteria, two patients (2.73%) in Group 1 but none in Group 2 achieved a CR; 20 patients (27.39%) in Group 1 and 10 patients (25.00%) in Group 2 achieved a PR; 27 patients (36.98%) in Group 1 and 12 patients (30.00%) achieved SD; 24 patients (32.90%) in Group 1 and 18 patients (45.00%) in Group 2 had PD; 22 patients (30.12%) in Group 1 and 10 patients (25.00%) in Group 2 achieved the ORR; 49 patients (67.10%) in Group 1 and 22 patients (55.00%) in Group 2 achieved the DCR. However, no significant between-group differences were found with regard to ORR ( P = 0.562), DCR ( P = 0.202), CR ( P = 0.756), PR ( P = 0.783), SD ( P = 0.455), and PD ( P = 0.202). (Table 2). Factors associated with OS and PFS In the Cox regression model of univariate analysis, subsequent therapy options (TACE combined with first-line TKI plus PD-1 inhibitors vs. TACE plus regorafenib), sex, liver cirrhosis, ALBI grade were risk factors associated with OS mortality ( P < 0.05) (Table 3). In the multivariate analysis, subsequent therapy options (hazard ratio [HR], 2.145; 95% CI, 1.183–3.889; P = 0.012), ALBI grade (HR, 1.928; 95% CI, 1.253–2.966, P = 0.003), were significant predictors of overall survival (Table 3). In the Cox regression model of univariate analysis, subsequent therapy options, sex, Child–Pugh score, liver cirrhosis, ALBI grade, AFP level were risk factors associated with PFS ( P < 0.05) (Table 4). In multivariate analysis, subsequent therapy options (HR, 2.096; 95% CI, 1.126–3.905, P = 0.020), cirrhosis (HR, 0.656; 95% CI, 0.490–0.879, P = 0.005), ALBI grade (HR,1.915; 95% CI, 1.096–3.345, P = 0.022) were significant predictors of PFS (Table 4). Treatment safety In Group 1, 60 patients (84.91%) had treatment-related TRAEs (Table 5). An incidence of 46.57% of patients experienced hand-to-foot skin reactions (HFSR), which is the most frequent TRAE; other TRAEs with an occurrence of >15% included hypertension (31.50%), thrombocytopenia (31.50%), fatigue (23.28%) hypothyroidism (23.28%), anorexia (19.17%), skin rash (19.17%), and diarrhea (17.80%). In Group 2, 35 patients (87.50%) experienced TRAEs (Table 5). An incidence of 47.50% of patients experienced thrombocytopenia, which is the most frequent TRAE; other TRAEs with an occurrence of >15% were HFSR (30.00%), hypertension (20%), and proteinuria (20%). When comparing all grades of TRAEs, no significant difference was found between the two study groups with regard to cholecystitis ( P = 0.664), liver abscess ( P = 0.457), hypertension ( P = 0.195), HFSR ( P = 0.088), diarrhea ( P = 0.474), skin rash ( P = 0.097), fatigue ( P = 0.295), bleeding (gingiva) ( P = 0.796), hoarseness ( P = 0.459), anorexia ( P = 0.578), hypothyroidism ( P = 0.082), elevated serum ALT or AST ( P = 0.897), thrombocytopenia ( P = 0.093), pruritus ( P = 0.759), oral ulcer ( P = 0.756), paresthesia ( P = 0.457), alopecia ( P = 0.664), and gastrointestinal bleeding ( P = 0.457). Meanwhile, the percentage of patients with proteinuria in Group 1 was significantly lower compared to Group 2 (2.73% vs. 20.00%, P = 0.006). When comparing the TRAEs with grade >3 severity, no significant differences were found between the two groups with regard to HFSR ( P = 0.898), hypertension ( P = 0.940), skin rash ( P = 0.485), and gastrointestinal bleeding ( P = 0.457). Furthermore, the percentage of patients with severe TRAEs (grade > 3) in Group 1 was insignificantly different than that in Group 2 (13.69% vs. 17.50%, P = 0.589) (Table 5). The TACE+sorafenib/lenvatinib+PD-1 inhibitor group experienced 19 (26.0%) dose reductions, and 3 (4.1%) discontinuations of both sorafenib/lenvatinib and PD-1 inhibitor due to HFSR (n=2) and skin rash (n=1), respectively, The TACE+regorafenib group experienced 12 (30.0%) dose reductions and 2 (5.0%) discontinuations of regorafenib due to HFSR (n=1) and hypertension (n=1). Discussion Our study investigated the efficacy and safety of subsequent therapy for HCC patients with disease progression after the failure of TACE combined with first-line TKI therapy. Our major findings were as follows: (1) the subsequent combination of PD-1 inhibitors (TACE combined with first-line TKI and PD-1 inhibitors) led to a better survival benefit compared to switching to regorafenib (TACE plus regorafenib); (2) a subsequent therapy option was one of the significant predictors for OS and PFS; (3) the percentage of patients with proteinuria in the TACE combined with first-line TKI plus PD-1 inhibitors treatment group was significantly lower than that in the TACE plus regorafenib treatment group, and no significant between-group differences in other TARE aspects were found. In this work, the median OS and PFS of patients treated with TACE plus regorafenib were 9.0 and 6.0 months, which were lower than those observed in another study, which reported 11.7 months for OS and 6.7 months for PFS [29]. Greater OS and PFS have also been documented in prior studies [30, 31]. Further, the DCR and ORR observed in patients who received TACE plus regorafenib in the present study were 55.0% and 25.0%, respectively, which were lower than those observed in another study in which ORR reached 42.3% and DCR reached 66.1% [32]. Possibly, differences in baseline patient characteristics contributed to the differences in oncological outcomes between our study and these previously mentioned trials. For example, previous trials enrolled small proportions (0%–2.6%) of patients with Child–Pugh class B, but the proportion of patients with Child-Pugh class B (20.00%) was relatively higher in our study. In addition, previous trials have also enrolled only small proportions of patients with high tumor burdens [33], while we enrolled HCC patients with a more severe tumor burden (the average largest median tumor diameter > 6.9 cm and 87.5% of patients with more than three tumors). It has been known that hepatic dysfunction and high tumor burden resulted in a poor survival prognosis. [34, 35]. In this work, the median OS of patients who received TACE plus first-line TKI plus PD-1 inhibitors was 15.0 months, consistent with prior work that revealed an OS of 14.9 months. The median PFS of these patients was 11.0 months in the present study, which was greater than that observed in a previous study that reported a PFS of 4.2 months [22]. This difference in PFS might be explained by variations in baseline patient characteristics: the previous trial included a larger proportion (88.4%) of patients with liver cirrhosis (only 52.1% in our study). It has been known that patients with liver cirrhosis had a poor survival prognosis. [36] PD-1 medications block programmed death-ligand 1 (PD-L1) from reaching its receptor on T-cells to inhibit tumor growth [37, 38]. When used alone, PD-1 antibodies are insufficient to intensify anticancer immunity in patients with uHCC [38, 39]. However, TACE has the potential to enhance clinical effectiveness by increasing the release of antigens [37]; further, first-line TKI like sorafenib or lenvatinib can increase PD-L1 expression in tumors and promote immune cell infiltration into the tumor [40]. Combining first-line TKI plus PD-1 inhibitors can result in unique immunomodulatory effects, which can overcome the challenges of a low response rate and TKI resistance [41-43]. Thereby, it was expected that combining TACE with first-line TKI plus PD-1 inhibitor therapy could increase the tumor response rate and would be effective in improving the prognosis of our study patients. As we found in our study, cirrhosis, ALBI grade, and subsequent therapy options were indicated as an independent risk factors for PFS; while ALBI grade, and subsequent therapy options were indicated as independent risk factors for OS. Better PFS was observed in patients without liver cirrhosis before the second-line treatment. This result was similar to previous research. [44, 45] Several independent research groups had validated the ALBI score, which was based solely on bilirubin levels and serum albumin, was an objective measure of liver function in HCC.[26, 46] ALBI grade may be applied to screen patients who may benefit from second-line treatment. It has been reported ALBI grade 2 ~3 was correlated with the poor survival of patients with HCC, which is similar to the results of our study. [47] In the present study, after TACE plus first-line TKI therapy failed, TRAEs with the subsequent combination of PD-1 inhibitors or switching to subsequent regorafenib were manageable and consistent with previous data. [23, 48-50] There were comparable incidences and severity of TRAEs between the TACE+sorafenib/lenvatinib+PD-1 inhibitor group and the TACE+regorafenib group. According to these results, TACE+ sorafenib/lenvatinib+PD-1 inhibitors showed good tolerability. Subsequent combining of PD-1 inhibitors did not significantly increase additional TRAEs risk, indicating an acceptable safety profile. However, the group receiving TACE plus regorafenib had a higher incidence of proteinuria. It may be due to the regorafenib application. By inhibition of vascular endothelial growth factor receptors signaling, proteinuria is frequently observed during regorafenib treatment as previous study reported [51]. According to these results, for these patients, the subsequent combination of PD-1 inhibitor therapy was acceptable and feasible. There were a few limitations. It was a single-center study with inherent drawbacks, which limited our ability to produce general conclusions. Moreover, due to the local medical insurance policy, both of the PD-1 inhibitors (camrelizumab and sintilimab) we used, whose effectiveness and safety have been confirmed for HCC [52-54], are widely used in China. Thereby, we only included data related to these two inhibitors rather than data for pembrolizumab and nivolumab, which were widely used worldwide as second-line therapy, limiting the application of our findings globally. [55] Finally, options for subsequent treatment in this study were determined based on the preferences of the physicians and the patients, which could lead to some selection bias. In conclusion, the subsequent combining of PD-1 inhibitors after the failure of TACE plus first-line TKI was a safe and effective therapeutic approach. This approach deserves consideration as a prioritized option during subsequent therapy. Our findings should be verified by randomized controlled trials and large-sample. Declarations Acknowledge Not applicable. Authors’ Contributions Concept and design of the study (WZY, JQL), acquisition of data (LWL, LYY, HX, RC), analysis and interpretation of data (LWL, KK, HX), drafting of the manuscript (LWL, RC), critical revision of the manuscript for important intellectual content (WZY, JQL, LWL), administrative, technical, or material support, study supervision (LYY, HX, LWL). Funding This work was supported by grants from the Fujian Province Natural Science Foundation (nos. 2022J01733 and 2019J01162), and Fujian Province Joint Funds for the Innovation of Science and Technology (no. 2100201). Data sharing statement All data are available upon reasonable request. The corresponding author can be contacted for further information Ethics statement This retrospective study was endorsed by the Ethics Committee of Fujian Medical University Union Hospital, Fuzhou, China. (no. 2022KY217) and was conducted according to the Declaration of Helsinki. 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Baseline characteristics of patients after failure of TACE combined with first‐line tyrosine kinase inhibitors therapy Characteristics Overall (n=113) TACE+sorafenib/lenvatinib+PD-1 (n=73) TACE+regorafenib (n=40) P value Age (years) Mean±SD 55.3±11.5 54.5±11.8 56.8±10.9 0.316 Gender, n ( %) Male 99(87.60%） 67(91.80%） 32(80.00%） 0.129 Female 14(12.40%) 6(8.20%） 8(20.00%） Etiology, n (%) Hepatitis B 100(88.49%) 64(87.70%) 36(90.00%) 0.769 Hepatitis C 2(1.76%) 1(1.40%) 1(2.50%） Non-hepatitis B and C 11(9.75%) 8(11.00%) 3(7.50%) Child-Pugh score, n (%) 5-6 96(84.95%) 64(87.70%) 32(80.00%) 0.255 7-9 17(15.05%) 9(12.30%） 8(20.00%) Cirrhosis, n (%） Present 61(53.98%) 38(52.10%) 23(57.50%） 0.803 Absent 52(46.02%) 35(47.90%) 17(42.50%) BCLC stage, n (%) B 43(38.05%) 30(41.09%） 13(32.50%) 0.368 C 70(61.95%) 43(58.91%) 27(67.50%) ALBI grade, n (%) 1 50(44.24%) 28(38.40%) 22(55.00%) 0.073 2 58(51.32%) 43(58.90%) 15(37.50%) 3 5(4.44%) 2(2.70%) 3(7.50%) Largest tumor size (cm, in diameter) 7.6±3.7 8.0±3.1 6.9±3.4 0.131 Tumor numbers, n (%）≤3 12(10.61%) 7(9.60%) 5(12.50%) 0.946 ＞3 101(89.39%) 66(90.40%) 35(87.50%) AFP (ng/ml), n (%) ＜400 49(43.36%) 32(43.84%) 17(42.50%) 0.070 ≥400 64(56.64%) 41(56.16%) 23(57.50%) PIVKA-II (mAU/ml), Mean±SD 32861.3±83742.1 40175.0±99443.9 19512.0±39988.1 0.122 Vascular invasion, n (%） Present 44(38.93%) 30(41.10%) 14(35.00%) 0.525 Absent 69(61.07%) 43 (58.90%) 26(65.00%) Extrahepatic metastases, n (%) 50(44.24%) 33(45.20%) 17(42.50%) 0.782 Involved disease sites, n (%) Lymph node 7(6.19%) 5 (6.84%) 2(5.00%) Lung 19(16.81%) 15 (20.54%) 8(20.00%) Bone 5(4.42%) 4 (5.47%) 2(5.00%) Peritoneum 3(2.65%) 2 (2.73%) 1(2.50%) Others 8(7.07%) 7 (9.58%) 4(10.00%) APFs, n (%) Present 7(6.19%) 5 (6.80%) 2(2.60%) 0.160 Absent 106(93.81%) 68 (93.20%) 38(97.40%) ECAs, n (%) Present 27(23.89%) 14 (19.20%) 13(32.50%) 0.112 Absent 86(76.11%) 59 (80.80%) 27(67.50%) PT (sec) Mean±SD 13.3±1.5 13.1±1.1 13.7±2.2 0.056 ALT (IU/L) Mean±SD 46.6±38.7 49.3±43.7 41.8±27.4 0.330 AST (IU/L) Mean±SD 72.0±57.9 75.6±60.5 65.2±52.0 0.363 ALP (IU/L) Mean±SD 182.6±121.6 194.3±136.1 161.2±87.1 0.167 GGT (IU/L) Mean±SD 204.9±202.6 219.6±213.2 178.2±181.3 0.301 CA199 (U/ml) Mean±SD Prior therapy, n (%) Resection 49.7±90.0 8(7.07%) 39.0±53.5 5(6.80%) 69.1±131.7 3(7.50%) 0.173 0.897 Ablation 9(7.96%) 6(8.21%) 3(7.5%) 0.893 Idoine 125 implant 6(5.31%) 4(5.47%) 2(2.60%) 0.913 HAIC 5(4.42%) 3(4.10%) 2(2.60%) 0.826 Data are presented as n (%) or mean ± SD values. Abbreviations: HCC, hepatocellular carcinoma; TACE, transcatheter arterial chemoembolization; HAIC, hepatic artery infusion chemotherapy; PD-1, programmed death-1. HBV, hepatitis B virus; BCLC, Barcelona Clinic Liver Cancer; ALBI grade, albumin–bilirubin grade; AFP, alpha-fetoprotein; PIVKA-II, protein induced by vitamin K absence or antagonist II; APFs, arterioportal fistulas; ECAs, extrahepatic collateral arteries; PT, prothrombin time; AST, aspartate aminotransferase; ALT, alanine aminotransferase; ALP, alkaline phosphatase; GGT, γ-glutamyl transferase; CA 19-9, carbohydrate antigen 199. Table 2. Treatment response was evaluated according to mRECIST criteria in two group. Curative effect TACE + sorafenib/lenvatinib + PD-1 TACE + regorafenib P value Complete response (CR) 2 (2.73%) 0 (0.00%) 0.756 Partial response (PR) 20 (27.39%) 10 (25.00%) 0.783 Stable disease (SD) 27 (36.98%) 12 (30.00) 0.455 Progressive disease (PD) 24 (32.90%) 18 (45.00%) 0.202 Overall response rate (ORR) 22 (30.12%) 10 (25.00%) 0.562 Disease control rate (DCR) 49 (67.10%) 22 (55.00%) 0.202 Abbreviations: mRECIST, Modified Response Evaluation Criteria in Solid Tumors; TACE, transcatheter arterial chemoembolization; PD-1, programmed death-1; Table 3. Results of univariable and multivariable Cox regression analyses for OS Abbreviations: CI, confidence interval; HR, hazards ratio; BCLC, Barcelona Clinic Liver Cancer; HBV, hepatitis B virus; ALBI grade, albumin–bilirubin grade; PIVKA-II, protein induced by vitamin K absence or antagonist II; AFP, alpha-fetoprotein; APFs, arterioportal fistulas. Table 4. Results of univariable and multivariable Cox regression analyses for time to progression. Abbreviations: CI, confidence interval; HR, hazards ratio; BCLC, Barcelona Clinic Liver Cancer; HBV, hepatitis B virus; ALBI grade, albumin–bilirubin grade; PIVKA-II, protein induced by vitamin K absence or antagonist II; AFP, alpha-fetoprotein; APFs, arterioportal fistulas. Table 5. TRAEs in the study population Abbreviations: AST, aspartate aminotransferase; ALT, alanine aminotransferase. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2694765","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":186532227,"identity":"4c21ed12-de1b-4fc6-b171-c28ced37e07c","order_by":0,"name":"Long-Wang Lin","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Long-Wang","middleName":"","lastName":"Lin","suffix":""},{"id":186532229,"identity":"28716517-ac3e-4afa-ad0b-76f31a05d47a","order_by":1,"name":"Hang Xie","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Xie","suffix":""},{"id":186532231,"identity":"5a8c45cc-1476-47d4-99db-82982ba7055c","order_by":2,"name":"Kun Ke","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Ke","suffix":""},{"id":186532233,"identity":"84fdb5ef-1d05-4e63-ade4-a432a093cac9","order_by":3,"name":"Le-Ye Yan","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Le-Ye","middleName":"","lastName":"Yan","suffix":""},{"id":186532235,"identity":"9f1dff72-68a1-4bb4-8256-28b8ca6712f9","order_by":4,"name":"Rong Chen","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Chen","suffix":""},{"id":186532236,"identity":"f67be381-8d34-4592-b262-50dc74d0cd96","order_by":5,"name":"Jun-Qing Lin","email":"","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jun-Qing","middleName":"","lastName":"Lin","suffix":""},{"id":186532239,"identity":"ce0401d6-dbc5-4089-ab00-32aec1837b37","order_by":6,"name":"Wei-Zhu Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIie3QvQrCMBDA8StCXc6P8YqivoAQCDiV9lUahLoKLh1bhLj4AILv4RzJ0KUPUHGwXZx9AlE3t2QUzG++P3cJgOP8KAYZ4aBbFO3DPqnCcbDXW07WazyZhqxeySFabahXzWbta4RLK4EgmsxzY5Ik/IAavaOQzRqWfKFMyTVRHEljZyR2jECJkzkROUem0Q/OktAuWQLHJEUkzzIJbvd3okIkFO9PZhZv6VflnfeeFMdl2baPLJoYk5kCn33daRj/mObQaSzmHMdx/tkLnBBBdhYETkwAAAAASUVORK5CYII=","orcid":"","institution":"Fujian Medical University Union Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Wei-Zhu","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2023-03-15 06:44:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2694765/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2694765/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":34942885,"identity":"c10a9ebd-adda-42ed-92da-83c2cc407f86","added_by":"auto","created_at":"2023-03-28 21:49:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1396601,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study patient selection process. \u003cem\u003eAbbreviations:\u003c/em\u003e HCC, hepatocellular carcinoma; ECOG-PS, Eastern Cooperative Oncology Group performance status; BCLC, Barcelona Clinic Liver Cancer; TACE, transcatheter arterial chemoembolization; PD-1, programmed cell death-1.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2694765/v1/249e08024c6e1caa909d281b.png"},{"id":34942884,"identity":"b2d7404d-1625-46a9-aad5-041c3d08540f","added_by":"auto","created_at":"2023-03-28 21:49:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49765,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier analysis of OS in patients receiving TACE combined with first-line tyrosine kinase inhibitors (sorafenib/lenvatinib) plus PD-1 inhibitor therapy and TACE plus regorafenib treatment. \u003cem\u003eAbbreviations:\u003c/em\u003eTACE, transcatheter arterial chemoembolization; PD-1, programmed cell death-1.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2694765/v1/9832aab36b9a4978a89d7ffe.png"},{"id":34942886,"identity":"3edf6f77-39e0-4c8a-8b8f-342d036248e0","added_by":"auto","created_at":"2023-03-28 21:49:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50226,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier analysis of PFS in patients receiving TACE combined with first-linetyrosine kinase inhibitors (sorafenib/lenvatinib) plus PD-1 inhibitor therapy and TACE plus regorafenib treatment. \u003cem\u003eAbbreviations:\u003c/em\u003e TACE, transcatheter arterial chemoembolization; PD-1, programmed cell death-1.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2694765/v1/841ecee55238de05f84cae48.png"},{"id":36321085,"identity":"f645aee2-4cf4-4487-89da-263a0a17ded5","added_by":"auto","created_at":"2023-04-26 11:14:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2382441,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2694765/v1/c22d544a-f259-42b9-a766-d8d5fb92a359.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Efficacy and safety of transarterial chemoembolization combined with first-line tyrosine kinase inhibitors and programmed death-1 inhibitors for progressed hepatocellular carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is the sixth-most common malignancy, as well as the third-leading cause of cancer death, accounting for 8.3% of all cancer deaths worldwide\u0026nbsp;[1]. Further, the majority of new patients with HCC are diagnosed with advanced stages, which renders them ineligible for curative resection\u0026nbsp;[2]. Patients with HCC are usually treated with transarterial chemoembolization (TACE) and systemic therapy\u0026nbsp;[3]. Based on the Barcelona Clinic Liver Cancer (BCLC) staging system, for HCC patients in BCLC stage B, TACE is the standard of care\u0026nbsp;[4]; while for HCC patients with BCLC stage C, systemic treatment incluing tyrosine kinase inhibitors (TKI) is the standard of care\u0026nbsp;[5].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough TACE and systemic treatment provide a survival benefit, TACE alone frequently results in incomplete tumor necrosis, eventually becoming less effective\u0026nbsp;[6]. Meanwhile, systemic treatment easily results in an intolerance to the medication or a failed treatment response among patients with HCC\u0026nbsp;[7]. In consideration of these limitations of TACE and systemic treatment alone, TACE combined with systemic treatment has become an important therapy for patients with unresectable HCC (uHCC)\u0026nbsp;[8, 9]. Many studies have suggested that this combination treatment for HCC leads to improved outcomes; for example, a study revealed that the prognosis of patients treated with TACE combined with systemic treatment (namely,\u0026nbsp;sorafenib/lenvatinib) was better than patients who were administered repeated TACE alone\u0026nbsp;[9].\u003c/p\u003e\n\u003cp\u003eUnfortunately, among patients receiving first-line TKI in combination with TACE, resistance to first-line TKI will inevitably appear after a period of progression-free survival (PFS), resulting in a poor prognosis\u0026nbsp;[10]. Currently, patients with progressive HCC do not yet have a universally accepted treatment\u0026nbsp;[11]. However, several other treatment options are available\u0026nbsp;[12-14]. For example, the approval of regorafenib was based on the efficacy data in which regorafenib monotherapy was demonstrated to have a survival benefit, extending the overall survival (OS) to 10.3 months\u0026nbsp;[15]. This action is attributed to the fact that compared with first-line TKI such as sorafenib, the molecular target of regorafenib is unique, and its pharmacological activity is stronger; it can more efficiently impede protein kinase activity required for tumor immunity\u0026nbsp;[16-18]. Additionally, for uHCC patients after TACE plus first-line TKI therapy failed, TACE plus regorafenib has been approved for sequential treatment.\u003c/p\u003e\n\u003cp\u003eAs another possible second-line choice of therapy for uHCC patients after failure of TACE combined with first-line TKI, immunotherapy with programmed death-1 (PD-1) inhibitors has been shown to be associated with a survival benefit\u0026nbsp;[19]. Specifically, for these HCC patients, the data from phase III trials have shown that as second-line therapy, PD-1 inhibitors prolong the OS to 13.9 months and the PFS to three months\u0026nbsp;[20]. It is known that PD-1 inhibitors can modulate the tumor immune response and enhance immunity; thus, they can bring a survival benefit to uHCC patients during second-line therapy\u0026nbsp;[21]. However, for these patients, the survival obtained by PD-1 inhibitor monotherapy is still unsatisfactory\u0026nbsp;[19]. Thereby, PD-1 inhibitors in combination with other treatments (such as TACE and TKI) are being considered for these uHCC patients during second-line therapy; for instance, a recent study suggested that, as the second-line therapy for uHCC patients, PD-1 inhibitors in combination with first-line TKI (e.g., sorafenib) can prolong the survival time (i.e., the median OS can reach 14.1 months and the median PFS can reach 5.3 months)\u0026nbsp;[22].\u003c/p\u003e\n\u003cp\u003eIn the above contexts, it is hypothesized that for HCC patients with disease progression during TACE combined with first-line TKI, the subsequent combination of PD-1 inhibitors (TACE combined with first-line TKI and PD-1 inhibitors) may bring a new idea to the second-line therapy. In fact, researchers have documented that, for HCC patients with disease progression during first-line therapy, TACE combined with first-line TKI plus PD-1 inhibitors carries the potential to further improve the efficacy, leading to a significant increase in tumor response and survival benefit [23-25]. Therefore, it is believed that subsequent combining PD-1 inhibitors for HCC patients with disease progression during TACE combined with first-line TKI might result in synergistic anti-tumor activity, leading to improved clinical outcomes. Thus, the purpose of this study was to examine the effectiveness of the subsequent combination of PD-1 inhibitors for these HCC patients, by comparing their outcomes with switching to second‐line TKI therapy (such as regorafenib).\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003ePatients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis\u0026nbsp;retrospective\u0026nbsp;study\u0026nbsp;initially evaluated 513 HCC patients from July 2019 to August 2022. The screening process is summarized in Figure 1. Finally, 113 HCC patients with disease progression during TACE combined with\u0026nbsp;first-line TKI\u0026nbsp;were enrolled in this study, stratified as 73 patients who were administered the subsequent treatment of TACE combined with\u0026nbsp;first-line TKI\u0026nbsp;plus PD-1 inhibitors (Group 1) and 40 patients who were administered the subsequent treatment of TACE plus regorafenib (Group 2).\u003c/p\u003e\n\u003cp\u003eThe inclusion criteria for enrollment were: (1) unresectable HCC, (2) status after the failure of TACE combined with first‐line TKI therapy, (3) receiving\u0026nbsp;subsequent therapy (TACE plus regorafenib or TACE combined with\u0026nbsp;first-line TKI\u0026nbsp;and PD-1 inhibitors), (4) Child\u0026ndash;Pugh class A or B, (5) Eastern Cooperative Oncology Group performance status (ECOG-PS) score\u0026nbsp;\u0026le;\u0026nbsp;1 point, and (6)\u0026nbsp;Barcelona Clinic Liver Cancer (BCLC) stage B or C.\u003c/p\u003e\n\u003cp\u003eMeanwhile, the exclusion criteria were: (1)\u0026nbsp;patients with upper gastrointestinal bleeding caused by portal hypertension within six months; (2) patients with\u0026nbsp;tumors at multiple sites; (3) patients with immunotherapy alone; (4) patients with sorafenib or lenvatinib treatment alone; (5) patients with incomplete data; (5) patients with ECOG-PS score \u0026gt; 1 point; (6) patients with TACE treatment alone; (7) patients with BCLC A; (8) patients with other subsequent treatments such as traditional Chinese medicine treatment, radiation therapy, or nilotinib alone, TACE plus nilotinib, and TACE plus PD-1\u0026nbsp;inhibitor treatment; (9) patients refusing subsequent treatment; and (10) contraindications for TACE, regorafenib, or PD-1\u0026nbsp;inhibitor treatment.\u003c/p\u003e\n\u003cp\u003eWe recorded patient demographic profiles, biochemistry data, and tumor characteristics at baseline and the point of disease progression. The data of interest were:\u0026nbsp;age, sex, etiology of cirrhosis, Child\u0026ndash;Pugh score, cirrhotic level, BCLC stage,\u0026nbsp;extrahepatic metastasis, albumin\u0026ndash;bilirubin (ALBI) grade, protein induced by vitamin K absence or antagonist II (PIVKA-II), alpha-fetoprotein (AFP), carbohydrate antigen 199 (CA 19-9), arteriovenous fistula (AVF), extrahepatic collateral arteries (ECAs), \u0026gamma;-glutamyl transferase (GGT), aspartate aminotransferase (AST), prothrombin time (PT), alanine aminotransferase (ALT), alkaline phosphatase (ALP). The ALBI grade was calculated and liver cirrhosis was defined as previously described.\u0026nbsp;[26, 27]\u0026nbsp;At baseline, a hepatitis marker and viral load were measured in all patients, and patients with antiviral indications were treated with antiviral drugs. Patients received sofosbuvir therapy for hepatitis C virus infection and entecavir for hepatitis B virus infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTreatment options\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter TACE plus first-line TKI therapy failed,\u0026nbsp;the subsequent therapy, including the combination of PD-1\u0026nbsp;inhibitor\u0026nbsp;or switching to regorafenib, was determined in accordance with the patient\u0026rsquo;s disease condition and wishes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTACE plus regorafenib\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003etreatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe TACE procedure was performed by experienced physicians. TACE was conducted within 7 days of diagnosis. TACE was repeated when a stable disease (SD) or partial treatment response (PR) was identified. After TACE treatment approximately every 6\u0026ndash;8 weeks, follow-up imaging examinations were performed.\u0026nbsp;One week after TACE, regorafenib (40 mg/pill; Bayer HealthCare AG, Leverkusen, Germany) was administered orally at a dosage of 160 mg daily. Regorafenib was administered for three weeks and was stopped for one week. Each four-week period comprised a treatment cycle.\u003c/p\u003e\n\u003cp\u003eIn the event of grade 3 or 4\u0026nbsp;treatment-related adverse events (TRAE), the dosage of regorafenib was decreased to 80 mg daily. If the TARE did not disappear or decrease within the week after dose adjustment, the patient was counseled to discontinue regorafenib therapy until their symptoms had alleviated or resolved. When the toxicity was below the baseline level (according to the discretion of the investigator), the dosage was recovered to 160 mg daily.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTACE combined with first-line TKI plus PD-1 inhibitor therapy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe same TACE procedure was conducted, as we described above. Then, three to five days following the first TACE treatment, first-line TKI (sorafenib/lenvatinib) and PD-1 inhibitors were administered simultaneously. Lenvatinib (4 mg/pill) was administered orally at a dosage of 12 mg (if body weight was over 60 kg) or 8 mg (if body weight was below 60 kg) daily. The sorafenib (200 mg/pill) was administered orally at a dosage of 400 mg daily. For PD-1 inhibitor therapy, camrelizumab (200 mg/bottle) or sintilimab (100 mg/bottle) was administered intravenously at a dosage of 200 mg every 3 weeks.\u003c/p\u003e\n\u003cp\u003eIn the event of grade 3 or 4 TRAE, the lenvatinib dose was reduced to a dosage of either 8 mg (if body weight was over 60 kg) or 4 mg (if body weight was below 60 kg) daily, and the sorafenib dose was reduced to 200 mg daily until the TRAE was alleviated or eliminated. Corticosteroids were considered if a severe immune-related TRAE associated with PD-1 inhibitor therapy occurred. After adjustment, when grade 3 or 4 TRAE persisted, discontinue sorafenib, lenvatinib, and PD-1 inhibitors; when toxicity had diminished and the patient was capable of tolerating the treatment, the dose could be recommenced. (according to the discretion of the investigator).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTreatment evaluation and follow-up\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary study outcomes were OS and PFS. For this study, OS was defined as the interval of time from the start of the subsequent therapy to death, while PFS was defined as the interval of time from the start of the subsequent therapy to the first documentation of PD or death.\u0026nbsp;Treatment response was categorized according to the Modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria\u0026nbsp;[28]\u0026nbsp;and included complete response (CR), PR, SD, and progression of disease (PD). The ORR was defined as the sum of CR\u0026thinsp;and\u0026thinsp;PR, while the DCR was defined as the sum of CR,\u0026thinsp;PR, and SD. Patients were followed up every six to eight weeks by imaging examination (computed tomography or magnetic resonance imaging) to monitor disease status. TRAEs were assessed using the Common Terminology Criteria for Adverse Events version 5.0.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll of the statistical analyses were conducted with SPSS software version 25.0 (IBM Corporation, Armonk, NY, USA). Continuous variables are displayed as mean \u0026plusmn; standard deviation (SD) values and categorical variables are expressed using numbers and percentages (\u003cem\u003en\u003c/em\u003e, (%)). Continuous variables were compared with an independent-samples\u0026nbsp;\u003cem\u003et\u003c/em\u003e test and categorical variables were compared utilizing the chi-squared test. The survival curve analysis was conducted using the Kaplan\u0026ndash;Meier method, and differences were evaluated using the log-rank test. Cox proportional-hazards modeling was used for univariate and multivariate analyses for OS and PFS. Univariate Cox proportional hazards models were performed for each variable; then, variables with\u003cem\u003e\u0026nbsp;P\u003c/em\u003e \u0026lt;0.05 were included in multivariate analyses to determine their value as independent predictors of OS and PFS. \u003cem\u003eP\u003c/em\u003e\u0026thinsp; \u0026lt;\u0026thinsp;0.05 noted a statistically significant difference.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 shows the characteristics of the included 113 patients. no significant difference between the two groups was found with regard to age (\u003cem\u003eP\u003c/em\u003e = 0.316), sex (\u003cem\u003eP\u003c/em\u003e = 0.129), etiology (\u003cem\u003eP\u003c/em\u003e = 0.769), Child\u0026ndash;Pugh score (\u003cem\u003eP\u003c/em\u003e = 0.255), cirrhosis (\u003cem\u003eP\u003c/em\u003e = 0.803), BCLC stage C (\u003cem\u003eP\u003c/em\u003e = 0.368), ALBI grade (\u003cem\u003eP\u003c/em\u003e = 0.073), tumor number (\u003cem\u003eP\u003c/em\u003e = 0.946), largest tumor size (\u003cem\u003eP\u003c/em\u003e = 0.131), AFP level (\u003cem\u003eP\u003c/em\u003e = 0.070), PIVKA-II (\u003cem\u003eP\u003c/em\u003e = 0.122), vascular invasion (\u003cem\u003eP\u003c/em\u003e = 0.525), extrahepatic metastasis (\u003cem\u003eP \u003c/em\u003e= 0.782), APFs (\u003cem\u003eP\u003c/em\u003e = 0.160), ECAs (\u003cem\u003eP\u003c/em\u003e = 0.112), PT (\u003cem\u003eP\u003c/em\u003e = 0.056), ALT (\u003cem\u003eP\u003c/em\u003e = 0.330), AST (\u003cem\u003eP\u003c/em\u003e = 0.363), ALP (\u003cem\u003eP\u003c/em\u003e = 0.167), GGT (\u003cem\u003eP\u003c/em\u003e = 0.301), and CA 19-9 (\u003cem\u003eP \u003c/em\u003e= 0.173). The median duration of follow-up was 28 months (range: 8\u0026ndash;42). The median treatment duration in the TACE+sorafenib/lenvatinib+PD-1 inhibitor was 8.6 months (Range: 3.6\u0026ndash;19.7) compared with 8.0 months (range: 3.3\u0026ndash;18.2 months) in the TACE+regorafenib group. Additionally, the median number of TACE treatments in each patient was 6 (range: 4\u0026ndash;13) in the TACE+sorafenib/lenvatinib+PD-1 inhibitor group compared with 5 (range: 3\u0026ndash;11) in the TACE+regorafenib group. Two types of PD-1 inhibitors including sintilimab (n = 28, 38.4%) and camrelizumab (n = 45, 61.6%) were applied; while two types of first-line TKI including sorafenib (n = 23, 31.5%) and lenvatinib (n = 50, 68.5%) were applied. The PD-1 inhibitor was injected in a mean of 10 cycles, ranging from 2 to 18. After the study deadline, in the TACE+sorafenib/lenvatinib+PD-1 inhibitors group, 15 patients continued to receive their original regimen, while 24 patients were treated with third-line therapy, including regorafenib plus PD-1 inhibitors (n=4), regorafenib plus TACE plus PD-1 inhibitors (n=8), the best supportive treatment (n=6), and radiotherapy (n=6); in the TACE+regorafenib group, 4 patients continued to receive the original regimen, while 12 patients were treated with third-line therapy, including TACE combined with PD-1 inhibitors plus regorafenib (n=6), HAIC plus regorafenib (n=3), the best supportive treatment (n=2), and radiotherapy (n=1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOS and PFS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were 59 patients total who died during the follow-up period, including 35 (47.94%) in Group 1 and 24 (60.00%) in Group 2. The median OS in Group 1 (15.0; 95% confidence interval [CI], 9.8\u0026ndash;20.1 months) was significantly higher than that in Group 2 (9.0; 95% CI, 6.6\u0026ndash;11.3 months) (\u003cem\u003eP \u003c/em\u003e= 0.016) (Figure 2).\u003c/p\u003e\n\u003cp\u003eTumor progression was observed in 42 patients overall, including 22 patients (32.87%) in Group 1 and 18 patients (45.00%) in Group 2. The median PFS in Group 1 (11.0; 95% CI, 8.4-13.5 months) was significantly higher compared to that in Group 2 (6.0; 95% CI, 4.6-7.3 months) (\u003cem\u003eP \u003c/em\u003e= 0.010) (Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTreatment response\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the mRECIST criteria, two patients (2.73%) in Group 1 but none in Group 2 achieved a CR; 20 patients (27.39%) in Group 1 and 10 patients (25.00%) in Group 2 achieved a PR; 27 patients (36.98%) in Group 1 and 12 patients (30.00%) achieved SD; 24 patients (32.90%) in Group 1 and 18 patients (45.00%) in Group 2 had PD; 22 patients (30.12%) in Group 1 and 10 patients (25.00%) in Group 2 achieved the ORR; 49 patients (67.10%) in Group 1 and 22 patients (55.00%) in Group 2 achieved the DCR. However, no significant between-group differences were found with regard to ORR (\u003cem\u003eP\u003c/em\u003e = 0.562), DCR (\u003cem\u003eP\u003c/em\u003e = 0.202), CR (\u003cem\u003eP\u003c/em\u003e = 0.756), PR (\u003cem\u003eP\u003c/em\u003e = 0.783), SD (\u003cem\u003eP\u003c/em\u003e = 0.455), and PD (\u003cem\u003eP\u003c/em\u003e = 0.202). (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactors associated with OS and PFS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the Cox regression model of univariate analysis, subsequent therapy options (TACE combined with first-line TKI plus PD-1 inhibitors \u003cem\u003evs. \u003c/em\u003eTACE plus regorafenib), sex, liver cirrhosis, ALBI grade were risk factors associated with OS mortality (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (Table 3). In the multivariate analysis, subsequent therapy options (hazard ratio [HR], 2.145; 95% CI, 1.183\u0026ndash;3.889; \u003cem\u003eP\u003c/em\u003e = 0.012), ALBI grade (HR, 1.928; 95% CI, 1.253\u0026ndash;2.966,\u003cem\u003e P \u003c/em\u003e= 0.003), were significant predictors of overall survival (Table 3).\u003c/p\u003e\n\u003cp\u003eIn the Cox regression model of univariate analysis, subsequent therapy options, sex, Child\u0026ndash;Pugh score, liver cirrhosis, ALBI grade, AFP level were risk factors associated with PFS (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (Table 4). In multivariate analysis, subsequent therapy options (HR, 2.096; 95% CI, 1.126\u0026ndash;3.905,\u003cem\u003e P\u003c/em\u003e = 0.020), cirrhosis (HR, 0.656; 95% CI, 0.490\u0026ndash;0.879, \u003cem\u003eP\u003c/em\u003e = 0.005), ALBI grade (HR,1.915; 95% CI, 1.096\u0026ndash;3.345, \u003cem\u003eP \u003c/em\u003e= 0.022) were significant predictors of PFS (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTreatment safety\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Group 1, 60 patients (84.91%) had treatment-related TRAEs (Table 5). An incidence of 46.57% of patients experienced hand-to-foot skin reactions (HFSR), which is the most frequent TRAE; other TRAEs with an occurrence of \u0026gt;15% included hypertension (31.50%), thrombocytopenia (31.50%), fatigue (23.28%) hypothyroidism (23.28%), anorexia (19.17%), skin rash (19.17%), and diarrhea (17.80%). In Group 2, 35 patients (87.50%) experienced TRAEs (Table 5). An incidence of 47.50% of patients experienced thrombocytopenia, which is the most frequent TRAE; other TRAEs with an occurrence of \u0026gt;15% were HFSR (30.00%), hypertension (20%), and proteinuria (20%).\u003c/p\u003e\n\u003cp\u003eWhen comparing all grades of TRAEs, no significant difference was found between the two study groups with regard to cholecystitis (\u003cem\u003eP \u003c/em\u003e= 0.664), liver abscess (\u003cem\u003eP\u003c/em\u003e = 0.457), hypertension (\u003cem\u003eP\u003c/em\u003e = 0.195), HFSR (\u003cem\u003eP\u003c/em\u003e = 0.088), diarrhea (\u003cem\u003eP\u003c/em\u003e = 0.474), skin rash (\u003cem\u003eP\u003c/em\u003e = 0.097), fatigue (\u003cem\u003eP \u003c/em\u003e= 0.295), bleeding (gingiva) (\u003cem\u003eP\u003c/em\u003e = 0.796), hoarseness (\u003cem\u003eP\u003c/em\u003e = 0.459), anorexia (\u003cem\u003eP\u003c/em\u003e = 0.578), hypothyroidism (\u003cem\u003eP\u003c/em\u003e = 0.082), elevated serum ALT or AST (\u003cem\u003eP\u003c/em\u003e = 0.897), thrombocytopenia (\u003cem\u003eP \u003c/em\u003e= 0.093), pruritus (\u003cem\u003eP\u003c/em\u003e = 0.759), oral ulcer (\u003cem\u003eP\u003c/em\u003e = 0.756), paresthesia (\u003cem\u003eP \u003c/em\u003e= 0.457), alopecia (\u003cem\u003eP\u003c/em\u003e = 0.664), and gastrointestinal bleeding (\u003cem\u003eP\u003c/em\u003e = 0.457). Meanwhile, the percentage of patients with proteinuria in Group 1 was significantly lower compared to Group 2 (2.73% \u003cem\u003evs. \u003c/em\u003e20.00%, \u003cem\u003eP \u003c/em\u003e= 0.006). When comparing the TRAEs with grade \u0026gt;3 severity, no significant differences were found between the two groups with regard to HFSR (\u003cem\u003eP \u003c/em\u003e= 0.898), hypertension (\u003cem\u003eP\u003c/em\u003e = 0.940), skin rash (\u003cem\u003eP\u003c/em\u003e = 0.485), and gastrointestinal bleeding (\u003cem\u003eP \u003c/em\u003e= 0.457). Furthermore, the percentage of patients with severe TRAEs (grade \u0026gt; 3) in Group 1 was insignificantly different than that in Group 2 (13.69% \u003cem\u003evs. \u003c/em\u003e17.50%, \u003cem\u003eP \u003c/em\u003e= 0.589) (Table 5).\u003c/p\u003e\n\u003cp\u003eThe TACE+sorafenib/lenvatinib+PD-1 inhibitor group experienced 19 (26.0%) dose reductions, and 3 (4.1%) discontinuations of both sorafenib/lenvatinib and PD-1 inhibitor due to HFSR (n=2) and skin rash (n=1), respectively, The TACE+regorafenib group experienced 12 (30.0%) dose reductions and 2 (5.0%) discontinuations of regorafenib due to HFSR (n=1) and hypertension (n=1).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study investigated\u0026nbsp;the efficacy and safety of subsequent therapy for HCC patients with disease progression after the failure of TACE combined with first-line TKI therapy.\u0026nbsp;Our major findings were as follows: (1) the subsequent combination of PD-1 inhibitors (TACE combined with first-line TKI and PD-1 inhibitors) led to a better survival benefit compared to switching to regorafenib (TACE plus regorafenib); (2) a\u0026nbsp;subsequent therapy option was one of the significant predictors for OS and PFS; (3)\u0026nbsp;the percentage of patients with proteinuria in the TACE combined with\u0026nbsp;first-line TKI\u0026nbsp;plus PD-1 inhibitors treatment group was significantly lower than that in the TACE plus regorafenib treatment group, and no significant between-group differences in other TARE aspects were found.\u003c/p\u003e\n\u003cp\u003eIn this work, the median OS and PFS of patients treated with TACE plus regorafenib were 9.0 and 6.0 months, which were lower than those observed in another study, which reported 11.7 months for OS and 6.7 months for PFS\u0026nbsp;[29]. Greater OS and PFS have also been documented in prior studies\u0026nbsp;[30, 31].\u0026nbsp;Further, the DCR and ORR observed in patients who received TACE plus regorafenib in the present study were 55.0% and 25.0%, respectively, which were lower than those observed in another study in which ORR reached 42.3% and DCR reached 66.1%\u0026nbsp;[32]. Possibly, differences in baseline patient characteristics contributed to the differences in oncological outcomes between our study and these previously mentioned trials. For example, previous trials enrolled small proportions (0%\u0026ndash;2.6%) of patients with Child\u0026ndash;Pugh class B, but the proportion of patients with Child-Pugh class B (20.00%) was relatively higher in our study. In addition, previous trials have also enrolled only small proportions of patients with high tumor burdens\u0026nbsp;[33], while we enrolled HCC patients with a more severe tumor burden (the average largest median tumor diameter \u0026gt; 6.9 cm and 87.5% of patients with more than three tumors). It has been known that hepatic dysfunction and high tumor burden resulted in a poor survival prognosis.\u0026nbsp;[34, 35].\u003c/p\u003e\n\u003cp\u003eIn this work, the median OS of patients who received TACE plus\u0026nbsp;first-line TKI\u0026nbsp;plus PD-1 inhibitors was 15.0 months, consistent with prior work that revealed an OS of 14.9 months. The median PFS of these patients was 11.0 months in the present study, which was greater than that observed in a previous study that reported a PFS of 4.2 months\u0026nbsp;[22].\u0026nbsp;This difference in PFS might be explained by variations in baseline patient characteristics: the previous trial included a larger proportion (88.4%) of patients with liver cirrhosis\u0026nbsp;(only 52.1% in our study). It has been known that patients with liver cirrhosis had a poor survival prognosis.\u0026nbsp;[36]\u003c/p\u003e\n\u003cp\u003ePD-1 medications block programmed death-ligand 1 (PD-L1)\u0026nbsp;from reaching its receptor on T-cells to inhibit tumor growth\u0026nbsp;[37, 38]. When used alone, PD-1 antibodies are insufficient to intensify anticancer immunity in patients with uHCC\u0026nbsp;[38, 39]. However, TACE has the potential to enhance clinical effectiveness by increasing the release of antigens\u0026nbsp;[37]; further, first-line TKI like sorafenib or lenvatinib can increase PD-L1 expression in tumors and promote immune cell infiltration into the tumor\u0026nbsp;[40]. Combining first-line TKI plus PD-1 inhibitors can result in unique immunomodulatory effects, which can overcome the challenges of a low response rate and TKI resistance\u0026nbsp;[41-43]. Thereby, it was expected that combining\u0026nbsp;TACE with\u0026nbsp;first-line TKI\u0026nbsp;plus PD-1 inhibitor therapy could increase the\u0026nbsp;tumor response rate and would be effective in improving the prognosis of our study patients.\u003c/p\u003e\n\u003cp\u003eAs we found in our study, cirrhosis, ALBI grade, and subsequent therapy options were indicated as an independent risk factors for PFS; while ALBI grade, and subsequent therapy options were indicated as independent risk factors for OS. Better PFS was observed in patients without liver cirrhosis before the second-line treatment. This result was similar to previous research.\u0026nbsp;[44, 45]\u0026nbsp;Several independent research groups had validated the ALBI score, which was based solely on bilirubin levels and serum albumin, was an objective measure of liver function in HCC.[26, 46] ALBI grade may be applied to screen patients who may benefit from second-line treatment. It has been reported ALBI grade 2 ~3 was correlated with the poor survival of patients with HCC, which is similar to the results of our study.\u0026nbsp;[47]\u003c/p\u003e\n\u003cp\u003eIn the present study, after TACE plus first-line TKI therapy failed, TRAEs with the subsequent combination of PD-1 inhibitors or switching to subsequent regorafenib were manageable and consistent with previous data.\u0026nbsp;[23, 48-50]\u0026nbsp;There were comparable incidences and severity of TRAEs between the TACE+sorafenib/lenvatinib+PD-1 inhibitor group and the TACE+regorafenib group. According to these results, TACE+ sorafenib/lenvatinib+PD-1 inhibitors showed good tolerability. Subsequent combining of PD-1 inhibitors did not significantly increase additional TRAEs risk, indicating an acceptable safety profile. However, the group receiving TACE plus regorafenib had a higher incidence of proteinuria. It may be due to the regorafenib application. By inhibition of vascular endothelial growth factor receptors signaling, proteinuria is frequently observed during regorafenib treatment as previous study reported\u0026nbsp;[51]. According to these results, for these patients, the subsequent combination\u0026nbsp;of PD-1 inhibitor therapy was acceptable and feasible.\u003c/p\u003e\n\u003cp\u003eThere were a few limitations. It was a single-center study with inherent drawbacks, which limited our ability to produce general conclusions. Moreover, due to the local medical insurance policy, both of the PD-1 inhibitors (camrelizumab and sintilimab) we used, whose effectiveness and safety have been confirmed for HCC\u0026nbsp;[52-54], are widely used in China. Thereby, we only included data related to these two inhibitors rather than data for pembrolizumab and nivolumab, which were widely used worldwide as second-line therapy, limiting the application of our findings globally.\u0026nbsp;[55]\u0026nbsp;Finally, options for subsequent treatment in this study were determined based on the preferences of the physicians and the patients, which could lead to some selection bias.\u003c/p\u003e\n\u003cp\u003eIn conclusion, the subsequent combining of PD-1 inhibitors after the failure of TACE plus first-line TKI was a safe and effective therapeutic approach. This approach deserves consideration as a prioritized option during subsequent therapy. Our findings should be verified by randomized controlled trials and large-sample.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledge\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcept and design of the study (WZY, JQL), acquisition of data (LWL, LYY, HX, RC), analysis and interpretation of data (LWL, KK, HX), drafting of the manuscript (LWL, RC), critical revision of the manuscript for important intellectual content (WZY, JQL, LWL), administrative, technical, or material support, study supervision (LYY, HX, LWL).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Fujian Province Natural Science Foundation (nos. 2022J01733 and 2019J01162), and Fujian Province Joint Funds for the Innovation of Science and Technology (no. 2100201).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data are available upon reasonable request. The corresponding author can be contacted for further information\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was endorsed by the Ethics Committee of Fujian Medical University Union Hospital, Fuzhou, China. (no. 2022KY217) and was conducted according to the Declaration of Helsinki. As this was a retrospective, anonymous study, the Fujian Medical University Union Hospital Ethics Committee waived informed consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research does not require patient consent due to the absence of any personal data or images involving the patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll of the authors declare that they have no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: \u003cstrong\u003eGlobal Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2021, 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\u003cstrong\u003e9\u003c/strong\u003e:233-263.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Baseline characteristics of patients after failure of TACE combined with first‐line tyrosine kinase inhibitors\u0026nbsp;therapy\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003cp\u003e(n=113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003eTACE+sorafenib/lenvatinib+PD-1\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n=73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003eTACE+regorafenib (n=40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u0026nbsp;\u0026nbsp;\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eAge (years) \u0026nbsp;Mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e55.3\u0026plusmn;11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e54.5\u0026plusmn;11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e56.8\u0026plusmn;10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eGender, \u003cem\u003en (\u003c/em\u003e%) \u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e99(87.60%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e67(91.80%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e32(80.00%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e14(12.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e6(8.20%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e8(20.00%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eEtiology, \u003cem\u003en\u0026nbsp;\u003c/em\u003e(%) \u0026nbsp;Hepatitis B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e100(88.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e64(87.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e36(90.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eHepatitis C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e2(1.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e1(1.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e1(2.50%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eNon-hepatitis B and C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e11(9.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e8(11.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e3(7.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eChild-Pugh score, \u003cem\u003en\u0026nbsp;\u003c/em\u003e(%) \u0026nbsp;5-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e96(84.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e64(87.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e32(80.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003e7-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e17(15.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e9(12.30%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e8(20.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eCirrhosis, \u003cem\u003en\u0026nbsp;\u003c/em\u003e(%）\u0026nbsp;Present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e61(53.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e38(52.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e23(57.50%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Absent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e52(46.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e35(47.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e17(42.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eBCLC stage, \u003cem\u003en\u0026nbsp;\u003c/em\u003e(%) \u0026nbsp; \u0026nbsp; B\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e43(38.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e30(41.09%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e13(32.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e70(61.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e43(58.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e27(67.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eALBI grade,\u003cem\u003e\u0026nbsp;n\u0026nbsp;\u003c/em\u003e(%) \u0026nbsp; \u0026nbsp; 1 \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e50(44.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e28(38.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e22(55.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e58(51.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e43(58.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e15(37.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e5(4.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e2(2.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e3(7.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eLargest tumor size (cm, in diameter)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e7.6\u0026plusmn;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e8.0\u0026plusmn;3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e6.9\u0026plusmn;3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eTumor numbers,\u003cem\u003e\u0026nbsp;n\u0026nbsp;\u003c/em\u003e(%）\u0026le;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e12(10.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e7(9.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e5(12.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.946\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003e＞3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e101(89.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e66(90.40%) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e35(87.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\u0026nbsp;\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eAFP (ng/ml), \u0026nbsp;\u003cem\u003en\u0026nbsp;\u003c/em\u003e(%)\u0026nbsp;＜400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e49(43.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e32(43.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e17(42.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026ge;400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e64(56.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e41(56.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e23(57.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003ePIVKA-II (mAU/ml), Mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e32861.3\u0026plusmn;83742.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e40175.0\u0026plusmn;99443.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e19512.0\u0026plusmn;39988.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eVascular invasion, \u003cem\u003en\u0026nbsp;\u003c/em\u003e(%）\u0026nbsp;Present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e44(38.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e30(41.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e14(35.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Absent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e69(61.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e43 (58.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e26(65.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eExtrahepatic metastases,\u003cem\u003e\u0026nbsp;n\u0026nbsp;\u003c/em\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e50(44.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e33(45.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e17(42.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eInvolved disease sites, \u003cem\u003en\u0026nbsp;\u003c/em\u003e(%) \u0026nbsp; Lymph node\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e7(6.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e5 (6.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e2(5.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eLung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e19(16.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e15 (20.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e8(20.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eBone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e5(4.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e4 (5.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e2(5.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003ePeritoneum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e3(2.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e2 (2.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e1(2.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e8(7.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e7 (9.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e4(10.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eAPFs, \u003cem\u003en\u0026nbsp;\u003c/em\u003e(%) \u0026nbsp; \u0026nbsp; Present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e7(6.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e5 (6.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e2(2.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e106(93.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e68 (93.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e38(97.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eECAs, \u003cem\u003en\u0026nbsp;\u003c/em\u003e(%) \u0026nbsp; \u0026nbsp;Present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e27(23.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e14 (19.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e13(32.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e86(76.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e59 (80.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e27(67.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003ePT (sec) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e13.3\u0026plusmn;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e13.1\u0026plusmn;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e13.7\u0026plusmn;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eALT (IU/L) \u0026nbsp; \u0026nbsp; Mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e46.6\u0026plusmn;38.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e49.3\u0026plusmn;43.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e41.8\u0026plusmn;27.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eAST (IU/L) \u0026nbsp; \u0026nbsp; Mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e72.0\u0026plusmn;57.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e75.6\u0026plusmn;60.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e65.2\u0026plusmn;52.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eALP (IU/L) \u0026nbsp; \u0026nbsp; Mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e182.6\u0026plusmn;121.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e194.3\u0026plusmn;136.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e161.2\u0026plusmn;87.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eGGT (IU/L) \u0026nbsp; \u0026nbsp; Mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e204.9\u0026plusmn;202.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e219.6\u0026plusmn;213.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e178.2\u0026plusmn;181.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eCA199 (U/ml) \u0026nbsp; Mean\u0026plusmn;SD \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePrior therapy, n (%) \u0026nbsp; Resection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e49.7\u0026plusmn;90.0\u003c/p\u003e\n \u003cp\u003e8(7.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e39.0\u0026plusmn;53.5\u003c/p\u003e\n \u003cp\u003e5(6.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e69.1\u0026plusmn;131.7\u003c/p\u003e\n \u003cp\u003e3(7.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eAblation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e9(7.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e6(8.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e3(7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eIdoine 125 implant\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e6(5.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e4(5.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e2(2.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"32.98969072164948%\"\u003e\n \u003cp\u003eHAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e5(4.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.649484536082475%\"\u003e\n \u003cp\u003e3(4.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e2(2.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.43298969072165%\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as \u003cem\u003en\u003c/em\u003e (%) or mean \u0026plusmn; SD values. \u003cem\u003eAbbreviations:\u003c/em\u003e HCC, hepatocellular carcinoma; TACE, transcatheter arterial chemoembolization; HAIC, hepatic artery infusion chemotherapy; PD-1, programmed death-1. HBV, hepatitis B virus; BCLC, Barcelona Clinic Liver Cancer; ALBI grade, albumin\u0026ndash;bilirubin grade; AFP, alpha-fetoprotein; PIVKA-II, protein induced by vitamin K absence or antagonist II; APFs, arterioportal fistulas; ECAs, extrahepatic collateral arteries; PT, prothrombin time; AST, aspartate aminotransferase; ALT, alanine aminotransferase; ALP, alkaline phosphatase; GGT, \u0026gamma;-glutamyl transferase; CA 19-9, carbohydrate antigen 199.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2. Treatment response was evaluated according to mRECIST criteria in two group.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"488\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.377049180327866%\"\u003e\n \u003cp\u003eCurative effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.24590163934426%\"\u003e\n \u003cp\u003eTACE + sorafenib/lenvatinib + PD-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.311475409836067%\"\u003e\n \u003cp\u003eTACE + regorafenib\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.065573770491802%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.377049180327866%\"\u003e\n \u003cp\u003eComplete response (CR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.24590163934426%\"\u003e\n \u003cp\u003e2 (2.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.311475409836067%\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.065573770491802%\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.377049180327866%\"\u003e\n \u003cp\u003ePartial response (PR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.24590163934426%\"\u003e\n \u003cp\u003e20 (27.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.311475409836067%\"\u003e\n \u003cp\u003e10 (25.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.065573770491802%\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.377049180327866%\"\u003e\n \u003cp\u003eStable disease (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.24590163934426%\"\u003e\n \u003cp\u003e27 (36.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.311475409836067%\"\u003e\n \u003cp\u003e12 (30.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.065573770491802%\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.377049180327866%\"\u003e\n \u003cp\u003eProgressive disease (PD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.24590163934426%\"\u003e\n \u003cp\u003e24 (32.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.311475409836067%\"\u003e\n \u003cp\u003e18 (45.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.065573770491802%\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.377049180327866%\"\u003e\n \u003cp\u003eOverall response rate (ORR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.24590163934426%\"\u003e\n \u003cp\u003e22 (30.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.311475409836067%\"\u003e\n \u003cp\u003e10 (25.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.065573770491802%\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.377049180327866%\"\u003e\n \u003cp\u003eDisease control rate (DCR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.24590163934426%\"\u003e\n \u003cp\u003e49 (67.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.311475409836067%\"\u003e\n \u003cp\u003e22 (55.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.065573770491802%\"\u003e\n \u003cp\u003e0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations:\u003c/em\u003e mRECIST, Modified Response Evaluation Criteria in Solid Tumors; TACE, transcatheter arterial chemoembolization; PD-1, programmed death-1;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3. Results of univariable and multivariable Cox regression analyses for OS\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg 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bnAgxEAAL5f8bGre+yYvJPFyEy+t+133BqGSpiIo4WY39DCpBTe8bXPz5EY66TebJUbSypE5Hop8NiIQziOsgXXTHnSMxC4TRi/z57shfDaNEelAXfIgov5rLybTxCjM0MNT7dDH6Df0kZr8BSMQRHLtuI4Eo0MvjGX/5aUxPsMTjNJ24osHI6yUV/ZTgpzaJ+Osdp9YdOVPZ2qiXqhT6j7y8/JInXMN58W9Ovou037XvUGopJkQTAx9nuHze+0fPXBjJJCpBSI8FGu/G6OsjB/kh/gclR9a3ID5zdqkkVVGBiatX0QE67URrBL1Qv1Q9wQK4LekBBCT6iwH9YygBD7jE4CEaQGG9h/9hvZQG9XimtfSSSMArf2emj5X/iyG/PEFI+QAl2CGNlX+bID2MulFhftKeU9YdPTFSaPXGffkp0+f9kubGM3Mn9xr8k8jyA/aAsFvtAfu0bW2safGka6kJTN+2+3+0yhMzJdp4xvg1jzTev+fZRk/RLrl8Q2sW85ID5Evyg6RzsR8xj7K/Gv9f8Illw32H+kca4g0JrZFrdxF9rH/z+0w1a5TDdci56XOchn8/dM//dOttLh2sRztJa5FpDNxPNo7uU/ENctpTLn/xrXK17S8JuSPPh75yraTt899LpT5czuMspHvAUxxLOX9J5TlLrpcD3EPm6bMm68/9ceU01DWN3+5Fvn6tLhvrvA/451J0q7w9s6P2qe9gbcUozne5qRhv2o/4bMyI33TPkvr4DIIlbRn+EzHp6Ff5XeA/OOoRfxv60n74VfqL/yMg5/rLNvPaJaNQagkSZKa8x8mSZIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWrOIFSSJEnNGYRKkiSpOYNQSZIkNWcQKkmSpOYMQiVJktScQagkSZKaMwiVJElScwahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1t7Ix1s2srHQJkiRJ0n4i/HQkVJIkSc1tjYRKkiRJrTgSKkmSpOYMQiVJktScQagkSZKaMwiVJElScwahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmps7CL1///5oZWWlmz59+tQtD3n79m2X78aNG33KwfPs2bPuHCad50HCNTtx4kS/pIOMfjWpb0XbZWI+RFpMtIlJ6WD+woUL/dL0fUffZ8rK+0eJtsk6tq9hn7Vysyij1s45h6G+TNlRT/aRYfnaDl2noTz5+s+SvohmPVfa4lA7ZLvcH5H7RlluLof9l9uWankoM8pnfcj3mWnnMy3f0D5mSY++Sz1MOo6DJO5lk+61tTxcu6iXXBfccyN9UplNbczh48ePG3kT5u/du9cv1bH++vXr/dKvZ+jY1tbWNtbX17t58kw7z18Zx8610+KgTdL/JvUt1nPdc7+lXWdsz/qhdNAPjh8/3s1j1n2DbVdXV6vzucw4xkn9jG3iONl3LW8+bvaVj5HloX3E/nM9sBxl6TvqhWvBxHxNLU/MxxTXfyh9EXGu0f5oa5xvTawbqgvacvQl0E7LPhwoJ9ox+2Y5b1saysOxcPzIZUYfY/+Typ0l39A+htKZDzmd8ofq46CgvqLOOP/a+UzKE9cx6g3kIS2mvO5nmWsk9Nu3b6PxSfRLo9H4gvdzBxNvCK9eveqXtrtz504/d/Ddvn27n9OiePjw4Wh8k+mXfsQbL3312LFj3cQ8aZcuXepzfMf6oXRcvXp19OHDh24e0/bNiMT4xtjNnz59uvvLvt+9e9eVBdJ5g4+39PPnz4/GN9DRrVu3uuWaQ4cObR3n2bNnR0eOHOnmM44tjps+nPv3ixcvto6r9ODBgx/WcX/7ZUYLfhEx2sS1YEKkhaE8T58+JWLoJsTzYyh9EdHuo43TlumXtVE71o0DhH5pO+ry3Llz/dIm2u/ly5e3jRKC9ks50SfYN/1skqE8XJfoz+Mgr4sH6Nf0OZw8eXJrP6VZ89X2gVo69Zb7LPek58+fd/P09YP+DH/06NHo6NGj3Tz3yidPnnTz2Sx5AvV19+7drp9FvX39+rX7+zPNFYRGI2AoFzQkGiwnl4d3madCshgyjhsUf2M5totycj7E0DL56GTT9hf7is8JsT72STqd4syZM13jJo3lwHrSWZ87dRxHPjaWmcpjIW8cU+RhQpRD2Tk9jjvKinylXE9l3lqZ4GaXzz+wfeSnXOoh8kRZsY/YL+mRpl9T3OS5VlxTgrzyxk963MCynM41Z9t5vHnzpgsSA/t9//796PPnz6MvX770qZvp3ATZB+2Uh0a0r5p8/OyjFjhnBEDR/iehX1y5cqVf+i72l+8Nyy5fv1CmDeWJIAQEEkwYSl8GtM8I1GdF2z916lS/tOnmzZtdcPH48eNt92ZewuK5vVtcl1w25eY+SSCcr2U2a77aPlBL596R+yYvpdxjwrVr17Y9qw+S2j0nDwRgljwZdZZf4sELwc82929COUkCmghUwMnlkZHyDY5onc5AOm9rIGKn09ChAqMkpBEARj4eSLz1kU7FvX79euL+aKhRNmiErKdM9kX6y5cvu05BOudCWu4kvEWRzvp4a2U0kYckb4hxzJw/5bI958fDNPZFXuqK4+dYycPbB8dD+aDDkE4HI1/UbTwQoz4yGl6kx37ZdlKZ4JjKt2vWE2yQn3SCbnCO1BHYLkaTGLFgGT6Yf31cN9otQWTtps+oQfkwQ05n9LIWqO4E/ZeHZKANHT58uNsHOF7aF8c81L7iRYh7yrQHDIFvjLxOQn+Oh12J+wLlaFh+8A8p83BfqrXJofRFlp890/B8q9VPlEH90Y/oP/Fyt1d4xtDv6H/xvAnRJ3OgWDMt39A+aukEUDyn4l7Bi06+VxGU5vjioItn7ySz5AF1RVwyT9vbLzv61/E0dAIXGsC0RgeCL06WGz3BEZ2DZRoUb3B0KtKoQNIIxEAaD62LFy92yzywpqEDEkxRDscXjZAyc+edN4jigsWDKralfMplXxx7PExJi7wEsUzcDOhEGecOAlaCazBcTrCH2ogCD8Q8MkX+2Ba1MoewPoKNuDbxwGU+AvBo2HRwzo1typuQfj1cy3gpqvVTAoNa8JXTZwkwZsVLEP2A/hL9k/2wD4JF7glMuR2WyM+9h3vKpE9PoO9PGy3lRWxa0FPrh/pulpeUnIf7Ovet8gE4lL6oZml7Gc+d/IVhCCOAfK7e60+tXB8GKHgG5K9poE+yrnzGlablG9pHLZ12wnOZNO4nvLyWL9XzPud/ZZznNLPkAffceL7/bHMFoTSEPPrAwy1GyeZF56NBElDFA5KHD2kx1R6Qs+C4oox5OvlO5OOtXVTqiw5CPeXfrwzhoUkHpfPUfvNWmiUwn9W0mz/nx3nyoChvQvq10H4I0iJoo/3Rf+e1k1FQHpR5BIJ9x2cfXl44Hh4o0R/YRw70aIe0a9pYBKzlqGe8bA3hnjItD3jJjX3Q72q/q5ulHy6LWl2UadPycP+Il4N8XYfSF1H0xXkCbr5Q0D5pqwy08BI3NNLJJ/69fDaAF0WuT3xdK68R6yIIog9FvypfgHO+0tA+htLjmcQznzLLmOGgvtDUjru81rPkqcn3xl+hn809EhqfycFoRZx0vsnEiGCJjkcjoqHwgOFByTIPKdLoVNE5qRzmKT9+bJwN7Y+glt+chNoI0F4haI4HFudSPrzAw5gHbq3BxI+uGfnMnw15OHPcdLoSD3MellFPnHvedqjMGn7mkPNwHWr7DBwT+yWQiJuBfk20A/rTEPpXbVSlTGdkYd7RUNpQjHREH87tn30wUh8viHzpyP94jm25H0TAylS2S+495T/OCPRDzoF9sv9JN1raceyDfkdfzS+T9Otf4XdTv4q4DrSvuNeQRh0TcFDfQ3nAfZ92GQFK1O1Q+iKiTRFwRzub9RkVARcTXzh4/pT3Ycqmv9L26UN7dZ+mXAI9/g5hXcQD+VjLgaCcLxvax1B6IJ0AlOdXxovtLCPHvyruR3Hv5TrGs5r+EW1mKM8Q+in3V+qLcn6JrzzjRjKzccPv/hMAbBbTuHF06/gbaePO0f0dV1C3LpaZAuvGFdGl3Sv+kxVMbINc7lB6ub9IZ8r58j75m/PGeQTyRr7Iw3Hm+fLYEPNxLPmcoizqMS/HOQXWl2lZLjP2g6Eyo37+/M///IftYh0T+83nlM+XfGwT+fN+1V6+NnEt4tpxHVHLE4auXy2dNpX7R63caNORL7fRENvV9pHzx/GX2C7y5DLyvnN7jink7TmWEus5jhDlabuob6a4VnH9ov5qeYauzaRrtmhyO48p6ox+Fu0y1x/pJdZH24x+X8tbtum8/9y2WY58Q3lq6TmtdpxhUj7SdrLvqKOh/U46noOCc+Ac8/2urJtaHtaTFhPtKtdhTFHvP9MK/zM+mAOBt2zeIBfh94i8hYxvHtURUt7sGOkpR3+mmVSmtFP0O96w92pU5aCwP2kRLGM7ZmSde9a8z1C1t6N/mKT9xc8P7Dz6VfBZj09d3NiXBZ8LeT83ANVBRzue9z+zdpDxqZqf+/gMPRgOTBDK6GD8GHvW39D8quJhXt4Y4sfcOzFUprQXCMaW6b+IsGyjvlpsy9Se+Q0qL846GA7U53hJkiQtBj/HS5IkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWrOIFSSJEnNGYRKkiSpOYNQSZIkNWcQKkmSpOYMQiVJktScQagkSZKaMwiVJElScwahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWrOIFSSJEnNGYRKkiSpOYNQSZIkNWcQKkmSpOYMQiVJktScQagkSZKaMwiVJElSczsOQu/fvz86ceJEv7R3Pn36NLpw4UK/tL84h2fPnvVL0sFy48aNbhpCX1pZWekm5sO86WA5+uWkfIG+VebJ28UUavmHcBzkH/L27dsfygf3K9LK+1YtfT/ubYtiluvPfTXycD1CpMVUbs+1Jd37srQcdhyEfv78ubtR5xvMblHW+fPnRy9evOhT9tetW7dGb968mfhAk35FBJ+PHj3ql+roSx8/fuwm5sO86WW/HMoXyP/69evRxsbGaH19fSvP+/fvu7SYVldXu/Sh/DUEKS9fvuyX6s6cObNVVgTO1NerV6+6dO5bEbwPpX/48KEaJGn69afO7ty509UpebgeILAkLabr16936YH6PnbsWLfu0qVLfaqkRbajIJSbzNGjR0fnzp0bvXv3rk/dvatXr3Y3/5YePnzYPQB92Oggod3eu3evX/oRgR1BFQ91JoIF0uZNR+6Xk/IF7glsg9OnT3f5yVMGFmyPofw1BMJl8JIR6MR6ygJ9m/qK/REgEXhiKB0EUBGUatMs15+XjWvXrnXz5OF6cF1qgWXUPS8LtGeuh6TlsaMglJvMqVOnRhcvXhw9fvy4T/0uPrXEFJ9WuHmxHKMTGTcybmghto1RSrZlAg+GWB9yGvujPOYjnb/xyY/1efSTB+Dz58/7JengI7DjJTHw0kjavOllvxzKl/GV5MuXL/3SZqDx9evXfmkT5Z49e7abnyX/rPiyEeWCsrhfZYcOHdq6l2RlegRIvqB+N8v151pyTQN58vVFDGTEPC85DAbEvVrScthREMoNhVEGbtLctPObMDcQ3mj5pMJfJt6ASX/y5EmXjghMAzeyuCkh8hHo4u7du92Niu3IF+VTLjex+KTGJzj2w/Gxnk+WpMeIJ/Oszw4fPtytk7Rd2S9nQRCYX07pn/Sx7OnTp6OTJ09287Pk30sEpTHymtXSa0GsJmOAIv9UhID0yJEj/dImXvrJB+qX50h8wmfb/EyRtLh2FITevn27e2Nl4vdZ+U04j0Jw44k3YoJEfhsU2zBikeU350AQGSOU8SbNdrF//lIuDwoCVEY34/dHofa7Iz7p8XvQrPXPAKSDoNYvp+Glk6Ai3yPi03gWI42z5t8r3ENqn4aH0stRPE3GteO+G9eToDJeOALtKq4x9cvoaiyzbTm6KmkxzR2E8obKaCNvrEz8biqPYnATZ5mbz+XLl7f9xoe8sV3525/aaEt87mef8daMtbW1rXIieGR/4NiGEHyyDXnLf4yUP8NJBx39JY/uM0/avOllvxzKV4q+Rl8tXwTzp9hQyx//Upqp/HIyhJfg/ILLS2oOgPhycvPmzX7pu6F0lKN4y2zW68/9nevJ/Zh/gBYvHDV5sAK0DetcWhLjG8Vcxg+Ifu678U1mY3yz6ebv3bu3NZ+xXd62LIdthsrO6eOH1Mbx48f7pc1y2JZ8yPMcS942thsHw1t5QJnklQ6Ssn2XaO+0dabcZ+ZJr/XLoe1LZV8NQ/eIofwljmdSf43bWr4XgG3YB1gX80PpYHvOU9/Nev2py9ojhvrNdQzyRT1H+ZIW31xBKDcHbhb5ocTNhDSmuJnHckwhp9VuMrWbD+WVD0EeDFFO3MxiOY4x/uY8lFOmwQeNDhr6WrTl6B+0YZYjwIsggCm373nTy35ZyxdpLMexlf02lOnT8me5D7Md8r6R70Eh3zPyuqF0UB7rtV3t+vOXZdZF/Q/VXe065zLzvVnSYlvhf8Ydf8/w2Sz/rip+YD7Lb7zIyz8MyL/PnGf7neAzHJ9/yt+IStpU65fLgJ8BjIOriZ+SJUk7t6N/mDQJvwPN+FewswaQ5OM3XPk/4cQP1PcrAOV3ofyGzABUGlbrl4uO34jzfm4AKkn7Z8+D0Hv37m39YwKm/K/lZ8FNn3+kwAgl2+/nD9QJPmv/GlbSdtEvl4X/tQxJ2n97/jlekiRJmmbPR0IlSZKkaQxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWrOIFSSJEnNGYRKkiSpOYNQSZIkNWcQKkmSpOYMQiVJktScQagkSZKaMwiVJElScwahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWpuR0Ho/fv3RysrK9104cKF0du3b7tpN27cuNGV9+nTpz6ljZ3ul+12c87sL+rw2bNnferuRbkc35DdHrt+vtx+prVd+itToM/GtiXaRq3tsA+2i/lp+z5x4kS3nr8lysnHE6LM2jaBdhv5huT7U27n8xzTpGNYdvkaDN1Hanlyu8n1G9eFaVpbPuhm6Tuh7LdhKD36dX6eUPfRn/M1GTJPH8nnMq3cWfZN2ZGnrJvaPSPnjza2aP2Wa8n5TTqvoTxc96ifXJ9D6T/Nxpzu3bu3kTdbX1/vltfW1vqUnTt+/PjGx48f+6V25t3v6urqrs839kk5lLeXKPP69ev9Ut1enIN+nmg/TMxPQv+k34K/9FnQRnLbY5m8Zdshf97HtH2zPetA+bk8lvPxhHJ5SNx7OKZav2G/OU8c306OibTYRt9RL9QtU9R1aVIerknZbrgey1Df0/pORn3U+kUtnbTcplE+W+I6cE1qfWfePlI+P2plhmn7zunM57qp7ZvjzGXm/KQvQjvKbWTomT6UJ9dJbgdD6T9T/Q4yIC48J5KxXDbInaByfkbjmWe/nGftxjCv6ED7ITfGSaIx6mChv+WbB9e67JOBdbTXWpuN/pyRr2w7uZ3Ms2+wrmxncUwZxzHt4VG2a46jzF8eX62fzXpMlP0r3KR/JVyDuE7Rfsp7/7Q81H2t/mObRTVP34n2WLbJWjpllvmQ63iWvpPN2keyXH42y74pN7cR8uS6Kfdd1iVtJ1B2XndQcb75nMvrgaE8uc6pj2npP9Ncn+OfP38+Gh/06PTp033KJpYvXbrUL30f0mcIPS/HkHweNo6h5PITYDk0H0PI8TeG35HT+BvlM5/zxnqmOLZSzlP7TP7kyZPRxYsXu/nIG2VxzHEepEU5ecibedLA3/jEEHnZPvLEJwjmOZaYz8ee9xPnWYo85XD9tWvXqueoX9u7d+9G586d65dGo6NHj3ZpJdrD2bNn+6UfHTt2rOvPk1DG+fPn+6XZ9x0OHTr0Q7sr0d7H96LR+MHSHc9QO37z5s228+H4379/3y9t4l708uXLrl1TzvgG3a/5bpZjAuUj999l9+XLl37uuzJtljzLaLf9tpZO2/zw4cPo9evX3T0+nj+0f+7vYZa+k83aR8LQMWOWfX/+/HlbGyHP169f+6UfTerni9JvuaanTp3qlzbjKM41G8pz8uTJ0aNHj7p56vru3bvd+qH0n2nX/zApAkAmGgTLBGo8VEDaq1evunlOONJpIEx37tzp0mik48i8W0cF0ShJH0fuXZkPHz7cemCSngPhWHfmzJluHReC42Gexhkdnf3zoCOdjltrpFevXu3WcyyXL1/uU7+j4Ucj5xgoj7Jw69at7sYC0iiHKfKDedLAX7YhSGR/LFNX3759686bfYF9xLGQJ9KjQZJG/qdPn3bLGYFrnBPBRAS9OHLkSHeD0GKiPeSXwxLtJz+oaug70aZ3ghsd7W+Ssj/t9sYY/YVy6F+lWY4pcGzlA1Pbca+eZpY82jTUb2vptE2ed/EcjQCD+zr3952ap4+AYyPA2Sme/48fP+6XNuODw4cP90t1k/r5svdbzp+YgJiIthDtZij9Z5o7CI1AMRAAEugxcUIEUZwgQSDBUg5yXrx40c+NukCLkdV4CLJtBJk8+OhMlEEji6ALV65c6ed+RKNEVHSIGyD75w2PcsvzAJ2XdNbHsUSgh1rQyoMzyiLgjlFSzitvOwnnx/7iuCJoXl1d7ToXx8z63NE4FvbNORGk1wJm8KbEOsqmTlnOauekg4+2ePPmzX6pjgdHLUjLdhs8zHujyy+XO0X/4V5Av8ovXWHeY3IUb7JZXlJ28yKzTIb67VA6bZPR1eg3169f79r/bu/rOwlQeO7uFPuKwSMm+u60e8G0fr7s/ZZrSP3w3Kf9hKH0n2WuIDSGfacdOIEUb2VMBKk7QVAbZTDtBYI1RgMpL4LMEoFf3m/uCEOdjLLo9FzcyMPDnTe0WW8GeZ/TAoNA2XRY3iJz0F2iwUXZ+UUAu7lx6OegH+aXifKTDPgaES82t2/f7ibaf+CmPemFLpTBwyz7DuxvWiBcE20y/9SEew7tnD4WeOEtR1948SNwpt/S3jnv3Ad3cky7GVFaNLW6KNNmybOMdtNvh9Kp1/yiSH8lrbyvz9J3wrx9hP6V7xM76bfg2USf5VlGMD3JtH6Og97meLmIASnUAvOhPNQ714R54rEYpBpK/5nmCkI5cBoHBz40ykeQ9+DBg35ps0EPoZHQeEADihFFOmakY1IZ86DxMw3h/LiIcW5csPI8CVLLxs4bHDeICLjphKCBzPJJgDLjTY6ya291NZTN9Zj0xsox5M+buS55U+QGoYOFdho/J2FinrQsbuhM8aUi2iftGmwzrb3RF/NDbpZ9gzJpWzwM6UOxz2lycJzPgTbOxNs7KJN+Vz5s+R3ZUB/fyTFxjrv5zLho4l7DlywmkEZdEnRQr0N5lt1u+u1QevQJygOftGmvtPM8Ehj5MNR3sJM+whfNHEzvpN8G9sdPC6YNXk3q51iEfstX1fiJAvVSC8yH8nDt8wtPGEr/qcYNZW7jN5XuX6PFNA7A+jWb8rpxYNmtZ55/scYU6zCutG6ePDHldKZxh9u2XCrzxnxO55jzcj6mSEM+N9aVopyMfOv9v+TjfGP7KDMr17NdmZaX8/mwnzhu/uZ8kV7WBWIdUxwnSNfBxHWMa0o7QLSHfI1BO4i2kNtHuX3Zd0K0tVDbd6SxnPtUTKHWPvN+I21I7p8h7xu1fcx7TIhz0Xb5+kdbi+vCX9TyUJ+RFhNp+f7EFGUsolwv0V6jXqKeAm2x1h/K9FxmrjuWs2l9Zyd9BPleMWTavimT+VpZQ/tehn4b9ZKf1VGXoZYH+XrmdjGU/rOs8D/jg9EcGOnkR9sH+e1+Ec5BbTB6QVth5GaZMLI3fqANjtpIvzJGxviMz8jkMrHfHiwGoTvEZ20+G9Y+Rf7qDvKx6+fg8xbtZlkeaHwyXLagW4uHF0j+AeK0z9uLwn578BiESpIkqbld/3dCJUmSpHkZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWrOIFSSJEnNGYRKkiSpOYNQSZIkNWcQKkmSpOYMQiVJktScQagkSZKaMwiVJElScwahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLU3FxB6IkTJ0YrKys/TM+ePetzbMe6Se7fv9/lefv2bZ/SFscd5/Dp06cu7caNG1tpzJdpFy5c6NKQ0ykr8odp60uRlynqJKf9h//wH7YtxxR5OYdI41qFqGemGrafdmy7EfWwV9eZ8n5Wm/lV5GsdbbcU68t8Q9vmdlKWyXK0/aHta7hW+f4QbYGpRPkcwzSz5JuUJx9TPpd8XLn/aLtZrz/1zFTKz5GQ78W5vUhacBtzWl1d3VhfX++XNrr5tbW1H+ZnVZbXysePHzeOHz/ezV+/fn3j3r173TyYz8tgmXyB42YKrKM6KRfT1g/hmMo8pEUd8TeXy3IuN5ZL7L9Wz1yvXN5eyvWVz2EvcMzztrVFEu2EKdpxVtYN1yLaSG1b5qPNl22d65b3MW3fgfW0xdr9gfJzH+N6krfsd6VZ8k3KUx5TWU+5L5CP/NpuluvP9aX+cjsC9V2r06hrJuYlLYddf44/ffr06NKlS9383bt3u78Hwbdv37ZGOx4+fDi6detWNz8LRuFevnw5evHiRZ+yWcb4odfNT1u/l6j/8YNz9P79+23L5Ujh0aNHu3WlO3fubDvOvcL+X7161S/tPY6ZY19G1C1t99ixY910/vz5H6539MmMvJO2jT5w6tSprr2Eq1evjj58+NDNz7Lv8ODBg9E4COmXRqNDhw5tHdfZs2dHR44c6ebB9cx5h8ySb1Ke8pjKeuKcwjggqo7kLbNZr//Q/Y4+Ow5ct23DaCppUSbz00bYJS2GXQWh3Eji0wk3awKvy5cvd/PcqJhC/twy9Jksf+bJn73ztnl9zsc+WS5vXrUyKe/MmTPd8ZI+9BAd8vTp0y7QK/EQ5yY6bf1e4tg5j5MnT/Ypm0EDxxCGbujUw7Vr1/ql75/JyB/1hmnXjrRYT162pX55iJOW9x/5Io3jz9vGvuJ6UnaUz7q8f46dtGXz7t270blz5/qlzRcM0oZQ1xFUDm2b2yVtJwJSrg+BRph131yXK1eu9Eub8j7evHlTDZT3U+2YMs6V4DjE8eb2u+xmvf5DeDHlvsD9Ifpu1DN9m7qmve31fVLSr2lHQSg3EIIC/gbefAm81tbWuvk8CsbN/cmTJ3xj6abbt29XgwfegCMPYhSCwJYbF+nkYZ7RDvZHwAUeLqSXN69amTz81tfXu+1Jr40QcoycY0wsZ9Nukru5iXLMed+cVxbBM1MEe3l/nN+jR4/6pdHo+fPno4sXL/ZL3xEI5NEoRrvYN6PElBcjGeSjvqircsSY6/j58+duHcfBtWJ78kfdx7FxrCwzEhUjt1y/vC3HznraT+zv9evX3TxtKOPYOTZNxvVndHMaAgDaFG0ngn0CjAhg58F1qfWreOlgH7V7wH4aOqZA8J1f5kDbjbaq3aM+mejv+UsG/Z17LAEozw9Jy2FHQWgEJPydRfn2THBTBg88nPLoITeoCGRzOqN1BDkgTwQm7COCnTCpzGk4Rs4xpv34lD6EG3TeN8FcFsEzE3lZXz7QCeRyWlk3qI3w8BD4+vVrv7Tp5s2b20ZWM65jjB6xD/Y7NDKS28uXL1+668PxE5TEOZKGPEIL8vDiUQbBjlJNx0vCpOArcP1oU7T1x48fd2lsOy8C2KFAguNgH7ST8qViP006pqzWT2ir2lvUc/5SRqDP/YH7QQw+SFp8u/oczwNlJ5/U8ujbEH4/FuJ3RAQi3LzigcpfRu9mDURymbtB0JVHGgNBH9O09XspAr/ygc7IMGmT6qb2wGXUi4cuD4II+MjHOeVP4UPmHTXLATVTLVgi+GQd1788hto5LDpGNRkdDszPMtKJWbbNgX55PWfZngCWa8VEP2CEu7xuvNhMEz+5Ydptv5l2TPSTobY7y/1qWcxy/WcVfZe6514VLygMFMTLqKTFtut/mIT8RlvDTYpPLREQMXpW/jaLGxCfmeNhw5txjIbxD54iSClHM8hDwFT73DypzN0g8CZ4yr9bjRsp66at30uUy0O1DMYiQOcfYtTqBgSW5SgPD1yuVYxuUrfsg8+U+eETyMcDPZBnaH+luD7xwOE61R4+0b4YJcnHwLHHcS6TuLZcFybmSauhTnMdzbIt1yB+B0rfzaOhs2xPWvRXXpD4iU45gk1fzF9HauLlg2m3/WbaMQ39ZIFzLD/RL7NZrv8scrvkyxb3AUlLaHxTntnx48e7/3xGOd3r/1Mo/GV5fJPfWjcOxraty/lzvnGAsfWfF2KK7cB8pDOxXWA7jmtIrUz+MyG5vCwfU+yndj7Ix1U7hqH1URccexZ5mTjuMu3f//t/v205pnxMGfuZVDdg+4y6yfWbz4F1Nbl+cp5I+9f/+l9vzed2wDnma8G+8nLUwVD5085tkeV2He2Iv7nekK9lqG2br0vZnqjn3FZr20dazgf2H9csX8fyuPK6uD/U1PKV+55WFutzO0J5PKC8ob61zGrXn78ssw65PUXd5r5d1nctv6TFt8L/jDv+L4235jwSwme0PIpRLh8EMeK3k1GEvUTdMkLLqNNBwigz/6hpr0eW9SPaav7PNC0LPt2Pg6ul/MmHJLWwJ5/j9xNBUvmPmPLnQdbP+vn3V8Ex8xODnx2AgiCO39wepH8MwLFyzAagbdBO+Z1e/nnJouMnILyfG4BK0v45ECOhPBAYkQgcMoEcv0W8fv26/0kPSZKkA+ZABKGSJElaLL/853hJkiQtHoNQSZIkNWcQKkmSpOYMQiVJktScQagkSZKaMwiVJElScwahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWrOIFSSJEnNGYRKkiSpOYNQSZIkNWcQKkmSpOYMQiVJktScQagkSZKaMwiVJElScwahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkprbURB6//790crKSjcxz/Tp06fRiRMnurSaGzdujJ49ezZ6+/bt1nYl0i9cuNAvfRfblNNOcBxsS5m/MuoqzrNWV1nUD/VfU6vTaaKeZjmGyMM24NinHbN2j/qOOq/JbYj5bNK2XLvy+tG/ox0xH+UyX8P2Q3kiPdprLi+mIRzztDyBvPm8h7atHetQX9Js139aHtoS6+L6TGqrkhbYxpyuX7++cfz48X5pY+PevXsbFLO+vr7x8ePHbn4atmEqUcbq6mq/tB1l5/2SLy/Pg+3Y16+sVj81Uf/UT2nW61GztrbWz21el6Hj4TpEXTIf25GftqL9Qd1ybSfVcbSLsh1M25Z1+XpzfXNfYz7KrfXB3I/LbWvtKLc1DN0DKCvycuxDbRJxzpF/aNtJxxr1p+2mXX9MylNre1HXTMxLWg5zjYTyRvvo0aPRq1ev+pTR6NatW6Pxzb1faufOnTuj8Q1r8E38IGMk4Pbt21NHMBkBJd/4Oo6OHTvWp353/vz5bt1OXLp0qZ8bjd69eze6ePFiv7Tdhw8fRqdPn+7muSZPnjzp5mkX+NVHnA+qhw8fjsaBVL/0I/rF+OHftQsm5qOvTNqW0cJy3dWrV7vrDK4no4RRLm2svMa0F7YBbYP8kYf2Wo6O5baGWlvGoUOHtvKePXt2dOTIkW6+5sGDB6NxoNMvDW876Vi5vwyNFi+rWa7/pDzc02hftMEwqa1KWmxzBaHPnz/fullk3NwjEAE3HB403IgQn7vKm1WITzM8EGb19OnT7lh42ETZ8UknPiXmTzwxZZFWu+Hlz0Vx3PlzXqSxbaRF0Bj54i95mSLftM9NJ0+e3Aosow5rqAMetFFuPg+OhYAw9hvHRt3Eg5WyY9tJPn/+/MM1B/vjQR140EewgitXrnTHqPbienG9uU4EArVrmNFWCNAy0tg20EfPnTvXL41GR48e/aHf0l6+fPnSL20ey9evX7vjoF2vr693fZeyS7VjCPn437x580PwGuhftL1saNuhY4155H617Ga5/kN5qEfuD69fv+7uOXEfinqep61KWgxz/yZ0UlAU7t69uzUCx02FUbHV1dVuucSNhxsW+bk5TULAE0FTjMjyRs0DDTxY8ujH5cuXu20omzw5YDpz5kyXTv7379/3qZs4Zm6CrKcMROBIGm/yEVxRLmlM4Maaj4l0AnRGW5jnGKLMIXEDphxuyENBK+fPDT6OKW7qPMhjhJKJh34Eh1wLtuEcuZb52GvIN4Tj5Dzj+L59+7atfRCU5lFztUXdM/JIG8ojT0No02VgR/BAe5kHQeTjx4/7pc02dPjw4a12HW2S+0SJY+AlbAhtO/r/UL8gyMwvxaG27dCxBo65vD9oZ6hH7g98MeGew3WIF5F526qkxbAv/zr+xYsX/dxmYDIJD4D41MvNaZIc8DHFQ21IDny5+eVj4SEY8kgIKJcbJKOH7IcHGg+2GF0hkONGyQ0074Pjz0FX5CdfBNARnMbNdxrKKI8vUGZ89qYOX7582c0TOFy7dq2bB8cfATgPX/JyjkyTgkzw4ChHlTJGWwmqOTcC+zwCghz4qy2uHe2caxAvKENoFzdv3uyXvmOkcF4EsvQ32gQT7bIMCmtBYpjUr9mOPsnLY/z0I+OldiiIqW07y7EO9T/Nh3rk/hD1y3WIUdR52qqkxTFXEHrq1KnuJl0GLgRU3Pz3UjwUmKYFSkMICgn6KIMH26QHX4mHFduzbQSM8ZluCCN/QwhWKTOmeY6F369RB7lOwLlF3XB+EeASOHCtsshLMB0P+RhpnSSPKlEPsf8Y8WRdnBP7KH87GsektrjWBFpxfXg5mvTiQ16uFdeWESkmgoFyFJR2lb9YMF+2NfAiyn75vXj+OpGVwSbHnPcXP4lhKkc9awEzeKmNbXiR5AWpvDeV20471km/PV02s1z/oTzUY36p4VrHvW2etippccwVhHKT4CZdBi58ao4RuXkRzPC7TsRbMbgZxTRpZAQRECGPRMbPApjm+cTDTZEHIOfLg4nj4rNdHnnhwcZ6gvJ4QPI2n0cgQ+SLGyv5Z73Jcg6M1lAHuU7AvqLuKDNflzIgpo4IMqgHzi9GG9hm0rGQN8RDgin/9hOUz7Hma8XI87QgV/uDuqfNzSoCMSZ+2hH/eITgIQcOtAGuPe2CKX72UUOb5EWu1vfoP+UIO785zwFNPqbyZwL0tXLUHRxPbMO9iv5b3ptq2w4dK+c46ecBy2aW6z+Uh2vIiwFp4IWBup23rUpaIOOb9dzGN3eioK0pjIOcbpn/5AlTrM/5//zP/3xrfvyA6LaL5dhm/ADs0sP6+vpWHqYS+WNdlME2+RiYOI58LHk78oePHz9u25Zl5LQ49nxsrEfeRyB/mY/6ivks6pEp9j0kjoltAvsqtyNfnGM+5rxdiXxxnkMmHSf1kOtVeye3XeoZXAOWo85reTCUHljPFGgj+frm9hPpkcZylF+Wnfebyw+1Y8lYH9vnvHnfGXmi/Q5tO3SsoLxa/1x2tevPX5aj7dXyIKfne0tuG7VrIWkxrfA/446/kBjdyCMojL7sdMR2v+zHMTHSwGhPOXrUEufFCNo8I9D6NTFSnv8zTcuCT/rjAGrqlxhJ0s7syz9M+hUQgPJ7xmwn/8hiP/HJfz9+b8ZDszz3lqh7GIAuBj6l8jOX+M98LQN+XsL7uQGoJO2fhR4J5UHCSEZY4FOVJEk6UBY6CJUkSdKvaWE/x0uSJOnXZRAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWrOIFSSJEnNGYRKkiSpOYNQSZIkNWcQKkmSpOYMQiVJktScQagkSZKaMwiVJElScwahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnM7CkLv378/WllZ6SbmmT59+tStu3HjxujZs2fd/CTkY/u3b9/2KTsz6/5q2DfbHzQnTpzo57Ss6G/RB6PvlWJ9mW/StvSHWp8g34ULF7bmh7bPIk9ur8yXacj3lEll0mcj35ChsmbZd9yPyjz6Ll+Doft3LU9uN2X9cg+PdVyPRTVL34n1Q/mon1od0T/Jn5+H1H3053xNJok8tT4QfShfd/Y77ZrNsu/cD/M5D6Wj3Pci9ts491znpaH7drldbn+1/D/FxpyuX7++cfz48X5pY+PevXsbFLO+vt6nzI5ydrLdXlhbW9tYXV3tlw4e6vzjx4/9kpYNfYfrz5T7Y6B9Z/TbaC9D25KHdsXfjD6a803bN7gvlPIx0PdiP5QffbHcVyluWXmbbKisoX2TlsvM+7aP1VEv1BVT1F1pUh7qONczeeJ6cG2GylwEnDdtiinXQZjUbwP1U/Yv0qIOQ/mMi3qlvmt9B7V+i+gn5fq4XkPbhWn7zunMR90MpWNo36QtSr/lHDlvRB2WuO6sK68/aUyxPXLds65sbz/DXL09GmJ5gTmRfKKzokHtZLu9ULsBHCRcg9ygtDzoM/nac/OZ1o/iBjVtW27o5c0s95VZ9x03wKGHAdtEuewz3wwpv1YmefKxka8sf5ay8r6Zz+fDMQf72I+o27iuTMzn+sa0PNR9blPkCeRd1Dov29pQ38nKvsgybTwHXpSZl0OuY+p/Wt8B9R/XLqOs8jqHOKYhs+x7qN8OpYfavimbfAcd55GvYVkXGetyHQe2H2pjQ9u0Ntfn+OfPn4/GJzU6duxYn7Lp0qVLo9OnT28NmzP8G/MM+cbwb17OQ8SxLoba85BxfAJE/tyQ9xFlleuHsP7atWvdfN5XfMZgPvabjz9M2k98rqDcKBv5k0LsJ5DGdqQzn895aLu4BlFnWh7v3r0bnTt3rl8ajY4ePdqlDaGNkAfzbkv7Pn/+fL802/bsb3xvGY1vft39otYXDx06tPXp7PPnz6MvX75086Btf/36tV/67s2bN6OzZ8/2S5v53r9/3y9tmqWsvG/uWy9fvtzqy+Mbc5cO+9iPct2GMm2WPBn1nO+VUe+LZt6+l/staJ+5/YM8Hz58GL1+/bqrv/jESnuOZxxm6TtD/Za/9Jc7d+50++CZNI/d9NtZ7w1ZtJ+D3m+po3zvPXLkSFeXe4Xycvv6Web+TWjcvGtu3bo1Gr+BbJuPhs1Ewwbz3Pxx5syZbnkckW81TPLFNqBjUQ6Ni7TLly936Xl/tfVDuJBcALDN+I2jmyeYxvhtY/TixYuuI3ORKJOH09BxZNwQOP5v3751+eKhxk2C/bBd7CfQ6VnHcbGehyLYP52QNNazv/xAp/yyM0slXh5PnTrVL82Hh+S8NyraJejjtO27d+92yxnt9urVq908D6jHjx9386CPHT58uF+azyxl5X2DY6RvcZzcUzL72HTco6aZlifuqXj06NG2+9yyKvvt06dPf3h20DYjQKT+ou7yM25WQ/02AuVXr151z6Hbt293/WovDfXbnd4b7LfT0UYuXrzYL/08+/6v4/PNHleuXOnnNtHYA288dKAILEHnovHTqOhgjBLS2SKIDdPWZ2UHYluC4BhpjDcvLhIdjrc//s5yHODtpXxb4zy4qQwhcH348GG/tHmM7D/eIOMYyzfnSSMMEggAJvWHSWYJMCYZ2i9tOx6o/OVBSj9j4iVsp8c7S1l536BPcR8ib22Uxz422SwvKbO+yMSAwLTRrmWQ+y3Ppps3b3bzGW2T0dXIF8+I3QaJuc9wHDzHeQYx8Xze6wBvqN/u5t5gvx1G+6BPcj1/trmCUN7KaARlAydwnHeIflZ8OgsEffFJoPamPG19qFU8wfGTJ0+6zp7fDhgVpVwmRjkxbT9cXDoAI6cxspJvJjtVu5HP+7arg49+yMh6YH7Wkc55ty3b3E72XfY3+kX5QOXLA/2K/saDFPGzFyb6DS9kBJCBl8KTJ0/2S9/Vygrlvum/8bBnm9ooj33su1pdlGmz5BlCW4kvZotmJ30n8GyiXugLtFEm2jL1ml8U6a+klX1u1r6TRRnxPAukTxqN3Ot+O6k/TzJrm/tVUUfUVaAOY1Bqt3Js8tONL+5cxo1g249lkZfHb0lbP4RlftxwunmQL9YhL1MuP5QFhxXb8Zf08Rvy1o+NIw2xv6H1NUPrOZ6cTr58bhzjLPshPZ9D5Oc4KaNEet4P27KfKCdQDumhXNbyoL1w7Zly2ynRhqIdhknb0p5zG6212Vn3DcqLPg6W43hIz8fG/LTyoj+wbfSrmlpZtX2X+aLvBfvYj6gj6o8prgf1GOmo5QnUd65z6jiWZ2lTBxnnNkvfibZZQztmCtRvtNEon21zHsR14JpM6jtg27iWlJevYXk987N7yKz7LvtjGEof2vei9FvOI65DWe8ZdVCLLaiz2D7kumG7n11Pw2c1ASdLhcQUcvr4TW1rnpPM68q8VETMU2FMsRwNljKYj/SyzP/zf/7PD+uj3Folk14iP1OWy6Qj1I6jRD6OLdAQIn/ZIBDr2CbyRofL50i5IY5Dyyn3kWiD/GU5t7HcDkNtW+R+WLbfnK+2faSxnMvJ/Sn3m5gQ+WvHWqIP5G1R23dZ1tC+kftYPl7Ks4/9KF9/5hHXJe5RtTzUZ6TFVLaXuO8h8sf2iyDXC+eH2nlO6gvUV26nucz8jGA5m7XvMOXykbctj3Nom2zWfZfnPZSOZei30TaYcr2zHNc66ogp11Mt7ojlmH6Felrhf8YHs7Dic3n5OxI+E/CJg6H+QBpD4OWnjF8RnzrGDfRAHKsONvoQvwmLn6MsC/uYDrLaM24Z2G8Pln3/h0k/E52Qf+FX+yEzP3jmd538NiLwu5eD0HD5oTbvDnYytUD/4bdJ/NZrWdjHdNDVnnGLzn578Cz8SOgseLjyD67W19erAaskSZL2lkGoJEmSmlvoz/GSJEn6NRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWrOIFSSJEnNGYRKkiSpOYNQSZIkNWcQKkmSpOYMQiVJktScQagkSZKaMwiVJElScwahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWrOIFSSJEnNGYRKkiSpOYNQSZIkNWcQKkmSpOYMQiVJktTcXEHoiRMnRisrK9umCxcu9Gu/u3///tZ65pk+ffq0bftnz571ubd7+/btVh7KZnko789048aNbefC8hDOYdL6SagzqYY2Nald0eeijTIfaK+RTtss0e+iXQe2j74+VG5N9PlyPxx3Lj/yzdLeOQ7uKUPy/Sf2m485pqxWJsf429/+tl9SmOX653rO+fK1ydvmvOU6SQtsY06rq6sb6+vr/dLm8vXr1/uljW7++PHj/dLGxr179zbYTWxTbp+tra11eT9+/Ngtk49lyphFPo79xDkwBfabjzvjnHLenRgqW8sr2tykNk8/pN0wRZ8s5ykjq5VJP8x9ulZuKcqu9d1YR98A+yMN5f2kxPqhchFlIx937CvkPjlUJmXNeu9ZJtOuf1nXcX1zffI3rvNQfkmLb9dBaL7Rc+PgZl7eQLjJxDbl9hnllDekfOOaJPa93zj22n44xtqNs3aTnhfl5oemhPwgL9FOc5shH2n0r9yfSI8+R/5aX8tteKjcUq0vB7bJ+80oa1qfYduhe0J5fEP3BMrIhsos8y27Wa9/FnWY749sM3QNrXNpeez6N6F3794dnT9/vpt//vz5aPwAGR07dqxbDpcuXRqdPn26X6rjs9n4JjU6efJkn7KJsm7dutUvfZc/GbIt+wVpLPMpLT7t5M9+kVaui8+B8clxyNOnT0fjm3C/9B3HWJ43ZV+7dq1f+r4PPjUxMY/8iSofa4hy/USlWb1792507ty5fmk0Onr0aJf25cuX0efPn/vUzXTSaFsfPnwYvX79umuH8ZmfvhT9G0PlZmxDW79z505XVv7MTfu+cuVKv/SjQ4cOzfRJfgj3mZcvX3b74TjGgU6/5jvSz5492y9N9pvf/KbLr02zXP+MdkUe5Psj99HafT3nl7T4dhSEnjlzZito4sby8OHDfs3uf8NYBnI13KjINw6iR5cvX+7SCGBBGg+yV69edfPjN+7RkydPunU8WHkokc5fJgJk0slDOmqBYDbLMeLNmzejI0eO9Euj7iFPsPzt27eujHhA8uDn+Nk/x1ND/vfv3/dL0s6cOnVq9OjRo35p1AWktFHaVgSOtEPyEHwRYMwbFERQQh+kXd++fXvrBYo+MemFlOO4evVqv7Qz9HnuC7wg1wIdAqDyZXfI7/3e742+fv3aL2leDEzQ5gLtgOcG7av8DS7K/JIW246CUG7yPKgiaNtt4DkvAjJuYoxacgzlQ431BHzc5AiYQx794MEbI0I8LCOwZhSFB+VeiAdvxqhS+VDjwc/NdxpGrKTdoK9cv3596yWSfkRARttihCv6EnkIJvOo6azYhkCSfsjElwOCS/pjfmGtoe8NvYjNiuPmHkVfrgU64LhmZb/bOdpCvj9T79yzeQF//Phxn/pdmV/SYtv15/ibN292ox2MmvAGy42/DL5YN/QwCHHjqY1C1j6RcyOLz321z2Wkg4dR4OHGjY91jJTkB2KMRDJNelASyOaRpMBxl8dee9AxqsRDjdHXGKWZdO5ZHlWVJqEvMsIemI8RJto37Zy+QYBIO80vZaCdklaOgk4qN0QbD5R/+PDhrb4XwS99MN8X6BPcT0L85IZpWt8I3AsikOEc8ygsmJ93ZNd+990s138WtRFqSctn10HogwcPur98AufGzwhK/g0ZGBUZuunwUIjfn62trXUPpvzQ4EFUBoWsJ539sU35myQeRDxcy33ywIvP7kyB443zQBxPDYEsZefAmOOh3HIEh4C1HEXhgcaDMUZloxxu5JNGYNnHrJ8QJfoGXwNoN0zMx8sO6COM/r948aJbpu0SGJIXBIy0N9plDk6nlYuLFy92bTxQbmwXfY/7BH03+ih9kz5BwMqxEXRybJF/1tFRvjLwZWPIvJ97//7v/74LoLVplusfuIb561PGNS6fE5PyS1pQ4xv8zPhXq2xSTuvFv47kXzfm9WGW7fkXs0Prwsf+X4tHHpYR5bNNrIs0jqksmynkNMq7d2/zPy0VZZfy/tnHENZnHAPHEnKdxLnG8ce+43ylEO2TKdoT7aTWjnJbij5Qa085P/kCbTT3g1q5ZZvNfS2OJ4v+iNyXYhqS7y3UAcp91/KE3PfCvPmXXe3685flfK3Lusttttb+rGtp+azwP+ObwlLgTTuPqPA2jqE3+WnrZ8E+GSWNEaed4HPk+CZf/bwv7Tf6AV8zGPVaJoz0MXLqp2NJ2h9LFYQSzOXT5bP70O8/9yJ4DDzE+Re5k35rOoR/9LVsD3/9egjI6C970R8OCs73z/7sz0a/+7u/26dIkvbSUgWh/O4s/1ZtbW1t5t+aSZIkae8sVRAqSZKkX8Ou/3W8JEmSNC+DUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWrOIFSSJEnNGYRKkiSpOYNQSZIkNWcQKkmSpOYMQiVJktScQagkSZKaMwiVJElScwahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWrOIFSSJEnNGYRKkiSpOYNQSZIkNWcQKkmSpOYMQiVJktScQagkSZKa23EQev/+/dGJEyf6pU1v374draysbE0l8uf1TBcuXOjXjrr5SL9x40afureePXvWlc/fWX369Knbhr8ljpPz3olcX9TnJJEv6oVt96uOdDBw/Se1gWjrTGV7z32xRFss2yNtP/fVafuO8ifdIzL2F+mT+tNQudm0sjjuWv+PsmMb8v32t7/t5vVdvoZD12oozyzpTNPuhwfZtL6T+22uo/x8zCb1c7aPfeU6HjKpf8W2eR1lTysTk8oNud/WnrVRRq4T5PRJ5R9E+drW6gRDeYbScz2X7eWn2Nih69evb6yurm6sr6/3KZs+fvy4cfz48X7pR+U2LFNWYP7evXv90v5gH2tra/3SZJwP1cTEfMaxz1pODdsj9lHWZcj1k4+DfUcZWi60CdpCbhulaCvRvgLtpmzLGXlzH6Rd5j49bd+kR/ll/47joMyy/aPcVzap3DCtrFif+22klfcd0vf7XnQQUVfULVPUdWkoz1A69cxyTHGdFw1tlvOrtV1w3tFmo12C+qHOwLb5vh/1lfOjfD7EOsrJ6WFS/6r1A8qJfkTeob4yqdyQj4n53G/jvMryh9KjPg66OD/+Us+1++JQnqF05qO+SKuV2dr3FjsHTowTiSlj3aQTo6FFZ0LZ4CY15r3CPqLzzKps2Pli7oVJ5533W9482Gbec9Fi4NrXbugo+yHz0Y6Ypz3nfhiiHea2WOvPk/ad5f5NO83b0JY5JvLkds2xTVPeN8K0stg/U+4zlDPUh2Y5x2VCPcW9kIn5su6G8kzadpnqeVLfoT5y3yvbKqLuYj73A+ZJi/lAGXmf0feGlP2L/cW1C3me8svjrBnqt5xz3p7jIy/IXyt7KJ3jyveAg4o6ietMXTAfdRKG8gylxzXjL2m/Qr/b0ef49+/fj06dOjW6ePHi6PHjx33qzty9e3d0/vz5fmmyPPTPFJ8nGIJnPobic57AcHTkzfIninmGpp88edKdP6KM+FzJcHd8AsmfUIaG03H06NHRkSNH+qXtjh071v3l+O7cudPNB46BY5GyaDO0RdodfSzSXr16NRrfhEZnzpzZ1uZpx2fPnu2XNpE2a/+sOXTo0Fafe/PmzbbyOR7uJadPnx69fPmyOxb2N76B9jmG5XKzSWWRduXKlX5pE3koh35FHy0/A//mN7/p8mjTly9f+rnvyrShPEPptM9Hjx519V+7psuE+vj8+XO/tPlcKOuNfjMOwLbmUfZz2vq1a9e6dRjqe0Ny/6LccawwGgcx3X6jP8S+QfmXLl3ql4YN9VvOOZ8nZX/9+nWwf07qt3Fck563B0FuB4E6yYbyDKVTN9RdtB/a18+2oyCUxsLNnhOiIcx7k+bhR8NhooyHDx/2a4axDx6edAYeoFTiixcvuo7BMo3xw4cPXWPkwUO+cZS/9ZClc5KPMrjhhatXr26Vefny5T51Oh500dipC46D/ePWrVtbF5c0ymeK/DWvX7+e2IkJajk+6i6jTI5FKtHWb9++3bX93MdoM0zRb8LTp09/aIPv3r3b1Y2KBx19bBr6D+2bl1L6zzSTyh0qiwclfTXj/BCBOfWVH16/93u/98ONX9vVHniloTyk52CIa8Bz4aAHEDvF4E5+PlE/5eAEz8IcYNb6OW19aFBjFrl/lc85+lXgWLheHHM8aycZ6rcEyHlAi+t/+PDhwf45rd9yzLldLYryhaRmKE+kcx2pM5T19jPsKAjlwCOIJACKBjErGnIEZqi9GZXywyMeoIGANNbz0GGizOjMdBSW4+FLcBrpcdOLNwPSpqldtHxh6YwxSsrNYlqZrJ/2oObmEvVV6+w/uyHp18NNmL5Gu4yR+Yy+EH2PNnXz5s1uPpslwJhk1hES7iEcK/eTcjSyZlK5tbL4W3vZ5fzoe3FvWF1d/eHhNcuNf5nN8pIylIf0kydPbj0PYuT6+fPn3d9lw3OE51M8X3mGUT8ZL4v55arWz3f7PBjqX/k5DJa5bhzzLF/khsoljXtRjisoe6h/Lmu/neXFYihPTqfOaDP49u1b9/dnmTsIJWDi4OOmQcPfzSd5HnyUMUvwR0AXjXRtba1P3Y6HKet5Q4pgcxIab5wLU9nJariANQSydH46WuThZsGb46SbAuujY5IvzpGpNPSpcuiYtJxoRzwU4iFBf6j1sWg35KX90uZ4yWTigbabUVC2z4Etox30jcAx8YDluHioxLHG23n+KUt+8SrLzYbK4h4VZfFgZ6SUwJTzyw8r6oMRmGw3I0qLplYXZdpQnqH0fO/ifjnLzzEWWQw48Jzl+ZTrhzabf1Iy1M/L58FQ36uZ1L9Qlo2cfyf9FnzZ5Bx4tseze6h/LkO/rd17y3McyjPLtrPEOk2ML/pcxo2jn/tu3FG2fjA7Dih/+OHx+KaytT7nBeVxGGwXy+Qvsb62b8rK+yNP/Fg55tmWfcR+yR95cjpp+diyfIzgPPIySMtVyjI4n9hfqTz2acr9Mh/70XKhXQ21Gdpxbou0s7Jt0yZr7ZJyow+yTW0fk/aN3OYpI+bjmEiLdsu63A/KvpYNlRtmKSvuC2Bdrqc8j7/4i7/4od6WHXVEnTBFfVGfkY5aHtTSuaZ522h7i2pa30FZb6COo25ot8yX+aKf57wh8rF+6JnBNtE3opws9lmq7S+bVm4gPfffof45rd+Wz8mDKM6Rv7leqMtIH8ozlM7fuPaT2kFL26/cFJwAJ5Y7ECdIWqTHfJ5yBdTWR6OmQiKt1knzNkzlvpHTYn/R6CM99hMXKKcjXyjE+tgGbFceI9vEuUQjiKlUrmeqnTNynig/5M6t5RE3otxuok1FG6nlye19qL2xHVOgP0S7R61c9sky+XI/jink/WeUE+l539lQuXnfmFYW63OfycdU9q+hOlpmUd+5vqIOo15reVBLz2lM+dosmlrfyf026jE/f5DbdEzR3mtlguUst/MQdT+p3+byc3/KxzSpn8zSb2MftXLycUebwVB6nMsiyOdYXu8451oe1NLzNftV6miF/xkf0C+Pzw78diEPIfNpIv82Zi/NUjafHfhdytBv0/Ybnzr4FMNnDGm/8HmPdh7/8G5ZcM/ht4n7dY+R9tOyPh/4GcA46Kr+bEC/nh3/Pya19uDBg23/SpUH43795oPgcpay6dz8zoZjaY19sm8DUO03Xvz4DRn9Yplwz/nDP/zDfkk6WBgc4b9+wW8xlwX/uIlxNQPQg+NAjYQe7/8FO1ZXVw3AJEmSDqgDE4RKkiRpcRyYz/GSJElaHAahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWrOIFSSJEnNGYRKkiSpOYNQSZIkNWcQKkmSpOYMQiVJktScQagkSZKaMwiVJElScwahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqbm5gtAbN26MVlZWfphOnDjR5zjYnj171p3P/fv3+5Sf69OnT1t1PO2YLly40OXfL7Vjob5+lbqSJEkHy9wjoR8/fhxtbGyMjh8/vjW/KEHopUuXRtevX++X2iPQy4HkgwcPuvqlnm/fvt2n/oj6v3PnzujYsWN9yt6rHQv1BV5O1FZ+KRh6+YiXKibmAy8skZ5FWky5XObZLuZreWpom+R7+/Ztt8zf2DbjZSbSI+8QjmOWl7IyTxzL0P0qH+tQHs12/Yfa2E7anqQFNg4sdmQchG6MA5J+aXGMg9CNe/fu9Uv772//9m+7/f3+7/9+V6dra2v9mo1t9bu6utrPbce2eZt5/dVf/dVM5zvpWKiz9fX1fkktRP9jYr6G7h15oqtzreNacd3iWpZtiHVxzcmf9zHLvmOfZduK46DM2Hc+vnJfJbaplZvV8uTzYT3LYdKxxjb6btr1H2pjQ+mT2p6kxbZnQSg37LghxUOA9ZHOjSZu9DGfb2CRlyluTtyMWI6/cQND7IO02C6nUx77Yp79lflZzvuMc2FfsV3kD5EWN0nmKYfz4G+UzfbkqfmHf/iHLvD74z/+4619sUz6EPLE8ZVyHUa9xr6jfJAvjr9EIExejmOa2rFw3kPnq71HfdPeAnWf2ym4RrltMF9et2jDNfl65nJm2TfYpgwuWM7lRh8syxw6pkAZ0a6HTMrD/sq6KY8VHFs+Ls1+/cNQG5u17UlabHv2D5PGN6J+bjR68eJFP7eZPr7hjN68ebP1KZf58b679fHZZfwg6NIinU+8Dx8+7NJB+unTp7t5xD6uXr3arRvfGLvPaHnft27d6tJR5v/w4cPo/Pnz3fz4pjd6//59tx4cI+njB1OXH3xG4vhJf/Xq1ejbt2/d+pcvX3bLlP/06dMuD4Y+J/23//bfRv/m3/ybbv5f/It/Mbp48eLo//v//r/R7/7u73ZpJfbL8XCspXIffB7nmCKd+gPLfF7k2JlKv/nNb7byUu9/93d/182Xho7l0KFDXR2ojXfv3o3OnTvXL41GR48e7dKy+GkGn6S5/lyz8ucaLEf/yshPmaBP5es9y77jczY/EeHTanwWp9+fPXu2mwf7p9/Rr+lH/GSAbcfBY59jf9Be43P70LEi6qvsZ8tsluufDbWxWdqepMXX5F/Hc7MhyImbegQ8IJjjQRDBIngg5KDmypUr/dyPcr6vX7/2c8MiPw+eJ0+edPP48uVLPzfaegjGbx65MfKQ5Dx4UBFoxo2X447z4uZJnlOnTm0LhjOCzv/1v/7X6Hd+53dG//2///fR7//+73fHwr7+8i//8ocHHuWwPybqKaPuSidPnuwCbPBAJRDn+JimPUz/8T/+x91fzrVm0rGQpl8LbT1eGnKfC1zDa9eu9UvfPX/+vGvDoJ3PGxRE32D/tAuOYVrb42X18uXLo7t373Ztdj8R+MbL5bRjpd/kF1TNZ6iNzdL2JC2+X/I/0cRIxa8oRhKZag9K0ljHg5RRwxoeaGfOnBn9zd/8zeif/bN/Nvrf//t/dw/Af/kv/2U3All74PEgZLS2VKsn8vIwLR/6BCG10VSQ90/+5E+6B8Cf/dmfTQwCho6lNqqhn4u2FF8iav94jJH72rX+/Pnz1lcH5ufFNgR5tBUmXtSmBXIEgxwrL0DlPyjaa4zIxgvmLMeaX1A1n6E2Nkvbk7T49iwIzQFROUo2Tf4cBx4CtbfkWeWAaGhUbxYcD6OU8XCKhyNBW+1ByYOec2fEMEYjSzz8/uEf/mH0b//tv+0ebv/qX/2rLjjkwcjn+X/+z/95n3M7AtXy5sxx1XD+lMlNnmONAIS08tr89V//dRd8/tEf/VGXf+hnAVl5LIzIDgW42nuMFL1+/bpfGnXz5egR152Rfq4TL0Zcs3ztab+TvjCEchR0ln2zTQ7caKeHDx/uPsXTzgPHxMg9xxXBB8cao5H5X1Lnf92/G/SFmzdv9kvDx5odOXKkn9Ms1z8MtbFZ256kJTC+6c+NzWJaTz9KHwc/Xdo4YOv+xjITPzaPZf5GHiZQTiyzDmyT82R5Hznfx48fty1HvvEDsPtb5s/zcS75OAPlRj6mvBz/AIKyYn/MI88Poay/+Iu/2PiDP/iDrbJq+6shf2wTOO7In+s1n888ph0L55fbgfZfXGOm2nWN6x7IE9eIf4ST21luP6xjCmxTtt9p+yY977s2T7nRz9lfLqfWxjKOp2zzpVoeluPc2D/zk44VHOOkY1lG064/qNtaGxtKB+vi+khaDtvvuNoX+Ua7H3gQRIDRGuc2LcjW3uN6EzDlgI2/LEdb4NpEnrhG/I20mGJ71K5lBB2htu9Ii2WCiciT22ZOz/JxTeovtXzlvmt54uUwT2HoWCkvAmV9V7v+/I36G2pjQ+mB9ZKWywr/M74ZaJ/EP4AqP6XvNT5d5n/81QKfSPmUWfttlxYHn8v53eTQT0wWVfwjxJZ9SpKWiUGopKnit8VD/9WHRcPL47IF3ZLUmkGoJEmSmvsl/xNNkiRJWmwGoZIkSWrOIFSSJEnNGYRKkiSpOYNQSZIkNWcQKkmSpOYMQiVJktScQagkSZKaMwiVJElScwahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWrOIFSSJEnNGYRKkiSpOYNQSZIkNWcQKkmSpOYMQiVJktScQagkSZKaMwiVJElScwahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1N1cQeuLEidHKyko3PXv2rE9VRr3k+uHvjRs3uvlfHcd6//79funn+fTp0+jChQv9kobQria1Leox+ivzgWtcS6fOI72Ur8lQudnQPkLcS96+fdunbOJ8pt1bOI5p7bSWh33FMZUineNC/NWP9uL6R1vL13pae5a0eOYKQj98+DBaXV0dra+vjy5dutSntsWDZOgh9SvcwKiX69ev90ubyw8fPuyX9t5enTMPjTdv3oxu3brVp/w8x44dG925c8dAYAKu+6NHj/qluvPnz48+fvzYTcwjAoKNjY3RvXv3Rg8ePOiWuf7UOem03wg4QZ9j+xcvXnTLtXIz8r9+/bori3tFzhMBzLVr17r1p0+f7tdsrpt2ThzXy5cv+6W6oTxnzpzZOqZ8fpw76Uzc48DfSUHWMpt2/YfaWKBe6eOsj+fILO1Z0uI5cJ/j7969289tR2C6bA+McqRnp6g3gob9DJbnRXDCtd6rc1w0XCse8EMIBAniedgzESyQhnjROHXq1Ojo0aPdPGkREN68eXNbEHf16tWt4GxSueHdu3fdNqBM8kce8q+trVVfdghW8gtcDYHwTvJwf4i0OM+4X9y+fbsacBJkOTK33SzXH7U2BoJ/2m15r5nWniUtpj0NQgkYuJkzMc/NiXlu5PE30vLEzZ8pliPwiLwssy0TD8fLly9veziQjzTWkZ+bJH8R+yZPpOfjyseTRRrbIM4tyitHY+P4Iz/y8edlphiJmfWYchr7ZuLhyehB7DOOLbaL5fhLGTXPnz/fChriPDg+ptg+RFrsM44/9lGKemPimGOZ+dgWMZ/rlVGSx48f90uaB4HguXPn+qVRFwiQRuAQnj59Wg0GyXP8+PFunjaTR7uGys0+f/48+vLlS7+0Wd7Xr1+7srjOjLhyraNfgOt+5cqVfmnvMcp/9uzZfmnzmN6/f9+19xgd5ZxzH4m6Io82zXL9h9oY9cjLDC+8XP98X5G0nPY0CCUo4obOCALBAyMOvN0SKJHO2y7BDjd8luPTPjctHnSkMVEONyxuYJQFltmebRhJyW/S7Ic01rH9q1ev+jWbb9jxQI10RtjIx3HFPNvGA4gHJfslneMi8OJGSp54aJU/RyAf27CP+KwU5x/YV5w7N2PKmvWYqDfS2AcBN/unbEZ3KIuHOA8E8pDODT6fO+kxAlTioXD48OFunmtBXRLQEyywP46JYyVoiOPgfFmO4+cBT3rGscdnWcp88uTJVj1yDmzL8fNAYp48BAsl9q29Q31S51zXHAgGrhufy0GAkUeyZkFbyC8P7I/2FcEK15p2Ff0cXPeh9rmfImBi3/RN+l7GeoJVzafWxqjHeAnhnsC6HPRLWj57GoRyYyEYisAn5E9jeVSFGzwjJNyweChx02ICNywefpTFJ534PdpeiLIoOwezcSyIh1Pt02SJGyk3V7ZhKj8FBvZ76NCh7hw532zSMVF+1E/UbXnz5iEenxX5G8Ehpo0wxafWjECRB3OcD9eDgJLgkX3wAGE5lEE5CDqivlifryEBJ2VzjQmao76j/rNv3771c9oL1DV9lXqvjTTn0StGNefFtY6RbSb6D22JsmgP7J+JNka7IkjJbf5nGQqC86iuZlNrY9Qjo6hRz9xXylFUSctlT4NQHjwEQ2WAlRHU8Q8EeDgRcETwQnDFTSsm0nkQMs/oRHy+bi2Co73A6GSM+EYwOSse2Ll+ag9MArtYXwssh3DdZhUjuUyzvBjsxQOcwF3z4cUtvyQwT1oWgWZGQJhfWspR0FnKBW2DNkKbjJcyyio/0zNCSpASASsvN7zocBzx0w+m/DONnWB0No+y85J28uTJfum7Wn8/cuRIP6dZr3/IbYx6zC81tAfrVlpuuw5CCSQJrmIEa9qIBnnLICZu/PGgYZSPefIyT755gqr8IOG4JgXFpdg2PiGx/6GRzUCQxGhPjE7mUciM9KF1kxBw5vKpm5gPPGT5zBWou1kxOsGIa5brm8CAlwLy5c+V0/bBwymPtEWdzqsWGGgy2gzXkPbPxHz54kIbyl8mov+Rj224XlzDHDjMUm6gPNpk3BMuXrzYjdIH2lWUF/cE+hqBK8FLBLJM8bK6U2zP/sB5x5eLrAzAwTnWgtVlNc/1R25jcQ3YDtwbrFtpyY1v8DM7fvw4P/r7YVpfX9+2vpZv/GDp8owfMtvSV1dXu3TKiDS2B3lZTxrzuHfv3rblMA40t7ZHbEdZ5XGxLtYz5WOinFxWHEvsN/KUOL9YH2X/+Z//+VYa6/N+5j2msnxEncUx5u3L/SGWS5QfZYBt47iirJDT2X8+/pp8DNRhrsc8X+YD+415bVfWHaLdRn/MfSrabN4uX7Nc/zHFNlzjmEet3EhjOfYRx5XldhzHmbFNbm+lWjvJ+0YtD/K+Q66PnBeUN9Sul1nt+vOXZdblOi3rL2+br3PeptZuJC2mFf5n3PGbYXQkj2ow+lD7LKj9Qf0z+lAbBWLEi1Er8vCPiGb53L5fGEHh94OMtOjnWtZrwc8AxsGVI/GStE/29Deh0/AwI7jJdvIPH7QzBJr8Lq72UOVFgE/65PnZ+FzHZ38D0F8Dn1v5GcnP+l32z8Dnet7PDUAlaf80HwnlQZb/tbkjDb8WAsDj/T+aunfvnqPUkiRpXzQPQiVJkqSmn+MlSZIkGIRKkiSpOYNQSZIkNWcQKkmSpOYMQiVJktScQagkSZKaMwiVJElScwahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWrOIFSSJEnNGYRKkiSpOYNQSZIkNWcQKkmSpOYMQiVJktScQagkSZKaMwiVJElScwahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1NyOg9D79++PTpw40S9tevv27WhlZeWHiXzkL9MvXLjQb7np06dPP6TNg/0zzYJjiuMvz2Ov3LhxY/Ts2bN+aXv9ZEPpYB3ltEDd5/rj2Dmmsn5q6ftVhxpGu5ilbdD3mDKuV9nm4roylf0o903mIx/zNbm/l2Uh9h/rcv6hMhHbzdLeyv6H2EfenmOI9DBL+csq11ft2oah9hnXMNd3qLVVSQtsY4euX7++sbq6urG+vt6nbPr48ePG8ePH+6VN5MXa2trWPNh9LFNOud287t27189NVx4nx0LaTpR1AMqiTM45RHWTn7oLQ+lsm5f3E/vmOOJccv3k6zYpPc5D+496p75zfxpCvtw3uG5lW8/XtbyWtIncV5gnT94my9uX28a6fDy53Zf5M86V7UH+Sece+8n9b+j+kI+17JexP31HvVBXTFF3paH2WWt7GdvMcx+XdLDtaCSUkYqjR4+Ozp07N3r37l2fOuzhw4f93Hbjm83o1atX3fzVq1dHHz586OZ36siRI/3c/MY3xplGlfDb3/62G2Eh/z/6R/9odPfu3X7Ndw8ePBiNb8D90uYoUyyfPn26+0s9DqXjzp07oxcvXnTzO8Hx/d3f/V2/NNnTp09H4wdwvzQaPX/+fHTt2rVu/tKlS1vXaSj92LFjo/FDaeY61O7Qp+g/03A9yny0q3Ggt20U6/3791vXlWtJm4xRxNw32YaRLPIwnT9//ofRsG/fvm21Jdo0fSuQfxyIjG7dutWnjLp7CPsA+Sm/NsLGObNPcA7R9mrK/ofbt293o295pHVS/5vnnrAsok0cOnSomxBp2VD7rLW9UGurkhbbjoJQHlinTp0aXbx4cfT48eM+tW7STZxt4yHG3xCfa0jnb/40RnnxKYcpPhGS9/Dhw1vry20jf+3mh3i45QdURjD3l3/5l6M/+ZM/Gf2n//SfRv/v//2/0b/7d/9u9H//7//9IVDkpnzlypV+adObN29GZ8+e7Zc290c9DqVTRgQFiDrh+JiYR/6MWT4MeBC8fPmyO2YC5yGUcfPmzX5p0+vXr7trHCIwGEoHD/FJgYHa4rrkthW4RgRYZ86c2WozX758GX3+/LmbBy+ZpFFG7psEjLx8BvKVL6K0A9odZbN9BBbM014IRGiv8dmV/bKvQB/4+vVrv1RHABR9u1Trf/SZ8Ut396KUg6Ch/hfzGLonLKN8nUItbUit7WGorUpabDsKQrnp8KDhJp2DkMBNJgKj0qNHj7atI1DiIcbDLEQgwwgjDw7wIGA/rCONffAwiQCQMjgmyiOdmxz5OD72xTwPw/KBmeUHUOmf/tN/Ovqf//N/dqOtPOAIQH/zm9/0a7fjwRajKjtFGXlkl5EozotRJo4zHuwEhdQF58fIZIkRpz/6oz8a/Zf/8l+qv7WKB2w8cHer1h70czC6XWsTXGsm2g0BIXixoG8GAkPaX9k3Z0Wwd/ny5a4Px6hn9L0IRBiZpP0RfOSXWdJ4oZyEfhqjp6Va/4v2TTrHVvt6UcN2Q/cEbcovL9PU2h6G2qqkxbajIDQ+azEx4lEGdgRLBEVMJT59xbr4xDd0E8sjjARf+cESN7IhPGhAPj7/hWk3zKG3esrjocf6//gf/2N37ozCMsr413/9132uzVFFAuHdqo2+MCJVjhBxI+cT+SS/+7u/O/on/+SfjP7mb/7mh8/zbJs/je6FaaNY2n+MMpWj2yX6Rowm0rfom9GvCUhPnjw5V4CRcU+gz3B/yCOe9KEIRPhkT4BH8BEvi3FPmfYSR6BZC1pm6X/Tyi7NM9K3jHbykpLb3ixtVdJimjsIZZSLh0sEkgSCkz7JzxKQzXMT4xN1PKwiuOSY8ifi3Rj6XSkPz3hz/8//+T+P/uEf/qFb/p3f+Z3Rf/2v/7VLB3URx8eDnNEgHoyM9vDgDIwG8ZAfSucmXaKeeCDyk4MIHOOBmj9tZaSTn5El5svR2/KFIj6Tlb/3jcBgKD2bNoql/ffkyZPuZZDryjVmqv00Jrcz+ip9mv5NgMi6sm/Szxh9D+XPM0B/JOCkXVBejHhG+w2UH22FF07y0qfjN5q85EXbzO2b8xgKWob6XynOe6j/Zbv5rfmiqdXFTusnrsGsbVXSAhrf+OcyfkD0c9/xL0rHD65ufhyUDv7rVv5lZG17ts3plJEPjfLIQ3pt+3vFv6aM/CA/+wX5YvvacXIepA/527/9242/+Iu/2PiDP/iD7vjIzzLpNXnfiHPi2Ng21NLZrjwv0sgbZUbesv4CaVEPs6C8yJ/rh/3V6i2nh7JOtX9ye56EfGVbAtcv2lLg+ue+V2tbXGPaQa0PgTJzOuVF/lx2nke5XQ3nEcfMsZXHn3HctfWUkftFHAdpuV+CZY5b31Ff1BVTeQ2zSe2T6zJ0bWptVdJiGr6DVPCA4KaTbyzcSEiL9JhnyrixRHp5o0c82GI+8jHl8mI+Jvafb1r5GPI+c3o+5jgW9h3zsd20h89f/dVfbfzxH//x4IOTfeYbbd5vNpRey5frPuqJKT9Ud4rzz+VEPZTnN5TOtvn4tH/Kto0I8sq2QF4m5LaWr1WkRx/IuM65L0TwwRTpkRbLlB15Yt/I+4/jjHOZ1nbyvSAmlPsOlMf+kOsrHw/yMWWUV6uPZZevf1xD6iov5/qO6zrU9jK2K6+PpMW1wv+Mbwo/HZ/w+OQ96T/TxCe98rehfGrbi9808ilofCPd+kQU/7im/NTcEp8g+VS1m/9MUytcm3GwwlO8T9GimKVvLqLyniBJ2ls7+odJ+4Fgj99jxX9yqYb/9l/+Ry88HPfi91r8QJ7gKR42BH/869mfGYCCf3jB704Pwu+j+EdTBqCLaZa+uWjKe4Ikae/9MiOhs4jRtrC6unogRgklSZK03YEKQiVJkrQYfpnP8ZIkSVoeBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWrOIFSSJEnNGYRKkiSpOYNQSZIkNWcQKkmSpOYMQiVJktScQagkSZKaMwiVJElScwahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWrOIFSSJEnNGYRKkiSpOYNQSZIkNTdXEHrjxo3RyspKNz179qxPHW2l3b9/v0/ZOydOnOimneI4Oe799OnTp10d4yRv377d9+PfD/tVH/qOdjGpbdAuo28yX6K/5j5LX4n8uX+D7S9cuLA1P6lcUG4tD+050rOcnzyTcByT7jW0PcqptcHatkPHRN3+9re/7ZcUZrn+iOuQ65X6L9PCtPYsaQFtzGl1dXXj3r17/dKmjx8/dun7gbKPHz/eLy2XtbW1favX/cZ120Hz0oyuX7/e1S9/h9BvuA5DfYjtc19mOfLna7e+vr5t+2nlkj/abbltlJvz5P2V+UtsUx53Rn1QHsib62do29oxgXKG9rPMpl1/cO+K6xCoS+oYXJdc17O0Z0mLZ+7P8Xfu3Bm9fv26X9r07du3Ll17izp98eJFv3SwHDt2bDR+4DiysU8ePnw4Gj/U+6UfMbrHSBTXgen8+fPbRhi5Lnl7RrTGAcVWfuZjlOvq1aujDx8+dPPTysW7d++6bXD69OkuP3kYXR0HGVvpYB/cP8YBSbdM+jh46eZr6A9RRg31wnGB/vPq1atuHrVth44JlPP58+duXptmuf6g7mlDed2tW7e26vjmzZujly9fdvOY1p4lLaa5g1BuIjyQ8meYp0+fdun5k1p88opPfPyNgIQbU+QjLZZjPfPc6GpiOyawTWzL33zTi3I5lryPyEt6HB8T55S3yevi2MgTy5HGcnyqRK4HygDznFOUmfPXkO/atWvdfBwTE/P5GBCfuHKdMc/EseRrhSgLlBfb1Y4bsb/4lMl87BvMx3aUF2gTOQhQOwSC586d65dGo6NHj3Zp4BqdPXu2mw8RuEV7IbggjbzMh0nlBgK3L1++9EubZX/9+nX05s2bbfsl/f379107ISChzbG/vQpGDh06tK1P1AwdU/jNb36zrU0vu1muP+j3vEycOXNm270kUM8EqZKW247+YRLB0fPnz/ul727fvs13re7m8/jx4y7tyZMnXRo3+8CNKfI9evSoewjlBw8jaDU8IMnHtoxecHPjDTpuZqRTVsjlxjz746HDPhjRvXTpUpfOxI0x8vHWzts8x0i5bAfOm20pIwKsfLwcEw/hOL/Lly93DzHysEw9sC6PAtSQ78iRI908x8T2nCfzHOfa2lp37tQJo06USbDAMvvjGnF8XJMSeUGwQXkR7FIfcb7US4j9xTGTJ86d/fEgYhuOkWubg16CAB/ivxZeGvP1DdFeaEe0LRBgcH3nQd+I/g/aw+HDh/ulOtoOfeXu3btd39sLBJMxIrtTv/d7v9cF0JoP9wwm7hW1r2Rxj5K03HYUhF68eHHrIcPN5MqVK908gQhBWH7D5UbECBmfX3iwkT8+f7EuAqJZ8HBiIrCJoDDEMUzDvuMBHJ8YOZ/8E4MI/ljPp0KOP7COQAuxfZZHVjg/9hcjBdRLPNyRg7VSuY5gMQK6vI7j5uHNMVInLDMCRDDx4MGDrn45jhKfPzk3yorz5WFRe7kIbBMBAg8XsL9Tp0518xwjefJIEnyI/zron/TFGq5bvCzFSPdOPkfTv2irMTrOywttYxL6CPsmLy82e4G+WAu255VHdTUf7j210WhehPbqZUPSwbWjIDRuLARE8SkepHHjjwAFBF0EQoyuxINtUvA1CQ9QHmqM2EQguxciSON82Ec8uPj8zMhMDpRZxzLnWbu5luYdRQq1wJFRHeqbQDE/XHl4c0xM/O6NbZknGKa+avVNHoJDAtUoK64jdbBTteOeNgqmvceLQX6xipcFvkzwMkS74EWFiX5JG2EdbYC2Qx+jP5Ttd6jcEu2QchhBj75Ke6TfBPZx8uTJbj8Eu7FvjonjiZ+ZMM3bJjmnoWA7GzqmLF7SNPv1z8p7Ai8Zsw4aSFpsOwpCEQFRiEAnj/SBBwnrGDXkBs+DhtGOyM/Dhfl8o6/9xgg8LHio1QKd3SrPBxxn+Q+DuIFyzJwnQWicR+Chxshk4CbNSOu8KKccgSFYZLQz1xW/zyJQDjx8eajzl/z8tKAcmQTBBccZDwOuE3ig5IfyNOw/f/LkGucAmesewa3aoc6pe9pn9D/SIjhkip+h0JYZFY+fW2S0hzwaOlRuDf2E0fW4J0T7BW2U/hMvQ7SbUj7W3KamoY/Sfyg7XiyHDB1T+Pu//3tfopJ5rj+oe65FiGvBNmy/V6Pekg6o8Q1+x9ic/xRHOH78eJcWf/lPcFy/fn1refzA6/KxDcuRBx8/bv5nWiKNv2yb0/J25b6YSjn/n/7pn27Ncxwxzz4Cy/l84jiY2A8T20Y6f/NxR1n5uKO8nCcfexwL5ZRIL7F9PkbkOlhfX9/6T81EWg37jeuBsowsn2OuO/aBvK+8LfO5frV38nWIOo7rFNeAv5Gn1r7KNlArE7SNvH2t3EhjOcqpXfvcJzPyRno+plItX953bosxhaF9DB0Tauew7GrXn78ssy7XZ66/XP8xxfZDbU/SYlvhf8YdXz8RIzAoRxQYNeATaR6NZeTgIPyWilGOcfDCU71P0UFF+2S0m1GvZUIb5qcv/nZRkvbHjj/Ha28QaPI5vfZJi0+FfM6M39IeJPwG2AB0MdA2+VweP9lYFvxe+g//8A/7JUnSXnMk9IDgt2ofP37spv34TawkSVJLBqGSJElqzs/xkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWrOIFSSJEnNGYRKkiSpOYNQSZIkNWcQKkmSpOYMQiVJktScQagkSZKaMwiVJElScwahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTm5gpCT5w4MVpZWflhevbsWZ9jb8R+Wrhw4cLo7du3/ZIC1/T+/fv90sHw6dOn7nouA841+h/zNVzDyFO28Rs3bnRTDXXINrlf57qdZd+IPPTnwD4jPRtKr+E4prXNKCv2nY85plLcd6irfMzabpbrH+tr+aKemUK0uZwmaQlszGl1dXVjfX29X9ro5tfW1vqlOtbnbab5+PHjxg4ObSaUfe/evX6pHfbJvufxM4/1+vXr/dLBQjs7fvx4v7S4OEfaB1PtfHM687k/cW1Zrl3jWnpZp9P2jVq7zfcK9hF5htJruP9wjJPy1NaV9yjKCVE/5XaksU7bTbv+ZV1zTaMeWVfWKfUezwfy5msjabHtOgidBTeqebbhJsUDYD9Me8jtl5080H7GsXKMB/0hwIPuZ1zjVuhL+RrRTsr+VdYBeXJwwDrSMsqs1VsONGbZN2jvZZvP8xxLHM9Q+pBp/aK271I+d86vts9F6At7bdbrn5V1zbUZ2oY6Z72k5bCr34Ty2So+2fF5LD6nxGdAPrvwmWV8YxmdOXNm65Nb/ksZsV187guxLn8aKz/bxXLef3z6yXnj8/KjR49Gt2/f7vYV6yN/PhbWZXFOlBGfjtgu5vPnwfIY4/jHN+CunCiLKfYT+6Yc0spjZV+xTa4P5pnIH+cRxzQtX83z589HV69e7eZzfTAfxxDHXNsPy6SX9YcoC5QX2+X6Ij3E/lkfeXI9x/6Z8jldunRp9Pjx435p8bx792507ty5fmk0Onr0aJeWffnyZfT58+d+aTMPaUOovw8fPoxev369VefgGpw/f76bxyz7pqzxvWU0DjS6Nh/X9NixY91fvHnzprtOGErfiaF9Z6SdPXt2a552eOfOnR/aVxxXblvLbpbrn1F35AmvXr3aeh5wHyxR51w3SUuiC0XnwFswm8WURxAYneBNl7dZ/gbm482XPHm3lBfrYtt4G4437khnXzECwt94w2Z95CWNKR8D5ef1eRQlykYcV+y/xLakU16cB/O5/KFjJG/sp7bPOGa2qR0r81HXrI/9sj6ON7aPfLF9Ld+QKDswH/WIKLu2H5aZ2CbOq5T3zzbI5ZfIwzZRbq6b2D9/y/3lOl401EnUHcpllNcg1xfIT1pgXb72bMv8tH2Vy6V8zRDHFdc0DKXXRHubptx3YPvcBsnDMhP7z+2mrLdlV17vcrnEOq5DiTqu9XvyTipP0mLZ0Ujo+EbB0637m926dasbNRnfXLpRlSHjG3s/Nxq9ePFidOjQoW4UYnxj6lM3sS58+/atGyVhZJC8/OWtOjCSgZs3b3bpvFFzDIxs8NY9DSMicVxsy/nVjG+Qo9OnT4+OHDnS5Wcecb6TjjGQl/MhT2C0gHo7derUtvMODx8+7EaI2Obly5ddGvXGPh48eNAdL8fNSNbly5e7fIykslzLN6S8bpwfI0XUTx4Rqu3n8OHDXRojI0P1N37gd+dOWdQh7t69Wx0VCdRzjI7F8VGvJ0+e7OZZR92Vo17sZ1lx3ag3rk9co6ivGkZJGeGK9sy2XMc8mroTUV5gmbZB+U+ePOlTh9N3o9x3Fn2A82Pkn2Um2uf79++7dWHSCLImo35r14G6zl9QwtOnT7vniKTlsKvP8dxcyk9nBBcEBPmz1iR89iNw5QHEdtOsra11eZnKgKnEwxdlsDxkrz67TTtGPiMTeLE+cONlmXTWl6hPzoc8PCjBjZxlPi2yLo4/XhKYCGiH8tXUHgw8pHk48Kk+X+9yPxFIIOq+xLF8/fq1C4ijLP4SvJdB5Dxqx03wvYh4USHoD8yTVuLFhevBdaLNUPdDeCEoP9+Tlj+lYtZ9Z7X98rJYk9Pzzy0mvaRMUu6btp/PifkcZJKfl6ksXpa0s+s/pLw23OOuXLnSL0laBnvy3wmNAICAkhFJAi9G3mZ5cDCiVRstrCGIihFPxO/WQGADAqVr1651AQ0P3lnfqgmgGGGMAI1j30lQOukYA/spRzvJxzGTXgtcudmXwTT52Y4gjhFaRnAYzSKQDVFumW8I20ddBrZjJC0/jGv7oc54kFDnjGjVgkoe+oyWxsMmAm7qbdJvy0q8uLDPQJ2VIy6Tgq6DjPPkfGmfTLVzD1wDvgTURtezuMbR5vlNLSOnBBg5OJ1n3xgKLKKtlnI6xxwvORzfvGr75v6Qg6aLFy9296pAHeTz4RwnjSAvm3muP/cD+nVNuS6eFZRFuVw7SUtgfIOf2fHjm/+ysZz4DQ9TzPMbqljH/Dgg6ebjb6Qjp0X58XccRHZTrEdejjLIn7cNkS/Wsa84tlrZ+bhZl+V1ca5M+fiZR+0YI43lvD6OnW0jPcrJx5r3GefzP/7H/9hWVoj1TOPAdeu3cTlfHHdp6Lda5I9zCeV+8rnVykC0lRDbM+Xf4oEyY11Z58j7z9tyHHkfiyjXTZw7f1mOa8E816NUq0vkMvO1pp5z/db2HWks5/LzdYg2x5T3O5Rek/NG2bPsO9TKj7pioqxAebX6W3a168/fsv7Kus71nNflaxpTlCtpsa3wP+NOf6AxEsvvyCaNyOhHjD4wylP7LMboF59zQ4xw/uoY+ePnA4zQaG8sa53yM4BxMLSwI+qS9LPtyed4HTwElfwOs/aAJdjkU9lB+yTGZzx+ImAAurd4ueMnM7XfKi8qXmx5PzcAlaT9c+BHQvldIL/jwgIM6v5yeBgzGuSIkCRJ2ksL8TlekiRJB4uf4yVJktScQagkSZKaMwiVJElScwahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWrOIFSSJEnNGYRKkiSpOYNQSZIkNWcQKkmSpOYMQiVJktScQagkSZKaMwiVJElScwahkiRJas4gVJIkSc0ZhEqSJKk5g1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJze04CL1///7oxIkT/dKmt2/fjlZWVn6YQP7auhs3bnTr50V5z54965cOBupnp+crZZ8+fdrqQ8zX5H6W8w2l058irexb5Llw4cLWfLltiXsD68t7RL4P0B9K9I9J/Xqo3BLHyr4yyo5910TZHNe08pfZLNc/cB3IF9c0lplqyvySFtuOg9DPnz93N+r8IDl9+vTo48ePo+PHj482Nja6aXV1tct369at0dra2uj69etb65h2IgK5S5cudX8PCurn7NmzWw9zaafOnz/f9TUm5ks8xHM/o99NSsfly5e3ymQ+0MfZx4sXL7rlafumf7569aorn74f/ZWA5fbt2136+vr66OrVq116YP2jR4/6pR8NlVuif718+bJf2sQ50PfinHOAGkHVtWvXuvX00w8fPnRp04KsZTTt+gfq79ixY12dcq+mzu/cubN1Dcr7YJlf0hIYd/i5jW8+G/fu3duaMtaNg9B+aWNj/LAh0uzSx0Hoxvjm06/ZGcrbbRk/G3VGXUg7QR8Yv9z1SxtdfyBtkqE+E+llv2WetJgP8+6bdbF9uW15+6Espln6Ri63hnLyvSnOBZSf90E5tX2yTT5ezX79yVM+GzLqNl//afklLaYdjYS+f/9+dOrUqdHFixdHjx8/7lPrnj592o2M8oabMTKRP7nwFsyb8dDoRqC8K1eudPNsz3YxYkGZzMcoB6MlUS5ixIN9lPuJdWwDyo7t+Ms6pjwyEvtnf5Enn1Psnymj3p48edIvSfN59+7d6Ny5c/3SaHT06NEubQhtljylnB79k7ZMOiNcpMUoaJh334cOHdrqU4wwMkJJH6HccdDRpYO06NezyOXOIt9/3rx5szXSxnFQDiN00ZdDbJP7/LKb5fpTX4wkv379uqvT2j2duuW5gFnyS1pMOwpCv3z50j1QuJFwA+dGno3fcreCLz6v8QktsEz6mTNn+pTNB9Da2lr3IJj0OQ6UxQMIPEjYbvwW3R0Lx8Qyn/65kRHojQPtLi/7eP78efcZkM9y+ZjA9hw3Eyg7brbcICmHKR5MIM/169e7T4wcO/vmAQeC0tg/D9v8wKSM8nOhtF9o97w0lsp0+gRtmaDz4cOHXRoBRi2AnRUvrPmzO/2PT/13797t+mmg39B/Z1WWO4t4SeUeEy+LEUBx7vR9zj8HnfRV9qXZUV8R2HP/o77LZwTL/PwBs+SXtJh2FIRyo44gk2CqfBPmDTeCNqYcuBG0kcbDKBw+fLh7MFEO6yaJIDEQCBIk8uDgxkVZ4KFCoBvHyEPuyJEjW8Ev25Q4To49yooHNDfLSTdFgsx4gMYDjH1GGg9bjjs/3FAuS/uB32/XArwynWCAfklbjdEo8uxGHnUEfZx90D9i1JG/EfTOqix3Fpwr9xfuQfElgvMjmKXvM/FCWwadvHRrdtQXL/DRtqjv8hnBF614CZklv6TFNHcQSjDGQyQCTB5Y0z7J13DDiYdIPBxA0DhJfMLJCBIZ1eHGFTcycGxxnDzk2B/zPMDyyGRG+rdv37qbZJTFzZKRm90EjbXjzsG5NCtejvh0GZivjXTOg7ZNYBZ9kZc4+no5CjrPvglkb9682S9t3jsi8GUfMerI/SNeahkF44U0/8SFKUYuUZY7r7wt55eDTPpkvMgGXl61aZbrT33llxfqONch1zb/9GJafkmLa+4gNAdn4KZN4DZppHAaHjDcmAj2eAueVBafCgkSs/htar5xke/Bgwf90uaDi32wLwJSjrkWVHI+jJbGgyp+F8qb+jyf5RhRidEk9sP+IuhkmfXSTtD/YvSfifncJzPaOz8/KZXp9KnaT0QIMHKAMOu+6WuUT5unP7O/r1+//vAzGFBGvCzS/+MnNfxr/EiPF9ZaufOiH8dPbbh3EAwHguB8PpzjyZMn+yXNcv25VtQj68G9OeowrhfbsJ7rOSm/pAU3vsHP7Pjx492/aBw/KPqUzX9pSlqkxzxTxr98jHT+JWRGGaSxjn2A5ZjP1ot/nRlIY10W+2P6+HHzX/THfmplgHPgeMA2uYxSPvd8fswjlpky1sc+pJ2grUfbop0i2mvuB7mvZrX03Ibzevph7AO1fUcay9HH8hTyPSL6SZb7X2mo3Lxv1PaR0/K5IffjXHdxLtqudv35m+sv54nrma9BTLF9Lb+kxbfC/4w7/i8pRkdLjDDyySavG8r7q2EkgM+e8d9clH51jDjyu0lGvZYJPwMYB0lbXzAkSXtrR/8wqYX4lF0T/4hhJ5/ifiYe5vwe1QBUBwmfTvmMHj9NWQb8fIb3cwNQSdo/v/RI6CwYrcABPw1JkqSlcuCDUEmSJB08v+zneEmSJC0ug1BJkiQ1ZxAqSZKk5gxCJUmS1JxBqCRJkpozCJUkSVJzBqGSJElqziBUkiRJzRmESpIkqTmDUEmSJDVnECpJkqTmDEIlSZLUnEGoJEmSmjMIlSRJUnMGoZIkSWrOIFSSJEnNGYRKkiSpuZWNsW5mZaVLkCRJknarDzEHbQWhkiRJUit+jpckSVJjo9H/H3u1ZJ+wz7IpAAAAAElFTkSuQmCC\" width=\"673\" height=\"811\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations:\u003c/em\u003e CI, confidence interval; HR, hazards ratio; BCLC, Barcelona Clinic Liver Cancer; HBV, hepatitis B virus; ALBI grade, albumin\u0026ndash;bilirubin grade; PIVKA-II, protein induced by vitamin K absence or antagonist II; AFP, alpha-fetoprotein; APFs, arterioportal fistulas.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Results of univariable and multivariable Cox regression analyses for time to progression.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"678\" height=\"814\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations:\u003c/em\u003e CI, confidence interval; HR, hazards ratio; BCLC, Barcelona Clinic Liver Cancer; HBV, hepatitis B virus; ALBI grade, albumin\u0026ndash;bilirubin grade; PIVKA-II, protein induced by vitamin K absence or antagonist II; \u0026nbsp;AFP, alpha-fetoprotein; APFs, arterioportal fistulas.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 5. TRAEs in the study population\u003cbr\u003e\u003cimg 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\" width=\"727\" height=\"723\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations:\u003c/em\u003e AST, aspartate aminotransferase; ALT, alanine aminotransferase.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"first-line tyrosine kinase inhibitors, regorafenib, PD-1 inhibitors, transcatheter arterial chemoembolization, unresectable hepatocellular carcinoma","lastPublishedDoi":"10.21203/rs.3.rs-2694765/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2694765/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: After the failure of transarterial chemoembolization (TACE) combined with first-line tyrosine kinase inhibitor therapy, the subsequent therapy for progressed hepatocellular carcinoma (HCC) patients is still controversial. This study was performed to evaluate the safety and efficacy of the subsequent combination of PD-1 inhibitors (TACE combined with first-line tyrosine kinase plus PD-1 inhibitors) relative to switching to the subsequent regorafenib (TACE plus regorafenib).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: The data of HCC patients who suffered from the failure of TACE combined with first-line tyrosine kinase inhibitor therapy from July 2019 to August 2022 were assessed in this single-center retrospective study. Primary study outcomes included progression-free survival (PFS) and overall survival (OS), while the secondary outcomes were treatment-related adverse events, disease control rate (DCR), and objective response rate (ORR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: We enrolled a final total of 113 patients, including 73 patients who received TACE combined with first-line tyrosine kinase and PD-1 inhibitors (Group 1) and 40 patients who received TACE plus regorafenib (Group 2). The OS in Group 1 (15.0; 95% confidence interval [CI], 9.8–20.1 months) was significantly higher compared to Group 2 (9.0; 95% CI, 6.6–11.3 months) (\u003cem\u003eP\u003c/em\u003e = 0.016). The PFS in Group 1 (11.0; 95% CI, 8.4–13.5 months) was also significantly higher compared to Group 2 (6.0; 95% CI, 4.6–7.3 months) (\u003cem\u003eP\u003c/em\u003e = 0.010). No significant between-group differences in the ORR (\u003cem\u003eP \u003c/em\u003e= 0.562) and DCR (\u003cem\u003eP\u003c/em\u003e= 0.202) were found; however, the percentage of patients with proteinuria in Group 1 was significantly lower compared to Group 2 (2.73% vs. 20.00%, \u003cem\u003eP\u003c/em\u003e= 0.006).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: The subsequent combining PD-1 inhibitors after the failure of TACE plus first-line tyrosine kinase inhibitor (TACE combined with first-line tyrosine kinase plus PD-1 inhibitors) may be associated with improved OS and PFS compared with switching to the subsequent regorafenib (TACE plus regorafenib).\u003c/p\u003e","manuscriptTitle":"Efficacy and safety of transarterial chemoembolization combined with first-line tyrosine kinase inhibitors and programmed death-1 inhibitors for progressed hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-28 21:49:13","doi":"10.21203/rs.3.rs-2694765/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":"10bddc9b-a709-4c0a-a0b2-3c62cb4e2e53","owner":[],"postedDate":"March 28th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-04-26T11:14:32+00:00","versionOfRecord":[],"versionCreatedAt":"2023-03-28 21:49:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2694765","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2694765","identity":"rs-2694765","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}