Prognostic Significance of PET-SUVmax in Patients Undergoing Resection of Hepatocellular Carcinoma >3 cm: A Retrospective Analysis

Prognostic Significance of PET-SUVmax in Patients Undergoing Resection of Hepatocellular Carcinoma >3 cm: A Retrospective Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Significance of PET-SUVmax in Patients Undergoing Resection of Hepatocellular Carcinoma >3 cm: A Retrospective Analysis Takuma Ishikawa, Shinji Itoh, Yoshiyuki Kitamura, Norifumi Iseda, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8405525/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and Aims Hepatocellular carcinoma (HCC) > 3 cm often recurs after resection, indicating a need for better risk stratification. The 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) maximum standardized uptake value (SUVmax) reflects tumor aggressiveness. In this study, we aimed to evaluate the prognostic significance of preoperative FDG-PET SUVmax in patients with resectable initial HCC > 3 cm. Methods This retrospective analysis included data of 136 patients who had undergone curative resection for HCC > 3 cm between 2012 and 2021, all of whom had undergone preoperative FDG-PET imaging. Patients were classified into low and high uptake groups based on tumor SUVmax. We investigated the association between PET findings and clinicopathological factors. Results Patients with high SUVmax had significantly worse overall survival than did those with low SUVmax (49.2% vs. 90.0%, p < 0.0001). According to multivariate analysis, high SUVmax was an independent predictor of both poor overall survival (HR 3.09, 95% CI 1.35–7.06, p = 0.0077) and poor recurrence-free survival (33.4% vs. 60.4%, p = 0.0006). High SUVmax was strongly associated with microscopic vascular invasion (mvi). Notably, the combination of high SUVmax and high alpha-fetoprotein significantly improved the preoperative prediction of mvi. Conclusions Preoperative FDG-PET SUVmax is an independent prognostic biomarker in patients with HCC > 3 cm and, when combined with serum alpha-fetoprotein, may enhance prediction of mvi noninvasively. These findings support incorporating metabolic imaging into preoperative risk models to guide surgical and perioperative management strategies. hepatocellular carcinoma microvascular invasion positron emission tomography Figures Figure 1 Figure 2 Figure 3 Introduction Hepatocellular carcinoma (HCC), one of the most common malignancies worldwide, is a leading cause of cancer-related mortality[ 1 ]. Despite advances in surgical and locoregional therapies, long-term survival remains unsatisfactory with high rates of recurrence and tumor progression. Accurate risk stratification of patients with HCC to guide treatment and follow-up is therefore critical[ 2 ]. In clinical practice, tumor size is a key determinant of therapy: early-stage tumors of diameter > 3 cm may be amenable to curative ablation, whereas larger tumors (> 3 cm) typically require surgical resection for optimal outcomes[ 3 , 4 ]. However, tumor size and stage alone are not enough to accurately determine the biological aggressiveness of HCC. Thus, there is growing interest in better characterizing tumor biology preoperatively by performing metabolic imaging. 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is an established imaging modality that reflects tumor glucose metabolism. In HCCs, FDG uptake is highly variable: well-differentiated tumors often exhibit low uptake because of high intratumoral glucose-6-phosphatase activity, whereas poorly differentiated tumors tend to show intense FDG uptake[ 5 ]. Prior studies have revealed that high FDG uptake by HCC is associated with adverse features such as poor differentiation, microvascular invasion (mvi), and early recurrence[ 6 , 7 ]. These findings suggest that FDG-PET could serve as a noninvasive indicator of biologic tumor aggressiveness in HCC. Despite this evidence, the prognostic utility of preoperative FDG-PET in patients with resectable HCC has not yet been fully elucidated. In particular, there may be a higher rate of FDG-PET positivity in the subgroup of patients with large tumors (> 3 cm) than in those with very small HCCs, because false-negativity occurs frequently in the latter. Thus, FDG-PET positivity is more informative in the patients with large tumors[ 8 ]. We hypothesized that, among patients undergoing surgical resection of an initial HCC > 3 cm, increased metabolic activity of the tumor as indicated by FDG-PET maximum standardized uptake value (SUVmax) would be associated with worse overall survival. To test this hypothesis, we performed a retrospective analysis of data of HCC patients, stratifying outcomes by SUVmax. We report our findings on the prognostic significance of preoperative PET SUVmax in patients with HCC > 3 cm, and discuss its potential role as a biomarker of tumor aggressiveness in this setting. Methods Patients and Study Design This was a retrospective study of data of 136 patients who had undergone curative surgical resection of initial HCCs of diameter > 3 cm in in our institution between 2012 and 2021. All included patients had undergone preoperative 18F-FDG PET/CT scans as part of their evaluation prior to surgery. Because our focus was on patients with initial, resectable HCCs, we excluded those with recurrent HCC and previous treatment before resection. Relevant clinical data, including patient characteristics, underlying liver disease, laboratory findings, tumor characteristics, and pathologic findings were collected from medical records. Mvi status was assessed histologically on resected specimens. Our Institutional Review Board approved the study protocol and waived the requirement for individual informed consent because it is not required for retrospective studies of anonymized data. FDG-PET Imaging and Assessment of SUVmax All patients underwent preoperative PET/computed tomography (CT) imaging after at least 4 hours of fasting. Each patient received an intravenous injection of 18F-FDG (at a dose of approximately 4 MBq/kg body weight), and FDG-PET/CT scans were acquired 60 minutes post-injection using a dedicated PET/CT scanner. Attenuation-corrected PET images were reconstructed and reviewed by experienced nuclear medicine physicians. The SUVmax of the tumor was measured by placing regions of interest over the lesion on transaxial PET images, the highest pixel value within the tumor being recorded as the SUVmax. In patients with multifocal tumors, the largest tumor (> 3 cm) was used as the index lesion for SUVmax measurement. An optimal cutoff value for tumor SUVmax was determined using receiver operating characteristic (ROC) curve analysis for overall survival. The study patients were then stratified into two groups based on their FDG uptake: a low SUVmax and a high SUVmax group. Subsequently, clinicopathological features and outcomes were compared between these two groups. The study patients were then stratified into two groups based on their FDG uptake: a low SUVmax and a high SUVmax group. Subsequently, clinicopathological features and outcomes were compared between these two groups. Follow-up and Outcome Measures Follow-up and Outcome Measures Patients were followed postoperatively with periodic imaging and serum tumor marker concentrations (alpha-fetoprotein [AFP] and des-gamma-carboxy prothrombin [DCP]) every 3–4 months, in accordance with our institutional protocols. The primary endpoint of the study was overall survival (OS), defined as the time from surgical resection to death from any cause. Surviving patients were censored at the date of last follow-up. Recurrence-free survival (RFS) was defined as the time from resection to the first documented HCC recurrence or to death, whichever occurred first. Recurrences were confirmed by imaging or pathological assessment and treated with appropriate therapies (repeat resection, ablation, transarterial therapy, or systemic therapy) as indicated. Statistical Analysis Continuous variables are expressed as median (range) and were compared using the Mann–Whitney U test because they were not normally distributed. Categorical variables were compared using the χ 2 test or Fisher’s exact test, as appropriate. Survival curves for OS and RFS were constructed by the Kaplan–Meier method and compared between groups using the log-rank test. Five-year survival rates were calculated from the Kaplan–Meier estimates. Univariate Cox proportional hazards regression was performed to identify predictors of OS. Continuous variables were dichotomized based on previously-reported clinically validated thresholds as follows: age ≥ 75 years[ 9 ], AFP ≥ 20 ng/mL[ 9 ], and PIVKA-II ≥ 150 mAU/mL[ 10 ]. Variables with p < 0.05 for differences between groups on univariate analysis and those of previously established clinical importance were used to construct a multivariate Cox regression model to identify independent prognostic factors. We have reported hazard ratios (HR) with 95% confidence intervals (CI) and p-values. Two-tailed p < 0.05 was considered to denote statistical significance. All analyses were performed using JMP software version 18 (SAS Institute). Results Patient Characteristics and SUVmax Groups The inclusion criteria for this study were met by 136 patients. 106 patients had a solitary HCC. ROC analysis identified an optimal tumor SUVmax cutoff of 4.0 for OS (AUC = 0.71) (Supplementary Fig. 1). The distribution of PET SUVmax values was right-skewed, with a median of 4.34 and mean of 5.35. Using the optimal cutoff of 4.0 determined by ROC analysis, 54 patients’ tumors were classified as low SUVmax (< 4) and 82 as high SUVmax (≥ 4). A comparison of clinicopathological characteristics between the two groups is summarized in Table 1 . Patients in the high SUVmax group were significantly older (p = 0.0386) and more likely to have multiple tumors (28.1% vs. 13.0%, p = 0.0379), microscopic intrahepatic metastasis (32.9% vs. 14.8%, p = 0.0181), and mvi (45.1% vs. 14.8%, p = 0.0002). Table 1 Clinical and pathological characteristics of patients with HCC > 3cm Factors Low SUVmax (n = 54) High SUVmax (n = 82) P Value Age (years) 68 (36–88) 72 (34–86) 0.0386 Sex, male/female 9 (16.7%) 23 (28.1%) 0.1257 BMI (kg/m2) 23.1 (17.6–31.2) 22.8 (15.7–33.6) 0.8469 HBs-Ag positive 8 (15.1%) 6 (7.32%) 0.1478 HCV-Ab positive 13 (24.5%) 30 (37.0%) 0.1293 Non-viral 35 (66%) 45 (54.9%) 0.1975 Diabetes mellitus 18 (33.3%) 34 (41.5%) 0.3398 Albumin (g/dL) 4.1 (3.2–4.6) 3.9 (2.6–5.1) 0.1487 Child-Pugh B 2 (3.7%) 8 (9.7%) 0.1858 ICGR15 (%) 7.3 (0.6–38.3) 10.5 (0.1–30.8) 0.1598 AFP (ng/mL) 5.7 (1.3-109458) 28.7 (0.8-77196) 0.8053 DCP (mAU/mL) 80 (14-173435) 591.5 (15-3917271) 0.3284 ALBI score 2 20 (37.0%) 39 (47.6%) 0.2256 Tumor size (cm) 4.2 (3.1–13.5) 4.4 (3.1–14.6) 0.0796 Multiple tumors 7 (13.0%) 23 (28.1%) 0.0379 Macro vascular invasion 5 (9.3%) 7 (8.5%) 0.8844 BCLC-Stage B or C 11 (20.4%) 26 (31.7%) 0.1461 Poor differentiation 18 (33.3%) 40 (48.8%) 0.0747 Microscopic vascular invasion 8 (14.8%) 37 (45.1%) 0.0002 Microscopic intrahepatic metastasis 8 (14.8%) 27 (32.9%) 0.0181 Histological liver fibrosis (F3/F4) 5 (9.8%) 15 (19.2%) 0.1481 Data are presented as n (%) or the median (range) HCC, hepatocellular carcinoma; SUVmax, the maximum standardized uptake value; BMI, body mass index; HBs-Ag, hepatitis B surface antigen; HCV-Ab, hepatitis C virus antibody; ICG R15, Indocyanine green retention rate at 15 minutes; AFP, α-fetoprotein; DCP, des-gamma-carboxyprothrombin; ALBI grade, Albumin-Bilirubin grade; BCLC, Barcelona Clinic Liver Cancer Overall Survival by SUVmax Group At the time of analysis, the median duration of follow-up of survivors was 49 months; 40 patients (29%) died during the follow-up period. Kaplan–Meier analysis showed that patients with high tumor FDG uptake had markedly worse OS than did those with low uptake. The 5-year OS rate was 49.2% in the high SUVmax group versus 90.0% in the low SUVmax group (p < 0.0001). The survival curves diverged early and continued to separate over time (Fig. 1 ). In addition to OS, we analyzed RFS as a secondary endpoint. Consistent with the OS findings, tumor recurrence was identified significantly earlier the high SUVmax than in the low SUVmax group. The 5-year RFS rate was 33.4% for patients with high SUVmax compared with 60.4% for those with low SUVmax (p = 0.0006). These data indicate that high FDG uptake portends both reduced long-term survival and a higher likelihood of early recurrence (Fig. 2 ). Univariate and Multivariate Analysis of Survival According to univariate Cox regression analysis for OS, the following factors were significantly associated with worse OS (Table 2 ): PET SUVmax ≥ 4 (HR 4.01, 95% CI: 1.95–8.28, p = 0.0002). According to multivariate Cox regression analysis for OS, the following factors were independently associated with worse OS: PET SUVmax ≥ 4 (HR 3.16, 95% CI: 1.42–7.03, p = 0.0048), ALBI Grade 2 (p = 0.0050), BCLC Stage B or C (p = 0.0180), intrahepatic metastasis (p = 0.0002), and multiple tumors (p = 0.0232). According to univariate Cox regression analysis for RFS (Table 3 ), the following factors were significantly associated with worse RFS: PET SUVmax ≥ 4 (HR 2.19, 95% CI: 1.31–3.67, p = 0.0027). According to multivariate Cox regression analysis for RFS, the following factors were independently associated with worse RFS: PET SUVmax ≥ 4 (HR 1.77, 95% CI: 1.02–3.19, p = 0.0487), ALBI Grade 2 (p = 0.0258), BCLC Stage B or C (p = 0.0229), mvi (p = 0.0014), and microscopic intrahepatic metastasis (p = 0.0003). Table 2 Univariate and multivariate analyses of factors related to OS in patients with HCC > 3cm Factors Univariate analysis Multivariate analysis HR (95%CI) P Value HR (95%CI) P Value Age ≥ 75 (years) 1.47 (0.84–2.57) 0.1798 Sex, male 1.04 (0.54–1.99) 0.9071 BMI ≥ 25 (kg/m 2 ) 1.03 (0.56–1.88) 0.9323 HBs-Ag 1.48 (0.53–4.12) 0.4587 HCV-Ab 1.75 (0.99–3.10) 0.0534 Child-Pugh B 2.25 (0.96–5.29) 0.0636 ICGR15 ≥ 15 (%) 1.62 (0.90–2.92) 0.1091 AFP ≥ 20 (ng/mL) 1.34 (0.77–2.35) 0.2969 DCP ≥ 150 (mAU/mL) 1.80 (0.97–3.36) 0.0610 ALBI grade 2 2.84 (1.59–5.06) 0.0004 2.40 (1.30–4.41) 0.0050 Tumor size ≥ 5 (cm) 1.75 (1.00-3.03) 0.0490 1.19 (0.64–2.18) 0.5786 Multiple tumors 2.18 (1.21–3.92) 0.0091 3.29 (1.17–9.19) 0.0232 BCLC-Stage B or C 3.52 (1.43–4.43) 0.0013 2.97 (1.21–7.31) 0.0180 Poor differentiation 1.97 (1.13–3.44) 0.0168 1.47 (0.82–2.64) 0.1999 Microscopic vascular invasion 3.42 (1.96–5.97) < 0.0001 1.37 (0.70–2.69) 0.3544 Microscopic intrahepatic metastasis 4.82 (2.76–8.44) < 0.0001 4.24 (1.99–9.07) 0.0002 Histological liver fibrosis (F3/F4) 1.35 (0.65–2.81) 0.4202 PET SUVmax ≥ 4 4.01 (1.95–8.28) 0.0002 3.16 (1.42–7.03) 0.0048 OS, overall survival; HCC, hepatocellular carcinoma; HR, Hazard ratio; CI, confidence intervals; BMI, body mass index; HBs-Ag, hepatitis B surface antigen; HCV-Ab, hepatitis C virus antibody; ICG R15, Indocyanine green retention rate at 15 minutes; AFP, α-fetoprotein; DCP, des-gamma-carboxyprothrombin; BCLC, Barcelona Clinic Liver Cancer; PET SUVmax, Positron Emission Tomography Standardized Uptake Value maximum Table 3 Univariate and multivariate analyses of factors related to RFS in patients with HCC > 3cm Factors Univariate analysis Multivariate analysis HR (95%CI) P Value HR (95%CI) P Value Age ≥ 75 (years) 0.85 (0.53–1.39) 0.5232 Sex, male 1.41 (0.79–2.54) 0.2493 BMI ≥ 25 (kg/m 2 ) 0.90 (0.53–1.51) 0.6783 HBs-Ag 1.16 (0.53–2.54) 0.7092 HCV-Ab 1.32 (0.81–2.18) 0.2676 Child-Pugh B 1.28 (0.56–2.97) 0.5574 ICGR15 ≥ 15 (%) 1.31 (0.79–2.17) 0.2965 AFP ≥ 20 (ng/mL) 1.10 (0.68–1.79) 0.6889 DCP ≥ 150 (mAU/mL) 1.42 (0.86–2.34) 0.1663 ALBI grade 2 2.09 (1.31–3.35) 0.0021 1.80 (1.10–2.97) 0.0201 Tumor size ≥ 5 (cm) 1.49 (0.75–2.99) 0.2603 Multiple tumors 2.56 (1.54–4.25) 0.0003 1.36 (0.69–2.93) 0.4306 BCLC-Stage B or C 2.48 (1.52–4.04) 0.0003 2.14 (1.11–4.11) 0.0229 Poor differentiation 1.59 (0.99–2.54) 0.0544 2.37 (1.40–4.02) 0.0014 Microscopic vascular invasion 3.10 (1.92–5.01) < 0.0001 2.36 (1.37–4.07) 0.0020 Microscopic intrahepatic metastasis 4.39 (2.70–7.14) < 0.0001 3.11 (1.68–5.75) 0.0003 Histological liver fibrosis (F3/F4) 1.30 (0.68–2.48) 0.4324 PET SUVmax ≥ 4 2.19 (1.31–3.67) 0.0027 1.80 (1.02–3.19) 0.0439 RFS, recurrence-free survival; HCC, hepatocellular carcinoma; HR, Hazard ratio; CI, confidence intervals; BMI, body mass index; HBs-Ag, hepatitis B surface antigen; HCV-Ab, hepatitis C virus antibody; ICG R15, Indocyanine green retention rate at 15 minutes; AFP, α-fetoprotein; DCP, des-gamma-carboxyprothrombin; BCLC, Barcelona Clinic Liver Cancer; PET SUVmax, Positron Emission Tomography Standardized Uptake Value maximum Subgroup Analyses by Metabolic and Clinical Factors We performed additional subgroup analyses to determine whether the prognostic impact of FDG uptake was consistent across different clinical subsets. Stratification of patients by the presence of diabetes mellitus revealed that high SUVmax was predictive of poor survival in both strata. Among patients without diabetes mellitus (n = 80), the 5-year OS was 84.9% in the low SUVmax versus 47.2% in the high SUVmax group (p = 0.0059) (Supplementary Fig. 2A). Similarly, in patients with comorbid diabetes (n = 52), 5-year OS was 100% for low SUVmax versus 51.4% for high SUVmax (p = 0.0026) (Supplementary Fig. 2B). There was no significant association between diabetes status and SUVmax effect on survival. Subgroup Analyses by Solitary Tumor We performed additional subgroup analyses on patients with solitary HCCs (n = 106) to evaluate both the prognostic significance and pathological correlates of FDG uptake in this subgroup. High SUVmax remained strongly predictive of worse OS, with a 5-year OS of 90.2% in the low SUVmax (n = 47) compared with 54.1% in the high SUVmax group (n = 59) (p = 0.0003) (Supplementary Fig. 3A). High SUVmax remained strongly predictive of worse OS, with a 5-year RFS of 63.3% in the low SUVmax (n = 47) compared with 37.5% in the high SUVmax group (n = 59) (p = 0.0027) (Supplementary Fig. 3B). In this subset, mvi was the only pathological feature that was significantly associated with high FDG uptake. Histologically confirmed mvi was present in 42.4% of patients with high SUVmax versus 14.9% in those with low SUVmax (p = 0.0022) (Supplementary Table 1). These findings indicate that, even among patients with unifocal disease, high FDG uptake reflects underlying tumor aggressiveness, correlates strongly with vascular invasion, and retains robust prognostic value. Correlation of SUVmax with Microvascular Invasion and Tumor Markers High FDG uptake was strongly associated with adverse pathological features, particularly mvi. Most (82%) of the 45 patients with histologically identified mvi were in the high SUVmax group. The rate of mvi positivity was significantly greater in the high SUVmax than the low SUVmax group (44% vs. 15%, p < 0.001) (Supplementary Fig. 4). According to univariate logistic regression analysis (Table 4 ), the following factors were significantly associated with mvi positivity: PET SUVmax ≥ 4 (OR 4.63, 95% CI: 2.02–11.69, p = 0.0002). According to multivariate analysis, the following factors were independently associated with mvi: AFP ≥ 20 ng/mL (p = 0.0007), PET SUVmax ≥ 4 (p = 0.0162). These findings suggest that high PET uptake and AFP concentrations are strong independent predictors of mvi in patients with HCC > 3 cm. We further explored whether combining SUVmax with preoperative serum tumor markers better predicted the presence of mvi. When stratified by a scoring system (1 point for SUVmax ≥ 4 and 1 point for AFP ≥ 20 ng/mL), the incidence of mvi was 5% in patients with a score of 0 (SUVmax < 4 and AFP < 20 ng/mL). It was 34% in those with a score of 1 (either SUVmax ≥ 4 or AFP ≥ 20 ng/mL), and 59% in those with a score of 2 (SUVmax ≥ 4 and AFP ≥ 20 ng/mL) (Fig. 3 ). Table 4 Univariate and multivariate analyses of preoperative factors related to microscopic vascular invasion with HCC > 3cm. Factors Univariate analysis Multivariate analysis OR (95%CI) P Value OR (95%CI) P Value Age ≥ 75 (years) 0.51 (0.23–1.08) 0.0776 Sex, male 0.66 (0.29–1.52) 0.3216 BMI ≥ 25 (kg/m2) 0.65 (0.28–1.43) 0.2844 HBs-Ag 0.64 (0.21–2.07) 0.445 HCV-Ab 2.54 (1.18–5.49) 0.0168 2.17 (0.92–5.21) 0.0780 Child-Pugh B 0.85 (0.18–3.22) 0.8148 ICGR15 ≥ 15 (%) 1.65 (0.74–3.64) 0.2184 AFP ≥ 20 (ng/mL) 4.76 (2.23–10.53) < 0.0001 4.48 (1.85–11.4) 0.0007 DCP ≥ 150 (mAU/mL) 2.23 (1.07–4.74) 0.0311 1.42 (0.53–3.81) 0.4726 Tumor size ≥ 5 (cm) 1.99 (0.96–4.15) 0.0635 Multiple tumors 1.88 (0.8–4.36) 0.1447 BCLC-Stage B or C 2.27 (1.03–5.01) 0.0420 2.38 (0.94–6.21) 0.0670 ALBI grade 2b 1.14 (0.46-3.00) 0.7789 PET SUVmax ≥ 4 4.63 (2.02–11.69) 0.0002 3.12 (1.23–8.60) 0.0162 OS, overall survival; HCC, hepatocellular carcinoma; OR, Odds ratio; CI, confidence intervals; BMI, body mass index; HBs-Ag, hepatitis B surface antigen; HCV-Ab, hepatitis C virus antibody; ICG R15, Indocyanine green retention rate at 15 minutes; AFP, α-fetoprotein; DCP, des-gamma-carboxyprothrombin; BCLC, Barcelona Clinic Liver Cancer; ALBI grade, Albumin-Bilirubin grade; PET SUVmax, Positron Emission Tomography Standardized Uptake Value maximum Discussion In this retrospective study of 136 patients with resected hepatocellular carcinoma > 3 cm, we found a strong association between high FDG-PET uptake and worse OS. To our knowledge, this is one of the largest published single-center analyses investigating the prognostic value of FDG-PET in patients with large HCCs. These findings indicate that preoperative metabolic imaging can stratify patients into distinct risk categories: those with low SUVmax having excellent long-term survival, whereas those with high SUVmax have significantly poorer outcomes. High SUVmax remains an independent predictor of mortality even after adjusting for other important factors, underscoring that FDG uptake provides unique prognostic information beyond conventional clinicopathological variables. A recent meta-analysis of 22 studies revealed that high SUV or tumor-to-normal uptake ratios on FDG-PET correlate with significantly worse post-treatment outcomes in patients with HCC[ 11 ]. Our findings are consistent with the belief that high glucose metabolic activity in HCCs reflects more aggressive tumor biology[ 12 ]. Mechanistically, FDG uptake in HCCs is related to tumor differentiation. Well-differentiated HCC cells retain large amounts of glucose-6-phosphatase, which dephosphorylates FDG-6-phosphate, leading to low intracellular FDG accumulation, whereas poorly differentiated cells have reduced enzyme activity and higher glycolytic rates, resulting in greater FDG retention[ 1 , 13 ]. Consequently, FDG-PET tends to preferentially identify biologically “high-grade” tumors. PET was positive (SUVmax ≥ 4) in 60% of our study patients with tumors > 3 cm, a higher proportion than typically reported in unselected cohorts of patients with HCC[ 14 ]. Exclusion of very small tumors likely selected a subgroup in which FDG-PET is more informative. This contention is supported by previous reports that tumors > 3 cm are more likely to show significant FDG uptake than are smaller tumors and that tumor SUVmax correlates positively with tumor size[ 15 ]. The strong association between high SUVmax and mvi is a particularly noteworthy finding of our study. Mvi is an established adverse prognostic factor in HCC, having been shown to be significantly associated with a higher risk of postoperative recurrence and reduction of survival[ 16 ]. However, mvi can only be definitively diagnosed by examination of a resected specimen. Its presence is often suspected preoperatively on the basis of indirect clues (e.g. irregular tumor margins on imaging or high concentrations of tumor markers), but not confirmed. Our data indicate that FDG-PET can serve as a valuable noninvasive indicator of mvi risk. We found that tumors with high SUVmax were almost three times more likely to have pathologic mvi than those with low SUVmax. Moreover, when we considered SUVmax in combination with serum AFP, the predictive value of mvi improved even further. Most patients with both low SUVmax and low AFP were negative for mvi. These findings complement those of previous studies that found that both high PET uptake and high AFP are individually linked to presence of mvi and early recurrence[ 17 ]. The prognostic significance of FDG-PET in HCC has been explored in several prior studies, many of which were in the context of liver transplantation or mixed tumor sizes[ 18 ]. Our findings are consistent with theirs, further demonstrating that high SUVmax has an impact on OS. Clinically, integration of FDG-PET into HCC management remains controversial because of its limited sensitivity to early tumors[ 19 ]. Our study of a modern patient cohort and our focus on tumors > 3 cm contribute additional evidence in a subgroup that is particularly relevant to surgical practice because smaller tumors may be treated by ablation and are often PET-negative. The consistency of our findings with those of other published reports reinforces the contention that FDG-PET captures biological aggressiveness. High FDG uptake in HCC may reflect activation of the nuclear factor erythroid 2– related factor 2 (NRF2) pathway, which upregulates glycolytic enzymes, leading to increased glucose influx and SUVmax[ 20 , 21 ]. NRF2 also enhances programmed death ligand 1 expression via hypoxia inducible factor 1α, linking tumor metabolism with immune evasion. These findings suggest that SUVmax on FDG-PET/CT could serve as a non-invasive surrogate marker for NRF2-driven aggressive biology and immune modulation. Preoperative identification of patients with a high likelihood of mvi has important implications for optimizing treatment strategies. Individualized management strategies, such as introduction of adjuvant therapies, including targeted agents or immune checkpoint inhibitors, should be considered for such high-risk patients with the aim of improving long-term outcomes[ 22 , 23 ]. We found that high SUVmax had a detrimental impact in both patients with diabetic and without diabetes. This finding is important because hyperglycemia can increase FDG uptake in normal liver tissue in patients with diabetes, potentially hindering accurate evaluation of tumor uptake[ 24 ]. Despite this potential confounder, we found that high SUVmax remained a strong predictor of poor outcomes in patients with diabetes. We also found that high FDG uptake even had an adverse prognostic impact among patients with solitary HCC > 3 cm. This is clinically important because small, solitary tumors are often considered favorable candidates for curative treatment. However, in our cohort, patients with high SUVmax had significantly worse OS than those with low SUVmax. Furthermore, high SUVmax was significantly associated with mvi. These findings suggest that FDG-PET provides prognostic information beyond conventional tumor size or staging variables and can augment identification of biologically aggressive disease, even in patients with solitary lesions that appear amenable to resection. Our study had several limitations. First, it was a single-center retrospective study and thus carried inherent risks of selection bias and unmeasured confounders. The patients who underwent PET/CT may have been those with more suspicious features on conventional imaging, potentially favoring inclusion of patients with aggressive tumors. However, in our institution we routinely perform FDG-PET when staging resectable HCC, thus selection would have been relatively uniform. Second, we selected SUVmax as the best variable for representing FDG uptake. While SUVmax is simple and widely used, other quantitative PET variables (such as metabolic tumor volume or tumor-to-liver ratio of SUVmax) could potentially provide additional prognostic information. Despite these limitations, the key message of our study is that FDG-PET provides extremely valuable information for predicting postoperative prognosis, including mvi, in patients with HCC larger than 3 cm. The findings support the use of preoperative PET as an adjunct to standard imaging in selected patients with HCC, thus enabling better patient counseling and postoperative management plans. High SUVmax identifies a subset of patients with a substantially high risk of recurrence and death who might benefit from novel adjuvant strategies or more intense monitoring. Conclusion In summary, this retrospective analysis identified FDG-PET SUVmax as independent prognostic factor in patients with initial HCCs > 3 cm undergoing resection. Patients with high FDG uptake had significantly poorer OS and higher recurrence rates than those with low uptake, indicating an association between FDG avidity and aggressive tumor biology. Our findings also suggest that combining PET findings with serum biomarkers can improve preoperative prediction of mvi and post-surgical risk. Declarations Ethics approval statement : This retrospective study was approved by the ethics committee of Kyushu University (approval code: 23029-03). Conflict of interest disclosure: The authors declare no conflicts of interest in association with the present study. Patient consent statement Informed consent was obtained from all patients using an Permission to reproduce material from other sources Not applicable Clinical trial registration Not applicable Competing interests: The authors declare no conflicts of interest in association with the present study. Authors’ contributions: Study conception: SI. Writing: TI. Final approval of the article: All authors. Accountability for all aspects of the work: All authors. Acknowledgment: We thank Dr Trish Reynolds, MBBS, FRACP, from Edanz ( https://jp.edanz.com/ac ) for editing a draft of this manuscript. This study was supported by the Medical Research Encouragement Prize from The Japan Medical Association and by JSPS KAKENHI grant number JP-23K08133. The funding sources had no role in the collection, analysis, or interpretation of the data or in the decision to submit the article for publication. Data availability statement: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Forner A, Reig M, Bruix J (2018) Hepatocellular carcinoma. Lancet 391:1301–1314. https://doi.org/10.1016/S0140-6736(18)30010-2 European Association for the Study of the Liver (2018) EASL Clinical Practice Guidelines: management of hepatocellular carcinoma. J Hepatol 69:182–236. https://doi.org/10.1016/j.jhep.2018.03.019 Reig M, Forner A, Rimola J et al (2022) BCLC strategy for prognosis prediction and treatment recommendation: the 2022 update. J Hepatol 76:681–693. https://doi.org/10.1016/j.jhep.2021.11.018 Shin H, Yu SJ (2025) A concise review of updated global guidelines for the management of hepatocellular carcinoma: 2017–2024. J Liver Cancer 25:19–30. https://doi.org/10.17998/jlc.2025.02.03 Zhang J, Jiang S, Li M et al (2023) Head-to-head comparison of 18F-FAPI and 18F-FDG PET/CT in staging and therapeutic management of hepatocellular carcinoma. Cancer Imaging 23:106. https://doi.org/10.1186/s40644-023-00626-y Ahn SG, Kim SH, Jeon TJ et al (2011) The role of preoperative 18F-fluorodeoxyglucose positron emission tomography in predicting early recurrence after curative resection of hepatocellular carcinoma. J Gastrointest Surg 15:2044–2052. https://doi.org/10.1007/s11605-011-1660-1 Toshida K, Itoh S, Toshima T et al (2025) Association of serum lactate dehydrogenase with prognosis and tumor metabolism in patients with hepatocellular carcinoma treated with atezolizumab plus bevacizumab therapy. Surg Today 55:370–379. https://doi.org/10.1007/s00595-024-02914-x Cho KJ, Choi NK, Shin MH, Chong AR (2017) Clinical usefulness of FDG-PET in patients with hepatocellular carcinoma undergoing surgical resection. Ann Hepatobiliary Pancreat Surg 21:194–198. https://doi.org/10.14701/ahbps.2017.21.4.194 Tomino T, Itoh S, Okamoto D et al (2023) Impact of portal-phase signal intensity of dynamic gadoxetic acid-enhanced magnetic resonance imaging in hepatocellular carcinoma. J Hepatobiliary Pancreat Sci 30:1089–1097. https://doi.org/10.1002/jhbp.1345 Iseda N, Itoh S, Ninomiya M et al (2025) Survival impact and recurrence prediction using oncologic resectability classification in hepatocellular carcinoma following hepatic resection: a Japanese multicenter study. Int J Clin Oncol. https://doi.org/10.1007/s10147-025-02840-z Kornberg A, Schernhammer M, Friess H (2017) 18F-FDG-PET for assessing biological viability and prognosis in liver transplant patients with hepatocellular carcinoma. J Clin Transl Hepatol 5:224–234. https://doi.org/10.14218/JCTH.2017.00014 Itoh S, Yoshizumi T, Kitamura Y et al (2021) Impact of metabolic activity in hepatocellular carcinoma: association with immune status and vascular formation. Hepatol Commun 5:1278–1289. https://doi.org/10.1002/hep4.1715 Yao Y, Civelek AC, Li XF (2023) The application of 18F-FDG PET/CT imaging for human hepatocellular carcinoma: a narrative review. Quant Imaging Med Surg 13:6268–6279. https://doi.org/10.21037/qims-22-1420 Asman Y, Evenson AR, Even-Sapir E, Shibolet O (2015) 18F-fluorodeoxyglucose positron emission tomography and computed tomography as a prognostic tool before liver transplantation, resection, and loco-ablative therapies for hepatocellular carcinoma. Liver Transpl 21:572–580. https://doi.org/10.1002/lt.24083 Zhang Y, Li B, He Y et al (2022) Correlation among maximum standardized 18F-FDG uptake and pathological differentiation, tumor size, and Ki-67 in patients with moderately and poorly differentiated intrahepatic cholangiocarcinoma. Hell J Nucl Med 25:38–42. https://doi.org/10.1967/s002449912435 Ding Z, Zeng J, Fang G et al (2025) Evaluating the efficacy of using large language models in preoperative prediction of microvascular invasion in hepatocellular carcinoma: a multicenter study. Sci Rep 15:27549. https://doi.org/10.1038/s41598-025-08502-4 Shirabe K, Toshima T, Kimura K et al (2014) New scoring system for prediction of microvascular invasion in patients with hepatocellular carcinoma. Liver Int 34:937–941. https://doi.org/10.1111/liv.12459 Hatano E, Ikai I, Higashi T et al (2006) Preoperative positron emission tomography with fluorine-18-fluorodeoxyglucose is predictive of prognosis in patients with hepatocellular carcinoma after resection. World J Surg 30:1736–1741. https://doi.org/10.1007/s00268-005-0791-5 Lee JD, Yang WI, Park YN et al (2005) Different glucose uptake and glycolytic mechanisms between hepatocellular carcinoma and intrahepatic mass-forming cholangiocarcinoma with increased 18F-FDG uptake. J Nucl Med 46:1753–1759 Iseda N, Itoh S, Yoshizumi T et al (2022) Impact of nuclear factor erythroid 2-related factor 2 in hepatocellular carcinoma: cancer metabolism and immune status. Hepatol Commun 6:665–678. https://doi.org/10.1002/hep4.1838 Toshida K, Itoh S, Iseda N et al (2025) The impact of TP53-induced glycolysis and apoptosis regulator on prognosis in hepatocellular carcinoma: association with tumor microenvironment and ferroptosis. Liver Cancer 14:36–57. https://doi.org/10.1159/000540180 Llovet JM, Pinyol R, Yarchoan M et al (2024) Adjuvant and neoadjuvant immunotherapies in hepatocellular carcinoma. Nat Rev Clin Oncol 21:294–311. https://doi.org/10.1038/s41571-024-00868-0 Nishio T, Yoh T, Nishino H et al (2025) Current perspectives on perioperative combination therapy for hepatocellular carcinoma. Liver Cancer 29:1–21. https://doi.org/10.1159/000546138 Eskian M, Alavi A, Khorasanizadeh M et al (2019) Effect of blood glucose level on standardized uptake value in 18F-FDG PET scan: a systematic review and meta-analysis. Eur J Nucl Med Mol Imaging 46:224–237. https://doi.org/10.1007/s00259-018-4194-x Supplementary Files Figuresupplement.pptx 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. 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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-8405525","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580947164,"identity":"dee4fed8-f4ca-45dd-ae58-de790470c249","order_by":0,"name":"Takuma Ishikawa","email":"","orcid":"","institution":"Kyushu University: Kyushu Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Takuma","middleName":"","lastName":"Ishikawa","suffix":""},{"id":580947165,"identity":"e3a1f289-4c50-4087-b318-788c125188db","order_by":1,"name":"Shinji Itoh","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-0382-2520","institution":"Kyushu University","correspondingAuthor":true,"prefix":"","firstName":"Shinji","middleName":"","lastName":"Itoh","suffix":""},{"id":580947166,"identity":"a8417c42-f8b5-4e1d-9253-a526002a8915","order_by":2,"name":"Yoshiyuki Kitamura","email":"","orcid":"","institution":"Kyushu University: Kyushu Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Yoshiyuki","middleName":"","lastName":"Kitamura","suffix":""},{"id":580947171,"identity":"fe00dd62-e943-4bcb-9845-c888b9e2b59b","order_by":3,"name":"Norifumi Iseda","email":"","orcid":"","institution":"Kyushu University: Kyushu Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Norifumi","middleName":"","lastName":"Iseda","suffix":""},{"id":580947172,"identity":"051a0bdb-5698-4e56-8a96-abdb0cade27c","order_by":4,"name":"Kyohei Yugawa","email":"","orcid":"","institution":"Kyushu University: Kyushu Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Kyohei","middleName":"","lastName":"Yugawa","suffix":""},{"id":580947174,"identity":"ff213d1f-b8b0-4c78-9197-a709f01bf77d","order_by":5,"name":"Shohei Yoshiya","email":"","orcid":"","institution":"Kyushu University: Kyushu Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Shohei","middleName":"","lastName":"Yoshiya","suffix":""},{"id":580947175,"identity":"9dadc81f-e3da-4ff3-b4cb-ddfa033f1679","order_by":6,"name":"Takashi Motomura","email":"","orcid":"","institution":"Kyushu University: Kyushu Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Takashi","middleName":"","lastName":"Motomura","suffix":""},{"id":580947176,"identity":"dcd9c14d-e471-456f-8df7-643bdec6627d","order_by":7,"name":"Takeo Toshima","email":"","orcid":"","institution":"Kyushu University: Kyushu Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Takeo","middleName":"","lastName":"Toshima","suffix":""},{"id":580947177,"identity":"fb58c067-38a0-42b0-b531-31e0814f5f60","order_by":8,"name":"Takuro Isoda","email":"","orcid":"","institution":"Kyushu University: Kyushu Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Takuro","middleName":"","lastName":"Isoda","suffix":""},{"id":580947179,"identity":"b1adbdf6-d5ff-4432-aacd-af17d09ac11d","order_by":9,"name":"Kousei Ishigami","email":"","orcid":"","institution":"Kyushu University: Kyushu Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Kousei","middleName":"","lastName":"Ishigami","suffix":""},{"id":580947182,"identity":"056d7b1d-dfed-4dcd-bd0e-52fdb72ca599","order_by":10,"name":"Tomoharu Yoshizumi","email":"","orcid":"","institution":"Kyushu University: Kyushu Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Tomoharu","middleName":"","lastName":"Yoshizumi","suffix":""}],"badges":[],"createdAt":"2025-12-19 13:55:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8405525/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8405525/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101434002,"identity":"47634cb2-4316-405b-bd5d-d13e11ffe8a7","added_by":"auto","created_at":"2026-01-29 16:03:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59960,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival of patients with hepatocellular carcinoma according to the maximum standardized uptake value (SUVmax). Kaplan–­ Meier curves showing the overall survival of patients with hepatocellular carcinoma according to the SUVmax.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8405525/v1/59776ee18d51243ada28474c.png"},{"id":101434001,"identity":"2e5c0d8d-be26-48be-a875-f2ad9150ec51","added_by":"auto","created_at":"2026-01-29 16:03:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64164,"visible":true,"origin":"","legend":"\u003cp\u003eRecurrence-­free survival of patients with hepatocellular carcinoma (HCC) according to the maximum standardized uptake value (SUVmax). Kaplan–­Meier curves showing the recurrence-­free survival of patients with hepatocellular carcinoma according to the SUVmax.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8405525/v1/421219c685f3d5839bd3ded7.png"},{"id":101434003,"identity":"4cc9f5f8-5556-4f84-bdaf-8efbe37f3ee3","added_by":"auto","created_at":"2026-01-29 16:03:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":24680,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction of microvascular invasion (mvi) based on a combination of alpha-fetoprotein AFP concentration and positron emission tomography (PET) maximum standardized uptake value (SUVmax). The figure shows the proportion of patients with pathologically confirmed mvi according to combined biomarker status. The mvi-positive rate was 5% in patients with both low AFP (\u0026lt;20 ng/mL) and low SUVmax (\u0026lt;4), and 59% in patients with both high AFP (≥20 ng/mL) and high SUVmax (≥4). These results indicate that combining AFP concentration and PET findings improves preoperative prediction of mvi.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8405525/v1/0bc606e17e79756b259176b9.png"},{"id":103823318,"identity":"bf5d705e-a825-444a-aad3-5b40b3be4be2","added_by":"auto","created_at":"2026-03-03 11:01:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1159854,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8405525/v1/88d57249-d17c-4b6e-b65f-99d0c96f441c.pdf"},{"id":101434004,"identity":"dbc965c4-d7ff-4f6b-a539-a365ac6f50cb","added_by":"auto","created_at":"2026-01-29 16:03:30","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":219700,"visible":true,"origin":"","legend":"","description":"","filename":"Figuresupplement.pptx","url":"https://assets-eu.researchsquare.com/files/rs-8405525/v1/2bc8b3f8657e0770c51afe0e.pptx"}],"financialInterests":"","formattedTitle":"Prognostic Significance of PET-SUVmax in Patients Undergoing Resection of Hepatocellular Carcinoma \u0026gt;3 cm: A Retrospective Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC), one of the most common malignancies worldwide, is a leading cause of cancer-related mortality[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite advances in surgical and locoregional therapies, long-term survival remains unsatisfactory with high rates of recurrence and tumor progression. Accurate risk stratification of patients with HCC to guide treatment and follow-up is therefore critical[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In clinical practice, tumor size is a key determinant of therapy: early-stage tumors of diameter\u0026thinsp;\u0026gt;\u0026thinsp;3 cm may be amenable to curative ablation, whereas larger tumors (\u0026gt;\u0026thinsp;3 cm) typically require surgical resection for optimal outcomes[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, tumor size and stage alone are not enough to accurately determine the biological aggressiveness of HCC. Thus, there is growing interest in better characterizing tumor biology preoperatively by performing metabolic imaging.\u003c/p\u003e \u003cp\u003e18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is an established imaging modality that reflects tumor glucose metabolism. In HCCs, FDG uptake is highly variable: well-differentiated tumors often exhibit low uptake because of high intratumoral glucose-6-phosphatase activity, whereas poorly differentiated tumors tend to show intense FDG uptake[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Prior studies have revealed that high FDG uptake by HCC is associated with adverse features such as poor differentiation, microvascular invasion (mvi), and early recurrence[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These findings suggest that FDG-PET could serve as a noninvasive indicator of biologic tumor aggressiveness in HCC.\u003c/p\u003e \u003cp\u003eDespite this evidence, the prognostic utility of preoperative FDG-PET in patients with resectable HCC has not yet been fully elucidated. In particular, there may be a higher rate of FDG-PET positivity in the subgroup of patients with large tumors (\u0026gt;\u0026thinsp;3 cm) than in those with very small HCCs, because false-negativity occurs frequently in the latter. Thus, FDG-PET positivity is more informative in the patients with large tumors[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. We hypothesized that, among patients undergoing surgical resection of an initial HCC\u0026thinsp;\u0026gt;\u0026thinsp;3 cm, increased metabolic activity of the tumor as indicated by FDG-PET maximum standardized uptake value (SUVmax) would be associated with worse overall survival. To test this hypothesis, we performed a retrospective analysis of data of HCC patients, stratifying outcomes by SUVmax. We report our findings on the prognostic significance of preoperative PET SUVmax in patients with HCC\u0026thinsp;\u0026gt;\u0026thinsp;3 cm, and discuss its potential role as a biomarker of tumor aggressiveness in this setting.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and Study Design\u003c/h2\u003e \u003cp\u003eThis was a retrospective study of data of 136 patients who had undergone curative surgical resection of initial HCCs of diameter\u0026thinsp;\u0026gt;\u0026thinsp;3 cm in in our institution between 2012 and 2021. All included patients had undergone preoperative 18F-FDG PET/CT scans as part of their evaluation prior to surgery. Because our focus was on patients with initial, resectable HCCs, we excluded those with recurrent HCC and previous treatment before resection. Relevant clinical data, including patient characteristics, underlying liver disease, laboratory findings, tumor characteristics, and pathologic findings were collected from medical records. Mvi status was assessed histologically on resected specimens. Our Institutional Review Board approved the study protocol and waived the requirement for individual informed consent because it is not required for retrospective studies of anonymized data.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFDG-PET Imaging and Assessment of SUVmax\u003c/h3\u003e\n\u003cp\u003eAll patients underwent preoperative PET/computed tomography (CT) imaging after at least 4 hours of fasting. Each patient received an intravenous injection of 18F-FDG (at a dose of approximately 4 MBq/kg body weight), and FDG-PET/CT scans were acquired 60 minutes post-injection using a dedicated PET/CT scanner. Attenuation-corrected PET images were reconstructed and reviewed by experienced nuclear medicine physicians. The SUVmax of the tumor was measured by placing regions of interest over the lesion on transaxial PET images, the highest pixel value within the tumor being recorded as the SUVmax. In patients with multifocal tumors, the largest tumor (\u0026gt;\u0026thinsp;3 cm) was used as the index lesion for SUVmax measurement. An optimal cutoff value for tumor SUVmax was determined using receiver operating characteristic (ROC) curve analysis for overall survival. The study patients were then stratified into two groups based on their FDG uptake: a low SUVmax and a high SUVmax group. Subsequently, clinicopathological features and outcomes were compared between these two groups.\u003c/p\u003e \u003cp\u003eThe study patients were then stratified into two groups based on their FDG uptake: a low SUVmax and a high SUVmax group. Subsequently, clinicopathological features and outcomes were compared between these two groups.\u003c/p\u003e\n\u003ch3\u003eFollow-up and Outcome Measures\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eFollow-up and Outcome Measures\u003c/div\u003e \u003cp\u003e Patients were followed postoperatively with periodic imaging and serum tumor marker concentrations (alpha-fetoprotein [AFP] and des-gamma-carboxy prothrombin [DCP]) every 3\u0026ndash;4 months, in accordance with our institutional protocols. The primary endpoint of the study was overall survival (OS), defined as the time from surgical resection to death from any cause. Surviving patients were censored at the date of last follow-up. Recurrence-free survival (RFS) was defined as the time from resection to the first documented HCC recurrence or to death, whichever occurred first. Recurrences were confirmed by imaging or pathological assessment and treated with appropriate therapies (repeat resection, ablation, transarterial therapy, or systemic therapy) as indicated.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are expressed as median (range) and were compared using the Mann\u0026ndash;Whitney U test because they were not normally distributed. Categorical variables were compared using the χ\u003csup\u003e2\u003c/sup\u003e test or Fisher\u0026rsquo;s exact test, as appropriate. Survival curves for OS and RFS were constructed by the Kaplan\u0026ndash;Meier method and compared between groups using the log-rank test. Five-year survival rates were calculated from the Kaplan\u0026ndash;Meier estimates. Univariate Cox proportional hazards regression was performed to identify predictors of OS. Continuous variables were dichotomized based on previously-reported clinically validated thresholds as follows: age\u0026thinsp;\u0026ge;\u0026thinsp;75 years[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], AFP\u0026thinsp;\u0026ge;\u0026thinsp;20 ng/mL[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and PIVKA-II\u0026thinsp;\u0026ge;\u0026thinsp;150 mAU/mL[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for differences between groups on univariate analysis and those of previously established clinical importance were used to construct a multivariate Cox regression model to identify independent prognostic factors. We have reported hazard ratios (HR) with 95% confidence intervals (CI) and p-values. Two-tailed p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to denote statistical significance. All analyses were performed using JMP software version 18 (SAS Institute).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics and SUVmax Groups\u003c/h2\u003e \u003cp\u003eThe inclusion criteria for this study were met by 136 patients. 106 patients had a solitary HCC. ROC analysis identified an optimal tumor SUVmax cutoff of 4.0 for OS (AUC\u0026thinsp;=\u0026thinsp;0.71) (Supplementary Fig.\u0026nbsp;1). The distribution of PET SUVmax values was right-skewed, with a median of 4.34 and mean of 5.35. Using the optimal cutoff of 4.0 determined by ROC analysis, 54 patients\u0026rsquo; tumors were classified as low SUVmax (\u0026lt;\u0026thinsp;4) and 82 as high SUVmax (\u0026ge;\u0026thinsp;4).\u003c/p\u003e \u003cp\u003eA comparison of clinicopathological characteristics between the two groups is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients in the high SUVmax group were significantly older (p\u0026thinsp;=\u0026thinsp;0.0386) and more likely to have multiple tumors (28.1% vs. 13.0%, p\u0026thinsp;=\u0026thinsp;0.0379), microscopic intrahepatic metastasis (32.9% vs. 14.8%, p\u0026thinsp;=\u0026thinsp;0.0181), and mvi (45.1% vs. 14.8%, p\u0026thinsp;=\u0026thinsp;0.0002).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical and pathological characteristics of patients with HCC\u0026thinsp;\u0026gt;\u0026thinsp;3cm\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow SUVmax\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh SUVmax\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;82)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (36\u0026ndash;88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (34\u0026ndash;86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male/female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.1 (17.6\u0026ndash;31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.8 (15.7\u0026ndash;33.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8469\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBs-Ag positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (7.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCV-Ab positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (24.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (37.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-viral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (54.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (41.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1 (3.2\u0026ndash;4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9 (2.6\u0026ndash;5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild-Pugh B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICGR15 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.3 (0.6\u0026ndash;38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5 (0.1\u0026ndash;30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1598\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.7 (1.3-109458)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.7 (0.8-77196)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCP (mAU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (14-173435)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e591.5 (15-3917271)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALBI score 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (37.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (47.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2 (3.1\u0026ndash;13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4 (3.1\u0026ndash;14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0796\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple tumors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacro vascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCLC-Stage B or C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (48.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0747\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicroscopic vascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicroscopic intrahepatic metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (32.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological liver fibrosis (F3/F4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (19.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are presented as \u003cem\u003en\u003c/em\u003e (%) or the median (range)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eHCC, hepatocellular carcinoma; SUVmax, the maximum standardized uptake value; BMI, body mass index; HBs-Ag, hepatitis B surface antigen; HCV-Ab, hepatitis C virus antibody; ICG R15, Indocyanine green retention rate at 15 minutes; AFP, α-fetoprotein; DCP, des-gamma-carboxyprothrombin; ALBI grade, Albumin-Bilirubin grade; BCLC, Barcelona Clinic Liver Cancer\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOverall Survival by SUVmax Group\u003c/h3\u003e\n\u003cp\u003eAt the time of analysis, the median duration of follow-up of survivors was 49 months; 40 patients (29%) died during the follow-up period. Kaplan\u0026ndash;Meier analysis showed that patients with high tumor FDG uptake had markedly worse OS than did those with low uptake. The 5-year OS rate was 49.2% in the high SUVmax group versus 90.0% in the low SUVmax group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The survival curves diverged early and continued to separate over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition to OS, we analyzed RFS as a secondary endpoint. Consistent with the OS findings, tumor recurrence was identified significantly earlier the high SUVmax than in the low SUVmax group. The 5-year RFS rate was 33.4% for patients with high SUVmax compared with 60.4% for those with low SUVmax (p\u0026thinsp;=\u0026thinsp;0.0006). These data indicate that high FDG uptake portends both reduced long-term survival and a higher likelihood of early recurrence (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eUnivariate and Multivariate Analysis of Survival\u003c/h3\u003e\n\u003cp\u003eAccording to univariate Cox regression analysis for OS, the following factors were significantly associated with worse OS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e): PET SUVmax\u0026thinsp;\u0026ge;\u0026thinsp;4 (HR 4.01, 95% CI: 1.95\u0026ndash;8.28, p\u0026thinsp;=\u0026thinsp;0.0002). According to multivariate Cox regression analysis for OS, the following factors were independently associated with worse OS: PET SUVmax\u0026thinsp;\u0026ge;\u0026thinsp;4 (HR 3.16, 95% CI: 1.42\u0026ndash;7.03, p\u0026thinsp;=\u0026thinsp;0.0048), ALBI Grade 2 (p\u0026thinsp;=\u0026thinsp;0.0050), BCLC Stage B or C (p\u0026thinsp;=\u0026thinsp;0.0180), intrahepatic metastasis (p\u0026thinsp;=\u0026thinsp;0.0002), and multiple tumors (p\u0026thinsp;=\u0026thinsp;0.0232). According to univariate Cox regression analysis for RFS (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the following factors were significantly associated with worse RFS: PET SUVmax\u0026thinsp;\u0026ge;\u0026thinsp;4 (HR 2.19, 95% CI: 1.31\u0026ndash;3.67, p\u0026thinsp;=\u0026thinsp;0.0027). According to multivariate Cox regression analysis for RFS, the following factors were independently associated with worse RFS: PET SUVmax\u0026thinsp;\u0026ge;\u0026thinsp;4 (HR 1.77, 95% CI: 1.02\u0026ndash;3.19, p\u0026thinsp;=\u0026thinsp;0.0487), ALBI Grade 2 (p\u0026thinsp;=\u0026thinsp;0.0258), BCLC Stage B or C (p\u0026thinsp;=\u0026thinsp;0.0229), mvi (p\u0026thinsp;=\u0026thinsp;0.0014), and microscopic intrahepatic metastasis (p\u0026thinsp;=\u0026thinsp;0.0003).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analyses of factors related to OS in patients with HCC\u0026thinsp;\u0026gt;\u0026thinsp;3cm\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;75 (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.47 (0.84\u0026ndash;2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (0.54\u0026ndash;1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;25 (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.56\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBs-Ag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.48 (0.53\u0026ndash;4.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCV-Ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.75 (0.99\u0026ndash;3.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild-Pugh B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.25 (0.96\u0026ndash;5.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICGR15\u0026thinsp;\u0026ge;\u0026thinsp;15 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62 (0.90\u0026ndash;2.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP\u0026thinsp;\u0026ge;\u0026thinsp;20 (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34 (0.77\u0026ndash;2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCP\u0026thinsp;\u0026ge;\u0026thinsp;150 (mAU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.80 (0.97\u0026ndash;3.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALBI grade 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.84 (1.59\u0026ndash;5.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.40 (1.30\u0026ndash;4.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size\u0026thinsp;\u0026ge;\u0026thinsp;5 (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.75 (1.00-3.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19 (0.64\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple tumors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.18 (1.21\u0026ndash;3.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.29 (1.17\u0026ndash;9.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCLC-Stage B or C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.52 (1.43\u0026ndash;4.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.97 (1.21\u0026ndash;7.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.97 (1.13\u0026ndash;3.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47 (0.82\u0026ndash;2.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicroscopic vascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.42 (1.96\u0026ndash;5.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37 (0.70\u0026ndash;2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicroscopic intrahepatic metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.82 (2.76\u0026ndash;8.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.24 (1.99\u0026ndash;9.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological liver fibrosis (F3/F4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35 (0.65\u0026ndash;2.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePET SUVmax\u0026thinsp;\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.01 (1.95\u0026ndash;8.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.16 (1.42\u0026ndash;7.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eOS, overall survival; HCC, hepatocellular carcinoma; HR, Hazard ratio; CI, confidence intervals; BMI, body mass index; HBs-Ag, hepatitis B surface antigen; HCV-Ab, hepatitis C virus antibody; ICG R15, Indocyanine green retention rate at 15 minutes; AFP, α-fetoprotein; DCP, des-gamma-carboxyprothrombin; BCLC, Barcelona Clinic Liver Cancer; PET SUVmax, Positron Emission Tomography Standardized Uptake Value maximum\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analyses of factors related to RFS in patients with HCC\u0026thinsp;\u0026gt;\u0026thinsp;3cm\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;75 (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.53\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41 (0.79\u0026ndash;2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;25 (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.53\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBs-Ag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16 (0.53\u0026ndash;2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCV-Ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32 (0.81\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild-Pugh B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28 (0.56\u0026ndash;2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICGR15\u0026thinsp;\u0026ge;\u0026thinsp;15 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31 (0.79\u0026ndash;2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP\u0026thinsp;\u0026ge;\u0026thinsp;20 (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10 (0.68\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCP\u0026thinsp;\u0026ge;\u0026thinsp;150 (mAU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.42 (0.86\u0026ndash;2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALBI grade 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.09 (1.31\u0026ndash;3.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80 (1.10\u0026ndash;2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size\u0026thinsp;\u0026ge;\u0026thinsp;5 (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.49 (0.75\u0026ndash;2.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple tumors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.56 (1.54\u0026ndash;4.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.36 (0.69\u0026ndash;2.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCLC-Stage B or C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.48 (1.52\u0026ndash;4.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.14 (1.11\u0026ndash;4.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59 (0.99\u0026ndash;2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.37 (1.40\u0026ndash;4.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicroscopic vascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.10 (1.92\u0026ndash;5.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.36 (1.37\u0026ndash;4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicroscopic intrahepatic metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.39 (2.70\u0026ndash;7.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.11 (1.68\u0026ndash;5.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistological liver fibrosis (F3/F4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30 (0.68\u0026ndash;2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePET SUVmax\u0026thinsp;\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.19 (1.31\u0026ndash;3.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80 (1.02\u0026ndash;3.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eRFS, recurrence-free survival; HCC, hepatocellular carcinoma; HR, Hazard ratio; CI, confidence intervals; BMI, body mass index; HBs-Ag, hepatitis B surface antigen; HCV-Ab, hepatitis C virus antibody; ICG R15, Indocyanine green retention rate at 15 minutes; AFP, α-fetoprotein; DCP, des-gamma-carboxyprothrombin; BCLC, Barcelona Clinic Liver Cancer; PET SUVmax, Positron Emission Tomography Standardized Uptake Value maximum\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup Analyses by Metabolic and Clinical Factors\u003c/h2\u003e \u003cp\u003eWe performed additional subgroup analyses to determine whether the prognostic impact of FDG uptake was consistent across different clinical subsets. Stratification of patients by the presence of diabetes mellitus revealed that high SUVmax was predictive of poor survival in both strata. Among patients without diabetes mellitus (n\u0026thinsp;=\u0026thinsp;80), the 5-year OS was 84.9% in the low SUVmax versus 47.2% in the high SUVmax group (p\u0026thinsp;=\u0026thinsp;0.0059) (Supplementary Fig.\u0026nbsp;2A). Similarly, in patients with comorbid diabetes (n\u0026thinsp;=\u0026thinsp;52), 5-year OS was 100% for low SUVmax versus 51.4% for high SUVmax (p\u0026thinsp;=\u0026thinsp;0.0026) (Supplementary Fig.\u0026nbsp;2B). There was no significant association between diabetes status and SUVmax effect on survival.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup Analyses by Solitary Tumor\u003c/h2\u003e \u003cp\u003eWe performed additional subgroup analyses on patients with solitary HCCs (n\u0026thinsp;=\u0026thinsp;106) to evaluate both the prognostic significance and pathological correlates of FDG uptake in this subgroup. High SUVmax remained strongly predictive of worse OS, with a 5-year OS of 90.2% in the low SUVmax (n\u0026thinsp;=\u0026thinsp;47) compared with 54.1% in the high SUVmax group (n\u0026thinsp;=\u0026thinsp;59) (p\u0026thinsp;=\u0026thinsp;0.0003) (Supplementary Fig.\u0026nbsp;3A). High SUVmax remained strongly predictive of worse OS, with a 5-year RFS of 63.3% in the low SUVmax (n\u0026thinsp;=\u0026thinsp;47) compared with 37.5% in the high SUVmax group (n\u0026thinsp;=\u0026thinsp;59) (p\u0026thinsp;=\u0026thinsp;0.0027) (Supplementary Fig.\u0026nbsp;3B). In this subset, mvi was the only pathological feature that was significantly associated with high FDG uptake. Histologically confirmed mvi was present in 42.4% of patients with high SUVmax versus 14.9% in those with low SUVmax (p\u0026thinsp;=\u0026thinsp;0.0022) (Supplementary Table\u0026nbsp;1). These findings indicate that, even among patients with unifocal disease, high FDG uptake reflects underlying tumor aggressiveness, correlates strongly with vascular invasion, and retains robust prognostic value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation of SUVmax with Microvascular Invasion and Tumor Markers\u003c/h2\u003e \u003cp\u003eHigh FDG uptake was strongly associated with adverse pathological features, particularly mvi. Most (82%) of the 45 patients with histologically identified mvi were in the high SUVmax group. The rate of mvi positivity was significantly greater in the high SUVmax than the low SUVmax group (44% vs. 15%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Supplementary Fig.\u0026nbsp;4). According to univariate logistic regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the following factors were significantly associated with mvi positivity: PET SUVmax\u0026thinsp;\u0026ge;\u0026thinsp;4 (OR 4.63, 95% CI: 2.02\u0026ndash;11.69, p\u0026thinsp;=\u0026thinsp;0.0002). According to multivariate analysis, the following factors were independently associated with mvi: AFP\u0026thinsp;\u0026ge;\u0026thinsp;20 ng/mL (p\u0026thinsp;=\u0026thinsp;0.0007), PET SUVmax\u0026thinsp;\u0026ge;\u0026thinsp;4 (p\u0026thinsp;=\u0026thinsp;0.0162). These findings suggest that high PET uptake and AFP concentrations are strong independent predictors of mvi in patients with HCC\u0026thinsp;\u0026gt;\u0026thinsp;3 cm. We further explored whether combining SUVmax with preoperative serum tumor markers better predicted the presence of mvi. When stratified by a scoring system (1 point for SUVmax\u0026thinsp;\u0026ge;\u0026thinsp;4 and 1 point for AFP\u0026thinsp;\u0026ge;\u0026thinsp;20 ng/mL), the incidence of mvi was 5% in patients with a score of 0 (SUVmax\u0026thinsp;\u0026lt;\u0026thinsp;4 and AFP\u0026thinsp;\u0026lt;\u0026thinsp;20 ng/mL). It was 34% in those with a score of 1 (either SUVmax\u0026thinsp;\u0026ge;\u0026thinsp;4 or AFP\u0026thinsp;\u0026ge;\u0026thinsp;20 ng/mL), and 59% in those with a score of 2 (SUVmax\u0026thinsp;\u0026ge;\u0026thinsp;4 and AFP\u0026thinsp;\u0026ge;\u0026thinsp;20 ng/mL) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate analyses of preoperative factors related to microscopic vascular invasion with HCC\u0026thinsp;\u0026gt;\u0026thinsp;3cm.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP Value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP Value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;75 (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.51 (0.23\u0026ndash;1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0776\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.66 (0.29\u0026ndash;1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;25 (kg/m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.65 (0.28\u0026ndash;1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHBs-Ag\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.64 (0.21\u0026ndash;2.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHCV-Ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.54 (1.18\u0026ndash;5.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.17 (0.92\u0026ndash;5.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0780\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild-Pugh B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.85 (0.18\u0026ndash;3.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICGR15\u0026thinsp;\u0026ge;\u0026thinsp;15 (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.65 (0.74\u0026ndash;3.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAFP\u0026thinsp;\u0026ge;\u0026thinsp;20 (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.76 (2.23\u0026ndash;10.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.48 (1.85\u0026ndash;11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDCP\u0026thinsp;\u0026ge;\u0026thinsp;150 (mAU/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.23 (1.07\u0026ndash;4.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.42 (0.53\u0026ndash;3.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4726\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor size\u0026thinsp;\u0026ge;\u0026thinsp;5 (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.99 (0.96\u0026ndash;4.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultiple tumors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.88 (0.8\u0026ndash;4.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1447\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBCLC-Stage B or C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.27 (1.03\u0026ndash;5.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.38 (0.94\u0026ndash;6.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0670\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALBI grade 2b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.14 (0.46-3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePET SUVmax\u0026thinsp;\u0026ge;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.63 (2.02\u0026ndash;11.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.12 (1.23\u0026ndash;8.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eOS, overall survival; HCC, hepatocellular carcinoma; OR, Odds ratio; CI, confidence intervals; BMI, body mass index; HBs-Ag, hepatitis B surface antigen; HCV-Ab, hepatitis C virus antibody; ICG R15, Indocyanine green retention rate at 15 minutes; AFP, α-fetoprotein; DCP, des-gamma-carboxyprothrombin; BCLC, Barcelona Clinic Liver Cancer; ALBI grade, Albumin-Bilirubin grade; PET SUVmax, Positron Emission Tomography Standardized Uptake Value maximum\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective study of 136 patients with resected hepatocellular carcinoma\u0026thinsp;\u0026gt;\u0026thinsp;3 cm, we found a strong association between high FDG-PET uptake and worse OS. To our knowledge, this is one of the largest published single-center analyses investigating the prognostic value of FDG-PET in patients with large HCCs. These findings indicate that preoperative metabolic imaging can stratify patients into distinct risk categories: those with low SUVmax having excellent long-term survival, whereas those with high SUVmax have significantly poorer outcomes. High SUVmax remains an independent predictor of mortality even after adjusting for other important factors, underscoring that FDG uptake provides unique prognostic information beyond conventional clinicopathological variables.\u003c/p\u003e \u003cp\u003eA recent meta-analysis of 22 studies revealed that high SUV or tumor-to-normal uptake ratios on FDG-PET correlate with significantly worse post-treatment outcomes in patients with HCC[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Our findings are consistent with the belief that high glucose metabolic activity in HCCs reflects more aggressive tumor biology[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Mechanistically, FDG uptake in HCCs is related to tumor differentiation. Well-differentiated HCC cells retain large amounts of glucose-6-phosphatase, which dephosphorylates FDG-6-phosphate, leading to low intracellular FDG accumulation, whereas poorly differentiated cells have reduced enzyme activity and higher glycolytic rates, resulting in greater FDG retention[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Consequently, FDG-PET tends to preferentially identify biologically \u0026ldquo;high-grade\u0026rdquo; tumors. PET was positive (SUVmax\u0026thinsp;\u0026ge;\u0026thinsp;4) in 60% of our study patients with tumors\u0026thinsp;\u0026gt;\u0026thinsp;3 cm, a higher proportion than typically reported in unselected cohorts of patients with HCC[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Exclusion of very small tumors likely selected a subgroup in which FDG-PET is more informative. This contention is supported by previous reports that tumors\u0026thinsp;\u0026gt;\u0026thinsp;3 cm are more likely to show significant FDG uptake than are smaller tumors and that tumor SUVmax correlates positively with tumor size[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe strong association between high SUVmax and mvi is a particularly noteworthy finding of our study. Mvi is an established adverse prognostic factor in HCC, having been shown to be significantly associated with a higher risk of postoperative recurrence and reduction of survival[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, mvi can only be definitively diagnosed by examination of a resected specimen. Its presence is often suspected preoperatively on the basis of indirect clues (e.g. irregular tumor margins on imaging or high concentrations of tumor markers), but not confirmed. Our data indicate that FDG-PET can serve as a valuable noninvasive indicator of mvi risk. We found that tumors with high SUVmax were almost three times more likely to have pathologic mvi than those with low SUVmax. Moreover, when we considered SUVmax in combination with serum AFP, the predictive value of mvi improved even further. Most patients with both low SUVmax and low AFP were negative for mvi. These findings complement those of previous studies that found that both high PET uptake and high AFP are individually linked to presence of mvi and early recurrence[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe prognostic significance of FDG-PET in HCC has been explored in several prior studies, many of which were in the context of liver transplantation or mixed tumor sizes[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our findings are consistent with theirs, further demonstrating that high SUVmax has an impact on OS. Clinically, integration of FDG-PET into HCC management remains controversial because of its limited sensitivity to early tumors[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Our study of a modern patient cohort and our focus on tumors\u0026thinsp;\u0026gt;\u0026thinsp;3 cm contribute additional evidence in a subgroup that is particularly relevant to surgical practice because smaller tumors may be treated by ablation and are often PET-negative. The consistency of our findings with those of other published reports reinforces the contention that FDG-PET captures biological aggressiveness. High FDG uptake in HCC may reflect activation of the nuclear factor erythroid 2\u0026ndash; related factor 2 (NRF2) pathway, which upregulates glycolytic enzymes, leading to increased glucose influx and SUVmax[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. NRF2 also enhances programmed death ligand 1 expression via hypoxia inducible factor 1α, linking tumor metabolism with immune evasion. These findings suggest that SUVmax on FDG-PET/CT could serve as a non-invasive surrogate marker for NRF2-driven aggressive biology and immune modulation.\u003c/p\u003e \u003cp\u003ePreoperative identification of patients with a high likelihood of mvi has important implications for optimizing treatment strategies. Individualized management strategies, such as introduction of adjuvant therapies, including targeted agents or immune checkpoint inhibitors, should be considered for such high-risk patients with the aim of improving long-term outcomes[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe found that high SUVmax had a detrimental impact in both patients with diabetic and without diabetes. This finding is important because hyperglycemia can increase FDG uptake in normal liver tissue in patients with diabetes, potentially hindering accurate evaluation of tumor uptake[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Despite this potential confounder, we found that high SUVmax remained a strong predictor of poor outcomes in patients with diabetes.\u003c/p\u003e \u003cp\u003eWe also found that high FDG uptake even had an adverse prognostic impact among patients with solitary HCC\u0026thinsp;\u0026gt;\u0026thinsp;3 cm. This is clinically important because small, solitary tumors are often considered favorable candidates for curative treatment. However, in our cohort, patients with high SUVmax had significantly worse OS than those with low SUVmax. Furthermore, high SUVmax was significantly associated with mvi. These findings suggest that FDG-PET provides prognostic information beyond conventional tumor size or staging variables and can augment identification of biologically aggressive disease, even in patients with solitary lesions that appear amenable to resection.\u003c/p\u003e \u003cp\u003eOur study had several limitations. First, it was a single-center retrospective study and thus carried inherent risks of selection bias and unmeasured confounders. The patients who underwent PET/CT may have been those with more suspicious features on conventional imaging, potentially favoring inclusion of patients with aggressive tumors. However, in our institution we routinely perform FDG-PET when staging resectable HCC, thus selection would have been relatively uniform. Second, we selected SUVmax as the best variable for representing FDG uptake. While SUVmax is simple and widely used, other quantitative PET variables (such as metabolic tumor volume or tumor-to-liver ratio of SUVmax) could potentially provide additional prognostic information.\u003c/p\u003e \u003cp\u003eDespite these limitations, the key message of our study is that FDG-PET provides extremely valuable information for predicting postoperative prognosis, including mvi, in patients with HCC larger than 3 cm. The findings support the use of preoperative PET as an adjunct to standard imaging in selected patients with HCC, thus enabling better patient counseling and postoperative management plans. High SUVmax identifies a subset of patients with a substantially high risk of recurrence and death who might benefit from novel adjuvant strategies or more intense monitoring.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this retrospective analysis identified FDG-PET SUVmax as independent prognostic factor in patients with initial HCCs\u0026thinsp;\u0026gt;\u0026thinsp;3 cm undergoing resection. Patients with high FDG uptake had significantly poorer OS and higher recurrence rates than those with low uptake, indicating an association between FDG avidity and aggressive tumor biology. Our findings also suggest that combining PET findings with serum biomarkers can improve preoperative prediction of mvi and post-surgical risk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cb\u003eEthics approval statement\u003c/b\u003e: This retrospective study was approved by the ethics committee of Kyushu University (approval code: 23029-03).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflict of interest disclosure:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest in association with the present study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePatient consent statement\u003c/strong\u003e \u003cp\u003e Informed consent was obtained from all patients using an\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003ePermission to reproduce material from other sources\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eClinical trial registration\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests:\u003c/strong\u003e \u003cp\u003eThe authors declare no conflicts of interest in association with the present study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthors\u0026rsquo; contributions:\u003c/h2\u003e \u003cp\u003eStudy conception: SI. Writing: TI. Final approval of the article: All authors. Accountability for all aspects of the work: All authors.\u003c/p\u003e\u003ch2\u003eAcknowledgment:\u003c/h2\u003e \u003cp\u003eWe thank Dr Trish Reynolds, MBBS, FRACP, from Edanz (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jp.edanz.com/ac\u003c/span\u003e\u003cspan address=\"https://jp.edanz.com/ac\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for editing a draft of this manuscript. This study was supported by the Medical Research Encouragement Prize from The Japan Medical Association and by JSPS KAKENHI grant number JP-23K08133. The funding sources had no role in the collection, analysis, or interpretation of the data or in the decision to submit the article for publication.\u003c/p\u003e\u003ch2\u003eData availability statement:\u003c/h2\u003e \u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eForner A, Reig M, Bruix J (2018) Hepatocellular carcinoma. 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Eur J Nucl Med Mol Imaging 46:224\u0026ndash;237. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00259-018-4194-x\u003c/span\u003e\u003cspan address=\"10.1007/s00259-018-4194-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"hepatocellular carcinoma, microvascular invasion, positron emission tomography","lastPublishedDoi":"10.21203/rs.3.rs-8405525/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8405525/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aims\u003c/h2\u003e \u003cp\u003eHepatocellular carcinoma (HCC)\u0026thinsp;\u0026gt;\u0026thinsp;3 cm often recurs after resection, indicating a need for better risk stratification. The 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) maximum standardized uptake value (SUVmax) reflects tumor aggressiveness. In this study, we aimed to evaluate the prognostic significance of preoperative FDG-PET SUVmax in patients with resectable initial HCC\u0026thinsp;\u0026gt;\u0026thinsp;3 cm.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective analysis included data of 136 patients who had undergone curative resection for HCC\u0026thinsp;\u0026gt;\u0026thinsp;3 cm between 2012 and 2021, all of whom had undergone preoperative FDG-PET imaging. Patients were classified into low and high uptake groups based on tumor SUVmax. We investigated the association between PET findings and clinicopathological factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePatients with high SUVmax had significantly worse overall survival than did those with low SUVmax (49.2% vs. 90.0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). According to multivariate analysis, high SUVmax was an independent predictor of both poor overall survival (HR 3.09, 95% CI 1.35\u0026ndash;7.06, p\u0026thinsp;=\u0026thinsp;0.0077) and poor recurrence-free survival (33.4% vs. 60.4%, p\u0026thinsp;=\u0026thinsp;0.0006). High SUVmax was strongly associated with microscopic vascular invasion (mvi). Notably, the combination of high SUVmax and high alpha-fetoprotein significantly improved the preoperative prediction of mvi.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePreoperative FDG-PET SUVmax is an independent prognostic biomarker in patients with HCC\u0026thinsp;\u0026gt;\u0026thinsp;3 cm and, when combined with serum alpha-fetoprotein, may enhance prediction of mvi noninvasively. These findings support incorporating metabolic imaging into preoperative risk models to guide surgical and perioperative management strategies.\u003c/p\u003e","manuscriptTitle":"Prognostic Significance of PET-SUVmax in Patients Undergoing Resection of Hepatocellular Carcinoma \u0026gt;3 cm: A Retrospective Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 16:03:25","doi":"10.21203/rs.3.rs-8405525/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":"ed58b0ce-e2e6-4aeb-9bec-ed70890dee2c","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-03T11:01:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 16:03:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8405525","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8405525","identity":"rs-8405525","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}