The Predictive Model for Recurrence After Hepatectomy in Hepatocellular Carcinoma Patients Based on the Tumor Burden Score Combined with the Prognostic Nutritional Index and Other Indicators

The Predictive Model for Recurrence After Hepatectomy in Hepatocellular Carcinoma Patients Based on the Tumor Burden Score Combined with the Prognostic Nutritional Index and Other Indicators | 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 The Predictive Model for Recurrence After Hepatectomy in Hepatocellular Carcinoma Patients Based on the Tumor Burden Score Combined with the Prognostic Nutritional Index and Other Indicators Zhening Yan, Yubo Zhao, Yibo Wang, Xiang Li, Chenguang Shi, Feng Liu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7155075/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 Objective: This study aimed to investigate the influence of the tumor burden score (TBS), prognostic nutritional index (PNI), prothrombin time (PT), and preoperative extrahepatic metastasis on the risk of postoperative recurrence in patients with hepatocellular carcinoma (HCC) undergoing hepatectomy. A simplified prognostic model, termed the PNTR scoring system, was developed and subsequently validated to assess recurrence risk. Methods: This retrospective study included 301 patients with hepatocellular carcinoma (HCC) from three medical centers, with data collected between 2015 and 2023. The PNTR scoring system was utilized to predict recurrence-free survival (RFS) in both the derivation cohort (n=232) and the validation cohort (n=69). Variables such as TBS, PNI, PT, and preoperative extrahepatic metastasis were evaluated for their association with postoperative recurrence. Subgroup analyses were conducted to assess recurrence patterns at various sites, including local tumor recurrence (LTR), intrahepatic distant recurrence (IDR), and extrahepatic recurrence (ER).Statistical analysis was performed using Cox proportional hazards regression models, and Kaplan-Meier survival curves were employed for subgroup comparisons. Results: A high TBS, prolonged PT, low PNI, and preoperative extrahepatic metastasis were identified as independent risk factors for recurrence. The PNTR scoring system demonstrated that higher scores were associated with an increased risk of recurrence. In both the derivation cohort and validation cohort, the model exhibited robust discriminatory ability, with the following area under the curve (AUC) values: at 6 months (AUC = 0.748 vs 0.692), at 1 year (AUC = 0.711 vs 0.676), and at 2 years (AUC = 0.775 vs 0.663). These findings indicate that the model effectively differentiates between recurrence patterns. Conclusion: The PNTR scoring system represents a precise and dependable instrument for predicting postoperative recurrence in patients with HCC, and it may aid in optimizing both monitoring protocols and therapeutic strategies. hepatocellular Carcinoma hepatectomy prognostic nutritional index recurrence tumor burden score Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Hepatocellular carcinoma (HCC) is the most prevalent primary malignancy of the liver, predominantly arising from hepatocytes. Epidemiological studies have shown a consistent increase in HCC incidence over the past decade, particularly among individuals with chronic viral hepatitis and liver cirrhosis, where the incidence is notably elevated [ 1 – 3 ]. Currently, HCC accounts for approximately 70–85% of primary liver cancer cases and remains one of the leading causes of cancer-related mortality worldwide [ 4 ]. Although various treatments are available, surgical resection remains the primary and most effective therapy for early-stage, resectable HCC [ 5 – 7 ]. However, the high rate of postoperative recurrence poses a significant challenge to long-term survival [ 8 ]. Recurrence risk is closely linked to various clinical, pathological, and biological factors, underscoring the importance of accurate recurrence prediction [ 9 ]. The five-year recurrence rate for HCC patients is as high as 50–70% [ 10 , 11 ]. Factors contributing to recurrence include tumor morphology (e.g., size and number), extrahepatic metastasis, and the patient’s metabolic status[ 12 , 13 ]. Even among patients with similar tumor characteristics, such as tumor size, number, and biomarkers like alpha-fetoprotein (AFP), postoperative recurrence patterns and prognoses can vary significantly [ 14 ]. Thus, accurate prediction of recurrence is vital for tailoring individualized treatment plans and optimizing patient management. Given the significant risk of postoperative recurrence, preoperative risk prediction has become a critical area of clinical research. Identifying high-risk patients before surgery allows clinicians to develop individualized treatment and follow-up strategies, enabling precise interventions during the perioperative period and improving postoperative outcomes. Enhanced care also improves overall quality of life by facilitating recovery and reducing long-term complications [ 15 ]. To address these needs, researchers have increasingly focused on predictive scoring systems that provide comprehensive prognostic information before surgery. These tools assist in clinical decision-making, guide neoadjuvant therapy strategies, and potentially reduce recurrence rates, thereby improving long-term outcomes. Although staging systems like the American Joint Committee on Cancer (AJCC) and Union for International Cancer Control (UICC) TNM classification are widely used for HCC [ 16 ], they rely heavily on postoperative pathological findings (e.g., tumor size, number, vascular invasion, and lymph node metastasis) [ 17 , 18 ]. Such reliance limits their utility in preoperative assessments, as these systems inadequately account for tumor biological behavior and patient health status, thus reducing predictive accuracy in some clinical contexts. Consequently, there is a pressing need for simplified yet accurate preoperative predictive models. The tumor burden score (TBS) offers a comprehensive morphological evaluation based on tumor size and number. By using continuous variables rather than categorical ones, TBS overcomes the limitations of traditional staging methods that rely on arbitrary cutoff values, which can reduce predictive accuracy [ 19 ]. Studies have demonstrated the prognostic value of TBS in stratifying risks for HCC, intrahepatic cholangiocarcinoma (ICC), and combined hepatocellular-cholangiocarcinoma (HCC-ICC) [ 20 ]. The simplicity of TBS, combined with its strong correlation with radiological imaging techniques like contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI), enhances its clinical utility. Advances in imaging technology, particularly in MRI and CT, have further improved the accuracy of TBS. When combined with biomarkers such as AFP, TBS significantly enhances the prediction of postoperative recurrence[ 21 ]. Patients with high TBS values exhibit greater recurrence risks and poorer survival outcomes, as increased tumor burden correlates with both tumor biological characteristics and clinical factors like immune function and inflammation status [ 20 ]. A comprehensive evaluation of tumor burden, alongside factors such as coagulation and nutritional status, further improves prognostic models. The prognostic nutritional index (PNI), calculated from serum albumin levels and peripheral blood lymphocyte count (LYM), reflects both the nutritional and immune status of patients. It serves as an indicator of systemic inflammatory response and plays a crucial role in HCC prognosis [ 22 ]. Low PNI values are closely associated with higher recurrence risks and poorer survival outcomes in HCC patients[ 23 , 24 ]. As an immune-nutritional assessment tool, PNI highlights malnutrition and immunodeficiency, both of which contribute to recurrence risks[ 25 , 26 ]. In other cancers, such as gastric and lung cancers, combining TBS and PNI significantly improves recurrence prediction accuracy [ 27 , 28 ]. Although research on HCC remains limited, preliminary studies suggest that the combined use of TBS and PNI has substantial potential for HCC prognosis [ 29 ]. Recent studies increasingly highlight the value of combining TBS and PNI to predict postoperative recurrence in HCC patients [ 23 ]. TBS reflects tumor burden, while PNI indicates immune and nutritional status. Together, these factors provide complementary insights, improving predictive accuracy. When combined with parameters such as prothrombin time (PT) and extrahepatic metastasis, this approach offers a comprehensive assessment of patient survival risks. 2. Methods 2.1. Study population and selection criteria This study retrospectively analyzed clinical data from patients with hepatocellular carcinoma (HCC) treated between January 2015 and December 2023 at three medical centers. A total of 301 patients who met the inclusion criteria were included, with 105 from Shanxi Medical University First Hospital, 41 from Shanxi People's Hospital, and 155 from Shanxi Provincial Cancer Hospital. All procedures involving human participants adhered strictly to the ethical guidelines established by institutional and national research committees and complied with the principles of the 1964 Declaration of Helsinki and its subsequent amendments or equivalent ethical standards. The study was approved by the Ethics Committees of Shanxi Medical University First Hospital, Shanxi People's Hospital, and Shanxi Provincial Cancer Hospital (Ethics Approval Number: JC2024063). Informed written consent was obtained from all patients or their authorized representatives. 2.2 Inclusion criteria The inclusion criteria for this study were as follows: (1) a pathologically confirmed diagnosis of HCC following surgical resection; (2) preoperative evaluation of tumor size and number conducted within two weeks prior to surgery using dynamic CT or MRI[ 30 ]; (3) preoperative measurement of serum biomarkers, including PT, albumin (ALB), LYM, and other relevant laboratory parameters, performed within one week before surgery; (4) completion of curative liver resection; and (5) absence of severe dysfunction in major organs, such as the heart, lungs, or kidneys. 2.3 Exclusion criteria The exclusion criteria for this study were: (1) pathological diagnosis of non-HCC liver malignancies; (2) receipt of preoperative treatments, including radiofrequency ablation, local interventional therapy, or chemotherapy; and (3) mortality within 30 days post-surgery or loss to follow-up shortly thereafter. Patients with intraoperatively detected extra-regional lymph node metastasis, peritoneal metastasis, or mesenteric metastasis were included in the analysis if the metastatic lesions were surgically resected alongside the primary tumor, achieving complete tumor resection. 2.4 Primary endpoint The primary endpoint of this study was recurrence-free survival (RFS), defined as the duration from the date of curative HCC resection to either the date of tumor recurrence or the last follow-up. Recurrence of HCC was confirmed through tumor biopsy or the identification of suspicious lesions detected via follow-up imaging examinations. 2.5 Liver resection and pathological evaluation All patients underwent comprehensive preoperative evaluations, including routine laboratory tests such as liver function assessments, hepatitis virus screening, and tumor marker analysis, prior to liver resection. Additionally, each patient was required to undergo diagnostic imaging, including abdominal ultrasound, enhanced CT, MRI, and examinations using three-dimensional reconstruction technology. These evaluations were conducted to assess tumor status and resectability, enabling the development of an individualized surgical plan[ 31 ]. The liver resection methods employed included traditional open surgery, laparoscopic surgery, anatomical liver resection, and non-anatomical liver resection. The optimal surgical approach was determined based on the tumor's specific location and distribution. Postoperatively, all resected tissues underwent pathological examination to confirm the absence of residual tumor at the resection margins. 2.6 TBS Definition and PNTR Scoring System The TBS is calculated by determining the Euclidean distance in a Cartesian plane, considering two variables: the maximum tumor size (x-axis) and the tumor count (y-axis). For patients with multiple nodules, the tumor size was defined as the size of the largest lesion. The TBS calculation formula follows the Pythagorean theorem: TBS 2 =(Maximum lesion size) 2 +(Tumor count) 2 [ 32 ]. The optimal cutoff values for TBS (4.38/8.86 units), PT (14 seconds), and PNI (50.35 units) were determined via X-tile software. The TBS is then categorized into three groups on the basis of the cutoff values: low ( 8.86), with scores of 1, 2, and 3, respectively. PNI is classified into low ( 50.35) groups, assigned scores of 2 and 1, respectively. PT is divided into low ( 14 seconds) categories, with scores of 1 and 2, respectively (Fig. 1 ). The presence of extrahepatic metastasis before surgery was assessed, with no metastasis assigned 1 point and metastasis assigned 2 points. The final PNTR score is derived by summing the individual scores. 2.7 Statistical analysis Statistical analyses for this study were conducted using the Cox proportional hazards model for both univariate and multivariate analyses. Regression coefficients (B) were utilized to estimate the effect of each variable on risk, and the Wald statistic was applied to evaluate the significance of each variable, with a significance threshold set at p < 0.05. For variables identified as significant, hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. The optimal cutoff values for the TBS, PNI and PT, were determined using the quantile function in the X-tile software by arranging the variables in ascending order. RFS was analyzed using Kaplan–Meier (KM) survival curves, and the model’s discriminatory ability was assessed through the area under the receiver operating characteristic curve (AUC). A nomogram based on preoperative variables was constructed to predict RFS. All statistical analyses were performed using SPSS® version 25.0 and R version 3.2.0 ( http://www.r-project.org/ ), with p < 0.05 considered statistically significant. 3. Results 3.1 Factors affecting RFS The multivariable Cox regression analysis of the derivation cohort and validation cohort identified several independent risk factors for RFS. The TBS was significantly associated with RFS, with a hazard ratio (HR) of 1.66 (95% CI: 1.08–2.50, p = 0.004), suggesting that a higher TBS increases the risk of relapse. The PNI also had a modest but significant effect on RFS (HR: 0.95, 95% CI: 0.91–0.99, p = 0.021), indicating that lower PNI values are linked to worse survival outcomes. PT showed a small but statistically significant association (HR: 1.09, 95% CI: 1.02–1.17, p = 0.014), emphasizing the role of coagulation status in predicting relapse risk. Interestingly, preoperative extrahepatic metastasis was associated with a slightly lower risk of relapse (HR: 1.72, 95% CI: 1.13–2.61. p = 0.011), which might reflect a different underlying biological behavior in these patients. Together, these findings highlight the importance of integrating clinical, nutritional, and laboratory factors when assessing relapse risk, which can guide more personalized treatment strategies (Table 1 ). Table 1 Univariable and multivariable Cox regression analyses for RFS Patients in the derivation cohort and validation cohort Variable UV HR(95% CI) UV P value MV HR(95% CI) MV P value Gender 0.78 (0.54–1.12) 0.180 Age 1.01 (0.99–1.03) 0.294 TBS 1.12 (1.08–1.17) < 0.001 1.66(1.08–2.50) 0.004 Milan Criteria 1.17 (1.00-1.37) 0.050 0.599(0.44–1.63) 0.599 Cirrhosis 0.97 (0.70–1.34) 0.836 HBV(+) 0.86 (0.62–1.20) 0.376 HCV(+) 0.82 (0.40–1.67) 0.582 Extrahepatic Metastasis 1.88 (1.25–2.82) 0.002 1.72(1.13–2.61) 0.011 Ascites 2.06 (1.35–3.13) 0.001 2.05(1.19–3.51) 0.064 ALT 1.00 (1.00–1.00) 0.735 AST 1.00 (1.00–1.00) 0.845 TP 1.01 (0.99–1.03) 0.165 ALB 0.99 (0.97–1.02) 0.456 TB 1.01 (1.00-1.02) 0.013 1.09(1.03–1.15) 0.955 DB 1.02 (1.01–1.03) 0.003 1.00(0.97–1.03) 0.674 CHE 1.15 (0.90–1.46) 0.272 ALP 1.00 (1.00-1.01) < 0.001 0.97(0.95-1.00) 0.628 γ-GT 1.00 (1.00–1.00) 0.257 TC 1.03 (0.59–1.82) 0.911 TG 0.96 (0.67–1.39) 0.844 HDL_C 1.34 (0.31–5.68) 0.694 LDL_C 1.39 (0.72–2.68) 0.326 SCr 1.00 (1.00–1.00) 0.642 ALBI 1.16 (0.85–1.57) 0.353 ALBI Score 1.01 (0.74–1.39) 0.935 AFP 1.00 (1.00–1.00) 0.029 1.00 0.279 PT 1.11 (1.04–1.19) 0.003 1.09(1.02–1.17) 0.014 INR 0.48 (0.12–1.88) 0.290 WBC 1.10 (1.03–1.18) 0.006 1.00(0.98–1.01) 0.114 RBC 1.04 (0.81–1.33) 0.770 Hb 1.00 (0.99–1.01) 0.609 PLT 1.00 (1.00–1.00) 0.114 LY 1.07 (0.86–1.33) 0.560 LY% 0.99 (0.97-1.00) 0.108 NEU 1.13 (1.04–1.23) 0.005 1.18(0.96–1.44) 0.327 NEU% 1.01 (0.99–1.02) 0.254 PNI 0.93 (0.89–0.97) 0.001 0.95(0.91–0.99) 0.021 NLR 1.04 (0.96–1.13) 0.352 PLR 1.00 (1.00–1.00) 0.513 Child-Pugh_Grading 1.34 (0.80–2.25) 0.266 TBS, tumor burden score; HBV, hepatitis B virus; HCV, hepatitis C virus; ALT, alanine aminotransferase; AST, aspartate aminotransferase; TP, total protein; ALB, albumin; TB, total bilirubin; DB, direct bilirubin; HE,cholinesterase; ALP, alkaline phosphatase; γ-GT, gamma-glutamyl transferase; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SCr, serum creatinine; ALBI, albumin-bilirubin index; ALBI score, albumin- bilirubin index; AFP, alpha-fetoprotein; PT, prothrombin time; PTA, prothrombin-to-lymphocyte ratio; INR, international normalized ratio; WBC, white blood cell; RBC, red blood cell; Hb, hemoglobin; PLT, platelet; LY, lymphocyte; LY%, lymphocyte percentage; NEU, neutrophil; NEU%, neutrophil percentage; PNI, prognostic nutritional index; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; CI, confidence interval; HR, hazard ratio; MV, multivariable; UV, univariable interval; 3.2 Nomogram for Predicting Early Recurrence Risk Previous studies have indicated that patients with early disease recurrence have a worse prognosis than those with late disease recurrence. A prevailing hypothesis among researchers is that early recurrence is attributed primarily to intrahepatic micrometastasis, whereas late recurrence is thought to arise from de novo carcinogenesis. In most studies, early recurrence is defined as recurrence occurring within two years postsurgery (Time to recurrence, TTR < 2 years) [ 33 , 34 ]. In our dataset, approximately 80% of the recurrences occurred within two years following surgery. Consequently, a nomogram was developed to predict the risk of early recurrence after hepatic resection based on the independent risk factors identified through multivariate analysis (TBS, PT, PNI, and preoperative extrahepatic metastasis) (Fig. 2 ). In this model, each risk factor is assigned a score, and the total score is utilized to estimate the risk of early recurrence postsurgery. The AUC for this nomogram in predicting early recurrence risk exceeded 0.65 at various time points in both the derivation and validation cohorts (6-month AUC = 0.748 vs. 0.692; 1-year AUC = 0.711 vs. 0.676; 2-year AUC = 0.775 vs. 0.663) (Fig. 3 ). 3.3 Impact of TBS, PNI, PT, preoperative extrahepatic metastasis, and PTNR on RFS At the conclusion of the final follow-up, 228 patients (75.7%) experienced recurrence. In the derivation cohort, the median RFS was 5 months. Patients with a high TBS, low PNI, prolonged PT, and preoperative extrahepatic metastasis presented significantly worse RFS than did those with a low TBS, high PNI, shorter PT, and no preoperative extrahepatic metastasis. Similarly, as the PTNR increased, there was a progressive deterioration in the RFS. In the derivation cohort and validation cohort, Kaplan‒Meier survival curves revealed significant differences between the groups (Fig. 4 ). 4. Discussion HCC is the most common type of liver malignancy worldwide, with its high incidence and recurrence rates making it a major challenge in global public health. Despite significant improvements in the early diagnosis and treatment of HCC in recent years, the five-year survival rate remains low, with postoperative recurrence rates still being high[ 35 ]. Therefore, enhancing the accuracy of prognostic prediction, developing personalized treatment strategies, and effectively managing the risk of recurrence are critical issues in the current treatment of hepatocellular carcinoma. In this study, we utilized a multicenter database to develop and validate a simplified preoperative prognostic scoring system—the PNTR score model. This model successfully stratifies the long-term prognosis of HCC patients undergoing radical hepatectomy, providing important insights for treatment decision-making. The PNTR score combines the radiological TBS, the PNI, the PT, and the presence of extrahepatic metastasis prior to surgery—indicators that are easily assessable preoperatively. TBS, in combination with other primary tumor factors, enhances the prognostic predictive power for HCC patients following liver resection. Previous researchers demonstrated that both the TBS and the neutrophil‒lymphocyte ratio (NLR) significantly impact the prognosis of HCC patients after resection[ 36 ]. The complexity and heterogeneity of HCC make it difficult for a single prognostic assessment indicator to comprehensively and accurately reflect the prognosis risk of patients. Therefore, the combined use of multiple clinical and biomarker indicators can provide more comprehensive and accurate prognostic information.The PNTR score system integrates tumor biological factors (such as the TBS and extrahepatic metastasis) and considers the nutritional and metabolic status of the body (such as the PNI and PT), thus offering a more comprehensive reflection of the patient's overall health.Although a direct comparison between the PNTR model and the TBS score was not performed in this study, the PNTR model integrates multiple clinically relevant prognostic variables, including tumor burden, coagulation function, and systemic immune-nutritional status. In contrast, the TBS score is solely based on tumor size and number. Given this broader scope, the PNTR model is theoretically expected to provide more accurate prognostic stratification. Our findings indicate that high TBS grade, prolonged PT (> 14 seconds), low PNI (< 50.35), and the presence of extrahepatic metastasis before surgery are independent risk factors for poor prognosis in HCC patients. These factors show strong potential in predicting RFS in HCC patients. By assigning scores based on the TBS, PT, PNI, and preoperative extrahepatic metastasis status, we observed that patients with higher scores presented significantly shorter recurrence times. External validation cohorts also supported this observation, indicating the consistency and reproducibility of the scoring system across different cohorts. Cox regression analysis of the derivation cohort further confirmed that TBS, PT, PNI, and extrahepatic metastasis are independent prognostic factors for RFS. Because of the four independent risk factors in the PNTR scoring system, we constructed a nomogram to predict early recurrence risk after liver resection. Nomograms are extensively utilized as visual tools for prognostic prediction across various cancer types. The AUC analysis further demonstrates that our nomogram is a reliable model for prognostic prediction. We classified patients based on different recurrence patterns (local tumor recurrence [LTR], intrahepatic distant recurrence [IDR], and extrahepatic recurrence [ER]). Among these patterns, IDR was the dominant type (n = 98, 57.3%), followed by ER (n = 39, 22.8%), LTR + IDR (n = 13, 7.6%), and IDR + ER (n = 21, 12.3%). Notably, among these patients, those with a PNTR score of 7 points had the highest proportion across all recurrence patterns, with 48.9%, 41.0%, 38.5%, and 23.8% in the LTR, IDR, ER, and LTR + IDR recurrence models, respectively. Additionally, patients with a score of 9 points were predominantly observed in the recurrence model with extrahepatic metastasis, suggesting that patients with a score of 9 points may have a greater risk of extrahepatic metastasis (Fig. 5 A). We performed additional statistical analysis in the validation cohort, which confirmed the same results (Fig. 5 B). Although the proportion of these patients is limited, they merit considerable clinical attention, necessitating a comprehensive discussion of the risks and benefits associated with preoperative surgery.The stratification of recurrence patterns by PNTR score provides novel insight into its potential role in recurrence pattern prediction. High PNTR scores were associated with extrahepatic or combined recurrence, which are known to portend worse prognosis and limited treatment options. This suggests that PNTR may serve not only as a prognostic marker but also as a tool to guide surveillance intensity and systemic therapy consideration after resection.Additionally, postoperative follow-up should be more intensive for these patients. Future studies should further expand the sample size to validate the generalizability of these findings. Currently, the application of molecular targeted therapies and immunotherapies has broadened the treatment options for HCC and improved patient prognosis [ 37 ]. A large retrospective cohort study revealed that while neoadjuvant therapy is typically used for more advanced tumors, it significantly improves survival rates compared with surgery for resectable HCC [ 38 ]. Overall, the PNTR scoring system is a reliable tool for predicting the prognosis of HCC patients undergoing liver resection. This system can assist clinicians in performing precise risk stratification, thereby facilitating the development of personalized treatment plans. It is particularly valuable in preoperative assessments and postoperative recurrence monitoring. There are several limitations to this study. First, it is retrospective in nature, with a limited sample size and inherent bias. Second, owing to the insufficient sample size, we were unable to explore the prognostic characteristics of patients with a PNTR score of 9. Third, the exact etiology of HCC remains unclear [ 39 , 40 ], and most of the patients in this study had HBV-related HCC, which may limit the generalizability of the results to other populations, particularly in Western countries where alcoholic liver disease and nonalcoholic fatty liver disease (NAFLD) are more common. Additionally, patients in the recurrence cohort commonly experienced IDR or IDR + ER recurrence, which may influence the external validation results of the scoring system. HCC patients with high TBSs, prolonged PTs, low PNI levels, and preoperative extrahepatic metastasis should undergo more frequent and stringent monitoring. Early detection of recurrence allows timely interventions that can improve patient outcomes. 5. Conclusions This study demonstrated that the PNTR score performs well in stratifying HCC patients with respect to RFS. Higher scores are strongly correlated with an elevated risk of recurrence and a notably shorter time to recurrence. The PNTR score serves as an invaluable tool in preoperative consultations, offering significant insights into a patient’s prognosis. It plays a crucial role in identifying individuals who may benefit from more thorough clinical observation and timely therapeutic interventions, ensuring that healthcare providers can implement appropriate strategies to optimize patient outcomes. Declarations This study was approved by the Ethics Committee of the First Hospital of Shanxi Medical University (2022 K-K0157), Shanxi Provincial Cancer Hospital (JC2024063). Written informed consent was obtained from all participants. Acknowledgments No acknowledgment Authors ’ contributions Zhening Yan and Yanbo Ma conceived the study and contributed to the study design, implementation, and manuscript revision. Zhening Yan was responsible for drafting the manuscript. Zhening Yan and Yi Chen performed the statistical analysis. Zhening Yan, Yubo Zhao, Xiang Li, Chenguang Shi, and Feng Liu participated in data collection. All the authors have read and approved the final version of the manuscript. Funding Supported by the General Program of Natural Science Research, Shanxi Provincial Department of Science and Technology, No. 202203021221248; and Scientific Research Project of Shanxi Provincial Health Commission, No. 2023065. Availability of data and materials The data used in the current study are available from the corresponding author upon reasonable request. Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. References Tan EY, Danpanichkul P, Yong JN, Yu Z, Tan DJH, Lim WH et al (2024) Liver cancer in 2021: Global burden of disease study. J Hepatol. ;:S0168-8278(24)02652-7. Xiong Y, Qiao W, Mei T, Li K, Jin R, Zhang Y (2024) Recurrence of Hepatocellular Carcinoma in Patients with Low Albumin-Bilirubin Grade in TACE Combined with Ablation: A Random Forest Cox Predictive Model. J Hepatocell Carcinoma 11:1375–1388 Shen Y, Cheng L, Xu M, Wang W, Wan Z, Xiong H et al (2023) SGLT2 inhibitor empagliflozin downregulates miRNA-34a-5p and targets GREM2 to inactivate hepatic stellate cells and ameliorate non-alcoholic fatty liver disease-associated fibrosis. Metabolism 146:155657 Youness RA, Hassan HA, Abaza T, Hady AA, El Magdoub HM, Ali M et al (2024) A Comprehensive Insight and In Silico Analysis of CircRNAs in Hepatocellular Carcinoma: A Step toward ncRNA-Based Precision Medicine. Cells 13:1245 Vogel A, Meyer T, Sapisochin G, Salem R, Saborowski A (2022) Hepatocellular carcinoma. Lancet 400:1345–1362 Dong Q, Jiang Z (2024) Platinum–Iron Nanoparticles for Oxygen-Enhanced Sonodynamic Tumor Cell Suppression. Inorganics 12:331 Zhang Y, Chen H, Chen S, Duan Y, Cheng L, Gao J et al (2022) Comparison of the Efficacy and Safety of Transarterial Chemoembolization Plus Microwave Ablation versus Surgical Resection in Patients with Solitary Large Hepatocellular Carcinoma: A Propensity Score-Matched Analysis. J Hepatocell Carcinoma 9:1309–1321 Wang K, Xiang Y-J, Yu H-M, Cheng Y-Q, Liu Z-H, Qin Y-Y et al (2024) Adjuvant sintilimab in resected high-risk hepatocellular carcinoma: a randomized, controlled, phase 2 trial. Nat Med 30:708–715 Safri F, Nguyen R, Zerehpooshnesfchi S, George J, Qiao L (2024) Heterogeneity of hepatocellular carcinoma: from mechanisms to clinical implications. Cancer Gene Ther 31:1105–1112 Chen R, Hou B, Zhou Y, Zhang T, Wang Z, Chen X et al (2023) Recurrence after percutaneous radiofrequency ablation of hepatocellular carcinoma: Analysis of the pattern and risk factors. Front Oncol 13:1018715 Abdelhamed W, El-Kassas M (2023) Hepatocellular carcinoma recurrence: Predictors and management. Liver Res 7:321–332 Wang K, Xiang Y-J, Yu H-M, Cheng Y-Q, Liu Z-H, Qin Y-Y et al (2024) Adjuvant sintilimab in resected high-risk hepatocellular carcinoma: a randomized, controlled, phase 2 trial. Nat Med 30:708–715 Xu X-F, Xing H, Han J, Li Z-L, Lau W-Y, Zhou Y-H et al (2019) Risk Factors, Patterns, and Outcomes of Late Recurrence After Liver Resection for Hepatocellular Carcinoma: A Multicenter Study From China. JAMA Surg 154:209–217 Tang S, Huang T, Luo C, Fu J, Zhang K, Chen Q et al (2024) Tumor burden score and alpha-fetoprotein level predict prognosis of patients with unresectable hepatocellular carcinoma treated with tyrosine kinase inhibitor and anti-PD-1 antibody. iLIVER 3:100109 Liu Z, Liu Y, Zhang W, Hong Y, Meng J, Wang J et al (2022) Deep learning for prediction of hepatocellular carcinoma recurrence after resection or liver transplantation: a discovery and validation study. Hepatol Int 16:577–589 Tellapuri S, Sutphin PD, Beg MS, Singal AG, Kalva SP (2018) Staging systems of hepatocellular carcinoma: A review. Indian J Gastroenterol 37:481–491 Kim DY (2024) Changing etiology and epidemiology of hepatocellular carcinoma: Asia and worldwide. J Liver Cancer 24:62–70 Dirican A, Uncu D, Sekacheva M, Artaç M, Aladashvil A, Erdogan A et al (2023) A multicentre, multinational study of clinical characteristics and prognosis of hepatocellular carcinoma. East Mediterr Health J 29:462–473 Sasaki K, Morioka D, Conci S, Margonis GA, Sawada Y, Ruzzenente A et al (2018) The Tumor Burden Score: A New Metro-ticket Prognostic Tool For Colorectal Liver Metastases Based on Tumor Size and Number of Tumors. Ann Surg 267:132–141 Ma W, Liu R, Wang J, Liu L, Qiu Z, Yu J et al (2024) High tumor burden score indicated the unfavorable prognosis in patients with hepatocellular carcinoma: A meta-analysis. PLoS ONE 19:e0308570 Ho S-Y, Liu P-H, Hsu C-Y, Ko C-C, Huang Y-H, Su C-W et al (2021) Tumor burden score as a new prognostic marker for patients with hepatocellular carcinoma undergoing transarterial chemoembolization. J Gastroenterol Hepatol 36:3196–3203 Wang D, Hu X, Xiao L, Long G, Yao L, Wang Z et al (2021) Prognostic Nutritional Index and Systemic Immune-Inflammation Index Predict the Prognosis of Patients with HCC. J Gastrointest Surg 25:421–427 Zhang N, Lin K, Qiao B, Yan L, Jin D, Yang D et al (2024) Machine Learning Model Based on Prognostic Nutritional Index for Predicting Long-Term Outcomes in Patients With HCC Undergoing Ablation. Cancer Med 13:e70344 Ho C-T, Chia-Hui Tan E, Lee P-C, Chu C-J, Huang Y-H, Huo T-I et al (2024) Prognostic Nutritional Index as a Prognostic Factor for Very Early-Stage Hepatocellular Carcinoma. Clin Transl Gastroenterol 15:e00678 Alwarawrah Y, Kiernan K, MacIver NJ (2018) Changes in Nutritional Status Impact Immune Cell Metabolism and Function. Front Immunol 9:1055 Ding P, Guo H, Sun C, Yang P, Kim NH, Tian Y et al (2022) Combined systemic immune-inflammatory index (SII) and prognostic nutritional index (PNI) predicts chemotherapy response and prognosis in locally advanced gastric cancer patients receiving neoadjuvant chemotherapy with PD-1 antibody sintilimab and XELOX: a prospective study. BMC Gastroenterol 22:121 Peng L, Wang Y, Liu F, Qiu X, Zhang X, Fang C et al (2020) Peripheral blood markers predictive of outcome and immune-related adverse events in advanced non-small cell lung cancer treated with PD-1 inhibitors. Cancer Immunol Immunother 69:1813–1822 Deng H, He Y, Huang G, Huang Y, Wu J, Qin X (2024) Predictive value of prognostic nutritional index in patients undergoing gastrectomy for gastric cancer: A systematic review and meta-analysis. Med (Baltim) 103:e39917 Lima HA, Moazzam Z, Endo Y, Alaimo L, Shaikh C, Munir MM et al (2023) TBS-Based Preoperative Score to Predict Non-transplantable Recurrence and Identify Candidates for Upfront Resection Versus Transplantation for Hepatocellular Carcinoma. Ann Surg Oncol 30:3363–3373 Chartampilas E, Rafailidis V, Georgopoulou V, Kalarakis G, Hatzidakis A, Prassopoulos P (2022) Current Imaging Diagnosis of Hepatocellular Carcinoma. Cancers (Basel) 14:3997 Gundavda KK, Patkar S, Varty GP, Shah N, Velmurugan K, Goel M (2025) Liver Resection for Hepatocellular Carcinoma: Recent Advances. J Clin Experimental Hepatol. ;15 Fu J, Zheng L, Tang S, Lin K, Zheng S, Bi X et al (2024) Tumor burden score and carcinoembryonic antigen predict outcomes in patients with intrahepatic cholangiocarcinoma following liver resection: a multi–institutional analysis. BMC Cancer 24:358 Yan W-T, Li C, Yao L-Q, Qiu H-B, Wang M-D, Xu X-F et al (2023) Predictors and long-term prognosis of early and late recurrence for patients undergoing hepatic resection of hepatocellular carcinoma: a large-scale multicenter study. Hepatobiliary Surg Nutr 12:155–168 Nevola R, Ruocco R, Criscuolo L, Villani A, Alfano M, Beccia D et al (2023) Predictors of early and late hepatocellular carcinoma recurrence. World J Gastroenterol 29:1243–1260 Liu Z, Liu X, Liang J, Liu Y, Hou X, Zhang M et al (2021) Immunotherapy for Hepatocellular Carcinoma: Current Status and Future Prospects. Front Immunol 12:765101 Wang J, Chen Z, Wang L, Feng S, Qiu Q, Chen D et al (2022) A new model based inflammatory index and tumor burden score (TBS) to predict the recurrence of hepatocellular carcinoma (HCC) after liver resection. Sci Rep 12:8670 Li Y-K, Wu S, Wu Y-S, Zhang W-H, Wang Y, Li Y-H et al (2024) Portal Venous and Hepatic Arterial Coefficients Predict Post-Hepatectomy Overall and Recurrence-Free Survival in Patients with Hepatocellular Carcinoma: A Retrospective Study. J Hepatocell Carcinoma 11:1389–1402 Llovet JM, Pinyol R, Yarchoan M, Singal AG, Marron TU, Schwartz M et al (2024) Adjuvant and neoadjuvant immunotherapies in hepatocellular carcinoma. Nat Rev Clin Oncol 21:294–311 Huang DQ, Mathurin P, Cortez-Pinto H, Loomba R (2023) Global epidemiology of alcohol-associated cirrhosis and HCC: trends, projections and risk factors. Nat Rev Gastroenterol Hepatol 20:37–49 Toh MR, Wong EYT, Wong SH, Ng AWT, Loo L-H, Chow PK-H et al (2023) Global Epidemiology and Genetics of Hepatocellular Carcinoma. Gastroenterology 164:766–782 Additional Declarations No competing interests reported. Supplementary Files data.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7155075","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":499682492,"identity":"23906c12-8dc0-4420-a5ee-b5a469a2b450","order_by":0,"name":"Zhening Yan","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhening","middleName":"","lastName":"Yan","suffix":""},{"id":499682493,"identity":"08043dc6-101f-4329-8885-f231772df30c","order_by":1,"name":"Yubo Zhao","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yubo","middleName":"","lastName":"Zhao","suffix":""},{"id":499682494,"identity":"86474788-b55b-4be4-8aab-6d563e220d97","order_by":2,"name":"Yibo Wang","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yibo","middleName":"","lastName":"Wang","suffix":""},{"id":499682495,"identity":"6cf3ef4f-914a-408e-8ee9-ef39d46ab925","order_by":3,"name":"Xiang Li","email":"","orcid":"","institution":"The First Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Li","suffix":""},{"id":499682496,"identity":"e8206009-2d00-4d3c-87ac-842aad1d47f6","order_by":4,"name":"Chenguang Shi","email":"","orcid":"","institution":"Shanxi Bethune Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chenguang","middleName":"","lastName":"Shi","suffix":""},{"id":499682497,"identity":"7b0ee8c9-ee85-4aec-bd0b-6f38a275cdf4","order_by":5,"name":"Feng Liu","email":"","orcid":"","institution":"Shanxi Provincial Cancer Hospital, Chinese Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Liu","suffix":""},{"id":499682498,"identity":"6665749f-245b-45f1-92f7-f944c83cec9f","order_by":6,"name":"Yi Chen","email":"","orcid":"","institution":"The First Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Chen","suffix":""},{"id":499682499,"identity":"355f4a55-6e53-4269-b20b-1e24b836096b","order_by":7,"name":"Yanbo Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACNvnzjx9I/pOQ42dvPvggoaKGsBY+CR42Aws2G2PJnmPJBg/OHCOsRU6Ch0Gigi0t0eCGj5nkwxZmIhwm3XvA4AbP4QSDG2xpFYkNbAz87d0J+LXInEt4OEPicJ7k7eZjNxJ3yDBInDm7Ab8WhgQDYwmDw8V8d46l3Ug8w8ZgIJFLWIv0n4TDiQ03cswKEtuYidAikWMgIXEgLXECUAsDcVp4jqUZSDZAAlki4cwxHoJ+kW9vPvxAsgESlR9/VNTI8bf34teCAXhIUz4KRsEoGAWjACsAABdXTYMZXJoMAAAAAElFTkSuQmCC","orcid":"","institution":"The First Hospital of Shanxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yanbo","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2025-07-18 07:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7155075/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7155075/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89266354,"identity":"eee7382b-9ec2-4561-9209-8f111242e98a","added_by":"auto","created_at":"2025-08-18 08:15:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33668,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe cutoff values of TBS, PNI, and PT\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7155075/v1/52b86712c277292d49e57306.jpg"},{"id":89266356,"identity":"0ccfefc3-73e8-40a4-9095-d028e42bef7c","added_by":"auto","created_at":"2025-08-18 08:15:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31427,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA nomogram for predicting the early recurrence risk after liver resection based on TBS, PNI, PT, and preoperative extrahepatic metastasis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7155075/v1/a5bfa846aef5fb2086e65fbe.jpg"},{"id":89266358,"identity":"20ae1a90-d295-4104-920f-ba0a3d1b3cd3","added_by":"auto","created_at":"2025-08-18 08:15:11","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":64857,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe ROC of the derivation cohort and validation cohort in the nomogram.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7155075/v1/08ae889cee18b3c06c74b095.jpg"},{"id":89268906,"identity":"b1a9754e-7848-42bd-93af-4027f999cc5a","added_by":"auto","created_at":"2025-08-18 08:31:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":286502,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCumulative RFS curves of patients stratified by the TBS (A, B), PNI (C, D), PT (E, F), extrahepatic metastasis (G, H), and PNTR (I, J) in the derivation cohort and the validation cohort.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7155075/v1/cc7c25138d76719cf0e246e5.png"},{"id":89268907,"identity":"711a776d-fa59-42bc-9606-8a7c84cad26d","added_by":"auto","created_at":"2025-08-18 08:31:11","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":45693,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe bar chart of PNTR scores under different recurrence patterns.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7155075/v1/c7bff65a1e85251e0ba8499e.jpg"},{"id":93199630,"identity":"e38b79f6-7323-4988-9cbb-fa6088518625","added_by":"auto","created_at":"2025-10-10 06:39:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1512144,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7155075/v1/596af51a-1c46-465d-a1ea-26a7629cc15e.pdf"},{"id":89266355,"identity":"f9997ae4-eeb5-4235-8a05-58317b73b288","added_by":"auto","created_at":"2025-08-18 08:15:11","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":93247,"visible":true,"origin":"","legend":"","description":"","filename":"data.zip","url":"https://assets-eu.researchsquare.com/files/rs-7155075/v1/35eb624282b78c8ed8197ab6.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Predictive Model for Recurrence After Hepatectomy in Hepatocellular Carcinoma Patients Based on the Tumor Burden Score Combined with the Prognostic Nutritional Index and Other Indicators","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHepatocellular carcinoma (HCC) is the most prevalent primary malignancy of the liver, predominantly arising from hepatocytes. Epidemiological studies have shown a consistent increase in HCC incidence over the past decade, particularly among individuals with chronic viral hepatitis and liver cirrhosis, where the incidence is notably elevated [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Currently, HCC accounts for approximately 70\u0026ndash;85% of primary liver cancer cases and remains one of the leading causes of cancer-related mortality worldwide [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although various treatments are available, surgical resection remains the primary and most effective therapy for early-stage, resectable HCC [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the high rate of postoperative recurrence poses a significant challenge to long-term survival [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Recurrence risk is closely linked to various clinical, pathological, and biological factors, underscoring the importance of accurate recurrence prediction [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe five-year recurrence rate for HCC patients is as high as 50\u0026ndash;70% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Factors contributing to recurrence include tumor morphology (e.g., size and number), extrahepatic metastasis, and the patient\u0026rsquo;s metabolic status[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Even among patients with similar tumor characteristics, such as tumor size, number, and biomarkers like alpha-fetoprotein (AFP), postoperative recurrence patterns and prognoses can vary significantly [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Thus, accurate prediction of recurrence is vital for tailoring individualized treatment plans and optimizing patient management.\u003c/p\u003e\u003cp\u003eGiven the significant risk of postoperative recurrence, preoperative risk prediction has become a critical area of clinical research. Identifying high-risk patients before surgery allows clinicians to develop individualized treatment and follow-up strategies, enabling precise interventions during the perioperative period and improving postoperative outcomes. Enhanced care also improves overall quality of life by facilitating recovery and reducing long-term complications [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To address these needs, researchers have increasingly focused on predictive scoring systems that provide comprehensive prognostic information before surgery. These tools assist in clinical decision-making, guide neoadjuvant therapy strategies, and potentially reduce recurrence rates, thereby improving long-term outcomes.\u003c/p\u003e\u003cp\u003eAlthough staging systems like the American Joint Committee on Cancer (AJCC) and Union for International Cancer Control (UICC) TNM classification are widely used for HCC [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], they rely heavily on postoperative pathological findings (e.g., tumor size, number, vascular invasion, and lymph node metastasis) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Such reliance limits their utility in preoperative assessments, as these systems inadequately account for tumor biological behavior and patient health status, thus reducing predictive accuracy in some clinical contexts. Consequently, there is a pressing need for simplified yet accurate preoperative predictive models.\u003c/p\u003e\u003cp\u003eThe tumor burden score (TBS) offers a comprehensive morphological evaluation based on tumor size and number. By using continuous variables rather than categorical ones, TBS overcomes the limitations of traditional staging methods that rely on arbitrary cutoff values, which can reduce predictive accuracy [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Studies have demonstrated the prognostic value of TBS in stratifying risks for HCC, intrahepatic cholangiocarcinoma (ICC), and combined hepatocellular-cholangiocarcinoma (HCC-ICC) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe simplicity of TBS, combined with its strong correlation with radiological imaging techniques like contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI), enhances its clinical utility. Advances in imaging technology, particularly in MRI and CT, have further improved the accuracy of TBS. When combined with biomarkers such as AFP, TBS significantly enhances the prediction of postoperative recurrence[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Patients with high TBS values exhibit greater recurrence risks and poorer survival outcomes, as increased tumor burden correlates with both tumor biological characteristics and clinical factors like immune function and inflammation status [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A comprehensive evaluation of tumor burden, alongside factors such as coagulation and nutritional status, further improves prognostic models.\u003c/p\u003e\u003cp\u003eThe prognostic nutritional index (PNI), calculated from serum albumin levels and peripheral blood lymphocyte count (LYM), reflects both the nutritional and immune status of patients. It serves as an indicator of systemic inflammatory response and plays a crucial role in HCC prognosis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Low PNI values are closely associated with higher recurrence risks and poorer survival outcomes in HCC patients[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. As an immune-nutritional assessment tool, PNI highlights malnutrition and immunodeficiency, both of which contribute to recurrence risks[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn other cancers, such as gastric and lung cancers, combining TBS and PNI significantly improves recurrence prediction accuracy [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Although research on HCC remains limited, preliminary studies suggest that the combined use of TBS and PNI has substantial potential for HCC prognosis [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Recent studies increasingly highlight the value of combining TBS and PNI to predict postoperative recurrence in HCC patients [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. TBS reflects tumor burden, while PNI indicates immune and nutritional status. Together, these factors provide complementary insights, improving predictive accuracy. When combined with parameters such as prothrombin time (PT) and extrahepatic metastasis, this approach offers a comprehensive assessment of patient survival risks.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study population and selection criteria\u003c/h2\u003e\u003cp\u003eThis study retrospectively analyzed clinical data from patients with hepatocellular carcinoma (HCC) treated between January 2015 and December 2023 at three medical centers. A total of 301 patients who met the inclusion criteria were included, with 105 from Shanxi Medical University First Hospital, 41 from Shanxi People's Hospital, and 155 from Shanxi Provincial Cancer Hospital. All procedures involving human participants adhered strictly to the ethical guidelines established by institutional and national research committees and complied with the principles of the 1964 Declaration of Helsinki and its subsequent amendments or equivalent ethical standards. The study was approved by the Ethics Committees of Shanxi Medical University First Hospital, Shanxi People's Hospital, and Shanxi Provincial Cancer Hospital (Ethics Approval Number: JC2024063). Informed written consent was obtained from all patients or their authorized representatives.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Inclusion criteria\u003c/h2\u003e\u003cp\u003eThe inclusion criteria for this study were as follows: (1) a pathologically confirmed diagnosis of HCC following surgical resection; (2) preoperative evaluation of tumor size and number conducted within two weeks prior to surgery using dynamic CT or MRI[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]; (3) preoperative measurement of serum biomarkers, including PT, albumin (ALB), LYM, and other relevant laboratory parameters, performed within one week before surgery; (4) completion of curative liver resection; and (5) absence of severe dysfunction in major organs, such as the heart, lungs, or kidneys.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Exclusion criteria\u003c/h2\u003e\u003cp\u003eThe exclusion criteria for this study were: (1) pathological diagnosis of non-HCC liver malignancies; (2) receipt of preoperative treatments, including radiofrequency ablation, local interventional therapy, or chemotherapy; and (3) mortality within 30 days post-surgery or loss to follow-up shortly thereafter.\u003c/p\u003e\u003cp\u003ePatients with intraoperatively detected extra-regional lymph node metastasis, peritoneal metastasis, or mesenteric metastasis were included in the analysis if the metastatic lesions were surgically resected alongside the primary tumor, achieving complete tumor resection.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Primary endpoint\u003c/h2\u003e\u003cp\u003eThe primary endpoint of this study was recurrence-free survival (RFS), defined as the duration from the date of curative HCC resection to either the date of tumor recurrence or the last follow-up. Recurrence of HCC was confirmed through tumor biopsy or the identification of suspicious lesions detected via follow-up imaging examinations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Liver resection and pathological evaluation\u003c/h2\u003e\u003cp\u003eAll patients underwent comprehensive preoperative evaluations, including routine laboratory tests such as liver function assessments, hepatitis virus screening, and tumor marker analysis, prior to liver resection. Additionally, each patient was required to undergo diagnostic imaging, including abdominal ultrasound, enhanced CT, MRI, and examinations using three-dimensional reconstruction technology. These evaluations were conducted to assess tumor status and resectability, enabling the development of an individualized surgical plan[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe liver resection methods employed included traditional open surgery, laparoscopic surgery, anatomical liver resection, and non-anatomical liver resection. The optimal surgical approach was determined based on the tumor's specific location and distribution. Postoperatively, all resected tissues underwent pathological examination to confirm the absence of residual tumor at the resection margins.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 TBS Definition and PNTR Scoring System\u003c/h2\u003e\u003cp\u003eThe TBS is calculated by determining the Euclidean distance in a Cartesian plane, considering two variables: the maximum tumor size (x-axis) and the tumor count (y-axis). For patients with multiple nodules, the tumor size was defined as the size of the largest lesion. The TBS calculation formula follows the Pythagorean theorem: TBS\u003csup\u003e2\u003c/sup\u003e=(Maximum lesion size)\u003csup\u003e2\u003c/sup\u003e+(Tumor count)\u003csup\u003e2\u003c/sup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The optimal cutoff values for TBS (4.38/8.86 units), PT (14 seconds), and PNI (50.35 units) were determined via X-tile software. The TBS is then categorized into three groups on the basis of the cutoff values: low (\u0026lt;\u0026thinsp;4.38), medium (4.38\u0026ndash;8.86), and high (\u0026gt;\u0026thinsp;8.86), with scores of 1, 2, and 3, respectively. PNI is classified into low (\u0026lt;\u0026thinsp;50.35) and high (\u0026gt;\u0026thinsp;50.35) groups, assigned scores of 2 and 1, respectively. PT is divided into low (\u0026lt;\u0026thinsp;14 seconds) and high (\u0026gt;\u0026thinsp;14 seconds) categories, with scores of 1 and 2, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The presence of extrahepatic metastasis before surgery was assessed, with no metastasis assigned 1 point and metastasis assigned 2 points. The final PNTR score is derived by summing the individual scores.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses for this study were conducted using the Cox proportional hazards model for both univariate and multivariate analyses. Regression coefficients (B) were utilized to estimate the effect of each variable on risk, and the Wald statistic was applied to evaluate the significance of each variable, with a significance threshold set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. For variables identified as significant, hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated.\u003c/p\u003e\u003cp\u003eThe optimal cutoff values for the TBS, PNI and PT, were determined using the quantile function in the X-tile software by arranging the variables in ascending order. RFS was analyzed using Kaplan\u0026ndash;Meier (KM) survival curves, and the model\u0026rsquo;s discriminatory ability was assessed through the area under the receiver operating characteristic curve (AUC). A nomogram based on preoperative variables was constructed to predict RFS. All statistical analyses were performed using SPSS\u0026reg; version 25.0 and R version 3.2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org/\u003c/span\u003e\u003cspan address=\"http://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Factors affecting RFS\u003c/h2\u003e\u003cp\u003eThe multivariable Cox regression analysis of the derivation cohort and validation cohort identified several independent risk factors for RFS. The TBS was significantly associated with RFS, with a hazard ratio (HR) of 1.66 (95% CI: 1.08\u0026ndash;2.50, p\u0026thinsp;=\u0026thinsp;0.004), suggesting that a higher TBS increases the risk of relapse. The PNI also had a modest but significant effect on RFS (HR: 0.95, 95% CI: 0.91\u0026ndash;0.99, p\u0026thinsp;=\u0026thinsp;0.021), indicating that lower PNI values are linked to worse survival outcomes. PT showed a small but statistically significant association (HR: 1.09, 95% CI: 1.02\u0026ndash;1.17, p\u0026thinsp;=\u0026thinsp;0.014), emphasizing the role of coagulation status in predicting relapse risk. Interestingly, preoperative extrahepatic metastasis was associated with a slightly lower risk of relapse (HR: 1.72, 95% CI: 1.13\u0026ndash;2.61. p\u0026thinsp;=\u0026thinsp;0.011), which might reflect a different underlying biological behavior in these patients. Together, these findings highlight the importance of integrating clinical, nutritional, and laboratory factors when assessing relapse risk, which can guide more personalized treatment strategies (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eUnivariable and multivariable Cox regression analyses for RFS Patients in the derivation cohort and validation cohort\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUV HR(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUV P value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMV HR(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMV P value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.78 (0.54\u0026ndash;1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.180\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\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.01 (0.99\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.294\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\u003e\u003cb\u003eTBS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.12 (1.08\u0026ndash;1.17)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.66(1.08\u0026ndash;2.50)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMilan Criteria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.17 (1.00-1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.599(0.44\u0026ndash;1.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.599\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCirrhosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.97 (0.70\u0026ndash;1.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.836\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\u003eHBV(+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.86 (0.62\u0026ndash;1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.376\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(+)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.82 (0.40\u0026ndash;1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.582\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\u003e\u003cb\u003eExtrahepatic Metastasis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.88 (1.25\u0026ndash;2.82)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.72(1.13\u0026ndash;2.61)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAscites\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.06 (1.35\u0026ndash;3.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.05(1.19\u0026ndash;3.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.735\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\u003eAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.845\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\u003eTP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.01 (0.99\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.165\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\u003eALB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.99 (0.97\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.456\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\u003eTB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.01 (1.00-1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.09(1.03\u0026ndash;1.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.955\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.02 (1.01\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00(0.97\u0026ndash;1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.674\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.15 (0.90\u0026ndash;1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.272\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\u003eALP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00 (1.00-1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.97(0.95-1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.628\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eγ-GT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.257\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\u003eTC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.03 (0.59\u0026ndash;1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.911\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\u003eTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.96 (0.67\u0026ndash;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.844\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\u003eHDL_C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.34 (0.31\u0026ndash;5.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.694\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\u003eLDL_C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.39 (0.72\u0026ndash;2.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.326\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\u003eSCr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.642\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\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.16 (0.85\u0026ndash;1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.353\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 Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.01 (0.74\u0026ndash;1.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.935\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\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e1.11 (1.04\u0026ndash;1.19)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.09(1.02\u0026ndash;1.17)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.48 (0.12\u0026ndash;1.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.290\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\u003eWBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.10 (1.03\u0026ndash;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.00(0.98\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.04 (0.81\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.770\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\u003eHb\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.609\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\u003ePLT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.114\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\u003eLY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.07 (0.86\u0026ndash;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.560\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\u003eLY%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.99 (0.97-1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.108\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\u003eNEU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.13 (1.04\u0026ndash;1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.18(0.96\u0026ndash;1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.327\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNEU%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.01 (0.99\u0026ndash;1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.254\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\u003e\u003cb\u003ePNI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.93 (0.89\u0026ndash;0.97)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.95(0.91\u0026ndash;0.99)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.04 (0.96\u0026ndash;1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.352\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\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.513\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_Grading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.34 (0.80\u0026ndash;2.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.266\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\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eTBS, tumor burden score; HBV, hepatitis B virus; HCV, hepatitis C virus; ALT, alanine aminotransferase; AST, aspartate aminotransferase; TP, total protein; ALB, albumin; TB, total bilirubin; DB, direct bilirubin; HE,cholinesterase; ALP, alkaline phosphatase; γ-GT, gamma-glutamyl transferase; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; SCr, serum creatinine; ALBI, albumin-bilirubin index; ALBI score, albumin- bilirubin index; AFP, alpha-fetoprotein; PT, prothrombin time; PTA, prothrombin-to-lymphocyte ratio; INR, international normalized ratio; WBC, white blood cell; RBC, red blood cell; Hb, hemoglobin; PLT, platelet; LY, lymphocyte; LY%, lymphocyte percentage; NEU, neutrophil; NEU%, neutrophil percentage; PNI, prognostic nutritional index; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; CI, confidence interval; HR, hazard ratio; MV, multivariable; UV, univariable interval;\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Nomogram for Predicting Early Recurrence Risk\u003c/h2\u003e\u003cp\u003ePrevious studies have indicated that patients with early disease recurrence have a worse prognosis than those with late disease recurrence. A prevailing hypothesis among researchers is that early recurrence is attributed primarily to intrahepatic micrometastasis, whereas late recurrence is thought to arise from de novo carcinogenesis. In most studies, early recurrence is defined as recurrence occurring within two years postsurgery (Time to recurrence, TTR\u0026thinsp;\u0026lt;\u0026thinsp;2 years) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In our dataset, approximately 80% of the recurrences occurred within two years following surgery.\u003c/p\u003e\u003cp\u003eConsequently, a nomogram was developed to predict the risk of early recurrence after hepatic resection based on the independent risk factors identified through multivariate analysis (TBS, PT, PNI, and preoperative extrahepatic metastasis) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In this model, each risk factor is assigned a score, and the total score is utilized to estimate the risk of early recurrence postsurgery. The AUC for this nomogram in predicting early recurrence risk exceeded 0.65 at various time points in both the derivation and validation cohorts (6-month AUC\u0026thinsp;=\u0026thinsp;0.748 vs. 0.692; 1-year AUC\u0026thinsp;=\u0026thinsp;0.711 vs. 0.676; 2-year AUC\u0026thinsp;=\u0026thinsp;0.775 vs. 0.663) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Impact of TBS, PNI, PT, preoperative extrahepatic metastasis, and PTNR on RFS\u003c/h2\u003e\u003cp\u003eAt the conclusion of the final follow-up, 228 patients (75.7%) experienced recurrence. In the derivation cohort, the median RFS was 5 months. Patients with a high TBS, low PNI, prolonged PT, and preoperative extrahepatic metastasis presented significantly worse RFS than did those with a low TBS, high PNI, shorter PT, and no preoperative extrahepatic metastasis. Similarly, as the PTNR increased, there was a progressive deterioration in the RFS. In the derivation cohort and validation cohort, Kaplan‒Meier survival curves revealed significant differences between the groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eHCC is the most common type of liver malignancy worldwide, with its high incidence and recurrence rates making it a major challenge in global public health. Despite significant improvements in the early diagnosis and treatment of HCC in recent years, the five-year survival rate remains low, with postoperative recurrence rates still being high[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Therefore, enhancing the accuracy of prognostic prediction, developing personalized treatment strategies, and effectively managing the risk of recurrence are critical issues in the current treatment of hepatocellular carcinoma.\u003c/p\u003e\u003cp\u003eIn this study, we utilized a multicenter database to develop and validate a simplified preoperative prognostic scoring system\u0026mdash;the PNTR score model. This model successfully stratifies the long-term prognosis of HCC patients undergoing radical hepatectomy, providing important insights for treatment decision-making. The PNTR score combines the radiological TBS, the PNI, the PT, and the presence of extrahepatic metastasis prior to surgery\u0026mdash;indicators that are easily assessable preoperatively. TBS, in combination with other primary tumor factors, enhances the prognostic predictive power for HCC patients following liver resection. Previous researchers demonstrated that both the TBS and the neutrophil‒lymphocyte ratio (NLR) significantly impact the prognosis of HCC patients after resection[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The complexity and heterogeneity of HCC make it difficult for a single prognostic assessment indicator to comprehensively and accurately reflect the prognosis risk of patients. Therefore, the combined use of multiple clinical and biomarker indicators can provide more comprehensive and accurate prognostic information.The PNTR score system integrates tumor biological factors (such as the TBS and extrahepatic metastasis) and considers the nutritional and metabolic status of the body (such as the PNI and PT), thus offering a more comprehensive reflection of the patient's overall health.Although a direct comparison between the PNTR model and the TBS score was not performed in this study, the PNTR model integrates multiple clinically relevant prognostic variables, including tumor burden, coagulation function, and systemic immune-nutritional status. In contrast, the TBS score is solely based on tumor size and number. Given this broader scope, the PNTR model is theoretically expected to provide more accurate prognostic stratification.\u003c/p\u003e\u003cp\u003eOur findings indicate that high TBS grade, prolonged PT (\u0026gt;\u0026thinsp;14 seconds), low PNI (\u0026lt;\u0026thinsp;50.35), and the presence of extrahepatic metastasis before surgery are independent risk factors for poor prognosis in HCC patients. These factors show strong potential in predicting RFS in HCC patients. By assigning scores based on the TBS, PT, PNI, and preoperative extrahepatic metastasis status, we observed that patients with higher scores presented significantly shorter recurrence times. External validation cohorts also supported this observation, indicating the consistency and reproducibility of the scoring system across different cohorts. Cox regression analysis of the derivation cohort further confirmed that TBS, PT, PNI, and extrahepatic metastasis are independent prognostic factors for RFS. Because of the four independent risk factors in the PNTR scoring system, we constructed a nomogram to predict early recurrence risk after liver resection. Nomograms are extensively utilized as visual tools for prognostic prediction across various cancer types. The AUC analysis further demonstrates that our nomogram is a reliable model for prognostic prediction.\u003c/p\u003e\u003cp\u003eWe classified patients based on different recurrence patterns (local tumor recurrence [LTR], intrahepatic distant recurrence [IDR], and extrahepatic recurrence [ER]). Among these patterns, IDR was the dominant type (n\u0026thinsp;=\u0026thinsp;98, 57.3%), followed by ER (n\u0026thinsp;=\u0026thinsp;39, 22.8%), LTR\u0026thinsp;+\u0026thinsp;IDR (n\u0026thinsp;=\u0026thinsp;13, 7.6%), and IDR\u0026thinsp;+\u0026thinsp;ER (n\u0026thinsp;=\u0026thinsp;21, 12.3%). Notably, among these patients, those with a PNTR score of 7 points had the highest proportion across all recurrence patterns, with 48.9%, 41.0%, 38.5%, and 23.8% in the LTR, IDR, ER, and LTR\u0026thinsp;+\u0026thinsp;IDR recurrence models, respectively. Additionally, patients with a score of 9 points were predominantly observed in the recurrence model with extrahepatic metastasis, suggesting that patients with a score of 9 points may have a greater risk of extrahepatic metastasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). We performed additional statistical analysis in the validation cohort, which confirmed the same results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Although the proportion of these patients is limited, they merit considerable clinical attention, necessitating a comprehensive discussion of the risks and benefits associated with preoperative surgery.The stratification of recurrence patterns by PNTR score provides novel insight into its potential role in recurrence pattern prediction. High PNTR scores were associated with extrahepatic or combined recurrence, which are known to portend worse prognosis and limited treatment options. This suggests that PNTR may serve not only as a prognostic marker but also as a tool to guide surveillance intensity and systemic therapy consideration after resection.Additionally, postoperative follow-up should be more intensive for these patients. Future studies should further expand the sample size to validate the generalizability of these findings. Currently, the application of molecular targeted therapies and immunotherapies has broadened the treatment options for HCC and improved patient prognosis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A large retrospective cohort study revealed that while neoadjuvant therapy is typically used for more advanced tumors, it significantly improves survival rates compared with surgery for resectable HCC [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOverall, the PNTR scoring system is a reliable tool for predicting the prognosis of HCC patients undergoing liver resection. This system can assist clinicians in performing precise risk stratification, thereby facilitating the development of personalized treatment plans. It is particularly valuable in preoperative assessments and postoperative recurrence monitoring.\u003c/p\u003e\u003cp\u003eThere are several limitations to this study. First, it is retrospective in nature, with a limited sample size and inherent bias. Second, owing to the insufficient sample size, we were unable to explore the prognostic characteristics of patients with a PNTR score of 9. Third, the exact etiology of HCC remains unclear [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and most of the patients in this study had HBV-related HCC, which may limit the generalizability of the results to other populations, particularly in Western countries where alcoholic liver disease and nonalcoholic fatty liver disease (NAFLD) are more common. Additionally, patients in the recurrence cohort commonly experienced IDR or IDR\u0026thinsp;+\u0026thinsp;ER recurrence, which may influence the external validation results of the scoring system. HCC patients with high TBSs, prolonged PTs, low PNI levels, and preoperative extrahepatic metastasis should undergo more frequent and stringent monitoring. Early detection of recurrence allows timely interventions that can improve patient outcomes.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study demonstrated that the PNTR score performs well in stratifying HCC patients with respect to RFS. Higher scores are strongly correlated with an elevated risk of recurrence and a notably shorter time to recurrence. The PNTR score serves as an invaluable tool in preoperative consultations, offering significant insights into a patient\u0026rsquo;s prognosis. It plays a crucial role in identifying individuals who may benefit from more thorough clinical observation and timely therapeutic interventions, ensuring that healthcare providers can implement appropriate strategies to optimize patient outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis study was approved by the Ethics Committee of the First Hospital of Shanxi Medical University (2022 K-K0157), Shanxi Provincial Cancer Hospital (JC2024063). Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo acknowledgment\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhening Yan and Yanbo Ma conceived the study and contributed to the study design, implementation, and manuscript revision. Zhening Yan was responsible for drafting the manuscript. Zhening Yan and Yi Chen performed the statistical analysis. Zhening Yan, Yubo Zhao, Xiang Li, Chenguang Shi, and Feng Liu participated in data collection. All the authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupported by the General Program of Natural Science Research, Shanxi Provincial Department of Science and Technology, No. 202203021221248; and Scientific Research Project of Shanxi Provincial Health Commission, No. 2023065.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTan EY, Danpanichkul P, Yong JN, Yu Z, Tan DJH, Lim WH et al (2024) Liver cancer in 2021: Global burden of disease study. J Hepatol. ;:S0168-8278(24)02652-7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiong Y, Qiao W, Mei T, Li K, Jin R, Zhang Y (2024) Recurrence of Hepatocellular Carcinoma in Patients with Low Albumin-Bilirubin Grade in TACE Combined with Ablation: A Random Forest Cox Predictive Model. J Hepatocell Carcinoma 11:1375\u0026ndash;1388\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShen Y, Cheng L, Xu M, Wang W, Wan Z, Xiong H et al (2023) SGLT2 inhibitor empagliflozin downregulates miRNA-34a-5p and targets GREM2 to inactivate hepatic stellate cells and ameliorate non-alcoholic fatty liver disease-associated fibrosis. Metabolism 146:155657\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYouness RA, Hassan HA, Abaza T, Hady AA, El Magdoub HM, Ali M et al (2024) A Comprehensive Insight and In Silico Analysis of CircRNAs in Hepatocellular Carcinoma: A Step toward ncRNA-Based Precision Medicine. Cells 13:1245\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVogel A, Meyer T, Sapisochin G, Salem R, Saborowski A (2022) Hepatocellular carcinoma. Lancet 400:1345\u0026ndash;1362\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDong Q, Jiang Z (2024) Platinum\u0026ndash;Iron Nanoparticles for Oxygen-Enhanced Sonodynamic Tumor Cell Suppression. Inorganics 12:331\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Chen H, Chen S, Duan Y, Cheng L, Gao J et al (2022) Comparison of the Efficacy and Safety of Transarterial Chemoembolization Plus Microwave Ablation versus Surgical Resection in Patients with Solitary Large Hepatocellular Carcinoma: A Propensity Score-Matched Analysis. J Hepatocell Carcinoma 9:1309\u0026ndash;1321\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang K, Xiang Y-J, Yu H-M, Cheng Y-Q, Liu Z-H, Qin Y-Y et al (2024) Adjuvant sintilimab in resected high-risk hepatocellular carcinoma: a randomized, controlled, phase 2 trial. Nat Med 30:708\u0026ndash;715\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSafri F, Nguyen R, Zerehpooshnesfchi S, George J, Qiao L (2024) Heterogeneity of hepatocellular carcinoma: from mechanisms to clinical implications. Cancer Gene Ther 31:1105\u0026ndash;1112\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen R, Hou B, Zhou Y, Zhang T, Wang Z, Chen X et al (2023) Recurrence after percutaneous radiofrequency ablation of hepatocellular carcinoma: Analysis of the pattern and risk factors. Front Oncol 13:1018715\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbdelhamed W, El-Kassas M (2023) Hepatocellular carcinoma recurrence: Predictors and management. Liver Res 7:321\u0026ndash;332\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang K, Xiang Y-J, Yu H-M, Cheng Y-Q, Liu Z-H, Qin Y-Y et al (2024) Adjuvant sintilimab in resected high-risk hepatocellular carcinoma: a randomized, controlled, phase 2 trial. Nat Med 30:708\u0026ndash;715\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu X-F, Xing H, Han J, Li Z-L, Lau W-Y, Zhou Y-H et al (2019) Risk Factors, Patterns, and Outcomes of Late Recurrence After Liver Resection for Hepatocellular Carcinoma: A Multicenter Study From China. JAMA Surg 154:209\u0026ndash;217\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTang S, Huang T, Luo C, Fu J, Zhang K, Chen Q et al (2024) Tumor burden score and alpha-fetoprotein level predict prognosis of patients with unresectable hepatocellular carcinoma treated with tyrosine kinase inhibitor and anti-PD-1 antibody. iLIVER 3:100109\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Z, Liu Y, Zhang W, Hong Y, Meng J, Wang J et al (2022) Deep learning for prediction of hepatocellular carcinoma recurrence after resection or liver transplantation: a discovery and validation study. Hepatol Int 16:577\u0026ndash;589\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTellapuri S, Sutphin PD, Beg MS, Singal AG, Kalva SP (2018) Staging systems of hepatocellular carcinoma: A review. Indian J Gastroenterol 37:481\u0026ndash;491\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim DY (2024) Changing etiology and epidemiology of hepatocellular carcinoma: Asia and worldwide. J Liver Cancer 24:62\u0026ndash;70\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDirican A, Uncu D, Sekacheva M, Arta\u0026ccedil; M, Aladashvil A, Erdogan A et al (2023) A multicentre, multinational study of clinical characteristics and prognosis of hepatocellular carcinoma. East Mediterr Health J 29:462\u0026ndash;473\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSasaki K, Morioka D, Conci S, Margonis GA, Sawada Y, Ruzzenente A et al (2018) The Tumor Burden Score: A New Metro-ticket Prognostic Tool For Colorectal Liver Metastases Based on Tumor Size and Number of Tumors. Ann Surg 267:132\u0026ndash;141\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa W, Liu R, Wang J, Liu L, Qiu Z, Yu J et al (2024) High tumor burden score indicated the unfavorable prognosis in patients with hepatocellular carcinoma: A meta-analysis. PLoS ONE 19:e0308570\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHo S-Y, Liu P-H, Hsu C-Y, Ko C-C, Huang Y-H, Su C-W et al (2021) Tumor burden score as a new prognostic marker for patients with hepatocellular carcinoma undergoing transarterial chemoembolization. J Gastroenterol Hepatol 36:3196\u0026ndash;3203\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang D, Hu X, Xiao L, Long G, Yao L, Wang Z et al (2021) Prognostic Nutritional Index and Systemic Immune-Inflammation Index Predict the Prognosis of Patients with HCC. J Gastrointest Surg 25:421\u0026ndash;427\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang N, Lin K, Qiao B, Yan L, Jin D, Yang D et al (2024) Machine Learning Model Based on Prognostic Nutritional Index for Predicting Long-Term Outcomes in Patients With HCC Undergoing Ablation. Cancer Med 13:e70344\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHo C-T, Chia-Hui Tan E, Lee P-C, Chu C-J, Huang Y-H, Huo T-I et al (2024) Prognostic Nutritional Index as a Prognostic Factor for Very Early-Stage Hepatocellular Carcinoma. Clin Transl Gastroenterol 15:e00678\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlwarawrah Y, Kiernan K, MacIver NJ (2018) Changes in Nutritional Status Impact Immune Cell Metabolism and Function. Front Immunol 9:1055\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDing P, Guo H, Sun C, Yang P, Kim NH, Tian Y et al (2022) Combined systemic immune-inflammatory index (SII) and prognostic nutritional index (PNI) predicts chemotherapy response and prognosis in locally advanced gastric cancer patients receiving neoadjuvant chemotherapy with PD-1 antibody sintilimab and XELOX: a prospective study. BMC Gastroenterol 22:121\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeng L, Wang Y, Liu F, Qiu X, Zhang X, Fang C et al (2020) Peripheral blood markers predictive of outcome and immune-related adverse events in advanced non-small cell lung cancer treated with PD-1 inhibitors. Cancer Immunol Immunother 69:1813\u0026ndash;1822\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeng H, He Y, Huang G, Huang Y, Wu J, Qin X (2024) Predictive value of prognostic nutritional index in patients undergoing gastrectomy for gastric cancer: A systematic review and meta-analysis. Med (Baltim) 103:e39917\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLima HA, Moazzam Z, Endo Y, Alaimo L, Shaikh C, Munir MM et al (2023) TBS-Based Preoperative Score to Predict Non-transplantable Recurrence and Identify Candidates for Upfront Resection Versus Transplantation for Hepatocellular Carcinoma. Ann Surg Oncol 30:3363\u0026ndash;3373\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChartampilas E, Rafailidis V, Georgopoulou V, Kalarakis G, Hatzidakis A, Prassopoulos P (2022) Current Imaging Diagnosis of Hepatocellular Carcinoma. Cancers (Basel) 14:3997\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGundavda KK, Patkar S, Varty GP, Shah N, Velmurugan K, Goel M (2025) Liver Resection for Hepatocellular Carcinoma: Recent Advances. J Clin Experimental Hepatol. ;15\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFu J, Zheng L, Tang S, Lin K, Zheng S, Bi X et al (2024) Tumor burden score and carcinoembryonic antigen predict outcomes in patients with intrahepatic cholangiocarcinoma following liver resection: a multi\u0026ndash;institutional analysis. BMC Cancer 24:358\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan W-T, Li C, Yao L-Q, Qiu H-B, Wang M-D, Xu X-F et al (2023) Predictors and long-term prognosis of early and late recurrence for patients undergoing hepatic resection of hepatocellular carcinoma: a large-scale multicenter study. Hepatobiliary Surg Nutr 12:155\u0026ndash;168\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNevola R, Ruocco R, Criscuolo L, Villani A, Alfano M, Beccia D et al (2023) Predictors of early and late hepatocellular carcinoma recurrence. World J Gastroenterol 29:1243\u0026ndash;1260\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Z, Liu X, Liang J, Liu Y, Hou X, Zhang M et al (2021) Immunotherapy for Hepatocellular Carcinoma: Current Status and Future Prospects. Front Immunol 12:765101\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang J, Chen Z, Wang L, Feng S, Qiu Q, Chen D et al (2022) A new model based inflammatory index and tumor burden score (TBS) to predict the recurrence of hepatocellular carcinoma (HCC) after liver resection. Sci Rep 12:8670\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y-K, Wu S, Wu Y-S, Zhang W-H, Wang Y, Li Y-H et al (2024) Portal Venous and Hepatic Arterial Coefficients Predict Post-Hepatectomy Overall and Recurrence-Free Survival in Patients with Hepatocellular Carcinoma: A Retrospective Study. J Hepatocell Carcinoma 11:1389\u0026ndash;1402\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLlovet JM, Pinyol R, Yarchoan M, Singal AG, Marron TU, Schwartz M et al (2024) Adjuvant and neoadjuvant immunotherapies in hepatocellular carcinoma. Nat Rev Clin Oncol 21:294\u0026ndash;311\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang DQ, Mathurin P, Cortez-Pinto H, Loomba R (2023) Global epidemiology of alcohol-associated cirrhosis and HCC: trends, projections and risk factors. Nat Rev Gastroenterol Hepatol 20:37\u0026ndash;49\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eToh MR, Wong EYT, Wong SH, Ng AWT, Loo L-H, Chow PK-H et al (2023) Global Epidemiology and Genetics of Hepatocellular Carcinoma. Gastroenterology 164:766\u0026ndash;782\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, hepatectomy, prognostic nutritional index, recurrence, tumor burden score","lastPublishedDoi":"10.21203/rs.3.rs-7155075/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7155075/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study aimed to investigate the influence of the tumor burden score (TBS), prognostic nutritional index (PNI), prothrombin time (PT), and preoperative extrahepatic metastasis on the risk of postoperative recurrence in patients with hepatocellular carcinoma (HCC) undergoing hepatectomy. A simplified prognostic model, termed the PNTR scoring system, was developed and subsequently validated to assess recurrence risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eThis retrospective study included 301 patients with hepatocellular carcinoma (HCC) from three medical centers, with data collected between 2015 and 2023. The PNTR scoring system was utilized to predict recurrence-free survival (RFS) in both the derivation cohort (n=232) and the validation cohort (n=69). Variables such as TBS, PNI, PT, and preoperative extrahepatic metastasis were evaluated for their association with postoperative recurrence. Subgroup analyses were conducted to assess recurrence patterns at various sites, including local tumor recurrence (LTR), intrahepatic distant recurrence (IDR), and extrahepatic recurrence (ER).Statistical analysis was performed using Cox proportional hazards regression models, and Kaplan-Meier survival curves were employed for subgroup comparisons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA high TBS, prolonged PT, low PNI, and preoperative extrahepatic metastasis were identified as independent risk factors for recurrence. The PNTR scoring system demonstrated that higher scores were associated with an increased risk of recurrence. In both the derivation cohort and validation cohort, the model exhibited robust discriminatory ability, with the following area under the curve (AUC) values: at 6 months (AUC = 0.748 vs 0.692), at 1 year (AUC = 0.711 vs 0.676), and at 2 years (AUC = 0.775 vs 0.663). These findings indicate that the model effectively differentiates between recurrence patterns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe PNTR scoring system represents a precise and dependable instrument for predicting postoperative recurrence in patients with HCC, and it may aid in optimizing both monitoring protocols and therapeutic strategies.\u003c/p\u003e","manuscriptTitle":"The Predictive Model for Recurrence After Hepatectomy in Hepatocellular Carcinoma Patients Based on the Tumor Burden Score Combined with the Prognostic Nutritional Index and Other Indicators","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-18 08:15:06","doi":"10.21203/rs.3.rs-7155075/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":"5f249b65-bd9d-4103-b094-caaf0c39294c","owner":[],"postedDate":"August 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-10T06:38:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-18 08:15:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7155075","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7155075","identity":"rs-7155075","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}