Published online Jul 15, 2026. doi: 10.4251/wjgo.v18.i7.121250
Revised: April 8, 2026
Accepted: April 22, 2026
Published online: July 15, 2026
Processing time: 116 Days and 10.7 Hours
Hepatocellular carcinoma (HCC) prognosis is influenced by tumor burden, liver function, performance status, and systemic inflammation. Conventional prog
To evaluate the prognostic significance of the pan-immune-inflammation value (PIV) compared with ALBI and MELD-Na scores in patients with HCC.
This retrospective cohort study included 97 patients diagnosed with HCC and treated at the Medical Oncology Department of Ankara Etlik City Hospital be
Median follow-up was 10 months, and 58 deaths occurred. In univariate analysis, Eastern Cooperative Oncology Group (ECOG) performance status, Barcelona clinic liver cancer stage, cirrhosis, treatment modality, ALBI, MELD-Na score, and PIV were significantly associated with OS (all P < 0.05). In multivariable Cox regression analysis, ECOG performance status (P < 0.001) and PIV (P = 0.016) remained independent prognostic factors. ROC analysis demonstrated that PIV had the highest discriminatory ability for predicting OS (AUC = 0.794, P < 0.001), followed by ALBI (AUC = 0.781, P < 0.001), alpha-fetoprotein (AUC = 0.712, P < 0.001), and MELD-Na (AUC = 0.699, P = 0.001). Patients with high PIV had significantly poorer survival, with a median OS of 5 months compared with 30 months in the low-PIV group.
These findings demonstrate that PIV provides superior prognostic performance compared with conventional liver function-based scores and may serve as a robust biomarker for risk stratification in patients with HCC.
Core Tip: Pan-immune-inflammation value (PIV) is a novel biomarker reflecting systemic inflammatory and immune status in hepatocellular carcinoma (HCC). In this retrospective study, PIV demonstrated superior prognostic performance compared with conventional liver function scores such as albumin-bilirubin and model for end-stage liver disease-sodium and remained an independent predictor of overall survival. These findings suggest that inflammation-based indices may improve risk stratification beyond traditional liver function-based models in patients with HCC.
- Citation: Karatlı S, Yazılıtaş D, Kaya S, İmamoğlu Gİ, Çelik S, Uyar GC. Prognostic value of pan-immune-inflammation index in hepatocellular carcinoma compared with albumin-bilirubin and model for end-stage liver disease sodium. World J Gastrointest Oncol 2026; 18(7): 121250
- URL: https://www.wjgnet.com/1948-5204/full/v18/i7/121250.htm
- DOI: https://dx.doi.org/10.4251/wjgo.v18.i7.121250
Hepatocellular carcinoma (HCC) is the most common malignancy of the liver worldwide and one of the leading causes of cancer-related mortality. The prognosis of the disease is determined by the complex interaction of multiple factors, including tumor burden, hepatic functional reserve, performance status, systemic inflammation, and therapeutic approach. Therefore, various clinical and biochemical scoring systems have been developed to predict survival in patients with HCC[1].
The Child-Pugh (CP), model for end-stage liver disease (MELD), MELD-sodium (MELD-Na), albumin-bilirubin (ALBI), and Barcelona Clinic Liver Cancer (BCLC) scores are widely used for prognostic assessment by evaluating liver function and tumor burden[2]. However, these models do not adequately reflect the host inflammatory response or the impact of the immune microenvironment.
In recent years, the pan-immune-inflammation value (PIV) has emerged as a novel hematology-based biomarker that reflects the systemic inflammatory and immune status more comprehensively than earlier indices such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), or systemic immune-inflammation index (SII). PIV is considered a composite parameter that integrates multiple components of the host immune-inflammatory response.
Previous studies have demonstrated the prognostic value of PIV in HCC, particularly in curative treatment settings. Karadağ et al[3] were among the first to suggest that PIV could serve as a potential prognostic marker at the time of diagnosis in patients with HCC. Liang et al[4] reported that a high PIV level (cut-off 120) was an independent predictor of worse recurrence-free survival (RFS) and overall survival (OS) in early-stage HCC patients treated with curative radiofrequency ablation. More recently, Fu et al[5] showed that elevated PIV independently predicted poorer OS and RFS after hepatectomy and exhibited superior discriminatory ability compared with other inflammation-based indices (NLR/PLR/SII). However, these studies were mostly limited to early-stage or curative-treatment cohorts, with relatively homoge
This study aimed to evaluate the prognostic significance of PIV in comparison with ALBI and MELD-Na scores in patients with HCC and to determine whether this inflammation-based biomarker provides additional prognostic value beyond conventional liver function-based models. Importantly, this study uniquely investigates PIV within a real-world HCC cohort encompassing both early- and advanced-stage patients and diverse treatment modalities, thereby addressing a critical gap in the current literature. In addition, the effects of clinical factors such as Eastern Cooperative Oncology Group (ECOG) performance status and treatment modality on OS were also analyzed.
This study was designed as a retrospective cohort study. A total of 97 patients diagnosed with HCC and having complete clinical and laboratory data were included, who were followed at the Medical Oncology Department of Ankara Etlik City Hospital between September 1, 2022, and September 1, 2025.
Inclusion criteria: (1) Patients diagnosed with HCC confirmed either by histopathological examination or by typical dynamic imaging (computed tomography/magnetic resonance imaging) consistent with international guidelines; and (2) Patients with complete clinical and laboratory data available at the time of diagnosis.
Exclusion criteria: (1) Incomplete clinical or laboratory data; (2) Presence of another concurrent primary malignancy; and (3) Patients under 18 years of age diagnosed with HCC. Patients with incomplete clinical or laboratory data were excluded because accurate calculation of the PIV, ALBI, and MELD-Na scores requires the availability of all component variables. Missing values in any of these parameters would preclude reliable score calculation and may lead to misclassification.
Although statistical approaches such as multiple imputation can be considered for handling missing data, missingness in our study primarily involved key variables required for the computation of composite prognostic indices. Imputation of these core variables may introduce bias and affect the validity of the results. Therefore, a complete-case analysis approach was adopted.
The demographic and clinical data of the patients were recorded. Laboratory evaluations included complete blood count (hemoglobin, platelet, lymphocyte, monocyte), biochemical tests (albumin, bilirubin, aspartate aminotransferase, alanine aminotransferase, creatinine), international normalized ratio (INR), and serum sodium levels. Using these parameters, CP, MELD-Na, ALBI, BCLC, and PIV scores were calculated. The CP score was determined to assess hepatic functional reserve based on serum albumin, bilirubin, INR, presence of ascites, and hepatic encephalopathy.
The classification according to total scores was as follows: 5-6 points: CP A; 7-9 points: CP B; 10-15 points: CP C. The CP score was calculated to evaluate hepatic functional reserve. However, it was excluded from the comparative statistical analyses because it contains subjective parameters such as ascites and hepatic encephalopathy, which are known to introduce significant interobserver variability[6-8]. These subjective components can reduce the reproducibility of the score, particularly in retrospective studies, and may lead to potential bias. Therefore, only objective laboratory-based scoring systems (ALBI, MELD-Na, and PIV) were comparatively analyzed in the receiver operating characteristic (ROC) and Cox regression models.
The ALBI score was calculated from albumin and bilirubin levels to quantitatively assess liver function: ALBI = [log10 bilirubin (μmol/L) × 0.66] + [albumin (g/L) × -0.085] The ALBI grades were classified as follows: Grade 1: ≤ -2.60; Grade 2: -2.60 to -1.39; Grade 3: > -1.39. The BCLC classification system stages the disease by integrating liver function, tumor burden, and patient performance status, providing a framework for clinical management.
The MELD-Na score is a biochemical index reflecting liver function and electrolyte balance: MELD-Na = 3.78 × ln [bilirubin (mg/dL)] + 11.2 × ln (INR) + 9.57 × ln [creatinine (mg/dL)] + 6.43 + 1.59 × [135 - Na (mmol/L)]. All values were evaluated in mg/dL and mmol/L units, with serum sodium (Na) restricted to a range of 125-137 mmol/L. PIV was calculated using the following formula: PIV = [neutrophil (103/mm3) × platelet (103/mm3) × monocyte (103/mm3)]/Lymphocyte (103/mm3).
OS was defined as the time from diagnosis to death from any cause or last follow-up. Survival curves were estimated using the Kaplan-Meier method, and differences between groups were compared using the log-rank test. Treatment modalities were categorized into two groups: Curative (surgical resection, radiofrequency ablation, or liver transplan
ROC curve analysis was performed to evaluate the predictive performance of PIV, ALBI, MELD-Na, and alpha-fetoprotein (AFP) for OS. The optimal cut-off values in the ROC curve analysis were determined using the Youden index. For Cox regression analyses, clinical variables were categorized as follows: Age (≥ 68 years vs < 68 years, according to the median value), ECOG performance status (≥ 2 vs 0-1), BCLC stage (C-D vs A-B), cirrhosis (present vs absent), ALBI grade (2-3 vs 1), MELD-Na (high vs low), and PIV (high vs low).
For Cox regression analyses, MELD-Na and PIV were dichotomized as high vs low according to the optimal cut-off values determined by ROC curve analysis using the Youden index. Patients with PIV ≥ 486.9 were classified as the high-PIV group, and those with PIV < 486.9 as the low-PIV group. Similarly, patients with MELD-Na score ≥ 8.3 were classified as the high MELD-Na group, and those with MELD-Na score < 8.3 as the low MELD-Na group.
Univariate Cox regression analysis was performed by including each variable separately to evaluate its association with OS. Variables with a P value < 0.10 in univariate analysis were considered candidates for inclusion in the mul
Both ALBI and MELD-Na reflect hepatic functional reserve; therefore, only ALBI was included in the multivariable model. Treatment modality was also excluded from the multivariable analysis because treatment allocation in HCC is largely determined by tumor stage and performance status, which may result in overadjustment. Although serum AFP is a well-known biomarker in HCC, ROC analysis in the present study showed lower prognostic discrimination compared with PIV and ALBI; therefore, AFP was not included in the regression analyses, as the main objective of the study was to compare inflammation-based and liver function-based prognostic scoring systems. Results were reported as hazard ratios (HRs) with 95% confidence intervals (CIs). The proportional hazards assumption of the Cox regression model was assessed using Schoenfeld residuals.
Statistical analyses were performed using IBM SPSS Statistics version 25.0 (IBM Corp. Armonk, NY, United States). A two-sided P value < 0.05 was considered statistically significant. Only patients with complete data were included in the final analysis.
The study was conducted in accordance with the principles of the Declaration of Helsinki. Ethical approval was obtained from the Ankara Etlik City Hospital Scientific Research Ethics Committee (Decision No: AEŞH-BADEK2-2025-438, date: September 16, 2025). Informed consent was waived due to the retrospective design and use of anonymized data.
A total of 97 patients diagnosed with HCC were included. The median follow-up duration for the entire cohort was 10 months (range: 1-36 months). During follow-up, 58 deaths were observed, while 39 patients were censored at the last follow-up. The median age of the patients was 68 years (range: 27-87 years), and the majority were male (79.4%). Most patients had good performance status, with ECOG 0-1 observed in 74.3% of the cohort. Chronic hepatitis B infection was the most common underlying etiology (54.6%), followed by non-alcoholic liver disease (21.6%) and alcohol-related liver disease (15.5%). Cirrhosis was present in 39.2% of the patients. According to BCLC staging, 30.9% of patients were stage A, 14.4% stage B, 35.1% stage C, and 19.6% stage D. Multiple tumors were observed in approximately half of the patients (50.5%), and vascular invasion was present in 35.1%. Regarding treatment modalities, sorafenib was the most frequently administered therapy (34.0%), followed by surgical resection (22.7%) and best supportive care (18.6%) (Table 1).
| Variable | |
| Age, years | Median 68 (range: 27-87) |
| Sex | |
| Female | 20 (20.6) |
| Male | 77 (79.4) |
| ECOG performance status | |
| 0 | 31 (32.0) |
| 1 | 41 (42.3) |
| 2 | 13 (13.4) |
| 3 | 11 (11.3) |
| 4 | 1 (1.0) |
| Etiology | |
| Hepatitis B | 53 (54.6) |
| Hepatitis C | 8 (8.3) |
| Alcohol-related | 15 (15.5) |
| Non-alcoholic | 21 (21.6) |
| Cirrhosis | |
| Absent | 59 (60.8) |
| Present | 38 (39.2) |
| BCLC stage | |
| A | 30 (30.9) |
| B | 14 (14.4) |
| C | 34 (35.1) |
| D | 19 (19.6) |
| Tumor number | |
| Single | 28 (28.9) |
| 2-3 lesions | 20 (20.6) |
| Multiple (≥ 4) | 49 (50.5) |
| Vascular invasion | |
| Absent | 63 (64.9) |
| Present | 34 (35.1) |
| Treatment modality | |
| Liver transplantation | 4 (4.1) |
| Surgical resection | 22 (22.7) |
| TACE/TARE | 9 (9.3) |
| Sorafenib | 33 (34.0) |
| Atezolizumab + bevacizumab | 3 (3.1) |
| Best supportive care | 18 (18.6) |
| Radiofrequency ablation | 7 (7.2) |
| FOLFOX | 1 (1.0) |
Baseline laboratory parameters and prognostic scores of the study population are summarized in Table 2. The median albumin level was 37 g/L (range: 20-50 g/L), and the median total bilirubin level was 0.9 mg/dL (range: 0.2-11.0 mg/dL). The median neutrophil, lymphocyte, and platelet counts were 4300/μL, 1500/μL, and 175 × 103/μL, respectively. Among the prognostic scores evaluated, the median ALBI score was -2.36 (range: -3.70 to -0.67), the median MELD-Na score was 8.65 (range: 3.11-28.92), and the median PIV score was 347.3 (range: 14-11320) (Table 2).
| Variable | Median (range) |
| Albumin (g/L) | 37 (20-50) |
| Total bilirubin (mg/dL) | 0.9 (0.2-11.0) |
| Neutrophil count (/μL) | 4300 (1350-16790) |
| Lymphocyte count (/μL) | 1500 (430-5140) |
| Monocyte count (/μL) | 600 (40-1200) |
| Platelet count (× 103/μL) | 175 (35-703) |
| AFP (ng/mL) | 72 (0-34.890) |
| ALBI score | -2.36 (-3.70 to -0.67) |
| MELD-Na score | 8.65 (3.11-28.92) |
| PIV score | 347.3 (14-11320) |
In the univariate Cox regression analysis, ECOG performance status (HR = 3.60, 95%CI: 2.05-6.33, P < 0.001), BCLC stage (HR = 7.32, 95%CI: 3.69-14.52, P < 0.001), presence of cirrhosis (HR = 1.73, 95%CI: 1.00-2.99, P = 0.048), treatment modality (HR = 9.46, 95%CI: 3.73-24.00, P < 0.001), ALBI grade (HR = 2.66, 95%CI: 1.47-4.79, P = 0.001), MELD-Na score (HR = 2.82, 95%CI: 1.62-4.89, P < 0.001), and PIV level (HR = 2.83, 95%CI: 1.65-4.86, P < 0.001) were significantly associated with OS. In contrast, age, etiology and sex were not significantly associated with OS (Table 3).
| Variable | HR | 95%CI | P value |
| Age (high vs low) | 1.36 | 0.80-2.33 | 0.247 |
| Sex (male vs female) | 0.67 | 0.36-1.25 | 0.205 |
| ECOG performance status (≥ 2 vs 0-1) | 3.60 | 2.05-6.33 | < 0.001 |
| BCLC stage (C-D vs A-B) | 7.32 | 3.69-14.52 | < 0.001 |
| Cirrhosis (present vs absent) | 1.73 | 1.00-2.99 | 0.048 |
| Treatment modality (non-curative vs curative) | 9.46 | 3.73-24.00 | < 0.001 |
| ALBI grade (2-3 vs 1) | 2.66 | 1.47-4.79 | 0.001 |
| MELD-Na (high vs low) | 2.82 | 1.62-4.89 | < 0.001 |
| PIV (high vs low) | 2.83 | 1.65-4.86 | < 0.001 |
| Etiology (viral vs nonviral) | 0.85 | 0.48-1.49 | 0.573 |
Variables with P < 0.10 in the univariate analysis were considered candidates for the multivariable Cox regression model. However, BCLC stage was not included in the multivariable model because it represents a composite staging system incorporating tumor burden, performance status, and liver function, which may lead to potential collinearity. Similarly, both ALBI and MELD-Na scores reflect hepatic functional reserve; therefore, to avoid potential collinearity, only the ALBI score was included in the multivariable model. In addition, treatment modality was not included in the multivariable analysis since treatment allocation in HCC is largely determined by tumor stage and performance status, which may result in overadjustment.
In the multivariable Cox regression analysis, ECOG performance status (HR = 2.91, 95%CI: 1.63-5.19, P < 0.001) and PIV level (HR = 2.01, 95%CI: 1.14-3.52, P = 0.016) were identified as independent prognostic factors for OS. In contrast, cirrhosis (HR = 1.20, 95%CI: 0.66-2.18, P = 0.538) and ALBI grade (HR = 1.82, 95%CI: 0.95-3.48, P = 0.071) were not statistically significant in the multivariable analysis (Table 4).
| Variable | HR | 95%CI | P value |
| ECOG performance status (≥ 2 vs 0-1) | 2.91 | 1.63-5.19 | < 0.001 |
| PIV (high vs low) | 2.01 | 1.14-3.52 | 0.016 |
| Cirrhosis (present vs absent) | 1.20 | 0.66-2.18 | 0.538 |
| ALBI grade (2-3 vs 1) | 1.82 | 0.95-3.48 | 0.071 |
In the ROC analysis, PIV demonstrated the highest discriminatory ability for predicting OS among the evaluated prognostic scores (AUC = 0.794, 95%CI: 0.705-0.883, P < 0.001), followed by the ALBI score (AUC = 0.781, 95%CI: 0.689-0.872, P < 0.001), AFP (AUC = 0.712, 95%CI: 0.609-0.815, P < 0.001), and the MELD-Na score (AUC = 0.699, 95%CI: 0.593-0.805, P = 0.001). The optimal cut-off values in the ROC curve analysis were determined using the Youden index. According to ROC analysis, the optimal cut-off values were 486.9 for PIV, -2.12 for ALBI, 91 for AFP, and 8.3 for MELD-Na (Figure 1 and Table 5).
| Prognostic score | AUC | 95%CI | P value | Cut-off value |
| PIV | 0.794 | 0.705-0.883 | < 0.001 | 486.9 |
| ALBI score | 0.781 | 0.689-0.872 | < 0.001 | -2.12 |
| AFP | 0.712 | 0.609-0.815 | < 0.001 | 91 |
| MELD-Na score | 0.699 | 0.593-0.805 | 0.001 | 8.3 |
Kaplan-Meier survival analysis demonstrated a significant difference in OS according to PIV levels. The median OS was 30 months in the low-PIV group and 5 months in the high-PIV group. The mean survival times were 21.6 months and 11.1 months in the low- and high-PIV groups, respectively, indicating poorer survival in patients with elevated PIV levels (Figure 2 and Table 6).
| PIV group | Mean survival (months) | Median survival (months) | 95%CI (median) |
| Low PIV | 21.6 | 30 | 21.5-38.5 |
| High PIV | 11.1 | 5 | 0.6-9.4 |
| Overall | 17.9 | 13 | 8.8-17.2 |
Various clinical and biochemical scoring systems have long been used to predict survival in patients with HCC. The prognostic value of these scores has been extensively investigated across different patient populations in the literature. Models such as CP, MELD, MELD-Na, ALBI, and BCLC aim to estimate patient prognosis by incorporating liver functional reserve, tumor burden, and performance status to varying degrees[9-11].
The CP and BCLC classifications are the most commonly used systems for predicting the prognosis of HCC. The CP score has been the standard method for assessing hepatic functional reserve for many years, and numerous studies have demonstrated its strong association with survival[12-14]. Similarly, the BCLC staging system, as the first comprehensive model integrating liver function, performance status, and tumor burden, has been supported by several studies showing significantly longer survival in early-stage patients (BCLC 0-A) compared with those at advanced stages (BCLC C-D)[15]. However, both systems have notable limitations.
The CP score includes subjective components such as ascites and hepatic encephalopathy, which can lead to interobserver variability and reduce its discriminative power, particularly in patients with borderline hepatic function[16-18]. Furthermore, the BCLC system relies solely on the CP classification to assess liver function, does not incorporate objective biomarkers such as AFP or ALBI, and restricts treatment decisions rigidly within staging categories, thereby limiting its flexibility in real-world clinical practice[18].
In our study, the CP score was not directly included in the comparison because it contains subjective parameters such as ascites and hepatic encephalopathy. Instead, more objective laboratory-based systems (ALBI, MELD-Na, and PIV) were evaluated. In the univariate analysis, BCLC stage showed a significant association with OS; however, it was not included in the multivariable model because it represents a composite staging system incorporating tumor burden, performance status, and liver function, which may lead to collinearity with other variables. In the multivariable Cox regression analysis, ECOG performance status and PIV were identified as independent prognostic factors for OS, whereas ALBI grade did not remain statistically significant. Additionally, ROC analysis demonstrated that PIV had the highest discriminatory ability for predicting OS among the evaluated scores. Although the difference between AUC values was modest, PIV showed the best overall prognostic performance in this cohort. Previous studies evaluating PIV in different malignancies have reported variable cut-off values depending on the study population and tumor type. In the present study, the optimal cut-off value for PIV was determined as 486.9 using the Youden index.
Overall, our findings suggest that inflammation-based indices such as PIV may provide additional prognostic infor
The MELD and MELD-Na scores are models that assess hepatic functional reserve based on objective laboratory parameters. Marrero et al[19] demonstrated that a MELD-Na value greater than 10 represents a threshold associated with significantly reduced survival. Similarly, a large-scale analysis by D’Amico et al[20] reported that the MELD score has independent prognostic value in patients with cirrhosis and HCC.
In our study, ROC analysis showed that the discriminative ability of the MELD-Na score for predicting OS was lower compared with the PIV and ALBI scores. In addition, since both MELD-Na and ALBI reflect hepatic functional reserve, only the ALBI score was included in the multivariable analysis to avoid potential collinearity. These findings suggest that although the MELD-Na score reflects liver function, its prognostic performance may be limited because it does not directly incorporate other important prognostic factors such as tumor biology and systemic inflammatory response. AFP also demonstrated moderate discriminatory ability (AUC = 0.712); however, its prognostic performance was lower than that of PIV and ALBI in our cohort.
The ALBI score is a novel parameter that objectively assesses liver function based solely on albumin and bilirubin levels, and it has demonstrated strong prognostic performance in multiple studies[21,22]. Johnson et al[23] reported that ALBI provides a more homogeneous distribution and greater predictive accuracy for survival compared with the CP score. Likewise, Hiraoka et al[24] identified ALBI as an independent prognostic factor in patients with early- and intermediate-stage HCC. However, some studies have indicated that, because ALBI does not include indicators of portal hypertension or inflammation, its discriminative power may be limited in patients with moderate hepatic dysfunction. Moreover, its prognostic accuracy alone may be insufficient, as it does not fully capture the substantial tumor heterogeneity and biological diversity observed in HCC[25]. In our study, although the ALBI classification showed a significant difference in OS according to the Kaplan-Meier analysis, it did not remain an independent prognostic factor in the multivariate Cox regression model. This finding suggests that, in advanced-stage patients, systemic inflammation and immune balance may have a greater influence on survival than liver function itself. PIV, as a biomarker that comprehensively reflects this process, is associated with tumor progression and treatment resistance through increased inflammatory burden and immune suppression. The observation that PIV exhibited an independent adverse prognostic effect in our study further supports this biological mechanism.
In recent years, the number of studies directly comparing the prognostic performance of these scoring systems has increased[17,26-28]. Collectively, these findings indicate that the prognostic models used in HCC each have their own advantages and limitations; however, no single model can sufficiently explain all clinical conditions. Therefore, the importance of comparative and integrative studies aimed at identifying the most accurate predictors of survival has been increasingly emphasized in the literature. As a result, new biomarkers based on inflammation and immune response have gained growing significance in evaluating HCC prognosis. A high PIV score reflects an imbalance characterized by increased neutrophil- and platelet-mediated proinflammatory activity and decreased lymphocyte-driven immune response. This imbalance is associated with mechanisms such as immune suppression within the tumor microenvironment, enhanced angiogenesis, and increased metastatic potential. Several studies conducted across different cancer types have shown that high PIV values are associated with poorer survival outcomes and reduced treatment response[29,30]. Therefore, PIV offers a more dynamic and biologically oriented perspective compared with conventional clinical scoring systems, as it reflects not only liver function but also tumor biology and the host’s immunoinflammatory status.
In our study, when the factors affecting survival were evaluated, ECOG performance status and PIV value emerged as independent prognostic factors for OS in the multivariable Cox regression analysis. This finding suggests that, in addition to the patient’s general performance status, systemic inflammatory burden also plays an important role in determining prognosis. Indeed, the association between higher PIV values and shorter survival further supports the significant role of inflammation and immune response in the biology of HCC. In contrast, some variables that were significant in the univariate analysis did not retain their independence in the multivariable model. BCLC staging system was not included in the multivariable analysis because it represents a composite system incorporating tumor burden, performance status, and liver function, which may lead to potential collinearity with other variables. ALBI grade did not remain statistically significant in the multivariable analysis. These findings suggest that clinical or morphology-based systems alone may be insufficient to fully explain HCC prognosis and that inflammation-based biomarkers may provide additional prognostic information. Overall, our results indicate that a comprehensive approach integrating clinical characteristics and inflammation-based parameters may contribute to more accurate prognostic stratification in patients with HCC.
This study has several limitations. First, due to its retrospective and single-center design, the sample size was relatively limited (n = 97), which may reduce the statistical power of the analyses and limit the generalizability of the findings. The small cohort size may also explain why some variables, such as ALBI grade (P = 0.071), remained at the threshold of statistical significance in the multivariable model. Nevertheless, previous studies evaluating the prognostic value of PIV in HCC have also been conducted in cohorts of similar size and have reported PIV as an independent prognostic factor. Second, the exclusion of patients with missing clinical or laboratory data may have introduced selection bias. Although this approach was necessary to ensure accurate calculation of prognostic scores, it may limit the generalizability of the findings. Third, inflammatory parameters were calculated only from hematological values obtained at the time of diagnosis, and dynamic changes during the disease course could not be evaluated. Inflammation-based indices such as PIV may change following treatment and their prognostic value may evolve over time; however, such analyses were not feasible due to the retrospective design and limited longitudinal data. Fourth, the study cohort included patients receiving different treatment modalities, including surgical resection, locoregional therapies, systemic treatments, and best supportive care. Because treatment allocation in HCC is largely determined by tumor stage and performance status, this heterogeneity may have introduced potential confounding effects. Although treatment modality was not included in the multivariable model to avoid overadjustment, residual confounding cannot be completely excluded. Finally, the optimal cut-off value for PIV (486.9) determined using the Youden index may be cohort-specific and may therefore have limited generalizability. Previous studies have reported considerable variability in PIV cut-off values depending on patient characteristics and tumor types[29,30]; therefore, the cut-off value identified in this study should be validated in independent cohorts.
Despite these limitations, the present study provides clinically relevant insights by comparatively evaluating the prognostic performance of PIV, ALBI, and MELD-Na scores within the same cohort of patients with HCC. Our findings suggest that inflammation-based biomarkers particularly PIV, which can be easily calculated from routine blood counts may provide complementary prognostic information beyond conventional clinical scoring systems. Validation of these findings in larger, multicenter, prospective studies will further clarify the role of inflammation-based markers in prog
In our study, multivariable analysis demonstrated that ECOG performance status and high PIV levels were independent adverse prognostic factors for OS. In contrast, among the conventional prognostic systems, ALBI grade did not retain its statistical significance in the multivariable analysis, while the BCLC staging system was not included in the multivariable model due to potential collinearity. These findings suggest that systemic inflammation may play an important role in determining the disease course in HCC. Our results indicate that the PIV score may serve as a strong prognostic bio
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