The prognostic importance of the pan-immune-inflammation value (PIV) in lung cancer: a systematic review and meta-analysis

Introduction

Lung cancer is defined as a malignant tumor originating from the mucosa or glands of the trachea and bronchi, which is comprised of distinct subtypes, including non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). With an estimated 2.48 million new cases and 1.82 million deaths per year, lung cancer is one of the most frequently diagnosed cancers worldwide, and is the leading cause of cancer-related death (1). In recent years, there has been continuous progress in the field of lung cancer treatment, resulting in combinations of surgical resection, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. However, the overall prognosis of patients remains unsatisfactory, with the 5-year survival of the lung cancer population being approximately 28.7% (2). Consequently, accurate prognostic indicators are imperative in guiding individualized treatment strategies.

Currently, the diagnosis and prognosis of lung cancer primarily rely on imaging examinations [such as computed tomography (CT) and positron emission tomography-CT], pathological biopsy, and molecular biomarker testing. However, there are certain limitations with these tools. Imaging modalities lack sufficient sensitivity for early-stage lesions, and molecular biomarker testing is costly, with its applications sometimes restricted to specific treatment strategies (3). It is well-known that inflammation influences every step of tumorigenesis, from tumor initiation and promotion to metastasis (4). According to previous studies, inflammation-related markers, such as the neutrophil-to-lymphocyte ratio (NLR), the systemic immune-inflammation index (SII), and the platelet-to-lymphocyte ratio (PLR), can serve as potential prognostic biomarkers for tumors (5-7).

PIV is a newly discovered biomarker combination that reflects the body’s immune response and systemic inflammatory status. It is defined as (neutrophil count × platelet count × monocyte count)/lymphocyte count (8). Since its introduction as a prognostic indicator for cancers, PIV has demonstrated prognostic efficacy in various cancers, including breast cancer, colorectal cancer, and esophageal cancer (9-11). Preliminary studies suggest that PIV has the potential to serve as a prognostic tool for lung cancer. However, there are limitations in these studies, including being exploratory, single-centered, and involving small sample sizes. Furthermore, the PIV cutoff values and subgroup populations vary across studies (4,12-20). In one study, a high pre-treatment PIV in lung cancer patients was associated with a hazard ratio (HR) of 0.818 for PFS, indicating a potentially favorable prognostic effect (21). In contrast, other studies have reported that elevated pre-treatment PIV is associated with poorer outcomes in this patient population. Therefore, to more comprehensively assess the prognostic value of PIV in lung cancer, we conducted a new meta-analysis to determine the relationships between PIV and lung cancer prognosis, providing a useful tool for clinicians in selecting personalized treatment strategies. We present this article in accordance with the PRISMA reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-518/rc).

Methods

Our project strictly followed the PRISMA2020 guidelines, and the registration (CRD42024622013) was completed on the PROSPERO platform after the search.

Eligibility criteria

Inclusion criteria

• Population (P): pathologically confirmed lung cancer patients, including both NSCLC and SCLC.
• Intervention (I): high PIV measured before treatment.
• Comparator (C): low PIV levels.
• Outcomes (O): studies reporting associations between PIV and survival outcomes, including OS, PFS, disease-free survival (DFS), recurrence-free survival (RFS), or distant metastasis-free survival (DMFS).
• Study design (S): observational studies (prospective or retrospective cohort studies) providing sufficient data to estimate HRs and 95% confidence intervals (CIs) for survival outcomes.

Exclusion criteria

• Reviews, case reports, meta-analyses, or conference abstracts;
• In vitro or animal experiments, and duplicate publications;
• Studies where the full text was unavailable or data were insufficient to calculate HRs and 95% CIs;
• Reports not in English were excluded to ensure consistency in methodological assessment and data quality.

Literature search strategies

A comprehensive literature search was conducted using PubMed, Cochrane, Embase, and Web of Science databases, published up to July 15, 2025, to identify all relevant studies on the prognostic value of PIV in lung cancer patients. The search used the following keywords: (lung neoplasms or lung cancer) AND (pan immune inflammation value or PIV). A detailed search strategy for each database is shown in Appendix 1. The references of the included studies and reviews were carefully examined to identify eligible studies.

Data extraction and outcomes

Two researchers (G.C. and Q.L.) independently performed literature screening and data extraction, followed by cross-verification. In the case of an unresolvable dispute, a third researcher (H.W.) was consulted for adjudication.

OS was used as the main outcome, with PFS, DMFS, DFS, and RFS as additional outcomes. The main outcome measure of this systematic review was the HR from multivariable Cox regression analyses, reflecting the relationships between PIV and prognoses in lung cancer.

The following information was recorded: the first author’s name, publication year, country, number of patients, age, study design, therapy, cut-off value of the PIV, median follow-up time and range, and HR with 95% CI. The HRs were directly extracted from studies that reported them using univariate and/or multivariate analyses. Preference was given to HRs from multivariate analyses, because they provided greater precision by accounting for confounding factors.

Quality assessment

The qualities of the included studies were assessed using the Newcastle-Ottawa Scale (NOS) for cohort studies (22). This scale evaluated study quality based on three domains: participant selection (four items), comparability (one item), and outcome assessment (three items). In the participant selection and outcome assessment domains, each item was scored up to 1 point, while the comparability domain was scored up to 2 points. The total maximum score was 9 points. Studies scoring 6–9 points were considered high quality. Two researchers used the NOS to evaluate the literature quality and cross-check after completion. A third investigator was invited to assist in adjudication if there was a dispute.

Statistical analysis

Summary statistics were performed using standard meta-analysis methods, with HRs used as an effective measure to assess the associations between PIV and prognoses in patients with lung cancer. If CIs were not reported, they were calculated from HRs and P values, where available. The statistical heterogeneity across studies was assessed using the Higgins I² statistic and Cochran’s Q test, with P<0.10 and/or I²>50% indicating significant heterogeneity. A random-effects model was used when I²>50%, and a fixed-effects model was used when I²<50%. Sensitivity analysis and subgroup analysis were performed to identify potential sources of heterogeneity. A funnel plot was used to visually evaluate publication bias among the included studies. Statistical significance was defined as P<0.05. Meta-analysis was conducted using Review Manager (Version 5.4, The Cochrane Collaboration, 2020) software.

Results

Literature search

Using the search strategy, we identified a total of 95 studies in the databases. After excluding duplicate studies, 50 studies remained. Based on the evaluation of titles and abstracts, 30 studies were excluded, leaving 20 studies for full text reviews. After reviewing the full texts, 10 studies were excluded for the following reasons: no available statistical data and controversial results. Finally, ten studies (4,12,14,16-21,23) were included in the current meta-analysis, comprising 1,969 patients. The process of literature selection is described in Figure 1.

Figure 1 Flow chart of the study selection. HR, hazard ratio.

Study characteristics

The included studies were published between 2021 and 2025, with sample sizes ranging from 59 to 899 patients. The studies were primarily conducted in China and Turkey. Three studies focused on SCLC patients, while seven studies involved NSCLC patients. Eight studies reported the prognostic effect of PIV on OS, and Eight studies reported the prognostic impact of PIV on PFS. The majority of treatments across all studies involved immunotherapy. According to the NOS, all studies scored above 6 points, indicating that they were of high quality. Detailed NOS scores for each study are provided in the Table S1. A detailed description of all included studies is presented in Table 1.

The association between PIV and OS/PFS in lung cancer

A fixed-effects model was used to pool effect sizes for eight studies (I²=0%, P=0.45). The results showed a significant association between PIV and OS (HR =2.86, 95% CI: 2.23–3.65, Z=8.37, P<0.001). A fixed-effects model was used in eight studies (I²=0%, P=0.51), showing a significant association between PIV and PFS (HR =2.06, 95% CI: 1.65–2.59, Z=6.29, P<0.001). Together, these results suggested that PIV was an independent prognostic predictor for lung cancer (Figure 2).

Figure 2 Forest plots demonstrating the effect of PIV on overall survival (A) and progression-free survival (B) in lung cancer patients based on meta-analysis results. CI, confidence interval; IV, inverse variance; PIV, pan-immune-inflammation value; SE, standard deviation.

The association between PIV and OS/PFS in the sub-type of lung cancer

In NSCLC patients (Figure 3), the pooled HR was 2.76 (95% CI: 2.14–3.56; P<0.001) for OS with low heterogeneity (I²=12%, P=0.33), and 1.94 (95% CI: 1.55–2.42; P<0.001) for PFS with moderate heterogeneity (I²=44%, P=0.13). In SCLC patients (Figure 4), stronger associations were observed, with HRs of 3.47 (95% CI: 2.21–5.44; P<0.001) for OS (I²=22%, P=0.28) and 2.33 (95% CI: 1.63–3.33; P<0.001) for PFS (I²=0%, P=0.95).

Figure 3 Forest plots demonstrating the effect of PIV on overall survival (A) and progression-free survival (B) in non-small cell lung cancer patients based on meta-analysis results. CI, confidence interval; IV, inverse variance; PIV, pan-immune-inflammation value; SE, standard deviation.

Figure 4 Forest plots demonstrating the effect of PIV on overall survival (A) and progression-free survival (B) in small cell lung cancer patients based on meta-analysis results. CI, confidence interval; IV, inverse variance; PIV, pan-immune-inflammation value; SE, standard deviation.

Publication bias

Publication bias was assessed using funnel plot analyses. The funnel plots for the associations between PIV and OS/PFS were approximately symmetrical (Figures 5), indicating no significant publication bias.

Figure 5 Funnel plots assessing publication bias for the association between PIV and overall survival (A) and progression-free survival (B) in lung cancer, overall survival (C) and progression-free survival (D) in non-small cell lung cancer, and overall survival (E) and progression-free survival (F) in small cell lung cancer, based on meta-analysis results. PIV, pan-immune-inflammation value; SE, standard deviation.

Subgroup analyses

We further performed subgroup analyses in the NSCLC patients (Table 2). For OS, high PIV generally remained a significant prognostic factor (P<0.05) across most subgroups, including country (China), sample size, cutoff value, treatment strategy, and analysis method. However, in the Turkey subgroup, the association was not statistically significant (P>0.05). For PFS, high PIV was significantly associated with poorer prognosis (P<0.05) in the majority of subgroups, including country (China), sample size, cutoff value, and treatment strategy. However, no significant associations were found in the Turkey subgroup or in the subgroup classified by the univariate analysis (both P>0.05).

Sensitivity analyses

When individual studies were sequentially excluded, the pooled results for OS and PFS remained unchanged. Therefore, we considered the evidence collected in the current meta-analysis to be reliable and credible.

Discussion

This meta-analysis included ten studies with a total of 1,969 patients. The results showed significant associations between PIV and both OS and PFS. Furthermore, PIV consistently retained its prognostic significance across most subgroups. To the best of our knowledge, this is the first meta-analysis to determine the prognostic value of PIV in lung cancer patients.

Lung cancer is one of the most prevalent cancers worldwide, and a leading cause of cancer-related mortality. It includes different sub-types, including NSCLC and SCLC, which differ in their treatment strategies (24). The treatment of NSCLC is based on staging and genetic mutation analyses. Early-stage patients primarily undergo surgery combined with chemotherapy or radiotherapy, while advanced-stage patients receive chemotherapy, targeted therapy, and immunotherapy (such as pembrolizumab and nivolumab). For SCLC, chemotherapy remains the main treatment. Limited-stage SCLC is typically treated with concurrent chemoradiotherapy and prophylactic cranial irradiation, whereas extensive-stage SCLC is treated with chemotherapy combined with immunotherapy (such as atezolizumab and durvalumab). However, the overall prognosis of lung cancer remains unsatisfactory, with a 5-year survival rate of only 28.7% (2). Therefore, accurate prognostic biomarkers are crucial for guiding individualized treatment strategies.

Inflammation plays a critical role in the initiation and progression of many malignancies. In the treatment of lung cancer, numerous prognostic biomarkers have emerged, including NLR, SII, PLR, and systemic inflammation response index (SIRI) (5-7,25). Most elevated inflammation-based prognostic biomarkers are associated with poor prognosis in lung cancer.

PIV is a newly identified combined biomarker that reflects the body’s immune response and systemic inflammatory status (8). It systematically reflects the complex interactions among platelets, neutrophils, monocytes, and lymphocytes within the tumor microenvironment, providing insight into the balance between systemic inflammatory and immune responses. Recently, numerous studies have reported associations between PIV and prognoses of various cancers, including colorectal cancer (26,27), breast cancer (9,28), nasopharyngeal carcinoma (29), esophageal squamous cell carcinoma (30), pancreatic cancer (31), oral squamous cell carcinoma (32), and central nervous system lymphoma (33). The findings suggest that PIV serves as a reliable prognostic marker across different malignancies, with elevated preoperative PIV levels often indicating poorer outcomes. In this study, PIV was significantly associated with OS and PFS in both NSCLC and SCLC patients. Specifically, in NSCLC, high PIV was associated with an HR of 2.76 for OS and 1.94 for PFS, while in SCLC patients, high PIV was associated with an HR of 3.47 for OS and 2.33 for PFS. A meta-analysis showed that in NSCLC patients receiving immune checkpoint inhibitors, high SII was associated with an HR of 1.54 for OS and 2.05 for PFS (34) , while high NLR was associated with an HR of 2.44 for OS and 2.06 for PFS, and high PLR was associated with an HR of 2.13 for OS and 1.61 for PFS (35). In NSCLC, high SIRI was associated with an HR of 2.08 for OS and 1.74 for PFS (25). In SCLC patients receiving first-line platinum-based chemotherapy, high NLR was associated with an HR of 1.39 for OS and 1.52 for PFS (36). PIV integrates four parameters: neutrophils, platelets, monocytes, and lymphocytes, rather than merely the ratio of two or three cell counts. Therefore, the relatively higher HR of PIV in lung cancer may indicate that it provides a more comprehensive reflection of the tumor microenvironment’s overall immune-inflammatory status than single inflammatory biomarkers.

Monocytes in the tumor microenvironment can differentiate into tumor-associated macrophages (TAMs) (37). TAMs significantly influence tumor malignancy by promoting tumor cell invasion, infiltration, and angiogenesis (38). Studies have shown that T-cell exhaustion, a dysfunctional state characterized by loss of effector function, sustains expression of inhibitory receptors and a unique transcriptional profile, which can both occur in cancers (39). In NSCLC, the density of B-cells within tertiary lymphoid structures is associated with long-term patient survival (40). Natural killer (NK) cells can directly induce tumor cell death without specific immunity (41). Studies have reported that platelets promote the formation of an early metastatic niche by releasing factors such as transforming growth factor-β (TGF-β), which recruit granulocytes (primarily neutrophils) (42,43). Platelets and neutrophils work synergistically, activating multiple pathways that enhance tumor cell capture, aggregation, colonization, and metastasis (44,45). Platelets and neutrophils work synergistically, activating multiple pathways that enhance tumor cell capture, aggregation, colonization, and metastasis (46). Additionally, neutrophils mediate tumor angiogenesis by producing prokineticin-2 and vascular endothelial growth factor A (VEGF-A). Under TGF-β stimulation, neutrophils release arginase-1, which suppresses CD8⁺ T lymphocyte-mediated anti-tumor immune responses, thereby promoting tumor proliferation (47-50). Furthermore, neutrophils can stimulate metastasis and dissemination by inhibiting NK cell function and facilitating tumor cell extravasation (51-53).

Previous meta-analyses have reported that PIV has significant prognostic value across various types of tumors. Moreover, the prognostic value of PIV is not affected by the country of publication, cutoff values, or tumor stage, suggesting that PIV has broad clinical applicability in predicting prognoses for cancer patients (54). This is consistent with the results of the subgroup analysis in our study, which showed that the prognostic value of PIV remained statistically significant (P<0.05) across sample sizes, treatment strategy, and analysis methods, suggesting that PIV can reliably serve as a prognostic marker for OS and PFS across diverse NSCLC patient populations. In this study, the cutoff values for PIV in NSCLC patients varied widely (ranging from 156.18 to 669.6). Regardless of whether a higher or lower cutoff value was used, PIV remained a statistically significant predictor of OS and PFS (P<0.05). However, if the cutoff value is set too low, some low-risk patients may be misclassified into the high PIV group, reducing the distinction between high-risk and low-risk patients. A higher PIV cutoff value may better differentiate between high-risk and low-risk patients. Future studies should further characterize the optimal PIV cutoff standard to enhance its clinical applicability. Additionally, research has suggested that PIV can predict recurrence in patients with left-sided colorectal cancer, but not in those with right-sided colorectal cancer, highlighting the importance of tumor location when considering PIV as a biomarker for colorectal cancer (55). Correspondingly, it remains unclear whether PIV differs among lung cancer patients based on different tumor locations.

Due to the ease of PIV detection, it has significant advantages in clinical practice for cancer treatments. In current clinical decision-making for cancer treatments, neoadjuvant therapy is often considered. For NSCLC patients receiving neoadjuvant immunochemotherapy, a high pre-treatment PIV is a positive biomarker for a pathological complete response (56). In addition, studies have indicated that high PIV were correlated with worse clinical outcomes in extensive-stage SCLC patients treated with anti-PD-1/PD-L1 inhibitor combined with chemotherapy (20). This suggests that PIV can be used to assess the prognosis of lung cancer patients. However, since this study is based solely on retrospective data, it cannot establish a causal relationship between PIV and treatment decisions. Future large-scale prospective cohort studies are needed to evaluate the actual value of PIV in clinical decision support.

There are some limitations in this study. (I) The number of cases included in the studies was relatively small, and more cross-ethnic and multi-center studies are needed. (II) All included studies were retrospective and predominantly from China/Turkey. This may limit generalizability to Western populations. Prospective multinational cohorts are needed to validate PIV’s global applicability. (III) There was no unified standard for the cutoff value, so future research is needed to establish a standardized cutoff. More multi-center and large-sample studies are therefore required to determine the optimal cutoff or reference range. (IV) The included studies were retrospective. Future prospective cohort studies are needed to further validate the prognostic value and clinical applicability of PIV.

Conclusions

PIV can serve as an independent prognostic biomarker for survival outcomes in lung cancer patients. Therefore, incorporating PIV into prognostic assessments may provide additional support for individualized treatment decision-making.

Acknowledgments

We thank International Science Editing (http://www.internationalscienceediting.com) for editing this manuscript.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-518/rc

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-518/prf

Funding: This work was supported by Guangdong Provincial Basic and Applied Basic Research Fund of China (grant No. 2020A1515110445).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-518/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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