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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Immunol . 2026 Mar 4;27:35. doi: 10.1186/s12865-026-00819-7 Search in PMC Search in PubMed View in NLM Catalog Add to search Prognostic application regarding PILE score and the pan-immune-inflammation value in diffuse large B-Cell Lymphoma Chun Chang Chun Chang 1 Aerospace Center Hospital, Beijing, 100049 China Find articles by Chun Chang 1 , Xiaoyong Man Xiaoyong Man 2 Peking University Aerospace School of Clinical Medicine, Beijing, 100049 China Find articles by Xiaoyong Man 2 , Qi Hao Qi Hao 1 Aerospace Center Hospital, Beijing, 100049 China Find articles by Qi Hao 1 , Wang Yan Wang Yan 1 Aerospace Center Hospital, Beijing, 100049 China Find articles by Wang Yan 1 , Zhen Wang Zhen Wang 1 Aerospace Center Hospital, Beijing, 100049 China Find articles by Zhen Wang 1 , Jingbo Wang Jingbo Wang 1 Aerospace Center Hospital, Beijing, 100049 China Find articles by Jingbo Wang 1, ✉ Author information Article notes Copyright and License information 1 Aerospace Center Hospital, Beijing, 100049 China 2 Peking University Aerospace School of Clinical Medicine, Beijing, 100049 China ✉ Corresponding author. Received 2025 Aug 30; Accepted 2026 Feb 18; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13067569 PMID: 41782081 Abstract Objective This research explored the pan-immune-inflammation value (PIV) as prognostic significance factors in patients with diffuse large B-cell lymphoma (DLBCL). And we examined the PILE score, a composite parameter derived from lactate dehydrogenase (LDH), and Eastern Cooperative Oncology Group performance status (ECOG PS) and PIV as well. The findings were intended to contribute to more reliable prognostic assessment in clinical settings. Methods We retrospectively analyzed 90 patients diagnosed with DLBCL who accepted standard therapy consisting of a CD20 monoclonal antibody combined with the CHOP regimen (cyclophosphamide, doxorubicin, vincristine, and prednisone),with appropriate dose or cycle adjustments made according to age, cardiac function, and performance status.Specifically, CD20 monoclonal antibody was administered at 375 mg/m 2 on day 0 of each 21-day cycle. CHOP consisted of cyclophosphamide 750 mg/m 2 , doxorubicin 50 mg/m 2 (reduced to 30–40 mg/m 2 in patients ≥70 years or with cardiac dysfunction), vincristine 1.4 mg/m 2 on day 1, and prednisone 100 mg/m 2 on days 1–5. Most patients received 6 cycles, while those with high-risk features received 8 cycles.A total of 16 patients with bulky disease, high-risk extranodal involvement, or suboptimal response to first-line chemotherapy as assessed by PET-CT received chemotherapy combined with radiotherapy.The PIV was established by integrating quantitative data on platelets, lymphocytes, monocytes and neutrophils obtained from routine peripheral blood tests. The PILE score was subsequently determined by integrating PIV with ECOG PS and LDH levels. Associations of PIV and PILE scores with treatment outcomes and prognosis were examined, and their potential relationships with pathological tumor characteristics were also evaluated. Results Elevated PIV was independently correlated with poorer overall survival (OS) (hazard ratio (HR) = 3.79; 95% confidence interval (CI): 1.31–10.97; p = 0.0135), PIV was statistically significant in univariate analysis for PFS (P<0.001), making it the most robust prognostic marker among all assessed immune-inflammatory indices. Similarly, patients exhibiting higher PILE scores experienced significantly reduced progression-free survival (PFS) and OS ( p < 0.01, respectively). We evaluated the association of PIV, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), and PILE with treatment outcomes by conducting both univariable Cox regression and multivariable logistic regression analyses, defining treatment response as the measured endpoint. While all markers showed significant association with the outcome in univariable analyses ( p < 0.01, respectively) at statistical level, none demonstrated independent predictive capacity in multivariable models. Among DLBCL patients, the PIV demonstrated an independent prognostic significance for OS outcomes. Furthermore, the PILE score, along with other systemic immune-inflammatory markers, demonstrated potential utility in forecasting treatment outcome or response. Keywords: PILE score, Pan-immune-inflammation value (PIV), Diffuse large B-cell lymphoma (DLBCL), Overall survival (OS), Treatment response Introduction Globally, hematologic malignancies comprise approximately 6.6% of cancer cases and cause around 7.2% of cancer mortalities [ 1 ]. Among these cancer types, non-Hodgkin lymphoma (NHL) emerges as the subtype most commonly diagnosed. DLBCL, as a biologically heterogeneous and clinically very aggressive subtype of NHL, comprises nearly one-third of all NHL cases. The standard therapy for DLBCL is chemoimmunotherapy based on the R-CHOP regimen. Despite this treatment, 45–50% of patients eventually experience disease relapse, particularly those with the activated B-cell–like (ABC) subtype or MYC/BCL2 double-expression lymphoma [ 2 ]. This high rate of relapse underscores the urgent need for reliable and practical methods to identify patients at elevated risk of poor prognosis following standard therapy, enabling optimization of treatment strategies. While numerous prognostic models have been proposed, this study specifically investigates peripheral blood-derived biomarkers given their ability to forecast clinical outcomes in individuals with DLBCL. Recent studies have identified a novel group of prognostic indicators—namely, inflammation-related markers—highlighting the ability of peripheral blood biomarkers to reflect systemic inflammatory status [ 3 – 5 ].In addition to peripheral blood biomarkers, ECOG PS and LDH levels are widely recognized as reliable prognostic indicators in DLBCL. To improve risk stratification, researchers have developed the PILE score, a composite index integrating PIV, ECOG PS, and LDH levels [ 6 ]. Evidence from prior studies indicates that patients with higher PILE scores tend to experience significantly worse clinical outcomes [ 7 ]. Despite growing interest, the predictive significance of pan-immune-inflammation indicators has not been fully established in DLBCL yet as we known, encompassing PIV, PLR, SII, NLR and the PILE score. This study seeks to evaluate how these inflammation-based markers relate to patient outcomes and treatment responses, aiming to provide insights that may inform clinical decision-making. Methods Patients recruitment In this retrospective analysis, we included 90 individuals with a confirmed diagnosis of DLBCL who received treatment at Aerospace Center Hospital between January 2019 and May 2024. In this study, blood samples used to calculate the Pan-Immune-Inflammation Value were all collected within 1 week after patients were diagnosed with DLBCL but before the initiation of first-line treatment. Eligibility for inclusion required patients to satisfy the following conditions: patients who have (1) a confirmed pathological diagnosis about DLBCL; (2) no history of other malignancies; (3) accessible comprehensive clinical, laboratory, and follow-up records from the hospital; (4) evidence of systemic disease confirmed through computed tomography (CT) or positron emission tomography–CT (PET-CT) scans of the pelvis, abdomen, and chest, accompanied by bone marrow aspiration; and (5) comprehensive clinical and follow-up records. No missing data were observed for the variables included in the analyses. This research adhered to the principles outlined in the Declaration of Helsinki, obtained approval by the Ethics Committee of Aerospace Center Hospital (approval no. 2024037-02), and all patients and participants who involved in this study signed informed consent forms. Data collection Clinical and laboratory data were systematically collected for all patients, including age, sex, TP53 mutation status, β2-microglobulin levels, Double-Expression and Double-Hit status, treatment response, OS, PFS, ECOG PS, Karnofsky Performance Status (KPS), LDH levels, neutrophil count, white blood cell count, monocyte count, platelet count, Ki-67 index, hemoglobin and lymphocyte count. All treatment regimens and corresponding responses were documented. Initial treatment responses were determined following the guidelines established by the International Working Group (IWG) for newly diagnosed DLBCL and categorized as progressive disease (PD), stable disease (SD), partial remission (PR), or complete remission (CR). Patients who reached either complete remission (CR) or partial remission (PR) were considered part of the “remission” cohort; in contrast, those presenting with stable disease (SD) or progressive disease (PD) were classified as “non-remission”. Disease progression during initial therapy was defined as primary refractory disease. Patients’ OS was measured from the point of diagnosis until death from any cause or last follow-up, in contrast, PFS was evaluated from diagnosis to the occurrence of disease progression or DLBCL-associated death. Basic systemic inflammation parameters Peripheral blood counts were used to calculate systemic immune-inflammation markers. The NLR was defined as the neutrophil count divided by the lymphocyte count, and the MLR as the monocyte count divided by the lymphocyte count. The PLR was determined by dividing the platelet count by the lymphocyte count. The SII was calculated by multiplying the neutrophil and platelet counts and then dividing by the lymphocyte count. The PIV incorporated neutrophil, platelet, and monocyte counts in the numerator, divided by the lymphocyte count. The PILE score was calculated by integrating the PIV, ECOG PS, and LDH levels. Specifically, a PIV below the cohort median was assigned a score of 0, whereas a PIV at or above the median received a score of 1. LDH level within the normal upper limit (ULN) scored 0 point, while the level exceeding the ULN scored 1 point. ECOG PS less than two scored 0 point, while ECOG PS equal to or larger than two scored 1 point. The sum of these points constituted the PILE score. Statistical analysis For descriptive analyses, data were expressed using the median values accompanied by their interquartile ranges (IQR). The survminer package in R was employed to determine the optimal cutoff values for PIV, MLR, PLR, NLR and SII, selecting values that best distinguished survival outcomes across different patient groups. We estimated survival probabilities by Kaplan–Meier method. Considering the significant heterogeneity within the DLBCL patient population, and given that most PIV cutoff values reported in the literature are derived from solid tumors or specific subtypes without a universally accepted standard, we prioritized sensitivity analyses based on the distribution of our own cohort (median) to ensure the reliability of the results.Associations between pathological features and PIV or PILE scores were assessed via the Fisher’s exact test or chi-square test, applied as appropriate to the data structure. Cox proportional hazards models were employed to perform both univariable and multivariable analyses. To mitigate the risk of overfitting, Only variables that reached statistical significance in the univariable assessment were subsequently entered into the multivariable models. Potential predictors of treatment response were examined using univariable and multivariable logistic regression analyses. All analyses were performed using R software (version 4.4.2). A two-tailed approach was applied for statistical testing, and results with a p-value less than 0.05 were interpreted as statistically significant. Results Patients’ demographic and characteristics This study enrolled 90 individuals diagnosed with DLBCL, and their clinicopathological profiles are presented in Table 1 . The median value of patients’ age was 61 years, with 47% of patients aged equal to or larger than 61 years old. Male patients accounted for 52% of the cohort. More than half of the patients (63.3%) had the non-GCB (non-germinal center B-cell-like) phenotype. Regarding ECOG performance status, 54% of patients had an ECOG PS score <2. Ten percent of cases were double-Hit Lymphoma. In 33% of patients, β2-microglobulin levels were below the normal range. In 74% of patients, KPS score ≥70. TP53 mutation was detected in 28% of patients, with the majority being TP53-negative. Higher LDH levels were present in 56% of cases. The overall “remission” rate (classified as CR and PR) was 71%, while the “non-remission” rate (classified as SD and PD) was 19%. Patients with PILE score ≥ 2 accounted for 48% of the cases. Table 1. Demographic and characteristics of the patients Characteristic N（%）/ median （IQR） Age ＜61 43（47.8%） ≥61 47（52.2%） Sex Man 52（57.8%） Woman 38（42.2%） Double-Expression Lymphoma Yes 35（38.9%） No 55（61.1%） GCB subtype 33（35.7%） Non-GCB subtype 57（63.3%） Double-Hit Lymphoma Yes 10（11.1%） No 80（88.9%） ECOG ＜2 54（60%） ≥2 36（40%） β_2-microglobulin Low 33（36.7%） High 57（63.3%） KPS ＜70 16（17.8%） ≥70 74（82.2%） TP53 Positive 28（31.1%） Negative 62（68.9%） LDH High 56（62.2%） Low 34（37.8%） Treatment Evaluation CR 40（44.4%） PR 31（34.4%） SD 6（6.7%） PD 13（14.4%） PLR 175.181（113.532-316.402） MLR 0.398（0.268-0.832） NLR 3.482（2.277-6.329） SII 650.087（273.296-1535.857） PIV 272.782（117.574-778.668） PILE 0 16（17.8%） 1 26（28.8%） 2 33（36.7%） 3 15（16.7%） Open in a new tab The median values (IQR) for inflammatory markers were as follows: PLR: 175.181 (113.532–316.402) MLR: 0.398 (0.268–0.832) NLR: 3.482 (2.277–6.329) SII: 650.087 (273.296–1535.857) PIV: 272.782 (117.574–778.668) Association between systemic inflammatory markers and patient prognosis In this study, PFS and OS were used as endpoints for identifying appropriate cutoff thresholds applicable to various systemic inflammation-related markers. Survival distributions were illustrated using Kaplan–Meier methodology according to the derived cutoff values. The optimal cutoff values were 900 for PIV, 10.74 for NLR, 338.94 for PLR, 1.13 for MLR, and 1479.43 for SII. As shown in Fig. 1 A and B, patients who remained progression-free within the first 12 months had a relatively lower risk of subsequent relapse. As shown in Fig. 1 C, patients classified in the low-PIV category demonstrated markedly superior OS at both 1 and 5 years in comparison with those in the high-PIV category (1 year: 89.1% vs. 35.6%; 5 years: 57.7% vs. 21.4%; p = 0.01). Similarly, as shown in Fig. 1 D, the low-PIV group also demonstrated markedly higher DFS rates at 1 and 5 years (1 year: 64.8% vs. 10.6%; 5 years: 43.2% vs. 5.29%; p = 0.01). Survival curves for NLR, PLR, MLR, and SII individually in relation to OS and DFS were generated using the same statistical approach (Figures 1 E–1M). Elevated values of NLR, PLR, MLR, and SII were linked to an unfavorable clinical outcome, with statistically significant differences observed (Figs. 2 and 3 ). Fig. 1. Open in a new tab Kaplan–Meier plots depicting survival outcomes of the study cohort. Overall survival (OS) and disease-free survival (DFS) are shown for the entire cohort ( A , B ) and stratified by baseline biomarker levels: pan-immune-inflammation value (PIV; C , D ), neutrophil-to-lymphocyte ratio (NLR; E , F ), platelet-to-lymphocyte ratio (PLR; G , H ), monocyte-to-lymphocyte ratio (MLR; I , K ), and systemic immune-inflammation index (SII; L , M ) Fig. 2. Open in a new tab Receiver operating characteristic (ROC) curves illustrating the discriminative ability of PIV compared with additional inflammatory markers at 1 year ( A ), 2 years ( B ), and 3 years ( C ). Abbreviations: AUC, area under the curve Fig. 3. Open in a new tab Decision curve analysis for OS at 2 years ( A ) and 3 years ( B ) across different predictive parameters Predictive PIV for OS Patients with poor performance status (ECOG ≥2 or KPS <70), Double-Expression Lymphoma, or Double-Hit Lymphoma exhibited a notable association with higher PIV values, as shown in Table 2 . Evaluation at one-, two-, and three-year intervals revealed that the PIV achieved consistently the highest area under the curves (AUCs) (e.g., 36-month AUC = 0.742), which was significantly higher than that of NLR (0.65), MLR (0.63), and other markers. In addition, decision curve analysis demonstrated that across most high-risk threshold probabilities (0.1–0.8), the PIV curve showed greater net clinical benefit compared to other markers, particularly within the 0.2–0.6 range. In univariable analysis for OS, an association was identified between elevated PIV and a substantially higher risk of mortality with HR to be equal to 4.30 (95% CI: 2.40–7.70; p < 0.001). Other factors significantly associated with OS included NLR, MLR, PLR, SII, and ECOG PS. Independent prognostic significance of PIV for OS was established through multivariable analysis, and HR was 3.79 (95% CI: 1.31–10.97; p = 0.0135) (Table 3 ). Table 2. Pathological features of PIV Characteristics Category N Median PIV p -value >Median ≤Median Age <61 42 23 (51.1%) 19 (42.2%) 0.526 >=61 48 22 (48.9%) 26 (57.8%) Sex Female 38 24 (43.7%) 14 (40.0%) 0.548 Male 52 31 (56.3%) 21 (60.0%) Hans Non-GCB 57 28 (62.2%) 29 (64.4%) 1.000 GCB 33 17 (37.8%) 16 (35.6%) ECOG_group <2 54 20 (44.4%) 34 (75.6%) 0.005 >=2 36 25 (55.6%) 11 (24.4%) LDH_group >250 56 29 (64.4%) 27 (60.0%) 0.828 <=250 34 16 (35.6%) 18 (40.0%) KPS_group <70 16 13 (28.9%) 3 (6.7%) 0.013 >=70 74 32 (71.1%) 42 (93.3%) Treatment Evaluation CR 40 15 (33.3%) 25 (55.6%) 0.068 PD 6 5 (11.1%) 1 (2.2%) PR 31 16 (35.6%) 15 (33.3%) SD 13 9 (20.0%) 4 (8.9%) β_2-microglobulin Low 57 30 (66.7%) 27 (60.0%) 0.662 High 33 15 (33.3%) 18 (40.0%) Double-Expression Lymphoma NO 55 28 (62.2%) 27 (60.0%) 1.000 YES 35 17 (37.8%) 18 (40.0%) Double-Hit Lymphoma NO 80 41 (91.1%) 39 (86.7%) 0.737 YES 10 4 (8.9%) 6 (13.3%) KI67_group <70% 13 4 (8.9%) 9 (20.0%) 0.230 >=70% 77 41 (91.1%) 36 (80.0%) bone marrow YES 27 12 ((26.7%) 15 (33.3%) 0.490 NO 63 33(73.3%) 30 (66.7%) TP53 NO 62 32 (71.1%) 30 (66.7%) 0.820 YES 28 13 (28.9%) 15 (33.3%) Open in a new tab Table 3. Results of single variable and multi-variable Cox regression Characteristics (N=90) Category OS Univariable analysis Multivariable analysis HR (95% CI) p -value HR (95% CI) p -value Age >=61/<61 1.637(0.804–3.333.804.333) 0.1740 Double-Hit Lymphoma Yes/No 1.688(0.724–3.938.724.938) 0.2255 Double-Expression Lymphoma Yes/No 1.655(0.827–3.313.827.313) 0.1549 β_2-microglobulin Yes/No 1.632(0.755–3.529.755.529) 0.2132 ki67 >=70/<70 1.822(0.621–5.346.621.346) 0.2745 ECOG >=2/<2 3.013(1.485–6.116.485.116) 0.0023 1.83 (0.85–3.95) 0.1186 MLR High/Low 5.745(2.752–11.992.752.992) <0.001 2.10 (0.80–5.51) 0.127 PLR High/Low 4.796(2.374–9.690.374.690) <0.001 1.71 (0.37–7.85) 0.4971 NLR High/Low 5.985(2.761–12.972.761.972) <0.001 1.95 (0.61–6.22) 0.2541 PIV High/Low 5.792(2.867–11.701.867.701) <0.001 3.79 (1.31–10.97) 0.0135 SII High/Low 4.225(2.100–8.498.100.498) <0.001 0.50 (0.10–2.48) 0.3982 TP53 Yes/No 1.105(0.531–2.300.531.300) 0.7890 Open in a new tab Predictive value of PILE score for treatment response and prognosis PILE was calculated based on PIV (dichotomized by median), LDH (divided by the upper limit of normal, ULN), and ECOG performance status (<2 vs. ≥2), with a total possible score ranging from 0 to 3. According to the calculation, the allocations of cases by PILE score (0, 1, 2, and 3) determined for the study cohort were 16 (17.78%), 22 (24.44%), 30 (33.33%), and 22 (24.44%), respectively. Patients were stratified according to PILE values, with 0–1 representing the low-PILE group and 2–3 indicating the high-PILE group. As shown in Fig. 4 A and B, patients with higher PILE scores exhibited significantly shorter survival times ( p < 0.0001). Out of the cohort, 48 individuals (53.33%) fell within the low-PILE group, whereas 42 (46.67%) were assigned to the high-PILE group. The findings indicated that membership in the high-PILE group corresponded with substantially inferior OS and PFS outcomes, whereas patients in the low-PILE group experienced longer survival (Fig. 4 C, p = 0.00052), (Fig. 4 D, p = 0.0027).We also investigated the associations between clinicopathological characteristics and the PILE score. The analysis showed that ECOG PS, LDH level, KPS score, and treatment response were all significantly associated with PILE (Table 4 ). Fig. 4. Open in a new tab Association between treatment response and PILE score categories. Kaplan–Meier curves demonstrate OS ( A , C ) and PFS ( B , D ) stratified by PILE score groups Table 4. Pathological features of PIV Characteristics Category N Low-PILE High-PILE p -value N =42 N =48 Age <61 42 20 (47.6%) 22 (45.8%) 1 >=61 48 22 (52.4%) 26 (54.2%) Sex Female 38 20 (47.6%) 18 (37.5%) 0.7429 Male 52 22 (52.4%) 30 (62.5%) Hans Non-GCB 57 28 (66.7%) 29 (60.4%) 0.693 GCB 33 14 (33.3%) 19 (39.6%) ECOG_group <2 54 40 (95.2%) 14 (29.2%) <0.001 >=2 36 2 (4.8%) 34 (70.8%) LDH_group >250 56 18 (42.9%) 38 (79.2%) 0.001 <=250 34 24 (57.1%) 10 (20.8%) KPS_group <70 16 0 (0.0) 16 (33.3%) <0.001 >=70 74 42 (100.0%) 32 (66.7%) Treatment Evaluation CR 40 22 (52.4%) 18 (37.5%) 0.046 PD 6 0 (0.0) 6 (12.5%) PR 31 16 (38.1%) 15 (31.2%) SD 13 4 (9.5%) 9 (18.8%) β_2-microglobulin Low 57 22 (52.4%) 35 (72.9%) 0.072 High 33 20 (47.6%) 13 (27.1%) Double-Expression Lymphoma NO 55 27 (64.3%) 28 (58.3%) 0.718 YES 35 15 (35.7%) 20 (41.7%) Double-Hit Lymphoma NO 80 39 (92.9%) 41 (85.4%) 0.433 YES 10 3 (7.1%) 7 (14.6%) KI67_group <70% 16 8 (19.0%) 8 (16.7%) 0.389 >=70% 74 34 (81.0%) 40 (83.3%) TP53 NO 62 29 (69.0%) 33 (68.8%) 1.000 YES 28 13 (31.0%) 15 (31.2%) Open in a new tab Associations between PIV values and treatment response Using the Kruskal–Wallis test, we found that the distribution of PIV values differed significantly across treatment response groups (Fig. 5 A, p = 0.0187), Among patients with reduced PIV, the likelihood of achieving better treatment response was 52% higher (Fig. 5 B). While there was a greater occurrence of poor responses in those exhibiting elevated PIV levels, including PD and SD (Fig. 5 B). In addition, patients exhibiting elevated PIV values showed a markedly greater likelihood of primary resistance (Fig. 5 C, p = 0.00296). Compared with the low-PIV group, patients exhibiting high PIV demonstrated a significantly increased frequency of primary resistance (Fig. 5 D, p < 0.001). Fig. 5. Open in a new tab Association between therapeutic response and PIV value. A PIV distribution in relation to treatment outcome in the cohort. B Proportions of complete remission (CR), progressive disease (PD), partial remission (PR) or stable disease (SD) across PIV categories. C PIV distribution with respect to primary resistance. D Proportions of primary resistance conditions stratified by PIV categories Figure 6 illustrates that treatment response distributions differed significantly between patients stratified by low- or high-PILE. Those in the low-PILE group exhibited a greater likelihood of achieving CR or PR, whereas patients with elevated PILE scores showed a greater proportion of SD or PD (Fig. 6 A, p = 0.0425). Similarly, the distribution of primary resistance also differed between the two groups, with a higher risk of primary resistance observed in the high-PILE group (Fig. 6 B). Despite the observed trend, statistical significance was not reached (p = 0.238). Fig. 6. Open in a new tab Associations between treatment response and PILE score. A Proportions of CR, PD, PR, or SD stratified by PILE groups. B Proportions of primary resistance conditions stratified by PILE groups To further evaluate predictors of treatment response, we performed univariable Cox regression and multivariable logistic regression analyses including PIV, SII, MLR, PLR, NLR, and PILE in the model. All six markers were significantly associated with treatment response in univariable analysis; however, none of them were identified as independent predictors in the multivariable model (Table 5 ). Table 5. Evaluation of therapeutic response using univariable and multivariable logistic regression Characteristics (N=90) Category Univariable analysis Multivariable analysis HR (95% CI) p -value HR (95% CI) p -value MLR High/Low 5.333(1.581 - 17.986) 0.007 1.594(0.319 - 7.957) 0.570 PLR High/Low 7.654(2.448 - 23.935) <0.001 1.606(0.226 - 11.392) 0.636 NLR High/Low 7.700(2.094 - 28.311) 0.002 0.915(0.129 - 6.517) 0.930 PIV High/Low 10.457(3.321 - 32.932) <0.001 3.540(0.578 - 21.674) 0.1722 SII High/Low 10.653(3.375 - 33.622) <0.001 3.149(0.344 - 28.844) 0.310 PILE High/Low 4.318(1.304 - 14.299) 0.017 0.870(0.152 - 4.985) 0.8766 Open in a new tab Discussion Over the past few years, multiple prognostic frameworks have been proposed for DLBCL, highlighting the need for straightforward and readily measurable indicators to anticipate patient outcomes. In this study, we retrospectively examined 90 DLBCL patients, focusing on peripheral blood cell counts and their derived ratios as potential predictors. Our analysis revealed that the PIV independently forecasts OS and demonstrates superior prognostic accuracy compared with other inflammatory markers. Greater PIV values were predictive of unfavorable survival. Although PIV did not demonstrate independent prognostic significance in median analysis, its prognostic association with OS and PFS in patients with DLBCL remained stable and highly significant as verified by analyses.Thus, PIV represents the pan-immune inflammatory index with the best prognostic performance in the present study.Meanwhile, PIV exhibited superior discriminative ability and clinical net benefit compared with other immune-inflammatory indices in ROC and DCA analyses.PIV remains a core indicator for evaluating the prognosis of DLBCL in this study.Additionally, the PILE score proved informative for predicting both PFS and OS, with individuals in the high-score group exhibiting markedly poorer outcomes. These findings further suggest that the pan-immune-inflammation biomarkers could act as valuable measures for anticipating therapeutic response in patients with DLBCL. Research has revealed that platelets play a dual role in cancer immunity. They not only create an immunosuppressive niche that shields tumor cells but also directly regulate immune cell functions [ 8 ]. Tumor cells release soluble activating factors, such as adenosine triphosphate (ATP), which accumulate within the tumor stroma and trigger platelet activation. After becoming activated, platelets establish attachment to tumor cells mediated by adhesion molecules on the cell surface, promoting tumor progression and angiogenesis [ 9 ]. Additionally, tumor cells may gain protection from immune detection through platelets, which also restrain effector lymphocyte responses, and contribute to a treatment-resistant tumor microenvironment [ 10 , 11 ]. TAMs, or tumor-associated macrophages, which largely differentiated from monocytes in the bloodstream, the predominantly represented immune cell populations within the tumor microenvironment across various cancers. Tumor progression, dissemination, and escape from immune surveillance are promoted through their ability by suppressing antitumor immune responses through multiple mechanisms [ 12 ]. Neutrophils exhibit a multifaceted role within the tumor milieu. In some contexts, they assist in eliminating tumor cells by modulating the microenvironment and releasing cytokines, chemokines, and growth factors. Conversely, neutrophils can also promote tumor growth, metastasis, and angiogenesis [ 13 , 14 ]. Tumor-infiltrating lymphocytes (TILs), which specifically target tumor cells, constitute a substantial fraction of immune elements operating within the tumor ecosystem compared with non-infiltrating lymphocytes. The principal types of these immune elements consist of T lymphocytes, B lymphocytes, and natural killer (NK) cells with diverse phenotypes and functions, enabling potent tumor-specific immune responses [ 15 , 16 ]. However, activated lymphocytes may restrain the growth of CD4⁺CD25⁻ and CD8⁺CD25⁺ T-cell, thereby reducing antitumor immunity and enhancing immune suppression [ 17 ]. Due to the accessibility of peripheral blood parameters, the PIV has emerged in recent studies as a valuable biomarker for predicting clinical outcomes. Peripheral blood-derived counts of platelets, lymphocytes, neutrophils and monocytes are integrated to form this index. PIV has shown strong prognostic value across various tumor in different organs, encompassing colorectal, hepatic, and esophageal cancers. [ 5 , 14 , 18 , 19 ]. For example, findings from Baykal and colleagues indicated that the PIV represents an autonomous determinant of clinical outcomes, functioning as an independent prognostic marker in patients with non-metastatic renal cell carcinoma [ 20 ], while Öztürk et al. reported that PIV independently predicts distant metastasis and classifies thyroid cancer patients into high-risk category, as determined by the American Thyroid Association (ATA) guideline framework [ 21 ]. These findings collectively support PIV functioning as a distinct marker for prognostic evaluation. In parallel, other peripheral blood parameters have also been studied for their prognostic significance. Xie et al. found that the NLR serves as a particularly effective marker in non-small cell lung cancer (NSCLC) [ 22 ]. Research has also examined the role of SII within DLBCL, though results remain inconsistent. For instance, REİS ARAS et al. analyzed 101 DLBCL patients and used ROC analysis to determine an optimal SII cutoff of 500 for survival prediction. No meaningful differences were observed in PFS or OS when comparing patients with SII ≤ 500 to those with SII > 500; however, lower SII values demonstrated a significant association with higher likelihood of mortality ( p = 0.017). [ 23 ]. Conversely, Fang Su [ 24 ] and Jing Wu [ 25 ] reported that elevated SII correlates with worse PFS and OS, findings consistent with our study. These discrepancies may be attributed to variations in sample size and geographic region, highlighting the need for further validation. Additional research by Ucar MA et al., which comprised 300 individuals affected by a range of hematologic cancers, evaluated SII, NLR, PLR, and PIV based on median values. Their results indicated that higher SII and PIV were associated with a marked decrease in OS, particularly in acute myeloid leukemia and multiple myeloma [ 26 ]. Similarly, Kejin Li et al. reported that elevated PIV, NLR, MLR, and PLR levels predicted worse OS in colorectal cancer patients [ 14 ]. Consistent with these previous studies, our findings demonstrate that patients with higher PLR, MLR, SII, NLR and PIV levels experienced a marked decline in OS and PFS in DLBCL. Notably, ROC analyses for cumulative OS over 1, 2, and 3 years revealed that PIV exhibited the highest AUC, reflecting superior predictive performance. Furthermore, both univariable and multivariable analyses confirmed that PIV serves as a standalone prognostic marker for OS in individuals with DLBCL. Guven et al. introduced a novel prognostic scoring system, PILE, which combines the PIV, ECOG PS, and LDH levels for the purpose of stratifying patients into low-PILE group and high-PILE group. Their findings indicated that patients with higher PILE scores experienced significantly reduced OS following immunotherapy [ 7 ]. Consistently, Yılmaz et al. reported that significant associations with OS and DFS in breast cancer were observed for PILE scores and PIV, with PIV remaining an independent prognostic determinant for both measures [ 27 ]. Comparable results have been observed in other malignancies: Zeng et al. reported similar trends in lung carcinoma of the small-cell type [ 6 ], and Karadağ and associates documented analogous associations in hepatocellular carcinoma [ 28 ]. A prospective observational study showed that a higher PIV value was associated with a lower complete response (CR) rate in the treatment of rectal cancer[ 29 ]. which is consistent with the findings of our study. In our cohort, the PILE score exhibited a significant association with both PFS and OS, and treatment response also demonstrated a meaningful association with PILE. These findings suggest that PILE, together with other pan-immune-inflammation markers, may serve as a valuable indicator of primary drug resistance. Specifically, for individuals classified in the low-PILE group, the probability of OS at both one year and five years reached 92.2% (95% CI: 84.1%–100%) and 76.3% (95% CI: 61.7%–94.5%), respectively. In contrast, patients belonging to the high-PILE group demonstrated OS probabilities of 62.6% after one year (95% CI: 49.9–78.5%) and 29.1% after five years (95% CI: 14.0–60.4%) (Figure 4 C, p = 0.00052). Regarding PFS, in the low-PILE group, PFS at one year was 70.2% (95% CI: 57.3%–85.9%), declining to 51.7% (95% CI: 37.5%–71.2%) at five years, whereas the high-PILE group had rates of 36.3% (95% CI: 24.6%–53.6%) and 19.9% (95% CI: 9.1%–43.5%) (Fig. 4 D).These pan-immune-inflammation markers can be used as a supplement to the IPI score, and their incremental value warrants further investigation in future studies. Peripheral blood cell counts were used in this study to calculate PIV, SII, MLR, PLR, NLR, and PILE score. These indices were applied to explore the prognostic importance of them as prognostic indicators in participants with DLBCL. As for results, PIV functions as an independent determinant of OS. In contrast, PILE score and other inflammation-based parameters may provide insight into primary resistance to treatment. Several limitations of this study should be recognized. First, recall bias might have influenced the accuracy of data collection. Second, the findings may be constrained by both the limited cohort size and the failure to validate results in an independent population. The present study has a relatively small sample size , which compromises the statistical power of the multivariable Cox regression analysis. To reduce the risk of overfitting, we adopted a rigorous variable selection strategy where only variables with statistical significance in the univariate analysis were included in the multivariable model. This approach is a well-recognized method for controlling overfitting in small-sample prognostic studies, which can effectively balance the efficiency of the model and the reliability of the results.Third, potential confounders, including biomarker measurement methods, timing of diagnosis, patients’ immune status, and coexisting infections, could have introduced analytical bias. Consequently, the predictive value of all markers and PILE score in detecting primary treatment resistance warrants confirmation through larger, prospective investigations. Conclusion In DLBCL patients, our study indicates that PIV is a key immune-related biomarker significantly associated with to the prognosis. Among all peripheral immune-inflammatory indices tested in this study, PIV exhibited the most robust prognostic significance. In addition, these markers—especially PIV—may help predict primary resistance to therapy, although confirmation in larger cohorts is still required. Acknowledgements I would like to thank Researcher Tiansheng JIN for reviewing the draft of this paper on multiple occasions. In particular, his valuable revision suggestions regarding the logic of result analysis and the standards of academic language have significantly enhanced the rigor of the paper. Authors’ contributions C.C.conceived and designed the study, and wrote the initial draft of the manuscript.C.C and XY.M.performed the experiments and analyzed the data. XY.M.prepared the figures,JB.W. revised the manuscript for important intellectual content. All authors read and approved the final manuscript. Funding This work was supported by China Capital Characteristic Clinic Project (Grant No. Z211100002921037) to Jingbo Wang. Data Availability The patient data used in this study were collected from hospitals and contain sensitive personal information, which is subject to privacy protection regulations and ethical approval requirements. Therefore, the raw patient data cannot be publicly shared to avoid potential privacy breaches. However, for researchers who meet the criteria for accessing confidential data (e.g., providing a research proposal approved by their institutional ethics committee and a commitment to data security), the raw data can be made available upon reasonable request. Interested parties may contact the corresponding author to discuss data access arrangements, and the data will be provided in accordance with relevant legal and ethical guidelines. All other materials (e.g., study protocols, analysis codes) that do not involve personal privacy are available from the corresponding author upon request. Declarations Ethics approval and consent to participate This research adhered to the principles outlined in the Declaration of Helsinki, obtained approval by the Ethics Committee of Aerospace Center Hospital (approval no. 2024037-02), and all patients and participants who involved in this study signed informed consent forms. Consent for publication I confirm the corresponding author has read the journal policies and submit this manuscript in accordance with those policies. Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. Sun K, Wu H, Zhu Q, et al. 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Role of Pretreatment Pan-Immune Inflammation Value as Predictive Marker of Response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer: A Prospective Observational Study in a Tertiary Cancer Center Indian J Med Paediatr Oncol. 2026; Indian J Med Paediatr Oncol. 10.1055/s-0045-1811967. Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement The patient data used in this study were collected from hospitals and contain sensitive personal information, which is subject to privacy protection regulations and ethical approval requirements. Therefore, the raw patient data cannot be publicly shared to avoid potential privacy breaches. However, for researchers who meet the criteria for accessing confidential data (e.g., providing a research proposal approved by their institutional ethics committee and a commitment to data security), the raw data can be made available upon reasonable request. Interested parties may contact the corresponding author to discuss data access arrangements, and the data will be provided in accordance with relevant legal and ethical guidelines. All other materials (e.g., study protocols, analysis codes) that do not involve personal privacy are available from the corresponding author upon request. 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