PIV and PILE Score at Baseline Predict Clinical Outcome of Anti-PD-1/PD-L1 Inhibitor Combined With Chemotherapy in Extensive-Stage Small Cell Lung Cancer Patients

The Construction of a Nomogram Using the Pan-Immune-Inflammation Value Combined with a PILE Score for Immunotherapy Prediction Prognosis in Advanced NSCLC

Purpose

The purpose of this study was to investigate the predictive value of Pan-Immune-Inflammation Value (PIV) combined with the PILE score for immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) and to construct a nomogram prediction model to provide reference for clinical work.

Patients and Methods

Patients with advanced NSCLC who received ICIs treatment in Qingdao Municipal Hospital from January 2019 to December 2021 were selected as the study subjects. The chi-square test, Kaplan-Meier survival analysis, and Cox proportional risk regression analysis were used to evaluate the prognosis. The results were visualized by a nomogram, and the performance of the model was judged by indicators such as the area under the subject operating characteristic curve (AUC) and C-index. The patients were divided into high- and low-risk groups by PILE score, and the prognosis of patients in different risk groups was evaluated.

Results

Multivariate Cox regression analysis showed that immune-related adverse events (irAEs) were prognostic factors for overall survival (OS) improvement, and ECOG PS score ≥2, bone metastases before treatment, and high PIV expression were independent risk factors for OS. The C index of OS predicted by the nomogram model is 0.750 (95% CI: 0.677-0.823), and the Calibration and ROC curves show that the model has good prediction performance. Compared with the low-risk group, patients in the high-risk group of PILE were associated with a higher inflammatory state and poorer physical condition, which often resulted in a poorer prognosis.

Conclusion

PIV can be used as a prognostic indicator for patients with advanced NSCLC treated with ICIs, and a nomogram prediction model can be constructed to evaluate the survival prediction of patients, thus contributing to better clinical decision-making and prognosis assessment.

Interactions between platelets and the cancer immune microenvironment

Cancer is a leading cause of death in both China and developed countries due to its high incidence and low cure rate. Immune function is closely linked to the development and progression of tumors. Platelets, which are primarily known for their role in hemostasis, also play a crucial part in the spread and progression of tumors through their interaction with the immune microenvironment. The impact of platelets on tumor growth and metastasis depends on the type of cancer and treatment method used. This article provides an overview of the relationship between platelets and the immune microenvironment, highlighting how platelets can either protect or harm the immune response and cancer immune escape. We also explore the potential of available platelet-targeting strategies for tumor immunotherapy, as well as the promise of new platelet-targeted tumor therapy methods through further research.

Nomogram incorporating preoperative pan-immune-inflammation value and monocyte to high-density lipoprotein ratio for survival prediction in patients with colorectal cancer: a retrospective study

OBJECTIVE

Using the preoperative pan-immune-inflammation value (PIV) and the monocyte to high-density lipoprotein ratio (MHR) to reflect inflammation, immunity, and cholesterol metabolism, we aim to develop and visualize a novel nomogram model for predicting the survival outcomes in patients with colorectal cancer (CRC).

METHODS

A total of 172 patients with CRC who underwent radical resection were retrospectively analyzed. Survival analysis was conducted after patients were grouped according to the optimal cut-off values of PIV and MHR. Univariate and multivariate analyses were performed using Cox proportional hazards regression to screen the independent prognostic factors. Based on these factors, a nomogram was constructed and validated.

RESULTS

The PIV was significantly associated with tumor location (P < 0.001), tumor maximum diameter (P = 0.008), and T stage (P = 0.019). The MHR was closely related to gender (P = 0.016), tumor maximum diameter (P = 0.002), and T stage (P = 0.038). Multivariate analysis results showed that PIV (Hazard Ratio (HR) = 2.476, 95% Confidence Interval (CI) = 1.410-4.348, P = 0.002), MHR (HR = 3.803, 95%CI = 1.609-8.989, P = 0.002), CEA (HR = 1.977, 95%CI = 1.121-3.485, P = 0.019), and TNM stage (HR = 1.759, 95%CI = 1.010-3.063, P = 0.046) were independent prognostic indicators for overall survival (OS). A nomogram incorporating these variables was developed, demonstrating robust predictive accuracy for OS. The area under the curve (AUC) values of the predictive model for 1-, 2-, and 3- year are 0.791,0.768,0.811, respectively. The calibration curves for the probability of survival at 1-, 2-, and 3- year presented a high degree of credibility. Furthermore, Decision curve analysis (DCA) for the probability of survival at 1-, 2-, and 3- year demonstrate the significant clinical utility in predicting survival outcomes.

CONCLUSION

Preoperative PIV and MHR are independent risk factors for CRC prognosis. The novel developed nomogram demonstrates a robust predictive ability, offering substantial utility in facilitating the clinical decision-making process.

Novel pretreatment nomograms based on pan-immune-inflammation value for predicting clinical outcome in patients with head and neck squamous cell carcinoma

Background

The prognostic value of an effective biomarker, pan-immune-inflammation value (PIV), for head and neck squamous cell carcinoma (HNSCC) patients after radical surgery or chemoradiotherapy has not been well explored. This study aimed to construct and validate nomograms based on PIV to predict survival outcomes of HNSCC patients.

Methods

A total of 161 HNSCC patients who underwent radical surgery were enrolled retrospectively for development cohort. The cutoff of PIV was determined using the maximally selected rank statistics method. Multivariable Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to develop two nomograms (Model A and Model B) that predict disease-free survival (DFS). The concordance index, receiver operating characteristic curves, calibration curves, and decision curve analysis were used to evaluate the nomograms. A cohort composed of 50 patients who received radiotherapy or chemoradiotherapy (RT/CRT) alone was applied for generality testing of PIV and nomograms.

Results

Patients with higher PIV (≥123.3) experienced a worse DFS (HR, 5.01; 95% CI, 3.25-7.72; p<0.0001) and overall survival (OS) (HR, 5.23; 95% CI, 3.34-8.18; p<0.0001) compared to patients with lower PIV (<123.3) in the development cohort. Predictors of Model A included age, TNM stage, neutrophil-to-lymphocyte ratio (NLR), and PIV, and that of Model B included TNM stage, lymphocyte-to-monocyte ratio (LMR), and PIV. In comparison with TNM stage alone, the two nomograms demonstrated good calibration and discrimination and showed satisfactory clinical utility in internal validation. The generality testing results showed that higher PIV was also associated with worse survival outcomes in the RT/CRT cohort and the possibility that the two nomograms may have a universal applicability for patients with different treatments.

Conclusions

The nomograms based on PIV, a simple but useful indicator, can provide prognosis prediction of individual HNSCC patients after radical surgery and may be broadly applicated for patients after RT/CRT alone.

Pan-immune inflammation value as a prognostic biomarker for cancer patients treated with immune checkpoint inhibitors

Background

Immune checkpoint inhibitors (ICIs) represent a paradigm shift in the development of cancer therapy. However, the improved efficacy of ICIs remains to be further investigated. We conducted a systematic review and meta-analysis to evaluate the pan-immunoinflammatory value (PIV) and PILE score used to predict response to ICI therapy.

Methods

We searched selected databases for studies on pan-immune inflammation values and their association with outcomes of treatment with immune checkpoint inhibitors. We used hazard ratios (HRS) and 95% confidence intervals (CI) to summarize survival outcomes. All data analyses were performed using STATA 15.0.

Results

7 studies comprising 982 patients were included in the meta-analysis. The pooled results showed that higher PIV was significantly associated with shorter overall survival OS (HR = 1.895, 95%CI: 1.548-2.318) and progression-free survival (PFS) (HR = 1.582, 95%CI: 1.324-1.890). Subgroup analyses also confirmed the reliability of the results.

Conclusions

High PIV and PILE metrics are associated with lower survival in cancer patients receiving ICIs.

Prognostic value of pan-immune-inflammation value and body mass index in geriatric patients with metastatic renal cell carcinoma treated with tyrosine kinase inhibitors as first line treatment. A single-center retrospective study

Introduction

Geriatric patients with metastatic renal cell carcinoma (mRCC) are underrepresented in clinical trials. Evaluation of the efficacy of the treatment and assignation of individuals to proper prognostic groups is an absolute necessity to guarantee them the best possible care.

Material and methods

A total of 138 geriatric patients with mRCC treated with first-line tyrosine kinase inhibitors (TKIs) at the Maria Skłodowska-Curie National Research Institute of Oncology were retrospectively analyzed to determine whether the body mass index (BMI) and pan-immune-inflammation value (PIV) are prognostic values for overall survival (OS) and progression-free survival (PFS) in this type of cancer. For this purpose, Cox's proportional hazard model was used.

Results

The median duration of follow-up for surviving patients was 46.6 (95% CI: 17.4-75.8) months. The median OS and PFS were respectively 33.8 months (95% CI: 23.8-47.8) and 19.1 months (95% CI: 15.0-23.3). BMI (p = 0.034) and PIV (p < 0.001) were statistically significantly associated with OS, and PIV (p = 0.001) was statistically significantly associated with PFS. The risk of death for patients from the high-PIV group (cut-off point: 548) was 3.4 times higher than for those with lower PIV values. The corresponding risk of progression for patients from the high-PIV group was 2.2 times higher. The G8 geriatric screening tool was not identified as a prognostic factor.

Conclusions

Lower PIV and obesity are associated with longer OS in geriatric mRCC patients treated with TKIs in the first line. These factors may be considered while making treatment decisions if further studies show the same results.

Prognostic significance of pan-immune-inflammation value (PIV) in nasopharyngeal carcinoma patients

Background

Blood-based immune-inflammation indexes have been widely used to predict survival in a variety of cancers. In this research, we seeked to evaluate a novel immune-inflammation marker, named the pan-immune-inflammation value (PIV), in patients with nasopharyngeal carcinoma (NPC) undergoing definitive radiotherapy.

Methods

A group of 377 patients with NPC was retrospectived analyzed. Clinical data and laboratory data were collected. Receiver operating characteristic (ROC) curve analysis was performed in order to determine the optimal PIV cut-off value. Survival curves were estimated by Kaplan-Meier method, and prognostic variables were identified using a Cox regression model. Additionally, we developed a nomogram and assessed its acuracy using the concordance index (C-index) and a calibration curve.

Results

The optimal PIV cut-off value was 146.24 according to ROC analysis. High PIV was related to poorer Eastern Cooperative Oncology Group Performance Status (ECOG PS) score (p = 0.017), more advanced T (p＜0.001) and clinical stages (p = 0.024). In univariate analysis, older Age, poorer ECOG PS, higher Epstein-Barr virus DNA (EBV-DNA), advanced T, N and clinical stage, and higher PIV levels were related to patients' poorer overall survival (OS). Poorer ECOG PS, higher EBV-DNA, later T stage, later clinical stage, and higher PIV were associated with patients' poorer progression free survival (PFS). Male sex and later T stage were associated with patients' poorer locoregional recurrence free survival (LRRFS). Poorer ECOG PS, higher EBV-DNA, later T stage, later clinical stage, and higher PIV were associated with patients' poorer distant metastasis free survival (DMFS). Multivariate analysis demonstrated that PIV was an independent prognostic index for OS (HR 2.231, 95 % CI 1.241-4.011, P = 0.007), PFS (HR 1.664, 95 % CI 1.003-2.760, P = 0.049), and DMFS(HR 2.081, 95 % CI 1.071-4.044, P = 0.031). Nomogram C-indexes for the nomogram of OS were 0.684, and PFS were 0.62, respectively. Survival predictions and actual survival were consistent according to the calibration curve.

Conclusions

Pre-treatment PIV is a promising biomarker for predicting survival in patients with NPC.

The prognostic importance of the pan-immune-inflammation value in patients with septic shock

INTRODUCTION

The purpose of this study was to determine whether the pan-immune-inflammation value (PIV), a novel biomarker combining neutrophil platelet, monocyte, and lymphocyte counts, some of the most widespread indicators of systemic inflammation, can predict mortality and prognosis in patients admitted to the intensive care unit (ICU) with septic shock.

METHOD

This prospective study was performed with 82 patients aged 18 or over admitted to a tertiary ICU with diagnoses of septic shock. Patients with hematological disease and neutropenia were excluded. PIV was calculated with the formula [neutrophil count (103/μL) × platelet count (103/μL) × monocyte count (103/μL)]/lymphocyte count (103/μL).

RESULTS

Median age, presence of hypertension, Acute Physiology and Chronic Health Evaluation II (APACHE II) levels, and neutrophil, monocyte, and platelet counts were lower in the low-PIV group than in the high-PIV group (p < 0.05). The highest area under ROC curve (AUC) was determined for Sequential Organ Failure Assessment (SOFA) (0.94 (0.89 - 0.99)), followed by Glasgow Coma Scale (GCS) (0.81 (0.70 - 0.91)), APACHE II (0.80 (0.69 - 0.91)) and lactate (0.77 (0.67 - 0.88)). Median survival was longer in the low-PIV group than in the high-PIV group (28 (15.25 - 40.76) vs 16 (9.46 - 22.55) days, respectively, p < 0.05). The univariate Cox proportional hazards (CPH) model showed that high PIV (HR = 2.13 (1.03-4.38)), low GCS (HR = 3.31 (1.34 - 8.15)), high SOFA (HR = 9.41 (2.86 - 30.95)), high APACHE II (HR = 3.08 (1.47 - 6.45)), high lactate (HR = 6.56 (2.73 - 15.75)), and high procalcitonin (PCT) (HR = 2.73 (1.11 - 6.69)) values were associated with a decreased survival time among ICU patients (p < 0.05). The multivariate CPH model showed the age-adjusted risk estimates for these six laboratory parameters. High lactate (HR = 7.97 (2.19 - 29.08)) and high SOFA scores (HR = 4.85 (1.22 - 19.32)) were significantly associated with shorter survival in ICU patients (p < 0.05).

CONCLUSION

The findings of this research suggest that PIV could predict the longer survival in patients with septic shock. Despite PIV score's capability to show inflammation, it is not significantly associated with mortality in the multivariate analysis.

Pretreatment Pan-Immune-Inflammation Value (PIV) in Predicting Therapeutic Response and Clinical Outcomes of Neoadjuvant Immunochemotherapy for Esophageal Squamous Cell Carcinoma

PURPOSE

The pan-immune-inflammation value (PIV), which reflects the balance between the host immune and inflammatory status, is a readily available index for evaluating cancer outcomes. Until now, however, no study has demonstrated the clinical response of PIV to neoadjuvant immunochemotherapy (NICT) in esophageal squamous cell carcinoma (ESCC).

METHODS

This retrospective study included 218 patients with ESCC who underwent NICT. The relationship between PIV and therapeutic response (pathological complete response [PCR]) and clinical outcomes (overall survival [OS] and disease-free survival [DFS]) was examined. Cox proportional, hazard-regression analyses and the Kaplan-Meier method were used for survival analyses. Recursive partitioning analysis (RPA) was used to establish a novel risk stratification model.

RESULTS

Sixty-six patients (30.3%) achieved PCR after NICT. Using PCR as the endpoint of interest, patients were compared in groups based on the optimal threshold. PIV was closely related to PCR (odds ratio [OR] 0.311, 95% confidence interval [CI] 0.140-0.690, P = 0.004). Compared with patients in the low PIV cohort, patients with high PIV had worse 3-year OS (58.7% vs. 83.6%, P < 0.001) and DFS (51.9% vs. 79.1%, P < 0.001). PIV was an independent predictor of OS (hazard ratio [HR] 2.364, 95% CI 1.183-4.724, P = 0.015) and DFS (HR 1.729, 95% CI 1.026-2.913, P = 0.040). Three risk groups with varied DFS and OS were staged by using an RPA method, and the prognostication accuracy was considerably improved.

CONCLUSIONS

Pretreatment PIV can predict the therapeutic efficacy of NICT for ESCC. Because of better prognostic stratification, pretreatment PIV is a novel, sensitive, and effective indicator in ESCC receiving NICT. The prognostic results of PIV need to be verified in additional prospective studies.

Comparison of Machine Learning and Logic Regression Algorithms for Predicting Lymph Node Metastasis in Patients with Gastric Cancer: A two-Center Study

OBJECTIVES

This two-center study aimed to establish a model for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning (ML) and logistic regression (LR) algorithms, and to evaluate its predictive performance in clinical practice.

METHODS

Data of a total of 369 patients who underwent radical gastrectomy in the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) from March 2016 to November 2019 were collected and retrospectively analyzed as the training group. In addition, data of 123 patients who underwent radical gastrectomy in the Department of General Surgery of Jining First People's Hospital (Jining, China) were collected and analyzed as the verification group. Besides, 7 ML and logistic models were developed, including decision tree, random forest, support vector machine (SVM), gradient boosting machine (GBM), naive Bayes, neural network, and LR, in order to evaluate the occurrence of lymph node metastasis in patients with gastric cancer. The ML model was established following 10 cross-validation iterations within the training dataset, and subsequently, each model was assessed using the test dataset. The model's performance was evaluated by comparing the area under the receiver operating characteristic curve of each model.

RESULTS

Compared with the traditional logistic model, among the 7 ML algorithms, except for SVM, the other models exhibited higher accuracy and reliability, and the influences of various risk factors on the model were more intuitive.

CONCLUSION

For the prediction of lymph node metastasis in gastric cancer patients, the ML algorithm outperformed traditional LR, and the GBM algorithm exhibited the most robust predictive capability.

High pre-chemoradiotherapy pan-immune-inflammation value levels predict worse outcomes in patients with stage IIIB/C non-small-cell lung cancer

BACKGROUND AND OBJECTIVES

We explored the prognostic usefulness of the pan-immune-inflammation value (PIV) in patients with stage IIIB/C non-small-cell lung cancer (NSCLC) who underwent concurrent chemoradiotherapy (CCRT).

METHODS AND PATIENTS

For all patients, the PIV was calculated using platelet (P), monocyte (M), neutrophil (N), and lymphocyte (L) measures obtained on the first day of CCRT: PIV = P × M × N ÷ L. Using receiver operating characteristic (ROC) curve analysis, we searched for the existence of an ideal cutoff that may partition patients into two groups with unique progression-free- (PFS) and overall survival (OS) results. The primary endpoint of this retrospective cohort research was to determine whether there were any significant relationships between pretreatment PIV measures and post-CCRT OS outcomes.

RESULTS

The present research included a total of 807 stage IIIB/C NSCLC patients. According to ROC curve analysis, the ideal PIV cutoff was 516 [area under the curve (AUC): 67.7%; sensitivity: 66.4%; specificity: 66.1%], which divided the whole cohort into two: low PIV (L-PIV: PIV < 516; N = 436) and high PIV (H-PIV: PIV ≥ 516; N = 371). The comparisons between the PIV groups indicated that either the median PFS (9.2 vs. 13.4 months; P < 0.001) or OS (16.7 vs. 32.7 months; P < 0.001) durations in the H-PIV group were substantially inferior to their L-PIV counterpart. Apart from the H-PIV (P < 0.001), the N3 nodal stage (P = 0.006), IIIC disease stage (P < 0.001), and receiving only one cycle of concurrent chemotherapy (P = 0.005) were also determined to be significant predictors of poor PFS (P < 0.05, for each) and OS (P < 0.05, for each) outcomes in univariate analysis. The multivariate analysis findings revealed that all four variables had independent negative impacts on PFS (P < 0.05, for each) and OS (P < 0.05, for each).

CONCLUSIONS

The findings of this hypothesis-generating retrospective analysis claimed that the novel PIV was an independent and steadfast predictor of PFS and OS in stage IIIB/C NSCLC patients.

Pan-immune-inflammation and its dynamics: predictors of survival and immune-related adverse events in patients with advanced NSCLC receiving immunotherapy

OBJECTIVES

Pan-immune-inflammation value (PIV) is defined by the neutrophil, platelet, monocyte, and lymphocyte counts and is associated with immune-checkpoint inhibitor (ICI) therapy outcomes in advanced non-small cell lung cancer (aNSCLC). However, PIV is dynamic under therapy and its longitudinal assessment may help predict efficacy. This study investigated the impact of baseline PIV and its dynamics on ICI efficacy and its immune-related adverse events (irAEs). The study additionally attempted to understand the biological significance of PIV.

PATIENTS AND METHODS

This retrospective study analyzed the clinical data of 269 consecutive patients with aNSCLC. PIV was calculated at baseline and at weeks 3-4 to determine its association with overall survival (OS), progression-free survival (PFS), and irAEs.

RESULTS

Results revealed that low baseline PIV was positively correlated with the incidence of irAEs. Moreover, a low PIV at baseline was significantly associated with a prolonged PFS (median PFS: 10 vs. 7 months, p = 0.0005) and OS (median OS: 29 vs. 21 months, p < 0.0001). When the PIV at baseline and weeks 3-4 was considered together, its low dynamics correlated with a higher incidence of irAEs (p = 0.001), a longer PFS (median PFS, 9 vs. 6 months, p = 0.012), and a longer OS (median OS; 28 vs. 21 months, p = 0.002).

CONCLUSION

Thus, PIV at baseline and its dynamics are novel and potent predictors of irAEs, PFS, and OS in patients with aNSCLC receiving immunotherapy. Moreover, the PIV dynamics may be an effective, novel surrogate marker to dynamically observe the efficacy of immunotherapy.

Prognostic significance of the pretreatment pan-immune-inflammation value in cancer patients: an updated meta-analysis of 30 studies

Background

The pan-immune-inflammation value (PIV) has been reported as a promising prognostic biomarker in multiple cancers but still remains inconclusive. The objective of this study is to systematically investigate the association of the pretreatment PIV with survival outcomes in cancer patients, based on available literature.

Methods

Online databases including PubMed, Embase and the Web of Science were thoroughly searched for studies evaluating the prognostic role of the pretreatment PIV in cancers from the inception to June 2023. Hazard ratios (HRs) with 95% confidence intervals (CIs) were always assessed using a random-effects model. Statistical analyses were performed using Stata 12.0.

Results

Thirty studies were finally included after comprehensively study searching. In total, 8,799 cancer patients were enrolled in this meta-analysis. The pooled results demonstrated that patients in the high PIV group had a significantly poorer overall survival (HR = 2.07; 95%CI: 1.77-2.41; I2 = 73.0%) and progression-free survival (HR = 1.83; 95%CI: 1.37-2.45; I2 = 98.2%) than patients in the low PIV group. The prognostic significance of the PIV score on overall survival and progression-free survival was observed across various geographical regions, tumor stages and treatment strategies. Sensitivity analyses supported the stability of the above combined results.

Conclusion

This meta-analysis demonstrated that the pretreatment PIV could be a non-invasive and efficacious prognostic biomarker for cancer patients.

Utility of pre-chemoradiotherapy Pan-Immune-Inflammation-Value for predicting the osteoradionecrosis rates in locally advanced nasopharyngeal cancers

PURPOSE

The aim of this retrospective study was to explore whether pretreatment Pan-Immune-Inflammation-Value (PIV) measurements might predict the risk of mandibular osteoradionecrosis (ORN) in patients receiving concurrent chemoradiotherapy (CCRT) for locally advanced nasopharyngeal cancer (LA-NPC).

METHODS

The platelet, monocyte, neutrophil, and lymphocyte counts acquired on the first day of CCRT were used to compute pretreatment PIV levels: PIV = (Platelets × Monocytes × Neutrophils) ÷ Lymphocytes. Receiver operating characteristic curve analysis was used to determine the association between ORN rates and PIV levels. Spearman correlation analysis was used to examine the probable intergroup correlations. The potential link between the pretreatment PIV levels and the post-treatment ORN rates was determined as the primary objective.

RESULTS

21 (10.0%) of 210 eligible patients were diagnosed with ORN. The optimal pre-CCRT PIV cutoff was 833, which separated patients into two PIV groups with divergent ORN prevalence estimates: Group 1: PIV < 833 (N = 153), and Group 2: PIV ≥ 833 (N = 57). The comparison analysis found that the PIV ≥ 833 cohort had significantly higher ORN rates than the PIV < 833 cohort (29.8% vs. 2.6%; P < 0.001). Other characteristics linked to significantly higher ORN rates were the patient's continuing smoking, the use of the Three-dimensional conformal radiation therapy technique, the mean mandibular dose of ≥ 58.1 Gy, the number of tooth extractions before CCRT ≥ 4, and the presence of tooth extractions after CCRT. The independent importance of all factors on higher ORN occurrence rates were retained in multivariate analysis (P < 0.05).

CONCLUSIONS

Our findings revealed a strong link between aggravated inflammatory response and ORN genesis, with high pretreatment PIV levels related to significantly higher ORN rates.

The Prognostic Value of the Novel Global Immune-Nutrition-Inflammation Index (GINI) in Stage IIIC Non-Small Cell Lung Cancer Patients Treated with Concurrent Chemoradiotherapy

BACKGROUND

We sought to determine the prognostic value of the newly developed Global Immune-Nutrition-Inflammation Index (GINI) in patients with stage IIIC non-small cell lung cancer (NSCLC) who underwent definitive concurrent chemoradiotherapy (CCRT).

METHODS

This study was conducted on a cohort of 802 newly diagnosed stage IIIC NSCLC patients who underwent CCRT. The novel GINI created first here was defined as follows: GINI = [C-reactive protein × Platelets × Monocytes × Neutrophils] ÷ [Albumin × Lymphocytes]. The receiver operating characteristic (ROC) curve analysis was used to determine the optimal pre-CCRT GINI cut-off value that substantially interacts with the locoregional progression-free (LRPFS), progression-free (PFS), and overall survival (OS).

RESULTS

The optimal pre-CCRT GINI cutoff was 1562 (AUC: 76.1%; sensitivity: 72.4%; specificity: 68.2%; Youden index: 0.406). Patients presenting with a GINI ≥ 1562 had substantially shorter median LRPFS (13.3 vs. 18.4 months; p < 0.001), PFS (10.2 vs. 14.3 months; p < 0.001), and OS (19.1 vs. 37.8 months; p < 0.001) durations than those with a GINI < 1562. Results of the multivariate analysis revealed that the pre-CCRT GINI ≥ 1562 (vs. <1562), T4 tumor (vs. T3), and receiving only 1 cycle of concurrent chemotherapy (vs. 2-3 cycles) were the factors independently associated with poorer LRPS (p < 0.05 for each), PFS (p < 0.05 for each), and OS (p < 0.05 for each).

CONCLUSION

The newly developed GINI index efficiently divided the stage IIIC NSCLSC patients into two subgroups with substantially different median and long-term survival outcomes.

Pretreatment absolute lymphocyte count is an independent predictor for survival outcomes for esophageal squamous cell carcinoma patients treated with neoadjuvant chemoradiotherapy and pembrolizumab: An analysis from a prospective cohort

BACKGROUND

The aim of the study was to analyze the relationship between pretreatment inflammatory biomarkers (IBs) and survival outcomes for patients with esophageal squamous cell carcinoma (ESCC) treated with neoadjuvant chemoradiotherapy (neo-CRT) and pembrolizumab.

METHODS

Clinical variables and IBs (absolute monocyte count [AMC], absolute lymphocyte count [ALC], platelet count [PLT], neutrophil-to-lymphocyte ratio [NLR], platelet-to-lymphocyte ratio [PLR], lymphocyte-to-monocyte ratio [LMR], pan-immune inflammation value [PIV], systemic immunoinflammatory index [SII], systemic immunoreactivity index [SIRI] and prognostic nutritional index [PNI]) were collected. Univariate and multivariate analysis were performed to identify the independent factors for outcomes of ESCC.

RESULTS

A total of 51 patients were included. Of these, 35 patients achieved pathological complete response (pCR) after neo-CRT and pembrolizumab (pCR: 68.6%). With a median follow-up of 20 months, the two-year PFS and OS of the cohort was 64% and 91%, respectively. Multivariate logistic regression analysis indicated that ALC (overall response [OR] 4.4, p = 0.051) and PLT (OR 6.7, p = 0.023) were two independent predictors for achieving pCR among ESCC treated with neo-CRT and pembrolizumab. Multivariate Cox regression analysis showed that ALC (HR 0.27, p = 0.028) and SIRI (HR 3.13, p = 0.048) were two independent predictors associated with PFS. Kaplan Meier analysis demonstrated that the PFS of ESCC with high baseline ALC was significantly better than those with low ALC (2-year PFS: 77% vs. 47%, p = 0.027), but not for overall survival (2-year OS: 96% vs. 87%, p = 0.46).

CONCLUSIONS

This retrospective analysis based on a prospective cohort for the first time demonstrates that pretreatment ALC is an independent predictor for achieving pCR and favorable outcomes of ESCC treated with neo-CRT and pembrolizumab.

Discovering the Clinical and Prognostic Role of Pan-Immune-Inflammation Values on Oral Cavity Squamous Cell Carcinoma

A newly introduced pan-immune-inflammation value (PIV) was not evaluated for its role in oral cavity squamous cell carcinoma (OSCC). In this study, the PIV was calculated with the following equation (neutrophil count × platelet count × monocyte count)/lymphocyte count from the results of the automated hematology analyzers in 853 OSCC patients from 2005 to 2017. The optimal cutoff for the preoperative PIV was 268, as determined by a receiver operating characteristic curve. Significant differences were observed for alcohol consumption, smoking, pT status, pN status, overall pathological status, extranodal extension, cell differentiation, depth of invasion, and perineural invasion between higher and lower PIV patients (all p values < 0.05). Kaplan-Meier and univariate regression analyses indicated that higher PIV was associated with worse overall survival, disease-free survival, locoregional recurrence-free survival, and distant metastasis-free survival (all p values < 0.001). Multivariate analyses adjusted by various factors further demonstrated that PIV was an independent prognostic factor for overall and distant metastasis-free survival (p = 0.027, HR: 1.281 and p = 0.031, HR: 1.274, respectively). In conclusion, a higher PIV level was associated with poor clinicopathological factors in OSCC patients and could be used to predict poor posttreatment outcomes, especially for overall and distant metastasis-free survival.

The association between pan-immune-inflammation value and survival in head and neck squamous cell carcinoma

PURPOSE

A significant portion of patients with locally advanced head and neck squamous cell carcinoma (HNSCC) relapse despite multimodality treatment denoting the need for biomarkers. The pan-immune-inflammation value (PIV) is a recently developed blood count-based prognostic biomarker. We evaluated the relationship between PIV and survival in locally advanced HNSCC patients treated with chemoradiotherapy (CRT).

METHODS

A total of 199 patients who underwent CRT at Hacettepe University Oncology Hospital were included. The relationship between clinical and laboratory parameters with overall survival (OS) and disease-free survival (DFS) was analyzed by multivariate analyses.

RESULTS

The median age was 59 years and 90.5% of the patients were male. 66.8% of the patients had laryngeal primaries, and 78.9% had T3-T4 disease. 84.9% of the patients received CRT with cisplatin. The optimal PIV threshold value was calculated as 404 in ROC analyses. This PIV value had 75.8% sensitivity and 70.4% specificity for OS prediction (AUC 0.781; 95% CI 0.715-0.846; p < 0.001). In multivariate analyses, high PIV levels (≤ 404 vs. > 404, HR 2.862; 95% CI 1.553-5.276; p = 0.001), higher NLR (≤ 2.5 vs. > 2.5, HR 1.827; 95% CI 1.017-3.281; p = 0.044) levels and ECOG performance score of 2 (HR 2.267; 95% CI 1.385-3.711; p = 0.001) were associated with shorter OS. These factors were associated with shorter DFS also (HR for PIV 2.485, 95% CI 1.383-4.467, p = 0.002).

CONCLUSIONS

We observed shorter OS and DFS in locally advanced HNSCC patients with high PIV levels. If prospective studies support our findings, the PIV score could be a prognostic biomarker in HNSCC.

Prognostic value of pan-immune-inflammation value in colorectal cancer patients: A systematic review and meta-analysis

Background

The pan-immune-inflammation value (PIV) has been reported as a novel prognostic biomarker in multiple malignancies. The aim of this study is to investigate the prognostic value of the PIV in patients with colorectal cancer.

Methods

We comprehensively searched electronic databases including PubMed, Embase and Web of Science up to August 2022. The endpoints were survival outcomes. Hazard ratios (HRs) with 95% confidence intervals (CIs) for survival data were collected for analysis.

Results

Six studies including 1879 participants were included. A significant heterogeneity in the PIV cut-off value among studies was observed. The combined results indicated that patients in the high baseline PIV group had a worse overall survival (HR=2.09; 95%CI: 1.67-2.61; P<0.0001; I^{2 =} 7%) and progression-free survival (HR=1.82; 95%CI: 1.49-2.22; P<0.0001; I^{2 =} 15%). In addition, early PIV increase after treatment initiation was significantly associated with decreased overall survival (HR=1.79; 95%CI: 1.13-2.93; P=0.01; I^{2 =} 26%), and a trend toward poor progression-free survival (HR=2.00; 95%CI: 0.90-4.41; P=0.09; I^{2 =} 70%).

Conclusion

Based on existing evidence, the PIV could act as a valuable prognostic index in patients with colorectal cancer. However, the heterogeneity in the PIV cut-off value among studies should be considered when interpreting these findings.

Pretreatment Pan-Immune-Inflammation Value Efficiently Predicts Survival Outcomes in Glioblastoma Multiforme Patients Receiving Radiotherapy and Temozolomide

Objectives

The purpose of this study was to determine the predictive significance of pretreatment pan-immune-inflammation value (PIV) in patients with newly diagnosed glioblastoma multiforme (GBM) who received postsurgical radiation (RT) and concurrent plus adjuvant temozolomide (TMZ).

Methods

The outcomes of 204 newly diagnosed GBM patients were analyzed retrospectively. Each eligible patient's PIV was calculated using the findings of peripheral blood platelet (P), monocyte (M), neutrophil (N), and lymphocyte (L) counts obtained on the first day of therapy: PIV = P × M × N ÷ L. We used receiver operating characteristic (ROC) curve analysis to discover the ideal cutoff values for PIV concerning progression-free (PFS) and overall survival (OS) outcomes. The primary and secondary end-points were the OS and PFS divergences across the PIV groups.

Results

In ROC curve analysis, the optimal PIV cutoff was 385, which substantially interacted with PFS and OS results and categorized patients into low PIV (L-PIV; N = 75) and high PIV (H-PIV; N = 129) groups. Comparative survival analyses showed that the patients in the H-PIV group had significantly shorter median PFS (6.0 vs. 16.6 months; P < 0.001) and OS (11.1 vs. 22.9 months; P < 0.001) durations than those in the L-PIV group. The results of multivariate Cox regression analysis indicated an independent and significant connection between an H-PIV measure and shorter PFS and OS outcomes.

Conclusions

The novel PIV was able to independently stratify newly diagnosed GBM patients into two groups with fundamentally different PFS and OS outcomes following RT and concurrent plus adjuvant TMZ.

Low Pre-ChemoradiotherapyPan-Immune-Inflammation Value (PIV) Measures Predict Better Survival Outcomes in Locally Advanced Pancreatic Adenocarcinomas

Objective

This study sought to determine whether pretreatment pan-immune-inflammation value (PIV) could be used to predict prognosis in patients with locally advanced pancreatic adenocarcinoma (LA-PAC) following definitive concurrent chemoradiotherapy (C-CRT).

Methods

The outcomes of 178 LA-PAC patients who received definitive C-CRT were analyzed retrospectively. For all patients, the PIV was calculated using the peripheral blood platelet (P), monocyte (M), neutrophil (N), and lymphocyte (L) counts obtained on the first day of C-CRT: PIV=P×M×N÷L. The optimum cutoff values for PIV connected to progression-free (PFS) and overall survival (OS) results were sought using receiver operating characteristic (ROC) curve analysis. The OS and PFS differences between the PIV groups constituted the primary and secondary endpoints, respectively.

Results

ROC curve analysis indicated that the ideal PIV cutoff was 464 (AUC: 75.9%, sensitivity: 74.1%, specificity: 71.9%), which categorized patients into two groups based on PFS and OS results: low PIV (L-PIV; N = 69) and high PIV (H-PIV; N = 109). According to comparative survival analyses, patients in the L-PIV group had significantly longer median PFS (14.3 vs 7.3 months; HR: 3.04; P<0.001) and OS (25.9 vs 13.3 months; HR: 2.86; P<0.001) than those in the H-PIV group. Although none of the H-PIV patients could survive beyond 5 years, the estimated 5-year OS rate was 29.7% in the L-PIV cohort. In multivariate analyses, besides the L-PIV, N₀ nodal stage, and CA 19–9 ≤ 90 U/mL appeared to be the independent predictors of better PFS (P < 0.05 for each) and OS (P < 0.05 for each) results.

Conclusion

The present results indicated that pre-C-CRT L-PIV measures were associated with favorable median and long-term PFS and OS results in LA-PAC patients, suggesting that the PIV is a potent and independent novel prognostic biomarker.

Pan-immune-inflammation value is associated with the clinical stage of colorectal cancer

Objective

We investigated the clinical significance of preoperative pan-immune-inflammation value (PIV) in patients with colorectal cancer (CRC).

Methods

In this retrospective study, 366 cases who underwent surgery for CRC were enrolled. Their clinical data were collected. PIV was calculated with the formula PIV = [neutrophil count (109/L)× platelet count (109/L) × monocyte count (109/L) /lymphocyte count (109/L). Patients were divided into high PIV (&gt; median PIV) and low PIV (&lt; median PIV) groups. The relationship between PIV and clinicopathological features of CRC was investigated. Receiver operating characteristic (ROC) curve was plotted to indicate the value of immune-inflammatory biomarkers (IIBs) in predicting the TNM stage of CRC, and the area under the curve (AUC) was calculated to evaluate the actual clinical value of IIBs. AUC &gt; 0.5 and closer to 1 indicated the better predictive efficacy. The influencing factors of PIV in CRC were analyzed.

Results

We found that PIV was positively correlated with tumor size (r = 0.300, p &lt; 0.05), carcinoembryonic antigen (CEA) (r = 0.214, p &lt; 0.05) and carbohydrate antigen 125 (CA-125) (r = 0.249, p &lt; 0.05), but negatively correlated with albumin (Alb) (r = −0.242, p &lt; 0.05). PIV was significantly different in patients with different tumor locations (left or right), surgical methods (laparotomy versus laparoscopic surgery) (p &lt; 0.05), and patients with different pathological T stages, N-stage and TNM stages (p &lt; 0.05). ROC curve analysis of IIBs showed the AUC of PIV was greater than other markers when combined with CEA or carbohydrate antigen 19–9 (CA19–9). Multivariate regression analysis identified T stage, CEA, Alb, and tumor size as the independent influential factors of PIV in CRC.

Conclusion

PIV is associated with the tumor stage in patients with CRC, which may be useful in preoperative assessment of CRC.

The Association between the Pan-Immune-Inflammation Value and Cancer Prognosis: A Systematic Review and Meta-Analysis

Background: Prognostic scores derived from the blood count have garnered significant interest as an indirect measure of the inflammatory pressure in cancer. The recently developed pan-immune-inflammation value (PIV), an equation including the neutrophil, platelet, monocyte, and lymphocyte levels, has been evaluated in several cohorts, although with variations in the tumor types, disease stages, cut-offs, and treatments. Therefore, we evaluated the association between survival and PIV in cancer, performing a systematic review and meta-analysis. Methods: We conducted a systematic review from the Pubmed, Medline, and Embase databases to filter the published studies until 17 May 2022. The meta-analyses were performed with the generic inverse-variance method with a random-effects model. Results: Fifteen studies encompassing 4942 patients were included. In the pooled analysis of fifteen studies, the patients with higher PIV levels had significantly increased risk of death than those with lower PIV levels (HR: 2.00, 95% CI: 1.51−2.64, p < 0.001) and increased risk of progression or death (HR: 1.80, 95% CI: 1.39−2.32, p < 0.001). Analyses were consistent across several clinical scenarios, including non-metastatic or metastatic disease, different cut-offs (500, 400, and 300), and treatment with targeted therapy or immunotherapy (p < 0.001 for each). Conclusion: The available evidence demonstrates that PIV could be a prognostic biomarker in cancer. However, further research is needed to explore the promise of PIV as a prognostic biomarker in patients with non-metastatic disease or patients treated without immunotherapy or targeted therapy.

Pan-immune-inflammation value independently predicts disease recurrence in patients with Merkel cell carcinoma

Purpose

We aimed to determine whether the pan-immune-inflammation value (PIV) of patients with Merkel cell carcinoma (MCC) at primary diagnosis differs from controls and whether it is associated with disease stage and outcome.

Methods

In this retrospective study, we recruited MCC patients with stage I–III. PIV was calculated from absolute complete blood cell counts obtained within one week at MCC diagnosis as follows: [neutrophils (10³/mm³) × platelets (10³/mm³) × monocytes (10³/mm³)]/lymphocytes (10³/mm³). As controls, we studied age–gender-matched cutaneous melanoma (CM, stage I–III) patients and healthy controls (HC). Univariate and multivariate statistics were used.

Results

The median PIV in MCC patients was significantly increased compared to both CM patients as well as healthy controls. PIV of MCC patients in stage II and III was significantly higher compared to stage I patients. ROC analysis revealed that MCC recurrence was significantly associated with a PIV greater than 372 [p < 0.0001, Youden index 0.58; hazard ratio: 4 (95% confidence interval: 1.7 to 9.2)]. In multivariate analysis, only a PIV greater than 372 and higher MCC stage were determined as independent predictors for disease recurrence.

Conclusion

We determined, for the first time, the prognostic ability of the promising blood-based biomarker PIV in MCC patients and observed that PIV is increased in MCC patients in dependence on disease stage and independently predicts MCC recurrence.

The Change of Soluble Programmed Death Ligand 1 (sPD-L1) in Plasma of Small Cell Lung Cancer and Its Clinical Significance

Background soluble programmed death-ligand 1 (sPD-L1) expression in lung squamous cell carcinoma and lung adenocarcinoma is associated with disease progression, and sPD-L1 expression in small cell lung cancer (SCLC) may have similar manifestations and become a potential marker for treatment. The purpose of this study was to observe the changes of plasma sPD-L1 expression in SCLC patients. Methods. 90 patients diagnosed with SCLC from January 2019 to November 2020 were selected as the test group, including 72 males and 18 women, 58.7 ± 6.6 years; 30 healthy subjects were selected from the physical examination center, including 18 males, 12 females, and 60.3 ± 7.0 years. There were no statistical difference in sex and age factors between the trial and control groups (p > 0.05). Selected SCLC used chemotherapy regimen: cisplatin + etoposide (EP), carboplatin + etoposide (CE), and SCLC group were divided into three subgroups of disease progression group, partial remission group, and disease stability group according to the treatment effect. Comparison of the differences in sPD-L1 expression content between the experimental and control populations. Plasma sPD-L1 levels were dynamically monitored pre- and posttreatment in 90 patients with small-cell lung cancer and were associated with efficacy among subgroups. Meanwhile, the risk factors for patient sPD-L1 expression content were analyzed by logistic regression. Results. Plasma sPD-L1 levels were higher in the SCLC group than in the healthy people group (t = 7.40, p < 0.01). In the disease progression group of the SCLC group, sPD-L1 levels were decreased in the SCLC group, sPD-L1 in some remission group was increased after treatment, and sPD-L1 levels in the disease-stable group (p > 0.05). Multivariate logistic regression analysis showed that factors promoting increased sPD-L1 expression in SCLC patients included increased smoking, brain metastasis, and ProGRP expression (both p values < 0.05). Conclusion. (1) Higher peripheral sPD-L1 expression in SCLC patients than in healthy patients, and the expression levels were closely related to efficacy. (2) Dynamic changes in s PD-L1 were correlated with clinical efficacy. (3) The progression of sPD-L1 and ProGRP in SCLC patients showed the same extent during remission and stabilization, suggesting the effect of s PD-L1 in the evaluation of SCLC tumors and the reflection of the tumor marker ProGRP.

The Managing Cancer and Living Meaningfully (CALM) Intervention Alleviates Chemotherapy-Related Cognitive Impairment in Patients with Breast Cancer by Modulating Pan-Immune-Inflammation Values

Background:

The number of patients with breast cancer is increasing worldwide, resulting in a growing number of patients with chemotherapy-related cognitive impairment (CRCI), which seriously affects their quality of life. CRCI is associated with inflammatory factors and systemic inflammatory markers such as pan-immune-inflammation value (PIV) and monocyte-to-lymphocyte ratio (MLR), which can reflect the level of inflammation in the body. While the Managing Cancer and Living Meaningfully (CALM) intervention has been demonstrated to alleviate CRCI in patients with breast cancer, the specific mechanism remains unclear.

Objective:

This study evaluated the impact of the CALM intervention on systemic inflammation.

Methods:

Ninety patients with breast cancer with CRCI were enrolled and randomized into care as usual (CAU) and CALM intervention groups. All patients were assessed using the Functional Assessment of Cancer Therapy-Cognitive Function (FACT-Cog), Mini-Mental State Exam (MMSE), and Functional Assessment of Cancer Therapy-Breast (FACT-B) before and after the CAU/CALM intervention. The blood levels of inflammatory markers were also analyzed before and after the intervention.

Results:

Compared to the CAU group, the CALM group showed significantly improved cognitive function and significantly decreased PIV ( P < .05). PIV was significantly negatively correlated with FACT-Cog ( P < .05). The levels of other inflammatory markers, including MLR, neutrophil-to-lymphocyte ratio (NLR), granulocyte-to-lymphocyte ratio (GLR), and systemic immune-inflammation index (SII), were also reduced in the CALM group.

Conclusion:

PIV is an important marker of inflammation. The CALM intervention may improve the cognitive function of patients by regulating the systemic inflammation marker PIV through the neuroimmune axis.