OL Oncology Letters 1792-1074 1792-1082 D.A. Spandidos 10.3892/ol.2024.14243 OL-27-3-14243 Articles Clinical impact of inflammatory and nutrition index based on metabolic tumor activity in non‑small cell lung cancer treated with immunotherapy ItoKoki 1 HashimotoKousuke 1 KairaKyoichi 1 YamaguchiOu 1 MouriAtsuto 1 ShionoAyako 1 MiuraYu 1 KobayashiKunihiko 1 ImaiHisao 1 KujiIchiei 2 KagamuHiroshi 1 Department of Respiratory Medicine, International Medical Center, Saitama Medical University, Hidaka, Saitama 350-1298, Japan Department of Nuclear Medicine, International Medical Center, Saitama Medical University, Hidaka, Saitama 350-1298, Japan Correspondence to: Dr Kousuke Hashimoto or Professor Kyoichi Kaira, Department of Respiratory Medicine, International Medical Center, Saitama Medical University, 1397-1 Yamane, Hidaka, Saitama 350-1298, Japan, E-mail: kkaira1970@yahoo.co.jp saitamamed@gmail.com 03 2024 19 01 2024 27 3 110 29092023 15122023 Copyright © 2024, Spandidos Publications 2024

The aim of the present study was to explore the relationship between tumor metabolic glycolysis and inflammatory or nutritional status in patients with advanced non-small cell lung cancer (NSCLC) who received programmed death-1 (PD-1) blockade. A total of 186 patients were registered in the present study. All of patients underwent 18F-FDG PET imaging before initial PD-1 blockade, and maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were assessed as indicators of 18F-FDG uptake. As inflammatory and nutritional index, neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ration (PLR), systemic immune inflammation index (SII), prognostic nutritional index (PNI), advanced lung cancer inflammation index (ALI) and Glasgow prognostic score (GPS) were evaluated based on previous assessment. 18F-FDG uptake by MTV and TLG significantly correlated with the scores of NLR, PLR, SII, PNI and ALI, in addition to the level of albumin, lactate dehydrogenase, C-reactive protein, white blood cells, neutrophils, lymphocytes and body mass index. The count of NLR, PLR and SII was significantly higher in patients with <1 year overall survival (OS) compared with in those with ≥1 year OS, and that of PNI and ALI was significantly lower in those with <1 year OS compared with those with ≥1 year OS. High MTV under the high PLR, SII and low ALI were identified as significant factors for predicting the decreased PFS and OS after PD-1 blockade in a first-line setting. In second or more lines, high MTV was identified as a significant prognostic predictor regardless of the levels of PLR, SII, ALI and GPS. In conclusion, metabolic tumor glycolysis determined by MTV was identified as a predictor for the outcome of PD-1 blockade under the high inflammatory and low nutritional conditions, in particular, when treated with a first-line PD-1 blockade. A high MTV under high PLR and SII and low ALI in the first-line setting could be more predictive of ICI treatment than other combinations.

 inflammatory nutrition 18F-FDG PET immunotherapy programmed death-1 lung cancer Funding: No funding was received. Introduction

Immunotherapy is effective in patients with various neoplasms. Currently, the greatest number of biomarkers are being investigated as potential predictors of immune checkpoint inhibitors (ICIs), such as programmed death-1 (PD-1) or PD ligand-1 (PD-L1) antibodies. However, the majority of biomarkers have limited capability to predict the efficacy of ICIs. Certain genetic mutations, such as epidermal growth factor receptor (EGFR) mutations, can affect the efficacy of ICIs. A previous report identified EGFR mutation as a negative predictor in ICIs therapy in patients with non-small cell lung cancer (NSCLC) (1). The patient's ethnicity may affect the efficacy of ICIs, because of different incidence between Asian and Caucasian population. Therefore, there is an urgent need to identify novel biomarkers for the clinical application of appropriate treatments. NSCLC is a potential candidate for ICI treatment. Although an increasing number of patients with advanced NSCLC have been receiving PD-1 blockade, PD-L1 expression within tumor specimens alone is clinically utilized rather than tumor mutation burden (TMB), tumor infiltrative lymphocytes (TILs), or peripheral blood mononuclear cells (PBMC) (2–4). Conventionally, convenient modalities, such as blood testing or radiographic imaging, are acceptable for clinical application as useful predictors for any therapeutic agent.

Recently, we reported several studies on the relationship between 2-deoxy-2-[fluorine-18]-fluoro-d-glucose (18F-FDG) uptake on positron emission tomography (PET) and the prognostic significance of PD-1 blockade (5–8). Our previous studies indicated that metabolic tumor activity on PET before immunotherapy effectively predicts tumor outcome, but cannot predict the objective response to PD-1 blockade (5–8). The maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) are generally used to assess 18F-FDG uptake within tumor specimens, reflecting tumor glucose metabolism (5,6). Based on previous evidence, we hypothesized that instead of SUVmax, MTV or TLG could be utilized as prognostic predictors of PD-1 blockade (5–10). However, identifying a novel prognostic predictor after ICI treatment using metabolic tumor activity alone on PET remains difficult.

Generally, routinely collected blood parameters, such as white blood cells, leukocytes, lymphocytes, albumin, and C-reactive protein (CRP), as well as body mass index (BMI) are useful, convenient, and economical if these biomarkers are established as novel predictors for immunotherapy. Several recent studies have demonstrated that inflammatory and nutritional indices in blood samples are important markers for predicting the outcome of PD-1 blockade therapy (11,12). By combining these inflammatory and nutritional markers, possible therapeutic prediction for immunotherapy can be explored, and future challenges are expected. Glycolysis is a nutritional index, in addition to amino and fatty acids, and a high accumulation of 18F-FDG is observed at inflammatory sites, described as a false-positive finding (13). A recent study demonstrated a close relationship between 18F-FDG uptake and inflammatory indices in patients with NSCLC (14). However, the prognostic value of combining 18F-FDG uptake with inflammatory or nutritional indices following immunotherapy remains unclear.

Based on this evidence, we investigated the association between metabolic tumor activity via 18F-FDG uptake and inflammatory/nutrition indices and the prognostic impact of PD-1 blockade treatment by combining these markers based on previous studies (4–6,8).

 Materials and methods <sec> <title>Patients

Between April 2018 and March 2021, 186 patients with advanced NSCLC who received PD-1 blockade monotherapy and underwent 18F-FDG PET immediately before the initial treatment at our institution were included in this study. These cases have been reported in our previous studies (4–6,8). Clinical data were extracted from medical records. This study was approved by the Institutional Ethics Committee of the International Medical Center of Saitama Medical University (approval no. 19-075). The requirement for written informed consent was waived by the Ethics Committee of Saitama Medical University due to the retrospective nature of the study (15).

 Treatment and evaluation

All patients were treated with PD-1 blockade monotherapy and combined chemotherapy with anti-PD-1/PD-L1 antibodies. IMpower 150 (atezolizumab 1,200 mg, bevacizumab 15 mg/kg, area under the concentration-time curve of 5 mg/ml per min carboplatin, and 175 mg/m paclitaxel), keynote 189 (carboplatin area under the plasma concentration-time curve 5 mg/ml min, pemetrexed 500 mg/m2 · and pembrolizumab 200 mg), and keynote 407 (carboplatin area under the plasma concentration-time curve 5 mg/ml · min, nab-paclitaxel 100 mg/m2, and pembrolizumab 200 mg) were intravenously administered (16–18). Physical examination, complete blood count, biochemical testing, and adverse event assessment were performed by a chief physician. Toxicity was graded based on the Common Terminology Criteria for Adverse Events, version 4.0. Tumor response was examined using the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 (19).

 Assessment of inflammatory and nutrition index

Clinical and biological data, such as total protein (TP), albumin, white blood cell (WBC), neutrophil, platelet, lymphocyte, C-reactive protein (CRP), height, and weight, were extracted from medical records before analysis. Six indices reflecting the systemic inflammatory and nutritional statuses, based on previous studies (11), were calculated at baseline within 1 week of the first treatment cycle, as follows: 1) neutrophil-to-lymphocyte ratio (NLR)=neutrophil count/lymphocyte count (12,20): 2) platelet-to-lymphocyte ration (PLR)=platelet count/lymphocyte count (12,21); 3) systemic immune inflammation index (SII)=platelet count × neutrophil count/lymphocyte count (12,22); 4) prognostic nutritional index (PNI)=10 × albumin + 0.005 × lymphocyte count (10); 5) advanced lung cancer inflammation index (ALI)=body mass index (BMI) + albumin/NLR (23); and 6) Glasgow prognostic score (GPS)=CRP >10, albumin <3.5 (total points: 0, good; 1, intermediate; 2, poor) (20). GPS values of 0 and 1/2 were defined as low and high, respectively.

 PET imaging and data analysis

The patients fasted for at least 6 h before 18F-FDG administration for PET, which was performed using a PET/computed tomography (CT) scanner. Three-dimensional data acquisition was initiated 60 min after the FDG injection. Eight bed positions were selected based on the imaging range. The attenuation-corrected transverse images obtained using 18F-FDG were reconstructed using an ordered subset expectation-maximization algorithm based on the point-spread function into 168×168 matrices with a slice thickness of 2.00 mm.

For semi-quantitative analysis, the standardized uptake value (SUV) was examined based on the injected dosage of 18F-FDG, the patient's body weight, and the cross-calibration factor between the PET and the dose calibrator. The SUV was defined as follows: SUV=radioactive concentration in the volume of interest (VOI) (MBq/g)/injected dose (MBq)/patient's body weight (g). CT for initial staging was performed using intravenous contrast medium, and board-certified radiologists interpreted the images. We used RAVAT software (Nihon Medi-physics Co. Ltd., Japan) on a Windows workstation to semi-automatically calculate the maximum SUV (SUVmax), MTV, and TLG, defined as MTV multiplied by SUVmean, of each lesion using the SUV thresholds obtained by the SUV in the liver VOI. Each threshold was defined as the average of 1.5 × SUV (SUVmean) plus 2 × standard deviations of SUV in the liver. These SUV thresholds were the optimum values for generating a three-dimensional VOI in which the entire tumor mass was enclosed in all cases, using the CT image as the reference. Regions of activity other than tumors, including the myocardium, gastrointestinal tract, kidneys, and urinary tract, were manually eliminated according to the orientation provided by a board-certified nuclear medicine physician.

 Statistical analysis

Statistical significance was set at P<0.05. Fisher's exact test was used to examine the association between two categorical variables. Correlations between SUVmax, MTV, TLG, and 18F-FDG uptake were analyzed using Pearson's rank test. Progression-free survival (PFS) was defined as the time from initial treatment to disease progression or death. Overall survival (OS) was defined as the time from initial treatment to death from any cause. The Kaplan-Meier method was used to estimate survival as a function of time, and survival differences were analyzed using the log-rank test. Univariate and multivariate analyses of the variables were performed using logistic regression. The optimal cut-off values of NLR, PLR, SII, PNI, ALI, SUVmax, MTV, and TLG for 18F-FDG uptake were determined using receiver operating characteristic (ROC) curve analysis. Sensitivity and specificity were calculated to determine the optimal cutoff value for differentiating responders from non-responders using ROC curves. Responders were defined as those with a PFS >12 months. Factors with a value greater than the cutoff value were defined as highly expressed. All statistical analyses were performed using GraphPad Prism (v.7.0; GraphPad Software, San Diego, CA, USA) and JMP Pro 6.0 (SAS Institute Inc., Cary, North Carolina, USA).

 Results <sec> <title>Patient demographics

The patient characteristics are shown in Table I. The median values for NLR, PLR, SII, PNI, ALI, SUVmax, MTV, and TLG before immunotherapy were 3.6 (range 0.9-73.3), 200.7 (range 67.1-167,2.7), 987,654 (range 164,252-17455,680), 43.0 (range 20.2-59.4), 21.0 (range 0.8-90.8), 7.8 (range 2.9-113.3), 48.1 (range 1.1-1,400.7), and 212.6 (range 3.9-7,473.0), respectively. The optimal cutoff values for NLR, PLR, SII, PNI, ALI, SUVmax, MTV, and TLG as determined using ROC curve analysis were 2.7 (sensitivity: 43.7%; specificity: 77.1%), 200 (sensitivity: 64.1%; specificity: 56.5%), 589,934 (sensitivity: 39.0%; specificity: 82.7%), 46.5 (sensitivity: 48.4%; specificity: 74.5%), 29.6 (sensitivity: 48.4%; specificity: 72.1%), 4.4 (sensitivity: 18.7%; specificity: 90.9%), 123 cm3/ml (sensitivity: 81.2%; specificity: 36.8%), and 537 cm3/ml (sensitivity: 79.6%; specificity: 36.1%), respectively. The areas under the ROC curve were 0.628 for NLR, 0.616 for PLR, 0.617 for SII, 0.627 for PNI, 0.622 for ALI, 0.498 for SUVmax, 0.580 for MTV, and 0.572 for MTV. High expression of NLR, PLR, SII, PNI, ALI, SUVmax, MTV, and TLG was observed in 69.9, 50.5, 75.3, 31.2, 31.2, 87.6, 30.6, and 31.7% of patients, respectively.

Next, we analyzed the relationship between drug-induced lung injury and high levels of these markers (NLR, PLR, SII, PNI, ALI, GPS, SUVmax, MTV, and TLG) and between grade 3 or 4 immune-related adverse events (irAEs) and these markers (Table SI). However, drug-induced lung injury and grade 3 or 4 irAEs were not significantly associated with high levels of these biomarkers. Our patients did not receive any prednisolone and antibiotics which affected the therapeutic efficacy of immunotherapy before treatment.

 Correlation of inflammatory and nutrition index with <sup>18</sup>F-FDG accumulation

Table II shows the correlations between inflammatory and nutritional markers and 18F-FDG uptake. The amount of 18F-FDG uptake based on SUVmax, MTV, and TLG was significantly correlated with PLR, SII, PNI, and ALI, but SUVmax and NLR were not significantly correlated (Table II). As individual markers, the levels of albumin, LDH, CRP, WBC, neutrophils, lymphocytes, and BMI closely correlated with the accumulation of 18F-FDG based on MTV and TLG (Table II).

 Therapeutic response and influence to survival rate according to different variables

Inflammatory and nutritional indices were analyzed according to different objective responses. No significant differences were observed in these indices between the complete response (CR)/partial response (PR) and stable disease (SD)/progressive disease (PD) groups. The PNI and PLR values were significantly different between patients with and without PD.

Of the 186 patients, 151 experienced disease recurrence after the initial PD-1 blockade and 148 died because of disease progression. The median PFS and OS of all the patients were 198 and 613 days, respectively. The 6-month and 1-, 2-, 3-, and 4-year PFS rates were 51.1, 33.8, 25.2, 20.4, and 20.4%, respectively. The 1-, 2-, 3-, 4-, and 5-year OS rates were 64.5, 44.5, 28.5, 22.5, and 15.7%, respectively. Inflammatory and nutritional indices were compared according to survival rates (Fig. 1). NLR, PLR, and SII were significantly higher and PNI and ALI were significantly lower in patients with <1-year OS than in those with ≥1-year OS (Fig. 1). However, there was no significant difference between <3 and ≥2-year OS. A significant difference was observed between the <6-month and <1-year PFS rates for NLR, the <1-year and <2-year PFS rates for SII and ALI, and the <6-month and <2-year PFS rates for PNI (Fig. 1). MTV and TLG on 18F-FDG uptake, but not SUVmax, were significantly different between <1-year and <2-year PFS and OS (Fig. 1).

 Survival analysis in inflammatory and nutrition index based on <sup>18</sup>F-FDG uptake

Table III shows the PFS and OS based on different variables in the univariate analysis. PS, PNI, MTV, and TLG were significant predictors of PFS in all patients, whereas PS, PLR, PNI, GPS, MTV, and TLG were significantly associated with poor OS. PS, PNI, GPS, MTV, and TLG were significant predictors of PFS and OS in patients who received first-line therapy, whereas PS, PNI, and MTV were significant predictors of PFS and OS in patients who received second-line therapy.

Next, the prognostic roles of the inflammatory and nutritional indices according to 18F-FDG uptake were analyzed (Tables IV and V). Overall, a high MTV was significantly associated with poor PFS in patients with high NLR, PLR, SII, and GPS, and low PNI and ALI (Table IV). In first-line therapy, high MTV was closely associated with poor PFS in the group with high PLR, high SII, and low ALI; high TLG was also related to the outcome in patients with high SII and low ALI (Table IV). In second-line therapy or beyond, a high MTV was closely associated with poor outcomes regardless of the PLR, SII, ALI, or GPS in the group with a high NLR and low PNI (Table IV). In contrast, a high MTV yielded a significantly poorer OS in the group with a high SII and GPS and low PNI and ALI, regardless of the NLR and PLR in patients receiving total therapy, and a high TLG was closely associated with poor OS in patients with a high NLR and SII and low ALI (Table V). In first-line therapy, high MTV was associated with significantly poor OS in patients with high NLR, PLR, and SII, and low ALI, and high TLG was closely related to poor OS in patients with high PLR and SII and low ALI (Table V). In second-line therapy or beyond, high MTV was closely associated with poor OS regardless of NLR, PLR, PNI, ALI, or GPS, except in the group with a high SII (Table V).

 Discussion

Metabolic tumor activity, based on 18F-FDG uptake within tumor tissues, is closely correlated with inflammatory and nutritional status. As our enrolled samples included the population receiving first- and second-line therapies or beyond, previous treatment may have affected the inflammatory and nutritional status. In the survival analysis, PNI was identified as a poor outcome regardless of the different therapeutic lines; however, GPS was related to poor outcomes in the first-line therapy, and NLR and PLR showed a weak relationship with poor outcomes in our population. Generally, systemic inflammation and nutrition play crucial roles in cancer development, therapeutic effects, and cancer cachexia (24). A comparison of different types of inflammatory and nutritional markers in lung cancer identified ALI as the most suitable predictor of the outcome (25). However, a previous study indicated that systemic inflammation or nutritional status is prognostic and independent of immunotherapy (11). Similarly, the results of the present study suggest that these markers are prognostic, but not predictive of immunotherapy for advanced NSCLC (11). In this study, systemic inflammatory and nutritional indices partially exhibited a prognostic role in the clinical course after PD-1 blockade treatment, whereas MTV or TLG on 18F-FDG uptake were confirmed to be prognostic after administration, which is consistent with our previous reports (5,6,8). Our study focused on the prognostic significance of PD-1 blockade in systemic inflammatory and nutritional indices based on different glucose metabolic activities. Resistance to immunotherapy may occur when metabolic tumor activity is markedly increased in environments with high inflammation and low nutrition. This phenomenon was observed in patients who received PD-1 blockade not as a second-line therapy, but as a first-line setting. Systemic chemotherapy and radiotherapy can affect the tumor environment (26). We hypothesized that prior chemotherapy could potentially affect the inflammatory and nutritional environments in the same way. Further large-scale studies are warranted to elucidate which combination of inflammatory and nutritional environments and tumor glycolytic metabolism is best for predicting ICIs.

Based on our survival data, high MTV with high PLR and SII and low ALI in the first-line setting seemed to be more predictive of ICI treatment than other combinations. In the second-line setting or beyond, the prognostic relationship between metabolic tumor glycolysis and the inflammatory or nutritional environment remains unclear. Dolan et al reported that elevated tumor metabolic activity determined by TLG was associated with greater nutritional risk (GPS) and systemic inflammatory response (NLR) in patients with NSCLC (14). As a plausible mechanism, tumor hypoxia with necrosis arising from metabolic tumor activity stimulates the production of proinflammatory cytokines such as interleukin-6 and CRP (24,27). Furthermore, the systemic inflammatory response reflects tumor immune cytokine activity and decreased nutritional status, such as appetite loss or fatigue (28,29). Thus, tumor hypoxia and a tumor environment with inflammatory infiltration may induce disorders in tumor immune cells, contributing to resistance to immunotherapy. The association between tumor metabolic glycolysis measured by MTV and inflammatory/nutritional indices measured by PLR, SII, PNI, and ALI remains unclear. Although indices that can accurately reflect the inflammatory or nutritional status related to immunotherapy are known, many challenges must be addressed before the discovery of established biomarkers for immunotherapy.

Our study suggests that metabolic tumor glycolysis under different inflammatory and nutritional conditions has different effects on the outcome after ICI treatment between first- and second-line settings or beyond. Currently, most candidates for ICI treatment undergo first-line immunotherapy and have not been treated previously. Evidence to explain this discrepancy is insufficient, but the influence of prior chemotherapy on the inflammatory or nutritional status can be speculated.

In the present study, we found that drug-induced lung injury and grade 3 or 4 irAEs were not significantly associated with high levels of inflammatory or nutritional markers or 18F-FDG uptake. Previous reports have shown that drug-induced lung injury caused by ICIs worsens the prognosis of patients with NSCLC (30,31). Furthermore, drug-induced lung injury occurs more frequently in groups with high CRP, SUVmax, or GPS (32–34). However, these studies had small sample sizes, which may have biased the relationship between CRP level, SUVmax, or GPS and the frequency of drug-induced lung injury. Patients with irAEs experience survival benefits from PD-1 blocker (35). The close relationship between irAEs and inflammatory and nutritional markers in patients with NSCLC remains unclear.

Our study has several limitations. First, sample collection was based on our previous approach. Therefore, the heterogeneous population might have biased the results. Second, the assessment of 18F-FDG uptake was inconsistent among all enrolled patients because of the pooled analysis of different studies (5,6,8). Furthermore, CRP and neutrophil levels are increased, and albumin and lymphocyte levels are decreased in several complications, such as obstructive pneumonia, thrombosis, and interstitial pneumonia, in addition to lung cancer. Thus, the influence of these complications may be the reason why inflammatory markers could not predict the therapeutic response in our study. Finally, the optimal index reflecting the inflammatory and nutritional status remains unclear. A previous study evaluated several types of scores for inflammatory and nutritional status; however, it was difficult to determine the appropriate index.

In conclusion, metabolic tumor glycolysis determined by MTV on 18F-FDG uptake was identified as a promising predictor of the outcome of PD-1 blockade under conditions of increased inflammation and decreased nutritional status, particularly in the first-line setting. A high MTV under high PLR and SII and low ALI in the first-line setting could be more predictive of ICI treatment than other combinations. Further investigation is warranted to confirm the results of this prospective study.

 Supplementary Material Supporting Data Acknowledgements

The authors would like to thank Ms Kozue Watanabe, Ms Chieko Ono, Ms Saki Toita, Ms Hiroko Noguchi, Mr. Joji Shiotani and Ms Koko Kodaira in Saitama Medical University (Hidaka, Japan) for assistance in preparing the manuscript.

 Availability of data and materials

The datasets generated and/or analyzed during the present study are available from the corresponding author upon reasonable request.

 Authors' contributions

KI, KH and KKa conceived and designed the study, and prepared the manuscript. HI, AM, AS, YM, HK and OY contributed to acquisition of data. KH, KKo, HK and IK performed analysis and interpretation of data. KI, KKa, KH, KKo, IK and HK revised the manuscript. KI, KH and KKa confirm the authenticity of all the raw data. All authors deeply contributed and agreed with the content of the manuscript.

 Ethics approval and consent to participate

This study was approved by the Institutional Ethics Committee of the International Medical Center at Saitama Medical University. The requirement for written informed consent was waived by the Ethics Committee of Saitama Medical University because of the retrospective nature of the study

 Patient consent for publication

Not applicable.

 Competing interests

KKa has received a speaker honorarium from Ono Pharmaceutical Co., Ltd., Chugai Pharmaceutical Co., Ltd. and AstraZeneca, and received research grants from AstraZeneca. AM and OY have received a speaker honorarium from Chugai Pharmaceutical Co., Ltd. and AstraZeneca. HK has received research grants and a speaker honorarium from Ono Pharmaceutical Co., Ltd., Bristol-Myers Squibb, Boehringer Ingelheim, Merck Sharp and Dohme, Chugai Pharmaceutical Co., Ltd. and AstraZeneca. KKo has received research grants and a speaker honorarium from AstraZeneca, and Bristol-Myers Squibb.

 References HastingsKYuHAWeiWSanchez-VegaFDeVeauxMChoiJRizviHLisbergATruiniALydonCAEGFR mutation subtypes and response to immune checkpoint blockade treatment in non-small-cell lung cancerAnn Oncol3013111320201910.1093/annonc/mdz14131086949 ReadyNHellmannMDAwadMMOttersonGAGutierrezMGainorJFBorghaeiHJolivetJHornLMatesMFirst-line nivolumab plus ipilimumab in advanced non-small-cell lung cancer (CheckMate 568): Outcome by programmed death ligand 1 and tumor mutational burden as biomarkersJ Clin Oncol379921000201910.1200/JCO.18.0104230785829 KagamuHKitanoSYamaguchiOYoshimuraKHorimotoKKitazawaMFukuiKShionoAMouriANishiharaFCD4+ T-cell immunity in the peripheral blood correlates with response to anti-PD-1 therapyCancer Immunol Res8334344202010.1158/2326-6066.CIR-19-057431871122 KairaKYamaguchiOKawasakiTHashimotoKMiuraYShionoAMouriAImaiHKobayashiKYasudaMPrognostic significance of tumor infiltrating lymphocytes on first–line pembrolizumab efficacy in advanced non–small cell lung cancerDiscov Oncol146202310.1007/s12672-023-00615-436662367 YamaguchiOKairaKHashimotoKMouriAShionoAMiuraYMurayamaYKobayashiKKagamuHKujiITumor metabolic volume by 18F-FDG-PET as a prognostic predictor of first-line pembrolizumab for NSCLC patients with PD-L1≧50Sci Rep1014990202010.1038/s41598-020-71735-y32929123 HashimotoKKairaKYamaguchiOMouriAShionoAMiuraYMurayamaYKobayashiKKagamuHKujiIPotential of FDG-PET as prognostic significance after anti-PD-1 antibody against patients with previously treated non-small cell lung cancerJ Clin Med9725202010.3390/jcm903072532156047 KairaKKujiIKagamuHValue of F-FDG-PET to predict PD-L1 expression and outcomes of PD-1 inhibition therapy in human cancersCancer Imaging2111202110.1186/s40644-021-00381-y33441183 HashimotoKKairaKImaiHMouriAShionoAMiuraYYamaguchiOKobayashiKKagamuHKujiIPrognostic potential of metabolic activity on 18 F–FDG Accumulation in advanced NSCLC receiving combining chemotherapy plus PD–1 BlockadeJ Immunother45349357202210.1097/CJI.000000000000043435980360 UmedaYMorikawaMAnzaiMAmeshimaSKadowakiMWasedaYShigemiHTsujikawaTKiyonoYOkazawaHIshizukaTPredictive value of integrated 18F-FDG PET/MRI in the early response to nivolumab in patients with previously treated non-small cell lung cancerJ Immunother Cancer8e000349202010.1136/jitc-2019-00034932345624 KairaKHiguchiTNaruseIArisakaYTokueAAltanBSudaSMogiAShimizuKSunagaNMetabolic activity by 18F-FDG-PET/CT is predictive of early response after nivolumab in previously treated NSCLCEur J Nucl Med Mol Imaging455666201810.1007/s00259-017-3806-128828507 MahiatCBihinBDuplaquetFStanciu PopCDupontMVander BorghtTRondeletBVanderickJAndréBPirardLSystemic inflammation/nutrition status scores are prognostic but not predictive in metastatic non-small-cell lung cancer treated with first-line immune checkpoint inhibitorsInt J Mol Sci243618202310.3390/ijms2404361836835030 SebanRDAssieJBGiroux-LeprieurEMassianiMABonardelGChouaidCDelevalNRichardCMezquitaLGirardNPrognostic value of inflammatory response biomarkers using peripheral blood and [18F]-FDG PET/CT in advanced NSCLC patients treated with first-line chemo- or immunotherapyLung Cancer1594555202110.1016/j.lungcan.2021.06.02434311344 KairaKOriuchiNOtaniYYanagitaniNSunagaNHisadaTIshizukaTEndoKMoriMDiagnostic usefulness of fluorine-18-alpha-methyltyrosine positron emission tomography in combination with 18F-fluorodeoxyglucose in sarcoidosis patientsChest13110191027200710.1378/chest.06-216017426205 DolanRDMaclayJDAbbassTColvilleDBualiFMacLeodNMcSorleySTHorganPGMcMillanDCThe relationship between 18F-FDG-PET CT-derived tumour metabolic activity, nutritional risk, body composition, systemic inflammation and survival in patients with lung cancerSci Rep1020819202010.1038/s41598-020-77269-733257741 EbaJNakamuraKOverview of the ethical guidelines for medical and biological research involving human subjects in JapanJpn J Clin Oncol52539544202210.1093/jjco/hyac03435349681 GandhiLRodríguez–AbreuDGadgeelSEstebanEFelipEDe AngelisFDomineMClinganPHochmairMJPowellSFPembrolizumab plus chemotherapy in metastatic non-small-cell lung cancerN Engl J Med37820782092201810.1056/NEJMoa180100529658856 Paz-AresLLuftAVicenteDTafreshiAGümüşMMazièresJHermesBÇay ŞenlerFCsősziTFülöpAPembrolizumab plus chemotherapy for squamous non-small-cell lung cancerN Engl J Med37920402051201810.1056/NEJMoa181086530280635 SocinskiMAJotteRMCappuzzoFOrlandiFStroyakovskiyDNogamiNRodríguez-AbreuDMoro-SibilotDThomasCABarlesiFAtezolizumab for first-line treatment of metastatic nonsquamous NSCLCN Engl J Med37822882301201810.1056/NEJMoa171694829863955 EisenhauerEATherassePBogaertsJSchwartzLHSargentDFordRDanceyJArbuckSGwytherSMooneyMNew response evaluation criteria in solid tumour: Revised RECIST guideline (version 1.1)Eur J Cancer45228247200910.1016/j.ejca.2008.10.02619097774 ImaiHKishikawaTMinemuraHYamadaYIbeTYamaguchiOMouriAHamamotoYKanazawaKKasaiTPretreatment Glasgow prognostic score predicts survival among patients with high PD-L1 expression administered first-line pembrolizumab monotherapy for non-small cell lung cancerCancer Med1069716984202110.1002/cam4.422034414673 PlatiniHFerdinandEKoharKPrayogoSAAmirahSKomariahMMaulanaSNeutrophil-to-Lymphocyte ratio and Platelet-to-Lymphocyte ratio as prognostic markers for advanced non-small-cell lung cancer treated with immunotherapy: A systemic review and meta-analysisMedicina (Kanuas)581069202210.3390/medicina58081069 HuBYangXRXuYSunYFSunCGuoWZhangXWangWMQiuSJZhouJFanJSystemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinomaClin Cancer Res2062126222201410.1158/1078-0432.CCR-14-044225271081 JafriSHShiRMillsGAdvance lung cancer inflammation index (ALI) at diagnosis is a prognostic marker in patients with metastatic non-small cell lung cancer (NSCLC): A retrospective reviewBMC Cancer13158201310.1186/1471-2407-13-15823530866 GuthrieGJRoxburghCSRichardsCHHorganPGMcMillanDCCirculating IL-6 concentrations link tumour necrosis and systemic and local inflammatory responses in patients undergoing resection for colorectal cancerBr J Cancer109131137201310.1038/bjc.2013.29123756867 SongMZhangQSongCLiuTZhangXRuanGTangMXieHZhangHGeYThe advanced lung cancer inflammation index is the optimal inflammatory biomarker of overall survival in patients with lung cancerJ Cachexia Sarcopenia Muscle1325042514202210.1002/jcsm.1303235833264 WangXTengFKongLYuJPD-L1 expression in human cancers and its association with clinical ouctomesOnco Targets Ther950235039201610.2147/OTT.S10586227574444 O'CallaghanDSO'DonnellDO'ConnellFO'ByrneKJThe role of inflammation in the pathogenesis of non-small cell lung cancerJ Thorac Oncol520242036201010.1097/JTO.0b013e3181f387e421155185 DiakosCICharlessKAMcMillanDCClarkeSJCancer-related inflammation and treatment effectivenessLancet Oncol15e495503201410.1016/S1470-2045(14)70263-3 LairdBJAFallonMHjermstadMJTuckSKaasaSKlepstadPMcMillanDCQuality of life in patients with advanced cancer: Different association with performance status and systemic inflammatory responseJ Clin Oncol3427692775201610.1200/JCO.2015.65.774227354484 SureshKPsoterKJVoongKRShankarBFordePMEttingerDSMarroneKAKellyRJHannCLLevyBImpact of checkpoint inhibitor pneumonitis on survival in NSCLC patients receiving immune checkpoint immunotherapyJ Thorac Oncol14494502201910.1016/j.jtho.2018.11.01630503891 GomatouGTzilasVKotteasESyrigosKBourosDImmune checkpoint inhibitor-related pneumonitisRespiration99932942202010.1159/00050994133260191 MinegishiYTakenakaKMizutaniHSudohJNoroROkanoTAzumaAYoshimuraAAndoMTsuboiEExacerbation of idiopathic interstitial pneumonias associated with lung cancer therapyIntern Med48665672200910.2169/internalmedicine.48.165019420811 AkaikeKSaruwatariKOdaSShiraishiSTakehashiHHamadaSIyamaSHorioYYusukeTSaekiSPredictive value of 18F-FDG PET/CT for acute exacerbation of interstitial lung disease in patients with lung cancer and interstitial lung disease treated with chemotherapyInt J Clin Oncol25681690202010.1007/s10147-019-01584-x31781994 KikuchRTakoiHTsujiTNagatomoYTanakeAKinoshitaHOnoMIshiwariMKazutoshiTKonoYGlasgow prognostic score predicts chemotherapy-triggered acute exacerbation-interstitial lung disease in patients with non-small cell lung cancerThorac Cancer12667675202110.1111/1759-7714.1379233480111 HarataniKHayashiHChibaYKudoKYonesakaKKatoRKanedaHHasegawaYTanakaKTakedaMNakagawaKAssociation of Immune-related adverse events with nivolumab efficacy in non-small-cell lung cancerJAMA Oncol4378378201810.1001/jamaoncol.2017.2925

Counting amount of inflammatory and nutrition index was compared according to different survival rates. The comparable assessment of (A) NLR, (B) PLR, (C) SII, (D) PNI, (E) ALI, (F) SUVmax, (G) MTV and (H) TLG in the group of <1, <2, <3, <4 and ≥4 years OS rate from the initial treatment with ICIs was shown. A significant difference in the OS was observed between the group of <1 and 2 years for NLR, PLR, SII, PNI, ALI, MTV and TLG, but not between <2 and <3 years for all groups. The assessment of (I) NLR, (J) PLR, (K) SII, (L) PNI, (M) ALI, (N) SUVmax, (O) MTV and (P) TLG in the group of <6 months and <1, <2, <3 and ≥3 years PFS rate from initial treatment with ICIs was observed. There was significantly different PFS between <6 months and <1 year for NLR; between <1 and <2 years for SII, ALI, MTV and TLG; and between <6 months and <2 years for PNI. *P<0.05. NS, not significant; ICI, immune checkpoint inhibitor; OS, overall survival; PFS, progression-free survival; NLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio; SII, systemic immune inflammation index; PNI, prognostic nutrition index; ALI, advanced lung cancer inflammation index; SUVmax, the maximum of standardized uptake value; MTV, metabolic tumor volume; TLG, total lesion glycolysis.

Demographics of the patients (n=186).

Variables N %
Age, years
  ≤69 109 58.6
  >69 77 41.4
Sex
  Male 149 78.8
  Female 37 21.2
ECOG PS
  0 65 34.9
  1 88 47.3
  2 23 12.4
  3 10 5.4
Smoking history
  Yes 162 87.1
  No 24 12.9
Histology
  AC 105 56.5
  Non-AC 81 43.5
Disease stage
  III 29 15.6
  IV 154 82.8
  Ope rec. 3 1.6
PD-L1 (TPS) (%)
  <1% 34 18.3
  1–49% 35 18.9
  50–100% 66 35.4
  Unknown 51 27.4
Treatment line
  1st line 98 52.7
  2nd or more line 88 47.3
Tumor response
  CR 6 3.2
  PR 69 37.1
  SD 52 27.9
  PD 48 25.8
  NE 11 6.0
PD-1 blockade
  Nivolumab 83 44.6
  Pembrolizumab 94 50.6
  Atezolizumab 9 4.8
Grade 3/4 irAE
  Yes 40 21.5
  No 146 78.5
Therapeutic regimen
  Monotherapy 141 75.8
  Chemoimmunotherapy 45 24.2
NLR
  High 130 69.9
  Low 56 30.1
PLR
  High 94 50.5
  Low 92 49.5
SII
  High 140 75.3
  Low 46 24.7
PNI
  High 58 31.2
  Low 128 68.2
ALI
  High 58 31.2
  Low 128 68.2
GPS
  0 98 52.7
  1 36 19.4
  2 52 27.9
SUVmax
  High 163 87.6
  Low 23 12.4
MTV
  High 57 30.6
  Low 129 69.4
TLG
  High 59 31.7
  Low 127 68.3

ECOG, eastern cooperative oncology group; PS, performance status; PD-L1, programmed death ligand-1; TPS, tumor proportional score; CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease; NE, not evaluable; PD-1, programmed death-1; irAE, immune-related adverse events; AC, adenocarcinoma; ope rec, recurrence after operation; NLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio; SII, systemic immune inflammation index; PNI, prognostic nutrition index; ALI, advanced lung cancer inflammation index; PLR, platelet to lymphocyte ratio; SII, systemic immune inflammation index; PNI, prognostic nutrition index; ALI, advanced lung cancer inflammation index. The cut-off value of NLR, PLR, SII, PNI, ALI, SUVmax, MTV and TLG was defined by receiver operating characteristics (ROC) analyses.

Correlation between glycolytic metabolism and other variables.

Pearson r value (95% CI)
Different variables SUVmax MTV TLG
TP −0.047 (0.190-0.096) 0.517 −0.142 (0.280-0.001) 0.052 −0.118 (0.257-0.026) 0.108
Albumin −0.235 (−0.366-0.094) 0.001 −0.444 (−0.552-0.321) <0.001 −0.429 (−0.540-0.304) <0.001
LDH 0.231 (0.090-0.363) 0.001 0.343 (0.209-0.464) <0.001 0.362 (0.231-0.481) <0.001
CRP 0.291 (0.153-0.417) <0.001 0.438 (0.314-0.547) <0.001 0.425 (0.299-0.536) <0.001
WBC 0.191 (0.048-0.326) 0.009 0.388 (0.259-0.504) <0.001 0.402 (0.273-0.516) <0.001
Neutrophil 0.212 (0.071-0.345) 0.003 0.416 (0.289-0.528) <0.001 0.437 (0.313-0.547) <0.001
Lymphocyte −0041 (−0.184-0.102) 0.570 −0.154 (−0.292-0.011) 0.035 −0.145 (−0.283-0.001) 0.047
Platelet 0.169 (0.026-0.305) 0.021 0.107 (0.036-0.247) 0.143 0.131 (−0.012-0.270) 0.073
BMI −0.091 (−0.231-0.053) 0.218 −0.151 (−0.288-0.007) 0.039 −0.151 (−0.288-0.007) 0.039
NLR 0.098 (−0.046-0.238) 0.181 0.275 (0.136-0.403) <0.001 0.270 (0.131-0.398) <0.001
PLR 0.151 (0.007-0.289) 0.039 0.280 (0.142-0.407) <0.001 0.277 (0.139-0.405) <0.001
SII 0.203 (0.061-0.337) 0.005 0.373 (0.242-0.491) <0.001 0.424 (0.298-0.535) <0.001
PNI −0.218 (−0.351-0.076) 0.002 −0.442 (−0.551-0.318) <0.001 −0.426 (−0.537-0.301) <0.001
ALI −0.219 (−0.352-0.078) 0.002 −0.382 (−0.498-0.252) <0.001 −0.359 (−0.478-0.226) <0.001

TP, total protein; LDH, lactate dehydrogenase; CRP, C-reactive protein; WBC, white blood cell; BMI, body mass index; NLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio; SII, systemic immune inflammation index; PNI, prognostic nutrition index; ALI, advanced lung cancer inflammation index; 95% CI, 95% confidence interval; SUVmax, the maximum of standardized uptake value; MTV, metabolic tumor volume; TLG, total lesion glycolysis.

Progression-free and overall survival according to different variables.

Progression-free survival [MST, days (P-value)] Overall survival [MST, days (P-value)]
Variables Groups Total therapy (n=186) First-line (n=98) Second or more lines (n=88) Total therapy (n=186) First-line (n=98) Second or more lines (n=88)
Age ≤69/>69 years 165/201 (0.781) 198/247 (0.607) 121/199 (0.233) 701/569 (0.567) 599/428 (0.081) 717/774 (0.494)
Sex Male/Female 200/125 (0.058) 238/185 (0.090) 181/75 (0.426) 693/436 (0.225) 564/468 (0.321) 730/436 (0.468)
PS 0-1/2 202/85 (0.011) 255/164 (0.018) 172/40 (0.025) 710/165 (<0.001) 613/208 (0.012) 743/115 (0.022)
Histology AC/non-AC 199/164 (0.381) 281/164 (0.085) 146/161 (0.731) 730/435 (0.186) 699/362 (0.035) 737/710 (0.871)
NLR High/Low 142/299 (0.089) 204/203 (0.551) 76/377 (0.041) 499/837 (0.051) 477/689 (0.299) 518/917 (0.080)
PLR High/Low 139/204 (0.099) 172/255 (0.389) 94/204 (0.075) 433/732 (0.047) 468/613 (0.292) 382/837 (0.088)
SII High/Low 176/299 (0.217) 204/198 (0.408) 125/372 (0.166) 518/842 (0.094) 486/727 (0.301) 539/848 (0.235)
PNI High/Low 324/160 (0.001) 544/172 (0.009) 205/94 (0.035) 890/440 (<0.001) 908/412 (0.003) 890/539 (0.039)
ALI High/Low 220/161 (0.320) 449/198 (0.261) 202/76 (0.341) 837/468 (0.080) 727/468 (0.203) 856/476 (0.261)
GPS High/Low 139/202 (0.276) 139/370 (0.006) 149/161 (0.374) 370/796 (0.030) 307/789 (0.001) 487/796 (0.742)
SUVmax High/Low 191/382 (0.154) 203/NR (0.268) 129/382 (0.189) 569/848 (0.176) 534/NR (0.283) 693/848 (0.406)
MTV High/Low 116/205 (0.003) 164/320 (0.022) 46/199 (<0.001) 264/793 (<0.001) 311/707 (0.005) 144/833 (<0.001)
TLG High/Low 129/204 (0.042) 136/314 (0.022) 54/181 (0.223) 303/730 (0.004) 307/707 (0.007) 210/793 (0.306)

PS, performance status; AC, adenocarcinoma; ope rec, recurrence after operation; NLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio; SII, systemic immune inflammation index; PNI, prognostic nutrition index; ALI, advanced lung cancer inflammation index; NR, not reached; MST, median survival time. The cut-off value of NLR, PLR, SII, PNI, ALI, SUVmax, MTV and TLG was defined by receiver operating characteristics (ROC) analyses.

Progression-free survival in inflammatory and nutrition index according to different glucose metabolic activity.

Total therapy (n=186) [MST, days (P-value)] First-line (n=98) [MST, days (P-value)] Second or more lines (n=88) [MST, days (P-value)]
Variables n SUVmax, MTV TLG n SUVmax MTV TLG n SUVmax MTV TLG
NLR
  High 130 146/125 (0.636) 108/191 (0.034) 116/181 (0.125) 60 172/185 (0.185) 134/320 (0.078) 132/296 (0.137) 58 75/143 (0.590) 46/146 (0.001) 52/127 (0.457)
  Low 56 204/479 (0.457) 198/372 (0.265) 198/377 (0.710) 38 203/NR (0.405) 291/203 (0.708) 194/294 (0.342) 30 215/479 (0.793) 89/382 (0.140) 1079/377 (0.595)
PLR
  High 94 138/161 (0.359) 108/190 (0.017) 129/172 (0.172) 55 168/172 (0.196) 136/320 (0.031) 136/285 (0.064) 39 75/142 (0.591) 46/146 (0.001) 52/127 (0.457)
  Low 82 204/401 (0.412) 191/220 (0.416) 153/240 (0.523) 43 281/160 (0.727) 198/305 (0.563) 153/356 (0.372) 49 200/420 (0.346) 59/205 (0.023) 144/205 (0.830)
SII
  High 14 181/161 (0.288) 122/203 (0.016) 129/202 (0.092) 83 203/204 (0.195) 151/320 (0.023) 134/314 (0.022) 57 102/143 (0.452) 40/164 (<0.001) 53/146 (0.476)
  Low 46 213/401 (0.863) 89/382 (0.432) 143/377 (0.803) 15 371/160 (0.941) 198/352 (0.984) 198/352 (0.984) 31 213/420 (0.805) 68/382 (0.035) 89/377 (0.665)
PNI
  High 58 234/NR (0.204) 203/361 (0.847) 148/361 (0.853) 29 519/NR (0.445) 308/621 (0.876) 98/621 (0.637) 29 202/822 (0.173) 89/212 (0.214) 199/212 (0.873)
  Low 128 146/161 (0.716) 116/181 (0.029) 129/172 (0.210) 69 178/160 (0.618) 151/202 (0.148) 138/200 (0.192) 59 72/161 (0.984) 45/161 (<0.001) 49/149 (0.274)
ALI
  High 63 204/401 (0.580) 385/212 (0.909) 482/220 (0.521) 21 472/160 (0.893) 765/326 (0.510) 765/417 (0.756) 42 200/400 (0.442) 68/212 (0.015) 199/204 (0.638)
  Low 123 162/161 (0.297) 116/205 (0.006) 116/200 (0.033) 77 194/198 (0.196) 136/320 (0.013) 134/296 (0.024) 46 73/143 (0.426) 40/135 (<0.001) 48/113 (0.092)
GPS
  High 88 152/47 (0.017) 108/247 (0.002) 116/204 (0.056) 52 139/139 (>0.999) 130/221 (0.098) 130/200 (0.156) 36 212/47 (0.037) 37/360 (<0.001) 52/220 (0.231)
  Low 98 200/420 (0.100) 237/201 (0.867) 198/204 (0.788) 46 370/NR (0.538) 308/449 (0.889) 371/417 (0.774) 52 114/420 (0.007) 61/172 (0.002) 82/162 (0.417)

NLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio; SII, systemic immune inflammation index; PNI, prognostic nutritional index; ALI, advanced lung cancer inflammation index; GPS, glasgow prognostic score; MST, median survival time; SUVmax, the maximum of standardized uptake value; MTV, metabolic tumor volume; TLG, total lesion glycolysis; n, number of patients; NR, not reached.

Overall survival in inflammatory and nutrition index according to different glucose metabolic activity.

Total therapy (n=186) [MST, days (P-value)] First-line (n=98) [MST, days (P-value)] Second or more lines (n=88) [MST, days (P-value)]
Variables n SUVmax MTV TLG n SUVmax MTV TLG n SUVmax MTV TLG
NLR
  High 130 513/452 (0.381) 237/685 (0.001) 264/589 (0.016) 60 431/449 (0.187) 277/870 (0.035) 270/711 (0.054) 58 346/731 (0.644) 177/730 (0.002) 210/476 (0.651)
  Low 56 768/869 (0.712) 444/865 (0.014) 764/837 (0.957) 38 689/NR (0.548) 590/689 (0.335) 557/717 (0.235) 30 1034/869 (0.753) 140/945 (<0.001) 1611/877 (0.248)
PLR
  High 94 411/1027 (0.273) 270/711 (0.002) 303/660 (0.068) 55 449/468 (0.196) 307/711 (0.027) 307/711 (0.036) 39 346/731 (0.644) 177/730 (0.002) 210/476 (0.651)
  Low 82 711/842 (0.597) 198/830 (0.031) 321/796 (0.277) 43 642/412 (0.831) 444/689 (0.217) 613/613 (>0.999) 49 796/848 (0.696) 131/848 (<0.001) 584/837 (0.911)
SII
  High 14 516/1027 (0.201) 237/711 (<0.001) 277/660 (0.007) 83 477/486 (0.208) 307/671 (0.004) 303/685 (0.006) 57 539/731 (0.433) 135/730 (<0.001) 208/589 (0.461)
  Low 46 832/842 (0.782) 724/848 (0.619) 843/842 (0.640) 15 844/412 (0.921) 962/717 (0.849) 962/717 (0.849) 31 832/848 (0.848) 432/865 (0.055) 724/856 (0.771)
PNI
  High 58 853/NR (0.352) 593/903 (0.626) 865/890 (0.889) 29 817/NR (0.630) 701/908 (0.811) 701/908 (0.896) 29 914/890 (0.326) 140/959 (<0.001) 1029/869 (0.814)
  Low 128 435/710 (0.926) 210/693 (<0.001) 264/589 (0.025) 69 419/412 (0.715) 290/559 (0.059) 277/564 (0.058) 59 520/773 (0.801) 149/713 (<0.001) 208/693 (0.359)
ALI
  High 63 799/842 (0.985) 737/837 (0.868) 943/830 (0.196) 21 732/412 (0.850) 857/642 (0.550) 857/670 (0.631) 42 865/848 (0.991) 432/877 (0.049) 1029/848 (0.247)
  Low 123 456/1027 (0.189) 209/711 (<0.001) 210/683 (0.001) 77 456/468 (0.203) 290/711 (0.004) 277/707 (0.007) 46 453/731 (0.400) 135/648 (<0.001) 177/560 (0.072)
GPS
  High 88 399/170 (0.139) 160/614 (<0.001) 206/559 (0.013) 52 307/307 (>0.999) 236/536 (0.067) 236/536 (0.072) 36 627/170 (0.091) 115/797 (<0.001) 149/716 (0.264)
  Low 98 727/890 (0.224) 701/837 (0.364) 712/814 (0.932) 46 789/NR (0.662) 737/870 (0.678) 771/803 (0.797) 52 693/890 (0.112) 278/833 (0.003) 535/814 (0.798)

NLR, neutrophil to lymphocyte ratio; PLR, platelet to lymphocyte ratio; SII, systemic immune inflammation index; PNI, prognostic nutritional index; ALI, advanced lung cancer inflammation index; GPS, glasgow prognostic score; MST, median survival time; SUVmax, the maximum of standardized uptake value; MTV, metabolic tumor volume; TLG, total lesion glycolysis; n, number of patients; NR, not reached.