Survival in stage IV non-small cell lung cancer patients based on radiation dose to immune cells: a retrospective analysis

Background: Programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) immune checkpoint inhibitors combined with chemotherapy represent the standard first-line treatment for stage IV non-small cell lung cancer (NSCLC) without driver mutations. Both concurrent and sequential thoracic radiotherapy (RT) have been shown to improve survival outcomes. This study aimed to evaluate the prognostic significance of the estimated dose of radiation to immune cells (EDRIC) in stage IV NSCLC patients receiving first-line immunotherapy (IT), as well as the predictive performance of EDRIC in combination with inflammatory parameters for overall survival (OS).

Methods: This multicenter retrospective study included 167 stage IV NSCLC patients who received concurrent or sequential RT in addition to IT. Spearman’s rank correlation was applied to assess associations between variables. Kaplan-Meier and Cox regression analyses were used to estimate OS and progression-free survival (PFS). A nomogram model was developed to evaluate the prognostic value of each parameter for OS. Receiver operating characteristic (ROC) curve analysis was performed to compare the predictive performance of the three models.

Results: GTV, PTV, and N staging were positively correlated with EDRIC (r=0.3564, p<0.001; r=0.6012, p<0.001; r=0.2592, p=0.0034), whereas lymphocyte nadir was negatively correlated (r=-0.3776, p<0.001). The Cox regression analyses identified EDRIC, lymphocyte nadir, and ALI as independent prognostic factors for OS (HR = 0.42, p=0.001; HR = 2.93, p=0.001; HR = 3.04, p=0.001). EDRIC was the only independent predictor of PFS (HR = 0.43, p=0.001). The combined EDRIC-lymphocyte nadir-ALI model demonstrated superior performance compared with single-factor models in predicting 2-year and 2.5-year OS in stage IV NSCLC patients. These findings were further confirmed in an external validation cohort.

Conclusions: In the IT era, higher EDRIC is associated with poorer OS and PFS, and the combination of EDRIC, lymphocyte nadir, and Advanced Lung Cancer Inflammation Index (ALI) provides more accurate prognostic assessment of OS than any single parameter alone.

1 Introduction

Lung cancer is the most common malignancy worldwide, with non-small cell lung cancer (NSCLC) representing the predominant histological subtype (1). Because early-stage NSCLC is often asymptomatic, most patients are diagnosed at an advanced stage with poor prognosis. With the advent of immunotherapy (IT), immune checkpoint inhibitors combined with chemotherapy have become a standard first-line treatment for advanced NSCLC (2, 3). Based on findings from the PEMBRO-RT and MDACC trials, administering concurrent or sequential thoracic radiotherapy (RT) may provide potential therapeutic benefit for patients with metastatic NSCLC (4–6).

RT can potentiate the effects of IT by inducing tumor cell apoptosis and releasing tumor-associated antigens, thereby activating antitumor immune responses. However, RT also exerts immunosuppressive effects, particularly through lymphocyte depletion (7, 8). Radiation-induced lymphocytopenia (RIL) is a recognized adverse prognostic factor in multiple solid tumors (9). Thus, quantifying the immune effects of RT is of critical importance.

Jin et al. (10) and Ladbury et al. (11) proposed and refined the estimated dose of radiation to immune cells (EDRIC), defined as the sum of equivalent uniform doses (EUD) to several organs, accounting for factors such as heart, lung, and whole-body mean dose, as well as the number of RT fractions. A retrospective study by Yu et al. (12) demonstrated that EDRIC was significantly correlated with lymphocyte depletion and served as an independent predictor of overall survival (OS) and progression-free survival (PFS) in patients with limited-stage SCLC. However, in this population, the standalone EDRIC model yielded only modest predictive performance, with a C-index of 0.617 for OS. In locally advanced NSCLC, the predictive ability of EDRIC for RIL was similarly limited, with an AUC of 0.596 (13). To improve prognostic accuracy, efforts have been made to integrate EDRIC with additional clinical indicators to develop multiparametric models.

Inflammation is a hallmark of cancer, contributing to tumor initiation, progression, and metastasis (14, 15), and is also influenced by RT and chemotherapy (16, 17). Circulating neutrophil, lymphocyte, and platelet counts reflect systemic immune-inflammatory status (18). Peripheral blood markers such as neutrophil-to-lymphocyte ratio (NLR) (19) and platelet-to-lymphocyte ratio (PLR) (20) are well-established prognostic factors in lung cancer. The Advanced Lung Cancer Inflammation Index (ALI), a composite index integrating inflammation and nutritional status, is calculated as BMI × (albumin/NLR). Several studies have demonstrated its prognostic value, with lower ALI levels associated with poor outcomes in lung cancer patients (21–24). Other biomarkers, including the systemic immune-inflammation index (SII) (25), lymphocyte-to-monocyte ratio (LMR) (26), and nutrition-based indicators such as the prognostic nutritional index (PNI) (27), have also been reported as predictors of adverse outcomes.

This study investigated the prognostic value of EDRIC and a range of inflammation- and nutrition-based indices (NLR, LMR, PLR, SII, ALI, and PNI) for OS and PFS in stage IV NSCLC patients treated with IT and RT. Furthermore, we evaluated their performance individually and in combination, aiming to develop a more accurate prognostic model for this patient population.

2 Materials and methods

2.1 Patients

This retrospective study included patients who received at least four cycles of first-line IT and thoracic RT at three centers between 2020 and 2023. Owing to the retrospective design, the requirement for informed consent was waived. A total of 167 patients with available dose measurement data were identified. Among them, 126 patients from Shandong Cancer Hospital were used as the training cohort and validation cohort, in which the prognostic model was developed and internally validated. The remaining 41 patients from Anhui Provincial Cancer Hospital and Anhui Provincial Hospital comprised an independent external validation cohort. The inclusion criteria were as follows: (1) age ≥18 years; (2) histologically confirmed NSCLC (including adenocarcinoma, squamous cell carcinoma, and large cell carcinoma); (3) clinical stage IV disease according to the AJCC TNM staging system, 8th edition; and (4) at least three complete blood counts (CBCs) obtained during thoracic RT. The exclusion criteria were: (1) prior targeted therapy for specific gene mutations (e.g., EGFR, ALK); (2) absence of detailed RT and dose measurement data; (3) active infection or ongoing corticosteroid therapy at the time of CBC assessment; and (4) concurrent or prior malignancies. This study was approved by the Ethics Committee of Shandong Cancer Hospital and Institute (ethics approval number: SDTHEC2022009020) and was conducted in accordance with the Declaration of Helsinki.

2.2 Data collection and EDRIC calculation

Patient data were collected by reviewing electronic medical records, including sex, age, height, weight, comorbidities (e.g., hypertension, diabetes), initial tumor pathology and stage, prior treatment history, and dates of diagnosis and disease progression. Baseline laboratory data obtained within two weeks prior to IT included serum albumin, neutrophil, platelet, lymphocyte, and monocyte counts. These values were used to calculate NLR, PLR, LMR, SII, ALI, and PNI. The EDRIC was calculated according to the methods developed by Jin et al. (10) and further refined by Ladbury et al. (11). Relevant parameters, including mean lung dose (MLD), mean heart dose (MHD), mean body dose (MBD), number of fractions, gross tumor volume (GTV), and planning target volume (PTV), were extracted from each patient’s RT treatment plan. The calculation was performed using the following formula:

$$\begin{array}{rr}\hfill EDRIC& =0.12\times MLD+0.08\times MHD\hfill \\ \hfill & \hfill +\left[0.45+0.35\times 0.85\times {\left(\frac{\#of fractions}{45}\right)}^{\frac{1}{2}}\right]\times MBD\end{array}$$

2.3 Statistical analysis

The primary endpoint of this study was OS, and PFS was defined as a secondary endpoint. OS was calculated from the date of diagnosis to death from any cause or the last follow-up. PFS was defined as the interval from diagnosis to local progression or death from any cause. Optimal cutoff values for each parameter were determined using ROC curve analysis. Spearman’s rank correlation was applied to evaluate associations between EDRIC and other variables. Kaplan-Meier methods were used to estimate OS and PFS, with comparisons made using log-rank tests. Cox proportional hazards models were employed for univariate and multivariate analyses to identify factors associated with OS and PFS and to determine independent prognostic indicators. Patients were randomly assigned into a training and validation cohort at a 2:1 ratio based on independent predictors. Nomogram models predicting OS using EDRIC, ALI, and combined EDRIC-lymphocyte nadir-ALI were constructed in R software (version 4.4.2). Model performance was compared using ROC curve analysis. All statistical analyses were performed using SPSS software (version 27.0), and a p-value<0.05 was considered statistically significant.

3 Results

3.1 Baseline patient characteristics

This study included 126 patients with advanced NSCLC. The median age was 64 years (range, 36–79 years), and the cohort comprised 96 males (76.2%) and 30 females (23.8%). A history of smoking was reported in 72 patients (57.1%), and 51 patients (40.5%) had a history of alcohol consumption. Clinical staging showed that 61 patients (48.4%) were at stage IVA and 65 patients (51.6%) at stage IVB. The median follow-up was 35.5 months (range, 11.6-62.1 months). During follow-up, 57 patients (45.2%) died, and 79 patients (62.7%) experienced disease progression. The median EDRIC was 4.76 Gy (range, 0.89-12.12 Gy), and the median lymphocyte nadir during radiotherapy was 0.52 × 10⁹/L (range, 0.1-2.1 × 10⁹/L) (Table 1). We compared the baseline characteristics across the training, validation, and external validation cohorts, as shown in Supplementary Table S1. Details of the patients’ radiotherapy, including treatment intent, thoracic dose, fractionation, and doses to critical organs, are provided in Supplementary Table S2.

Table 1

Table 1. Baseline characteristics of patients.

3.2 Factors affecting prognosis

ROC curves were plotted using patient survival as the state variable, and optimal cutoff values for each parameter were determined by the maximum Youden index: EDRIC = 5.63 Gy; lymphocyte nadir = 0.56 × 10⁹/L; NLR = 2.79; PLR = 143.8; LMR = 2.07; SII = 844; ALI = 306.9; PNI = 49.7. In univariate Cox regression for NSCLC, NLR, PLR, SII, LMR, ALI, lymphocyte nadir, and EDRIC were associated with OS. In multivariate analysis, ALI ≤306.9 (HR 2.64, 95% CI 1.01-6.92, P = 0.048), lymphocyte nadir ≤0.56 × 10⁹/L (HR 2.20, 95% CI 1.19-4.06, P = 0.012), and EDRIC ≤5.63 Gy (HR 0.52, 95% CI 0.29-0.91, P = 0.022) were independent prognostic factors for OS (Table 2). Univariate analysis also showed significant associations of NLR, PLR, SII, ALI, lymphocyte nadir, and EDRIC with PFS. Multivariate analysis identified EDRIC ≤ 5.63 Gy as an independent predictor of PFS (HR 0.50, 95% CI 0.31-0.81, P = 0.005) in NSCLC patients receiving neoadjuvant chemo-immunotherapy (Table 3).

Table 2

Table 2. Cox regression analysis of overall survival.

Table 3

Table 3. Cox regression analysis of progression-free survival.

3.3 Impact of EDRIC, lymphocyte nadir, and ALI on patient prognosis

Among 126 patients, 83 (65.9%) had EDRIC ≤5.63 Gy, whereas 43 (34.1%) had EDRIC >5.63 Gy. Kaplan-Meier analysis revealed significantly improved OS in patients with EDRIC ≤5.63 Gy compared with those with EDRIC >5.63 Gy. The 12-, 24-, and 36-month OS rates were 92.8%, 66.3%, and 26.5% for patients with EDRIC ≤5.63 Gy, versus 88.4%, 37.2%, and 14.0% for those with EDRIC >5.63 Gy, respectively (Figure 1A).

Figure 1

Figure 1. Kaplan-Meier survival curves in patients with stage IV NSCLC who received consolidative thoracic RT following first-line chemoimmunotherapy: (A) OS comparing the EDRIC >5.63 Gy and EDRIC ≤ 5.63 Gy groups; (B) OS comparing the lymphocyte nadir ≤ 0.56×10⁹/L and lymphocyte nadir >0.56×10⁹/L groups; (C) OS comparing the ALI ≤ 306.9 and ALI >306.9 groups; (D) PFS comparing the EDRIC >5.63 Gy and EDRIC ≤ 5.63 Gy groups. NSCLC, non-small cell lung cancer; RT, radiotherapy; OS, overall survival; EDRIC, estimated dose of radiation to immune cells; Gy, Gray; ALI, advanced lung cancer inflammation index; PFS, progression-free survival; HR, hazard ratio; CI, confidence interval.

Patients with a lymphocyte nadir >0.56 × 10⁹/L exhibited significantly prolonged OS compared with those with ≤ 0.56 × 10⁹/L. The 12-, 24-, and 36-month OS rates were 100%, 76.4%, and 30.9% versus 84.5%, 40.8%, and 15.5%, respectively (Figure 1B). Similarly, patients with ALI >306.9 demonstrated significantly better OS compared with those with ALI ≤306.9. The 12-, 24-, and 36-month OS rates were 95.7%, 77.1%, and 27.1% versus 85.7%, 30.4%, and 16.1%, respectively (Figure 1C). Consistent with the OS findings, patients with EDRIC ≤5.63 Gy had significantly improved PFS compared with those with EDRIC >5.63 Gy. The 12-, 24-, and 36-month PFS rates were 84.3%, 53.0%, and 19.3% versus 70.1%, 23.3%, and 11.6%, respectively (Figure 1D). We also generated Kaplan–Meier curves for PFS based on lymphocyte nadir (Supplementary Figure S1A) and ALI (Supplementary Figure S1B).

Spearman’s rank correlation analysis demonstrated positive correlations between EDRIC and GTV (r = 0.3564, P< 0.001; Figure 2A), PTV (r = 0.6012, P< 0.001; Figure 2B), and N stage (r = 0.2592, P = 0.0034; Figure 2C). In contrast, EDRIC was negatively correlated with lymphocyte nadir (r = -0.3776, P< 0.001; Figure 2D).

Figure 2

Figure 2. Spearman’s correlation coefficients for (A) GTV and EDRIC; (B) PTV and EDRIC; (C) N-stage and EDRIC; (D) Lymphocyte nadir and EDRIC. GTV, gross tumor volume; PTV, planning target volume; EDRIC, estimated dose of radiation to immune cells; HR, hazard ratio; CI, confidence interval; Gy, Gray;.

3.4 Comparison of nomogram prognostic models

The training cohort comprised 84 patients, while the validation cohort included 42 patients. No significant differences were observed across clinical variables between groups. The predictive performance of three nomogram models, namely EDRIC (Figure 3A), ALI (Figure 3B), and the combined EDRIC, lymphocyte nadir, and ALI model (Figure 3C), was evaluated, with the results summarized in Table 4. The calibration curve of the combined model closely aligned with the ideal reference line (Figure 3C).

Figure 3

Figure 3. (A) EDRIC-based model and ROC curves for predicting OS in NSCLC patients receiving neoadjuvant chemoimmunotherapy. (B) ALI-based model and ROC curves for predicting OS. (C) Combined model and ROC curves for predicting OS, along with the calibration curve of the combined model. EDRIC, estimated dose of radiation to immune cells; ROC, receiver operating characteristic; OS, overall survival; NSCLC, non-small cell lung cancer; ALI, advanced lung cancer inflammation index.

Table 4

Table 4. Comparison of nomogram models.

Comparison of the area under the ROC curve (AUC) demonstrated that the combined model significantly outperformed the single-parameter EDRIC- and ALI-based models in predicting 2-year OS. Similar results were observed for 2.5-year OS prediction (Table 4).

The combined model was further validated in an independent external cohort of 41 patients, including 26 (63.4%) with squamous cell carcinoma and 15 (36.6%) with adenocarcinoma. Within this cohort, 24 (58.5%) patients had EDRIC ≤5.63 Gy, and 19 (46.3%) had ALI >306.9. The combined model again demonstrated superior predictive ability for both 2-year and 2.5-year OS compared with the single-factor models (Figure 4).

Figure 4

Figure 4. Combined model and ROC curves for predicting OS, along with the calibration curve of the combined model in the external validation cohort. ROC, receiver operating characteristic; OS, overall survival.

Taken together, the nomogram based on EDRIC and ALI provided excellent discrimination and calibration, supporting its clinical utility in predicting survival outcomes in stage IV NSCLC patients receiving IT combined with RT.

4 Discussion

Based on findings from the PEMBRO-RT (4) and MDACC (5) trials, combining RT with IT has shown potential efficacy for patients with metastatic NSCLC. To our knowledge, this study is the first to investigate the prognostic value of integrating EDRIC with nutritional and inflammatory parameters in this population. Our results demonstrate that lower EDRIC scores are associated with significantly improved survival outcomes, both in terms of PFS and OS. Moreover, low baseline ALI and reduced lymphocyte nadir levels emerged as independent prognostic factors for inferior OS. While prognostic models constructed from single indicators displayed limited accuracy, the integration of EDRIC, baseline ALI, and lymphocyte nadir into a multidimensional model markedly enhanced predictive performance. This approach provides clinicians with more robust decision-making support and may facilitate the optimization of individualized treatment strategies.

In a secondary analysis of RTOG 0617, Jin et al. first proposed a model estimating the radiation dose delivered to the immune system. Following the establishment of the EDRIC model, subsequent studies confirmed that elevated EDRIC values were associated with poorer OS and PFS in stage III NSCLC (27). These findings suggest that optimizing treatment planning to minimize EDRIC could improve prognosis. Similarly, a retrospective study by Yin et al. (28) identified EDRIC as an independent prognostic factor for locally advanced NSCLC, with its impact closely correlated with GTV and N staging—results consistent with the present study. Likewise, Yang et al. (13) reported that higher EDRIC levels predicted worse OS and PFS in stage III NSCLC patients, underscoring its potential role as a biomarker for consolidation immunotherapy. These findings align closely with our results.

We also extended the analysis to the prognostic significance of ALI. A high baseline ALI was associated with longer OS, consistent with prior reports by Jafri and Mandaliya et al. (21, 29). Taken together, these findings suggest that the combined model of EDRIC, ALI, and lymphocyte nadir provides a biologically plausible and clinically useful tool for survival prediction in stage IV NSCLC.

The superior performance of the combined model may be explained by the synergistic biological effects of its components. First, lymphocytes are highly radiosensitive, with doses as low as 1 Gy substantially reducing cell survival (30, 31). Previous studies have confirmed correlations between EDRIC and peripheral lymphocyte depletion (12, 32). Since CD4+ and CD8+ T lymphocytes are critical for cytotoxic antitumor immune responses, their depletion may compromise immune surveillance and promote tumor proliferation, invasion, and metastasis (33). Second, ALI captures the interplay between systemic inflammation and nutritional status, where reduced neutrophil levels may diminish secretion of pro-angiogenic, growth, and anti-apoptotic factors, thereby limiting tumor progression (34).

Importantly, all three parameters—EDRIC, ALI, and lymphocyte nadir—are clinically simple, cost-effective, and readily available in routine practice. Once their prognostic significance is further validated, these markers could be widely implemented by oncologists worldwide, supporting clinical translation.

Nevertheless, this study has limitations. First, as a retrospective analysis, it is subject to potential selection bias and uncontrolled confounding factors. Second, EDRIC, while a promising prognostic marker, has several limitations that should be considered. It may be influenced by variations in radiotherapy planning, including target volume delineation, radiation field configuration, and dose constraints, which can differ across institutions and lead to variability in the estimated immune cell dose exposure. The EDRIC model is based on simplified assumptions and does not incorporate specific dose distributions to immune substructures such as lymph nodes or major vessels. Furthermore, although EDRIC quantifies radiation exposure to circulating immune cells, it does not capture a patient’s intrinsic immune resilience or capacity to recover from lymphocyte depletion, which may explain why combining EDRIC with host-related immune parameters (e.g., ALI or lymphocyte nadir) enhances prognostic performance. Finally, the relatively small sample size underscores the need for large-scale, multicenter, prospective trials to validate the prognostic value of EDRIC and ALI in stage IV NSCLC patients undergoing first-line immunotherapy.

5 Conclusions

In the era of immunotherapy, EDRIC, lymphocyte nadir, and ALI serve as independent predictors of OS in patients with stage IV NSCLC, while EDRIC alone is an independent predictor of PFS. Notably, combining EDRIC with ALI further enhances prognostic accuracy, providing a more reliable tool for outcome prediction.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

This study was approved by the Ethics Committee of Shandong Cancer Hospital and Institute (ethics approval number: SDTHEC2022009020) and was conducted in accordance with the Declaration of Helsinki. The studies were conducted in accordance with the local legislation and institutional requirements. This study was a retrospective analysis using previously collected, anonymized clinical data and did not involve any interventions or additional risk to patients. Some patients were lost to follow-up, making it impracticable to obtain informed consent. The study protocol was reviewed and approved by the institutional ethics committee, which granted a waiver of informed consent.

Author contributions

CF: Formal Analysis, Investigation, Methodology, Writing – original draft. KW: Formal Analysis, Methodology, Writing – original draft. TH: Investigation, Writing – original draft. FX: Data curation, Writing – original draft. LL: Data curation, Writing – original draft. SY: Funding acquisition, Supervision, Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This study was partially funded by the National Natural Science Foundation of China (NSFC82073345), the Natural Science Innovation and Development Joint Foundation of Shandong Province (ZR202209010002), the Jinan Clinical Medicine Science and Technology Innovation Plan (202019060), and the Taishan Scholars Program to Shuanghu Yuan.

Conflict of interest

The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2025.1715751/full#supplementary-material

Supplementary Figure 1 | Kaplan-Meier survival curves in patients with stage IV NSCLC who received consolidative thoracic RT following first-line chemoimmunotherapy: (A) PFS comparing the lymphocyte nadir ≤ 0.56×10⁹/L and lymphocyte nadir >0.56×10⁹/L groups; (B) PFS comparing the ALI ≤ 306.9 and ALI >306.9 groups; NSCLC, non-small cell lung cancer; RT, radiotherapy; PFS, progression-free survival; ALI, advanced lung cancer inflammation index; HR, hazard ratio; CI, confidence interval.

Supplementary Table 1 | Baseline characteristics of the training, validation cohort and external validation set.

Supplementary Table 2 | RT characteristics of patients, including RT intent, thoracic RT dose, fractionation, and doses to critical organs.

References

1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. (2021) 71:209–49. doi: 10.3322/caac.21660

PubMed Abstract | Crossref Full Text | Google Scholar

2. Gandhi L, Rodríguez-Abreu D, Gadgeel S, Esteban E, Felip E, De Angelis F, et al. Pembrolizumab plus chemotherapy in metastatic non-small-cell lung cancer. N Engl J Med. (2018) 378:2078–92. doi: 10.1056/NEJMoa1801005

PubMed Abstract | Crossref Full Text | Google Scholar

3. Paz-Ares L, Luft A, Vicente D, Tafreshi A, Gümüş M, Mazières J, et al. Pembrolizumab plus chemotherapy for squamous non-small-cell lung cancer. N Engl J Med. (2018) 379:2040–51. doi: 10.1056/NEJMoa1810865

PubMed Abstract | Crossref Full Text | Google Scholar

4. Theelen WSME, Peulen HMU, Lalezari F, van der Noort V, de Vries JF, Aerts JGJV, et al. Effect of pembrolizumab after stereotactic body radiotherapy vs pembrolizumab alone on tumor response in patients with advanced non-small cell lung cancer: results of the PEMBRO-RT phase 2 randomized clinical trial. JAMA Oncol. (2019) 5:1276–82. doi: 10.1001/jamaoncol.2019.1478

PubMed Abstract | Crossref Full Text | Google Scholar

5. Welsh J, Menon H, Chen D, Verma V, Tang C, Altan M, et al. Pembrolizumab with or without radiation therapy for metastatic non-small cell lung cancer: a randomized phase I/II trial. J Immunother Cancer. (2020) 8:e001001. doi: 10.1136/jitc-2020-001001

PubMed Abstract | Crossref Full Text | Google Scholar

6. Theelen WSME, Chen D, Verma V, Hobbs BP, Peulen HMU, Aerts JGJV, et al. Pembrolizumab with or without radiotherapy for metastatic non-small-cell lung cancer: a pooled analysis of two randomised trials. Lancet Respir Med. (2021) 9:467–75. doi: 10.1016/S2213-2600(20)30391-X Erratum in: Lancet Respir Med. (2021) 9(3):e29. doi: 10.1016/S2213-2600(21)00012-6

PubMed Abstract | Crossref Full Text | Google Scholar

7. Shang S, Liu J, Verma V, Wu M, Welsh J, Yu J, et al. Combined treatment of non-small cell lung cancer using radiotherapy and immunotherapy: challenges and updates. Cancer Commun (Lond). (2021) 41:1086–99. doi: 10.1002/cac2.12226

PubMed Abstract | Crossref Full Text | Google Scholar

8. Zhai D, An D, Wan C, and Yang K. Radiotherapy: Brightness and darkness in the era of immunotherapy. Transl Oncol. (2022) 19:101366. doi: 10.1016/j.tranon.2022.101366

PubMed Abstract | Crossref Full Text | Google Scholar

9. Koukourakis MI and Giatromanolaki A. Lymphopenia and intratumoral lymphocytic balance in the era of cancer immuno-radiotherapy. Crit Rev Oncol Hematol. (2021) 159:103226. doi: 10.1016/j.critrevonc.2021.103226

PubMed Abstract | Crossref Full Text | Google Scholar

10. Jin JY, Hu C, Xiao Y, Zhang H, Paulus R, Ellsworth SG, et al. Higher radiation dose to the immune cells correlates with worse tumor control and overall survival in patients with stage III NSCLC: A secondary analysis of RTOG0617. Cancers (Basel). (2021) 13:6193. doi: 10.3390/cancers13246193

PubMed Abstract | Crossref Full Text | Google Scholar

11. Ladbury CJ, Rusthoven CG, Camidge DR, Kavanagh BD, and Nath SK. Impact of radiation dose to the host immune system on tumor control and survival for stage III non-small cell lung cancer treated with definitive radiation therapy. Int J Radiat Oncol Biol Phys. (2019) 105:346–55. doi: 10.1016/j.ijrobp.2019.05.064

PubMed Abstract | Crossref Full Text | Google Scholar

12. Yu Y, Fu P, Jin JY, Gao S, Wang W, Machtay M, et al. Impact of effective dose to immune cells (EDIC) on lymphocyte nadir and survival in limited-stage SCLC. Radiother Oncol. (2021) 162:26–33. doi: 10.1016/j.radonc.2021.06.020

PubMed Abstract | Crossref Full Text | Google Scholar

13. Yang G, Yoon HI, Lee J, Kim J, Kim H, Cho J, et al. Risk of on-treatment lymphopenia is associated with treatment outcome and efficacy of consolidation immunotherapy in patients with non-small cell lung cancer treated with concurrent chemoradiotherapy. Radiother Oncol. (2023) 189:109934. doi: 10.1016/j.radonc.2023.109934

PubMed Abstract | Crossref Full Text | Google Scholar

14. Ritter B and Greten FR. Modulating inflammation for cancer therapy. J Exp Med. (2019) 216:1234–43. doi: 10.1084/jem.20181739

PubMed Abstract | Crossref Full Text | Google Scholar

15. Diakos CI, Charles KA, McMillan DC, and Clarke SJ. Cancer-related inflammation and treatment effectiveness. Lancet Oncol. (2014) 15:e493–503. doi: 10.1016/S1470-2045(14)70263-3

PubMed Abstract | Crossref Full Text | Google Scholar

16. Proctor MJ, Morrison DS, Talwar D, Balmer SM, O'Reilly DS, Foulis AK, et al. An inflammation-based prognostic score (mGPS) predicts cancer survival independent of tumour site: a Glasgow Inflammation Outcome Study. Br J Cancer. (2011) 104:726–34. doi: 10.1038/sj.bjc.6606087

PubMed Abstract | Crossref Full Text | Google Scholar

17. Fernandes JV, Cobucci RN, Jatobá CA, Fernandes TA, de Azevedo JW, and de Araújo JM. The role of the mediators of inflammation in cancer development. Pathol Oncol Res. (2015) 21:527–34. doi: 10.1007/s12253-015-9913-z

PubMed Abstract | Crossref Full Text | Google Scholar

18. Greten FR and Grivennikov SI. Inflammation and cancer: triggers, mechanisms, and consequences. Immunity. (2019) 51:27–41. doi: 10.1016/j.immuni.2019.06.025

PubMed Abstract | Crossref Full Text | Google Scholar

19. Diem S, Schmid S, Krapf M, Flatz L, Born D, Jochum W, et al. Neutrophil-to-Lymphocyte ratio (NLR) and Platelet-to-Lymphocyte ratio (PLR) as prognostic markers in patients with non-small cell lung cancer (NSCLC) treated with nivolumab. Lung Cancer. (2017) 111:176–81. doi: 10.1016/j.lungcan.2017.07.024

PubMed Abstract | Crossref Full Text | Google Scholar

20. Lee SC, Lee JG, Lee SH, Kim EY, Chang J, Kim DJ, et al. Prediction of postoperative pulmonary complications using preoperative controlling nutritional status (CONUT) score in patients with resectable non-small cell lung cancer. Sci Rep. (2020) 10:12385. doi: 10.1038/s41598-020-68929-9

PubMed Abstract | Crossref Full Text | Google Scholar

21. Mandaliya H, Jones M, Oldmeadow C, and Nordman II. Prognostic biomarkers in stage IV non-small cell lung cancer (NSCLC): neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), platelet to lymphocyte ratio (PLR) and advanced lung cancer inflammation index (ALI). Transl Lung Cancer Res. (2019) 8:886–94. doi: 10.21037/tlcr.2019.11.16

PubMed Abstract | Crossref Full Text | Google Scholar

22. Mountzios G, Samantas E, Senghas K, Zervas E, Krisam J, Samitas K, et al. Association of the advanced lung cancer inflammation index (ALI) with immune checkpoint inhibitor efficacy in patients with advanced non-small-cell lung cancer. ESMO Open. (2021) 6:100254. doi: 10.1016/j.esmoop.2021.100254

PubMed Abstract | Crossref Full Text | Google Scholar

23. Şeber ES, Elçiçek >ÖF, Çavdar E, Yalıcı Ö, Bilen YG, Karaduman İ, et al. Inflammation-based prognostication in extensive-stage small cell lung cancer in the first-line setting: the advanced lung inflammation index and the others. Asia Pac J Clin Oncol. (2025) 21(6):654–9. doi: 10.1111/ajco.14200

PubMed Abstract | Crossref Full Text | Google Scholar

24. Ma S, Li Z, and Wang L. The advanced lung cancer inflammation index (ALI) predicted the postoperative survival rate of patients with non-small cell lung cancer and the construction of a nomogram model. World J Surg Oncol. (2024) 22:158. doi: 10.1186/s12957-024-03432-3

PubMed Abstract | Crossref Full Text | Google Scholar

25. Yang R, Chang Q, Meng X, Gao N, and Wang W. Prognostic value of Systemic immune-inflammation index in cancer: A meta-analysis. J Cancer. (2018) 9:3295–302. doi: 10.7150/jca.25691

PubMed Abstract | Crossref Full Text | Google Scholar

26. Jin CX, Liu YS, Qin HN, Teng YB, Sun R, Ma ZJ, et al. Peripheral inflammatory factors as prognostic predictors for first-line PD-1/PD-L1 inhibitors in advanced non-small cell lung cancer. Sci Rep. (2025) 15:11206. doi: 10.1038/s41598-024-84469-y

PubMed Abstract | Crossref Full Text | Google Scholar

27. Okada S, Shimada J, Kato D, Tsunezuka H, Teramukai S, and Inoue M. Clinical significance of prognostic nutritional index after surgical treatment in lung cancer. Ann Thorac Surg. (2017) 104:296–302. doi: 10.1016/j.athoracsur.2017.01.085

PubMed Abstract | Crossref Full Text | Google Scholar

28. Yin X, Luo J, Xu C, Meng C, Zhang J, Yu H, et al. Is a higher estimated dose of radiation to immune cells predictive of survival in patients with locally advanced non-small cell lung cancer treated with thoracic radiotherapy? Radiother Oncol. (2021) 159:218–23. doi: 10.1016/j.radonc.2021.03.026

PubMed Abstract | Crossref Full Text | Google Scholar

29. Jafri SH, Shi R, and Mills G. Advance lung cancer inflammation index (ALI) at diagnosis is a prognostic marker in patients with metastatic non-small cell lung cancer (NSCLC): a retrospective review. BMC Cancer. (2013) 13:158. doi: 10.1186/1471-2407-13-158

PubMed Abstract | Crossref Full Text | Google Scholar

30. Davuluri R, Jiang W, Fang P, Xu C, Komaki R, Gomez DR, et al. Lymphocyte nadir and esophageal cancer survival outcomes after chemoradiation therapy. Int J Radiat Oncol Biol Phys. (2017) 99:128–35. doi: 10.1016/j.ijrobp.2017.05.037

PubMed Abstract | Crossref Full Text | Google Scholar

31. Repussard J. Understanding low dose radiation exposure effects: MELODI’s views on developing international cooperation. Int J Radiat Biol. (2019) 95:1354–7. doi: 10.1080/09553002.2018.1547439

PubMed Abstract | Crossref Full Text | Google Scholar

32. Xu C, Jin JY, Zhang M, Liu A, Wang J, Mohan R, et al. The impact of the effective dose to immune cells on lymphopenia and survival of esophageal cancer after chemoradiotherapy. Radiother Oncol. (2020) 146:180–6. doi: 10.1016/j.radonc.2020.02.015

PubMed Abstract | Crossref Full Text | Google Scholar

33. Mantovani A, Allavena P, Sica A, and Balkwill F. Cancer-related inflammation. Nature. (2008) 454:436–44. doi: 10.1038/nature07205

PubMed Abstract | Crossref Full Text | Google Scholar

34. Paramanathan A, Saxena A, and Morris DL. A systematic review and meta-analysis on the impact of pre-operative neutrophil lymphocyte ratio on long term outcomes after curative intent resection of solid tumours. Surg Oncol. (2014) 23:31–9. doi: 10.1016/j.suronc.2013.12.001

PubMed Abstract | Crossref Full Text | Google Scholar