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)

Mandaliya, Hiren; Jones, Mark; Oldmeadow, Christopher; Nordman, Ina

doi:10.21037/tlcr.2019.11.16

Cited by 229 publications

(209 citation statements)

References 13 publications

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“…Therefore, PLR, the combination of these two parameters, may reflect a balance between tumor development and tumor suppression. Several reports have shown that PLR is an inflammatory marker used as a prognostic factor in lung cancer, hepatocellular carcinoma, and gastric cancer [ 8 , 18 , 19 ]. The current study showed that PLR was higher in lung cancer patients compared to healthy subjects, confirming the role of platelet activity and lymphocyte in the progression of lung cancer.…”

Section: Discussionmentioning

confidence: 99%

“…A meta-analysis demonstrated that high pretreatment NLR was closely related to poorer progression-free survival and overall survival in patients with small-cell lung cancer [ 7 ]. A retrospective descriptive study showed that high PLR was significantly associated with poor overall survival in patients with stage IV non-small-cell lung cancer [ 8 ]. As for the diagnostic ability, both NLR and PLR have moderate abilities to detect ovarian cancer patients from healthy controls [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio in Blood to Distinguish Lung Cancer Patients from Healthy Subjects

et al. 2020

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Objective. Inflammation-driven markers play a crucial role in tumorigenesis and tumor progression. The neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in blood are systemic inflammatory response markers. Some reports have showed that NLR and PLR are related to a poor prognosis in patients with lung cancer. However, little studies have reported whether NLR and PLR can be diagnostic markers for lung cancer. The aim of the current study is to investigate the roles of NLR and PLR in diagnosing lung cancer. Methods. This study analyzed data from lung cancer patients and healthy individuals in Wuxi People’s Hospital Affiliated with Nanjing Medical University. The Mann–Whitney U test was performed to compare differences between the lung cancer group and the control group. Based on white blood cell (WBC) counts, both lung cancer patients and healthy individuals were divided into the low-level group, moderate-level group, and high-level group. The Kruskal-Wallis test was applied to compare differences of NLR and PLR among those groups with different WBC counts. Spearman correlation analysis was used to assess correlations. Receiver operating characteristic (ROC) curves were performed to determine diagnostic accuracy. Results. 210 patients diagnosed with lung cancer and 261 healthy subjects were enrolled in this study. Levels of NLR and PLR increased in the lung cancer group compared with the control group ( P < 0.001 ). For the lung cancer group, NLR levels could rise with the increasing of WBC levels ( P < 0.001 ) while PLR levels had no significant variation with the increasing of WBC levels ( P = 0.206 ). For the control group, NLR levels could rise with the increasing of WBC levels ( P < 0.001 ) while PLR levels would decline with the increasing of WBC levels ( P < 0.001 ). In the lung cancer group, both NLR and PLR had no significant correlations with aspartate transaminase, urea, and glucose. The area under the curve (AUC) with 95% confidence interval (95% CI) of NLR and PLR to distinguish lung cancer patients from healthy subjects was, respectively, 0.684 (0.634-0.735) and 0.623 (0.571-0.674). When NLR and PLR were combined, AUC (95% CI) increased to 0.691 (0.642-0.740). Conclusions. NLR and PLR alone have moderate ability to distinguish lung cancer patients from healthy subjects. Furthermore, combination forms of NLR and PLR can improve diagnostic ability.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio in Blood to Distinguish Lung Cancer Patients from Healthy Subjects

et al. 2020

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“…Although SAPS III had a better predictive ability, there were too many missing values due to the need for data within 1 hour after admission [42], so we chose to use SAPS II. Some studies had suggested that PLR also had the ability to predict mortality [10,43,44], therefore, we evaluated the predictive power of the PLR, and found that it does have prediction ability but not as good as NLR. When we continued to add PLR to the new SAPS II scoring model with NLR, the AUC value did not increase signi cantly, so we did not incorporate PLR into this model.…”

Section: Discussionmentioning

confidence: 98%

Neutrophil-to-lymphocyte Ratio as a Predictor of Mortality in ICU Patients: An Analysis of MIMIC-III Database

Luo

et al. 2020

Preprint

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Background: Identifying high-risk patients in intensive care unit (ICU) is very important because of the high mortality rate. Existing scoring systems are numerous but lack effective inflammatory markers. Our objective was to identify and evaluate a low-cost, easily accessible and effective inflammatory marker that can predict mortality in ICU patients.Methods: We conducted a retrospective study using data from the Medical Information Mart for Intensive Care III database. We first divided the patients into the survival group and the death group based on in-hospital mortality. Receiver operating characteristic analyses were performed to find the best inflammatory marker (i.e. neutrophil-to-lymphocyte ratio, NLR). We then re-divided the patients into three groups based on NLR levels. Univariate and multivariate logistic regression were performed to evaluate the association between NLR and mortality. The area under the curve (AUC), Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) were used to assess whether the incorporate of NLR can improve the predictive power of existing predictive model. Results: A total of 21,822 patients were included in this study, with an in-hospital mortality rate of 14.43%. Among all inflammatory marker in routine blood test results, NLR had the best predictive ability, with a median (interquartile range) NLR of 5.40 (2.95, 10.46) in the survival group and 8.32 (4.25, 14.75) in the death group. We then re-divided the patients into low (≤1), medium (1-6) and high (≥6) groups based on NLR levels. Compared with the median NLR group, the in-hospital mortality rates were significantly higher in the low (odds ratio [OR] = 2.09; 95% confidence interval [CI], 1.64 to 2.66) and high (OR=1.64; 95%CI, 1.50-1.80) NLR groups. The addition of NLR to Simplified Acute Physiology Score II (SAPS II) improved the AUC from 0.789 to 0.798 (P<0.001), with NRI of 16.64% (P<0.001) and IDI of 0.27% (P<0.001).Conclusion: NLR is a good predictor of mortality in ICU patients, both low and high levels of NLR are associated with elevated mortality rate. The inclusion of NLR might improve the predictive power of SAPS II.

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“…These were credible evidence supporting our analysis results. The ALI was devised to assess the degree of systemic in ammation in patients with advanced non-small cell lung cancer patients [30]. Subsequently, this index was found to be a prognostic factor of survival in some cancers [31][32][33].…”

Section: Building and Validating A Predictive Nomogrammentioning

confidence: 99%

A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers

Lai¹,

Zhang²,

Lv³

et al. 2020

Preprint

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Background: This study aims to develop and validate a novel prognostic model to estimate overall survival (OS) in nasopharyngeal carcinoma (NPC) patients based on clinical features and blood biomarkers. We assessed the model’s incremental value to the TNM staging system, clinical treatment, and Epstein-Barr virus (EBV) DNA copy number for individual OS estimation. Methods: We retrospectively analyzed 519 consecutive patients with NPC. A prognostic model was generated using the Lasso regression model in the training cohort (n= 346). Then we compared the predictive accuracy of the novel prognostic model with TNM staging, clinical treatment, and EBV DNA copy number using concordance index (C-index), time-dependent ROC (tdROC), and decision curve analysis (DCA). Subsequently, we built a nomogram for OS incorporating the prognostic model, TNM staging, and clinical treatment. Finally, we stratified patients into high-risk and low-risk groups according to the model risk score, and the survival time of these two groups was analyzed using Kaplan–Meier survival plots. All results were validated in the independent validation cohort (n = 173). Results: Using the Lasso regression, we established a prognostic model consisting of 13 variables with respect to patient prognosis. The C-index, tdROC and DCA all showed that the prognostic model had good predictive accuracy and discriminatory power in the training cohort than did TNM staging, clinical treatment, and EBV DNA copy number. Nomogram consisting of the prognostic model, TNM staging, clinical treatment, and EBV DNA copy number showed some superior net benefit. Based on the model risk score, we split the patients into two subgroups: low-risk (risk score ≤ -1.423) and high-risk (risk score > -1.423). There were significant differences in OS between the two subgroups of patients. Similar results were observed in the validation cohort.Conclusions: The proposed novel prognostic model based on clinical features and serological markers represents a promising tool for estimating OS in NPC patients.

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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)

Cited by 229 publications

References 13 publications

Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio in Blood to Distinguish Lung Cancer Patients from Healthy Subjects

Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio in Blood to Distinguish Lung Cancer Patients from Healthy Subjects

Neutrophil-to-lymphocyte Ratio as a Predictor of Mortality in ICU Patients: An Analysis of MIMIC-III Database

A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers

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