Prognostic value of preoperative hematological markers in patients with glioblastoma multiforme and construction of random survival forest model

Integrating nutritional status and hematological biomarkers for enhanced prognosis prediction in glioma patients: A systematic review

PURPOSE

Multiple inflammatory and nutritional biomarkers have been established as independent prognostic factors across various solid tumors, but their role in outcomes prediction for glioma is still under investigation. Aim of the present systematic review is to report the available evidence regarding the impact of nutritional assessment and intervention for glioma prognosis and patients' quality of life (QoL).

MATERIALS AND METHODS

Our systematic review conformed to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The PubMed and EMBASE databases were searched to identify studies assessing the impact of nutritional status and intervention and hematological biomarkers on survival outcomes and quality of life in patients with newly diagnosed gliomas. In the search strategy Medical Subject Headings (MeSH) terms were used. Search terms included ("nutritional status" or "nutritional assessment" or "nutritional intervention") AND ("glioma" or "glioblastoma" or "high-grade glioma" or "low-grade glioma" or "anaplastic astrocytoma" or "anaplastic oligodendroglioma") AND ("prognosis" or "survival outcomes"). The quality of each study was investigated based on the Newcastle-Ottawa Scale (NOS) criteria. Selected papers were in English and included publications in humans. This study was registered on PROSPERO (Registration No. CRD42024555442).

RESULTS

Our search retrieved 20 papers published between 2015 and 2023, all aiming at investigating correlations between hematological biomarkers (albumin, prealbumin, fibrinogen) and/or nutritional tools (Controlling Nutritional Score, CONUT; Prognostic Nutritional Index, PNI) and survival outcomes and quality of life of glioma patients. Nutritional intervention as well was evaluated for outcomes prediction. Overall, most papers contributed to the evidence of how nutritional assessment and inflammatory biomarkers could play an independent prognostic role also in the management of glioma patients.

CONCLUSIONS

PNI, CONUT score and hematological biomarkers (e.g. albumin, globulin, neutrophils, lymphocytes) may serve as useful predictors in patients with gliomas, potentially influencing clinical decisions. Additional large-scale studies are required to validate these findings and determine the mechanisms by which nutritional status, systemic inflammation and immune status affect prognosis in glioma patients.

Prognostic values of combined ratios of white blood cells in glioma: a systematic review and meta-analysis

Gliomas, the most prevalent type of neurological tumor, pose a challenging prognosis for patients. Recent studies have underscored the importance of inflammatory markers such as the neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), and monocyte/lymphocyte ratio (MLR) in predicting the prognosis of gliomas. We undertook a thorough meta-analysis to elucidate the role of these inflammatory markers in forecasting the prognosis of glioma patients. We extracted hazard ratios (HR) and their corresponding 95% confidence intervals (95% CI) from each study for analysis. To assess heterogeneity and identify influential studies, we conducted sensitivity analysis. Subgroup analysis was performed to investigate sources of heterogeneity, and we employed Egger's test to evaluate publication bias in the meta-analysis. Higher NLR levels were associated with shorter overall survival (HR = 1.46, 95% CI: 1.33-1.60) and progression-free survival (HR = 1.24, 95% CI: 1.04-1.48). There was no significant correlation between PLR levels and overall survival (HR = 1.01, 95% CI: 1.00-1.01) or progression-free survival (HR = 1.00, 95% CI: 0.98-1.02) in glioma patients. Elevated MLR levels were associated with decreased overall survival in glioma patients (HR = 1.78, 95% CI: 1.36-2.34). SII levels did not show any significant association with overall or progression-free survival in glioma patients (HR = 1.00, 95% CI: 0.99-1.01).In the sensitivity analysis, two studies potentially contributed to the instability. Subgroup analyses showed patient population and area were identified as potential sources of heterogeneity. Egger's test showed that there was publication bias in the relationship between NLR and PLR and overall survival (P < 0.05).All randomized controlled models, except for these, were not affected by publication bias. NLR and MLR are two reliable indicators of inflammation in the prognosis of glioma patients; PLR and SII do not have significant value in the prognosis of glioma patients.

Can Platelet-to-Lymphocyte Ratio (PLR) and Neutrophil-to-Lymphocyte Ratio (NLR) Help Predict Outcomes of Patients With Recurrent Glioblastoma?

BACKGROUND/AIM

In patients with recurrent glioblastoma, very little data are available regarding the prognostic value of platelet-to-lymphocyte (PLR) and neutrophil-to-lymphocyte (NLR) ratios. This study investigated potential associations between PLR or NLR and treatment outcomes.

PATIENTS AND METHODS

PLR and NLR at diagnosis of recurrence plus 10 additional characteristics were retrospectively analyzed for associations with progression-free survival (PFS) and overall survival (OS) in 75 patients with recurrent glioblastoma.

RESULTS

On multivariate analyses, maximal cumulative diameter of recurrent lesion(s) <40 mm (p=0.015) and systemic therapy (p<0.001) were associated with improved PFS. On multivariate analysis of OS, improved outcomes were significantly associated with PLR ≤150 (p=0.029), maximal cumulative diameter <40 mm (p=0.030), and systemic therapy (p=0.010).

CONCLUSION

In addition to other characteristics, PLR at the time of recurrence was identified as an independent predictor of OS in patients with recurrent glioblastoma. PLR may be useful when designing personalized treatment approaches or clinical trials.

Unraveling the Predictive Value of the Novel Global Immune-Nutrition-Inflammation Index (GINI) on Survival Outcomes in Patients with Grade 4 Adult-Type Diffuse Gliomas

BACKGROUND

This investigation evaluated the predictive and prognostic efficacy of the newly developed global immune-nutrition-inflammation index (GINI) in patients with grade 4 adult-type diffuse gliomas, comparing it with other established indices such as the systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and pan-immune-inflammation value (PIV).

METHOD

A retrospective cohort included 198 patients diagnosed with isocitrate dehydrogenase (IDH)-mutant gr4 (grade 4) astrocytoma and IDH-wt (wilde-type) glioblastoma (GBM) gr4 treated with surgical resection, radiotherapy, and temozolomide. Patients were stratified into two groups based on their GINI values: low GINI (<5815) and high GINI (≥5815). The primary endpoint was overall survival (OS).

RESULTS

High GINI was significantly associated with older age, poor performance status, multifocal tumors, and higher SII, SIRI, and PIV values (p < 0.005). The GINI demonstrated strong correlations with SII (r = 0.694), SIRI (r = 0.516), and PIV (r = 0.657) (p < 0.001). Patients with high GINI exhibited poorer OS (5.0 vs. 17.0 months) and PFS (5.0 vs. 13.0 months) in comparison to those with low GINI. Kaplan-Meier survival analysis revealed significantly prolonged OS and PFS among patients with low GINI (p < 0.001). Multivariate analysis identified high GINI as an independent negative risk factor for both PFS and OS.

CONCLUSIONS

GINI is a robust predictor of clinical outcomes in IDH-mutant gr4 astrocytoma and IDH-wt GBM gr4, highlighting the crucial impact of nutrition and cancer cachexia. It shows superior prognostic value relative to the SII, SIRI, and PIV.

Prognostic Role of Platelet-to-Lymphocyte and Neutrophil-to-Lymphocyte Ratios in Patients Irradiated for Glioblastoma Multiforme

Background/Aim

Previous studies suggested pre-operative platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) to be predictive factors in patients with glioblastoma multiforme (GBM). This study investigated the prognostic role of PLR and NLR prior to or at the beginning of radiotherapy.

Patients and Methods

In 80 patients with GBM receiving conventionally fractionated radiotherapy plus concurrent temozolomide following resection or biopsy, 12 factors including PLR and NLR were retrospectively evaluated regarding progression-free survival (PFS) and overall survival (OS).

Results

On multivariable analyses, PLR ≤150, Karnofsky performance score (KPS) 90-100, and O6-methylguanine-DNA methyltransferase promoter methylation were significantly associated with improved PFS. Single lesion, KPS 90-100, and adjuvant chemotherapy were significantly associated with OS; PLR ≤150 showed a trend. NLR ≤3 showed a trend for associations with PFS and OS on univariable analyses.

Conclusion

PLR prior to or at the beginning of radiotherapy was associated with treatment outcomes in patients irradiated for GBM and should be considered in future clinical trials.

Sequential Evaluation of Hematology Markers as a Prognostic Factor in Glioblastoma Patients

In our study, we investigated the prognostic significance of hematological markers-NLR (Neutrophil-to-Lymphocyte Ratio), PLR (Platelet-to-Lymphocyte Ratio), and RDW-CV (Red Blood Cell Distribution Width-Coefficient of Variation)-in 117 glioblastoma patients. The data collected from January 2016 to December 2018 included demographics, clinical scores, and treatment regimens. Unlike previous research, which often examined these markers solely before surgery, our unique approach analyzed them at multiple stages: preoperative, postoperative, and before adjuvant therapies. We correlated these markers with the overall survival (OS) and progression-free survival (PFS) using statistical tools, including ANOVA, Cox regression, and Kaplan-Meier survival analyses, employing SPSS version 29.0. Our findings revealed notable variations in the NLR, PLR, and RDW-CV across different treatment stages. The NLR and PLR decreased after surgery, with some stabilization post-STUPP phase (NLR: p = 0.007, η2p = 0.06; PLR: p = 0.001, η2p = 0.23), while the RDW-CV increased post-surgery and during subsequent treatments (RDW-CV: p < 0.001, η2p = 0.67). Importantly, we observed significant differences between the preoperative phase and other treatment phases. Additionally, a higher NLR and RDW-CV at the second-line treatment and disease progression were associated with an increased risk of death (NLR at 2nd line: HR = 1.03, p = 0.029; RDW-CV at progression: HR = 1.14, p = 0.004). We proposed specific marker cut-offs that demonstrated significant associations with survival outcomes when applied to Kaplan-Meier survival curves (NLR at 2nd line < 5: p < 0.017; RDW-CV at progression < 15: p = 0.007). An elevated NLR and RDW-CV at later treatment stages correlated with poorer OS and PFS. No significant preoperative differences were detected. These biomarkers may serve as non-invasive tools for glioblastoma management.

Systemic and local immunosuppression in glioblastoma and its prognostic significance

The effectiveness of tumor therapy, especially immunotherapy and oncolytic virotherapy, critically depends on the activity of the host immune cells. However, various local and systemic mechanisms of immunosuppression operate in cancer patients. Tumor-associated immunosuppression involves deregulation of many components of immunity, including a decrease in the number of T lymphocytes (lymphopenia), an increase in the levels or ratios of circulating and tumor-infiltrating immunosuppressive subsets [e.g., macrophages, microglia, myeloid-derived suppressor cells (MDSCs), and regulatory T cells (Tregs)], as well as defective functions of subsets of antigen-presenting, helper and effector immune cell due to altered expression of various soluble and membrane proteins (receptors, costimulatory molecules, and cytokines). In this review, we specifically focus on data from patients with glioblastoma/glioma before standard chemoradiotherapy. We discuss glioblastoma-related immunosuppression at baseline and the prognostic significance of different subsets of circulating and tumor-infiltrating immune cells (lymphocytes, CD4+ and CD8+ T cells, Tregs, natural killer (NK) cells, neutrophils, macrophages, MDSCs, and dendritic cells), including neutrophil-to-lymphocyte ratio (NLR), focus on the immune landscape and prognostic significance of isocitrate dehydrogenase (IDH)-mutant gliomas, proneural, classical and mesenchymal molecular subtypes, and highlight the features of immune surveillance in the brain. All attempts to identify a reliable prognostic immune marker in glioblastoma tissue have led to contradictory results, which can be explained, among other things, by the unprecedented level of spatial heterogeneity of the immune infiltrate and the significant phenotypic diversity and (dys)functional states of immune subpopulations. High NLR is one of the most repeatedly confirmed independent prognostic factors for shorter overall survival in patients with glioblastoma and carcinoma, and its combination with other markers of the immune response or systemic inflammation significantly improves the accuracy of prediction; however, more prospective studies are needed to confirm the prognostic/predictive power of NLR. We call for the inclusion of dynamic assessment of NLR and other blood inflammatory markers (e.g., absolute/total lymphocyte count, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, systemic immune-inflammation index, and systemic immune response index) in all neuro-oncology studies for rigorous evaluation and comparison of their individual and combinatorial prognostic/predictive significance and relative superiority.

The Prognostic Value of Preoperative Inflammatory Markers for Pathological Grading of Glioma Patients

Introduction: The independent diagnostic value of inflammatory markers neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) and the diagnostic efficacy of NLR, derived neutrophil to lymphocyte ratio (dNLR), PLR, and lymphocyte-to-monocyte ratio (LMR) in glioma cases remain unclear. We investigated the correlation of preoperative peripheral blood inflammatory markers with pathological grade, Ki-67 Proliferation Index, and IDH-1 gene phenotype in patients with glioma, focusing on tumor grade and prognosis. Methods: We retrospectively analyzed the clinical, pathological, and laboratory data of 334 patients with glioma with varying grades and 345 with World Health Organization (WHO I) meningioma who underwent initial surgery at the Affiliated Hospital of Jining Medical University from December 2019 to December 2021. The diagnostic value of peripheral blood inflammatory markers for glioma was investigated. Results: The proportion of men smoking and drinking was significantly higher in the glioma group than in the meningioma group (P < .05); in contrast, the age and body mass index (Kg/m2) were significantly lower in the glioma group (P = .01). Significant differences were noted in the pathological grade (WHO II, III, and IV), Ki-67 Proliferation Index, and peripheral blood inflammatory markers such as lymphocyte median, NLR, dNLR, and PLR between the groups (P < .05). No significant correlation existed between peripheral blood inflammatory factors and IDH-1 gene mutation status or tumor location in patients with glioma (P > .05). LMR, NLR, dNLR, and PLR, varied significantly among different glioma types (P < .05). White blood cell (WBC) count, neutrophil, NLR, and dNLR correlated positively with glioma risk. Further, WBC, neutrophil, NLR, dNLR, and LMR had a high diagnostic efficiency. Conclusion: Peripheral blood inflammatory markers, serving as noninvasive biomarkers, offer high sensitivity and specificity for diagnosing glioma, differentiating it from meningioma, diagnosing GBM, and distinguishing GBM from low-grade glioma. These markers may be implemented as routine screening tools.

Prognostic value of pretreatment lymphocyte-to-monocyte ratio in patients with glioma: a meta-analysis

BACKGROUND

Many studies have explored the prognostic role of the lymphocyte-to-monocyte ratio (LMR) in patients with glioma, but the results have been inconsistent. We therefore conducted the current meta-analysis to identify the accurate prognostic effect of LMR in glioma.

METHODS

The electronic databases of PubMed, Web of Science, Embase, and Cochrane Library were thoroughly searched from inception to July 25, 2023. The pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated to estimate the prognostic role of LMR for glioma.

RESULTS

A total of 16 studies comprising 3,407 patients were included in this meta-analysis. A low LMR was significantly associated with worse overall survival (OS) (HR = 1.35, 95% CI = 1.13-1.61, p = 0.001) in glioma. However, there was no significant correlation between LMR and progression-free survival (PFS) (HR = 1.20, 95% CI = 0.75-1.91, p = 0.442) in glioma patients. Subgroup analysis indicated that a low LMR was significantly associated with inferior OS and PFS in glioma when using a cutoff value of ≤ 3.7 or when patients received mixed treatment.

CONCLUSIONS

This meta-analysis demonstrated that a low LMR was significantly associated with poor OS in glioma. There was no significant correlation between LMR and PFS in glioma patients. The LMR could be a promising and cost-effective prognostic biomarker in patients with glioma in clinical practice.

Interpretable machine learning model based on the systemic inflammation response index and ultrasound features can predict central lymph node metastasis in cN0T1-T2 papillary thyroid carcinoma

Background

It is arguable whether individuals with T1-T2 papillary thyroid cancer (PTC) who have a clinically negative (cN0) diagnosis should undergo prophylactic central lymph node dissection (pCLND) on a routine basis. Many inflammatory indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and systemic immune-inflammatory index (SII), have been reported in PTC. However, the associations between the systemic inflammation response index (SIRI) and the risk of central lymph node metastasis (CLNM) remain unclear.

Methods

Retrospective research involving 1,394 individuals with cN0T1-T2 PTC was carried out, and the included patients were randomly allocated into training (70%) and testing (30%) subgroups. The preoperative inflammatory indices and ultrasound (US) features were used to train the models. To assess the forecasting factors as well as drawing nomograms, the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were utilized. Then eight interpretable models based on machine learning (ML) algorithms were constructed, including decision tree (DT), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). The performance of the models was evaluated by incorporating the area under the precision-recall curve (auPR) and the area under the receiver operating characteristic curve (auROC), as well as other conventional metrics. The interpretability of the optimum model was illustrated via the shapley additive explanations (SHAP) approach.

Results

Younger age, larger tumor size, capsular invasion, location (lower and isthmus), unclear margin, microcalcifications, color Doppler flow imaging (CDFI) blood flow, and higher SIRI (≥0.77) were independent positive predictors of CLNM, whereas female sex and Hashimoto thyroiditis were independent negative predictors, and nomograms were subsequently constructed. Taking into account both the auROC and auPR, the RF algorithm showed the best performance, and superiority to XGBoost, CatBoost and ANN. In addition, the role of key variables was visualized in the SHAP plot.

Conclusions

An interpretable ML model based on the SIRI and US features can be used to predict CLNM in individuals with cN0T1-T2 PTC.