Preoperative blood testing for glioblastoma, brain metastases, and primary central nervous system lymphoma differentiation

Wang, Song-Quan; Yuan, Qing; Zhang, Guangtao; Qian, Hai-Peng; Liu, Zhidan; Wang, Jiawei; Cai, Hong‐Qing; Wan, Jinghai

doi:10.21037/tcr-21-1957

Cited by 6 publications

(4 citation statements)

References 31 publications

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“…TT has been previously found to be a marker to differentiate brain metastases from other intracranial malignancies. 50 In this study, the prolongation of TT was positively correlated with the development of lung cancer and was significantly different between patients with lung cancer and benign lung disease ( P < 0.05), with longer TT in patients with lung cancer than in patients with benign lung disease. However, in the data collected in this study, the median values of TT in both categories of patients remained within the normal reference range.…”

Section: Discussionmentioning

confidence: 46%

Artificial neural network–based diagnostic models for lung cancer combining conventional indicators with tumor markers

Luo

Yuan

Qin

et al. 2023

Exp Biol Med (Maywood)

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This study set out to establish a lung cancer diagnosis and prediction model uses conventional laboratory indicators combined with tumor markers, so as to help early screening and auxiliary diagnosis of lung cancer through a convenient, fast, and cheap way, and improve the early diagnosis rate of lung cancer. A total of 221 patients with lung cancer, 100 patients with benign pulmonary diseases, and 184 healthy subjects were retrospectively studied. General clinical data, the results of conventional laboratory indicators, and tumor markers were collected. Statistical Product and Service Solutions 26.0 was used for data analysis. The diagnosis and prediction model of lung cancer was established by artificial neural network – multilayer perceptron. After correlation and difference analysis, five comparison groups (lung cancer-benign lung disease group, lung cancer-health group, benign lung disease-health group, early-stage lung cancer-benign lung disease group, and early-stage lung cancer-health group) obtained 5, 28, 25, 16, and 25 valuable indicators for predicting lung cancer or benign lung disease, and then established five diagnostic prediction models, respectively. The area under the curve (AUC) of each combined diagnostic prediction model (0.848, 0.989, 0.949, 0.841, and 0.976) was higher than that of the diagnostic prediction model established only using tumor markers (0.799, 0.941, 0.830, 0.661, and 0.850), and the difference in the lung cancer-health group, the benign lung disease-health group, the early-stage lung cancer-benign lung disease group, and early-stage lung cancer-health group was statistically significant ( P < 0.05). The artificial neural network–based diagnostic models for lung cancer combining conventional indicators with tumor markers have high performance and clinical significance in assisting the diagnosis of early lung cancer.

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Section: Discussionmentioning

confidence: 46%

Artificial neural network–based diagnostic models for lung cancer combining conventional indicators with tumor markers

Luo

Yuan

Qin

et al. 2023

Exp Biol Med (Maywood)

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“…Assessment of inflammatory processes by conventional blood tests is often beneficial in the diagnosis of early stages of diseases as well as in the clinical prognosis of brain tumors [ 7 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. According to Massara et al [ 5 ], NLR higher than 4 was associated with poor prognosis when measured before standard treatments.…”

Section: Introductionmentioning

confidence: 99%

Prognostic Values of Systemic Inflammatory Immunological Markers in Glioblastoma: A Systematic Review and Meta-Analysis

Jarmużek

Kozłowska

Defort

et al. 2023

Cancers

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Background. Neutrophils are an important part of the tumor microenvironment, which stimulates inflammatory processes through phagocytosis, degranulation, release of small DNA fragments (cell-free DNA), and presentation of antigens. Since neutrophils accumulate in peripheral blood in patients with advanced-stage cancer, a high neutrophil-to-lymphocyte ratio can be a biomarker of a poor prognosis in patients with glioblastoma. The present study aimed to explore the prognostic value of the preoperative levels of neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune inflammation index (SII), systemic inflammation response index (SIRI), and cell-free DNA (cfDNA) to better predict prognostic implications in the survival rate of glioblastoma patients. Methods. The meta-analysis was carried out according to the recommendations and standards established by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Databases of PubMed, EBSCO, and Medline were systematically searched to select all the relevant studies published up to December 2022. Results. Poorer prognoses were recorded in patients with a high NLR or PLR when compared with the patients with a low NLR or PLR (HR 1.51, 95% CI 1.24–1.83, p < 0.0001 and HR 1.34, 95% CI 1.10–1.63, p < 0.01, respectively). Similarly, a worse prognosis was reported for patients with a higher cfDNA (HR 2.35, 95% CI 1.27–4.36, p < 0.01). The SII and SIRI values were not related to glioblastoma survival (p = 0.0533 and p = 0.482, respectively). Conclusions. Thus, NLR, PLR, and cfDNA, unlike SII and SIRI, appeared to be useful and convenient peripheral inflammatory markers to assess the prognosis in glioblastoma.

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“…5,8,10 Considering the accessibility, convenience, and cost-benefit analysis among the assessing methods, biomarkers based on complete blood count (CBC) test are relatively promising in clinic because almost all the patients with brain metastases have the CBC test that is routinely measured in clinic. 8,[10][11][12][13][14] Specifically, four blood cell ratios have been highlighted by previous studies, including systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-tolymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) based on peripheral neutrophil, lymphocyte, monocyte, and platelet counts. A vast of studies have indicated that these four blood cell ratios could serve as potential biomarkers for cancer incidence risk, early identification of disease, prognosis, and the response to immune checkpoint inhibitors in various human malignancies.…”

Section: Introductionmentioning

confidence: 99%

“…Multiple molecules including various types of immune cells, blood cells, and other biochemical or hematological cells have been used to map the systemic immune‐inflammation landscape in clinic and labs 5,8,10 . Considering the accessibility, convenience, and cost–benefit analysis among the assessing methods, biomarkers based on complete blood count (CBC) test are relatively promising in clinic because almost all the patients with brain metastases have the CBC test that is routinely measured in clinic 8,10–14 . Specifically, four blood cell ratios have been highlighted by previous studies, including systemic immune‐inflammation index (SII), neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR), and lymphocyte‐to‐monocyte ratio (LMR) based on peripheral neutrophil, lymphocyte, monocyte, and platelet counts.…”

Section: Introductionmentioning

confidence: 99%

Systemic immune‐inflammation landscape in brain metastasis needing neurosurgical resection: Analysis of 230 consecutive cases in a single center

Wang

Qian

et al. 2022

Immunity Inflam & Disease

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Preoperative blood testing for glioblastoma, brain metastases, and primary central nervous system lymphoma differentiation

Cited by 6 publications

References 31 publications

Artificial neural network–based diagnostic models for lung cancer combining conventional indicators with tumor markers

Artificial neural network–based diagnostic models for lung cancer combining conventional indicators with tumor markers

Prognostic Values of Systemic Inflammatory Immunological Markers in Glioblastoma: A Systematic Review and Meta-Analysis

Systemic immune‐inflammation landscape in brain metastasis needing neurosurgical resection: Analysis of 230 consecutive cases in a single center

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