Prediction value of preoperative findings on meningioma grading using artificial neural network

Kashani, Hamid Reza Khayat; Azhari, Shirzad; Nayebaghayee, Hossein; Salimi, Sohrab; Mohammadi, Hasan Reza

doi:10.1016/j.clineuro.2020.105947

Cited by 10 publications

(12 citation statements)

References 22 publications

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“…At the end of their study, they demonstrated that a low LMR was associated with high tumor grade. 13,25 In the present study, all data of the patients revealed that most of the patients were female patients < 65 years old. The inflammatory cell count results were found to be within normal intervals.…”

Section: Discussionsupporting

confidence: 47%

“…In the literature, it has been suggested that serum CRP levels, which reflect the systemic response to inflammation, are related to progression of the cancer, and elevated CRP levels may be associated with poor prognosis. 8,[13][14][15][16] In those studies, it has been concluded that the CRP level may be a biomarker for many cancer types. 17,18 However, in their literature review, Heikkilä et al found no strong evidence for a causal role of serum CRP level in malignancy.…”

Section: Discussionmentioning

confidence: 99%

“…The neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), and systemic inflammation response index (SIRI) were also calculated as defined in the literature. [11][12][13] these data were analyzed using the independent samples t-test (p < 0.05). Nonparametric data were expressed as median (minimum-maximum), and to find out the differences between the study groups, these data were analyzed using the Mann Whitney U-test (p < 0.05).…”

Section: Biochemical Analysismentioning

confidence: 99%

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Can Routine Blood Biochemistry Parameters be Predictive Prognostic Marker(s) in Operated Patients with Meningioma WHO Grade 1?

Yüksel

Özdemır

Kısa

et al. 2021

Arquivos Brasileiros de Neurocirurgia: Brazilian Neurosurgery

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Background Today, there is a need for new and independent additional advanced markers that can predict the prognosis of meningioma patients, postoperatively. The present study aimed to find out postoperative short-term prognostic markers in patients with meningioma using their demographic data and routine blood biochemistry findings evaluated preoperatively. Methods The Glasgow Coma Scale (GCS), and Glasgow Outcome Scale (GOS) scores of the patients were recorded. Additionally, preoperatively obtained serum glucose, C-reactive protein (CRP), sodium, potassium, creatinine, blood urea nitrogen, aspartate aminotransferase (AST), alanine aminotransferase, and hemoglobin level values, platelet, leukocyte, neutrophil, lymphocyte, eosinophil, basophil, and monocyte count results, erythrocyte sedimentation rate (ESR), neutrophil-lymphocyte ratio, platelet-lymphocyte ratio (PLR) and lymphocyte-monocyte ratio (LMR) values were evaluated. Results In the present study, 23 operated patients with meningioma World Health Organization (WHO) grade 1 (17 females, 6 males) were included. Correlation test results revealed that the GCS score, platelet count, and serum potassium level values could directly predict the short-term prognosis of these patients. Additionally, these test results suggested that the lymphocyte, monocyte, and eosinophil count values, PLR, LMR, ESR, serum glucose, CRP, and AST level values could be indirect markers in predicting the short-term prognosis. However, likelihood ratio test results revealed that only monocyte count value, LMR value, and serum CRP level value could be the markers for prediction of the short-term prognosis. Conclusion At the end of the present study, it was concluded that the monocyte count value, LMR value, and serum CRP level value could be the best markers in predicting the short-term prognosis of the operated meningioma patients.

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Section: Discussionsupporting

confidence: 47%

Section: Discussionmentioning

confidence: 99%

Section: Biochemical Analysismentioning

confidence: 99%

See 1 more Smart Citation

Can Routine Blood Biochemistry Parameters be Predictive Prognostic Marker(s) in Operated Patients with Meningioma WHO Grade 1?

Yüksel

Özdemır

Kısa

et al. 2021

Arquivos Brasileiros de Neurocirurgia: Brazilian Neurosurgery

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“…Artificial neural network (ANN) is a mathematical or computational model that uses structures similar to synaptic connections in the brain to process information [ 9 ]. ANN models have been applied to risk assessment of many diseases, including colon cancer, lung cancer, hepatocellular carcinoma, meningioma, and so on and have shown reliable and accurate performance in disease prediction and evaluation [ 10 – 13 ]. However, no studies have been reported on predicting prostate cancer risk based on ANN models.…”

Section: Introductionmentioning

confidence: 99%

Identification of Signature Genes and Construction of an Artificial Neural Network Model of Prostate Cancer

Dong

Wang

2022

Journal of Healthcare Engineering

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This study aimed to establish an artificial neural network (ANN) model based on prostate cancer signature genes (PCaSGs) to predict the patients with prostate cancer (PCa). In the present study, 270 differentially expressed genes (DEGs) were identified between PCa and normal prostate (NP) groups by differential gene expression analysis. Next, we performed Metascape gene annotation, pathway and process enrichment analysis, and PPI enrichment analysis on all 270 DEGs. Then, we identified and screened out 30 PCaSGs based on the random forest analysis and constructed an ANN model based on the gene score matrix consisting of 30 PCaSGs. Lastly, analysis of microarray dataset GSE46602 showed that the accuracy of this model for predicating PCa and NP samples was 88.9 and 78.6%, respectively. Our results suggested that the ANN model based on PCaSGs can be used for effectively predicting the patients with PCa and will be helpful for early PCa diagnosis and treatment.

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“…A complete list of the excluded articles and the respective reasons for exclusion is provided in Supplementary Table S1 . Thirty three articles were excluded due to predicting only pathological features, e.g., grade (n = 16), or differentiating between tumor entities (n = 8) [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Thirty four articles were excluded due to predicting only clinical parameters, e.g., tumor consistency (n = 7), response/treatment outcome (n = 12) or brain/bone invasion (n = 4) [ 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ...…”

Section: Resultsmentioning

confidence: 99%

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

Windisch

Koechli

Rogers

et al. 2022

Cancers

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Objectives: To summarize the available literature on using machine learning (ML) for the detection and segmentation of benign tumors of the central nervous system (CNS) and to assess the adherence of published ML/diagnostic accuracy studies to best practice. Methods: The MEDLINE database was searched for the use of ML in patients with any benign tumor of the CNS, and the records were screened according to PRISMA guidelines. Results: Eleven retrospective studies focusing on meningioma (n = 4), vestibular schwannoma (n = 4), pituitary adenoma (n = 2) and spinal schwannoma (n = 1) were included. The majority of studies attempted segmentation. Links to repositories containing code were provided in two manuscripts, and no manuscripts shared imaging data. Only one study used an external test set, which raises the question as to whether some of the good performances that have been reported were caused by overfitting and may not generalize to data from other institutions. Conclusion: Using ML for detecting and segmenting benign brain tumors is still in its infancy. Stronger adherence to ML best practices could facilitate easier comparisons between studies and contribute to the development of models that are more likely to one day be used in clinical practice.

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Prediction value of preoperative findings on meningioma grading using artificial neural network

Cited by 10 publications

References 22 publications

Can Routine Blood Biochemistry Parameters be Predictive Prognostic Marker(s) in Operated Patients with Meningioma WHO Grade 1?

Can Routine Blood Biochemistry Parameters be Predictive Prognostic Marker(s) in Operated Patients with Meningioma WHO Grade 1?

Identification of Signature Genes and Construction of an Artificial Neural Network Model of Prostate Cancer

Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review

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