Risk stratification of pediatric high-grade glioma: a newly proposed prognostic score

Muhammed, Amr; Gaber, Mohmed Soliman; El-Beltagy, Mohamed; Hemaly, Ahmed El; Taha, Hala; Refaat, Amal; Zaghluol, Mohamed S

doi:10.1007/s00381-019-04257-2

Cited by 6 publications

(7 citation statements)

References 17 publications

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“…Firstly, we did not consider the tumor size and intratumor features, such as necrosis size and texture features extracted from images. Previous studies showed no relationship between tumor size, necrosis size and patient survival [39][40][41]. It is challengeable for texture features used in predicting survival outcomes due to unsolved issued of reproducibility and interpretability before application in clinical setting.…”

Section: Discussionmentioning

confidence: 99%

Machine learning classifiers for predicting 3-year progression-free survival and overall survival in patients with gliomas after surgery

et al. 2021

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Background: To develop machine-learning based models to predict the progression-free survival (PFS) and overall survival (OS) in patients with gliomas and explore the effect of different feature selection methods on the prediction. Methods: We included 505 patients (training cohort, n = 354; validation cohort, n = 151) with gliomas between January 1, 2011 and December 31, 2016. The clinical, neuroimaging, and molecular genetic data of patients were retrospectively collected. The multi-causes discovering with structure learning (McDSL) algorithm, least absolute shrinkage and selection operator regression (LASSO), and Cox proportional hazards regression model were employed to discover the predictors for 3-year PFS and OS, respectively. Eight machine learning classifiers with 5-fold cross-validation were developed to predict 3-year PFS and OS. The area under the curve (AUC) was used to evaluate the prognostic performance of classifiers. Results: McDSL identified four causal factors (tumor location, WHO grade, histologic type, and molecular genetic group) for 3-year PFS and OS, whereas LASSO and Cox identified wide-range number of factors associated with 3-year PFS and OS. The performance of each machine learning classifier based on McDSL, LASSO, and Cox was not significantly different. Logistic regression yielded the optimal performance in predicting 3-year PFS based on the McDSL (AUC, 0.872, 95% confidence interval [CI]: 0.828-0.916) and 3-year OS based on the LASSO (AUC, 0.901, 95% CI: 0.861-0.940). Conclusions: McDSL is more reproducible than LASSO and Cox model in the feature selection process. Logistic regression model may have the highest performance in predicting 3-year PFS and OS of gliomas.

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Section: Discussionmentioning

confidence: 99%

Machine learning classifiers for predicting 3-year progression-free survival and overall survival in patients with gliomas after surgery

et al. 2021

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“…5,16 The incidence of seizure has been reported in about 30% of affected children. 17,18 The reported survival for PHGG ranges from 8 to 70 months. Several factors including the extent of resection, tumor grade, age, and KPS have been suggested as the predictors of outcome in PHGG.…”

Section: Discussionmentioning

confidence: 99%

Pediatric Non–Brain Stem High-Grade Glioma: A Single-Center Experience

Alimohammadi

Bagheri

Delfani

et al. 2020

Indian Journal of Neurosurgery

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Background Pediatric high-grade gliomas (PHGGs) consist of a heterogeneous class of central nervous system (CNS) neoplasms with a poor prognosis. We aimed to present our 10-year experience in the management of children with high-grade glioma focusing on patients’ survival and related factors. Methods All pediatric patients with high- grade glioma (HGG) who were admitted to our center between May 2009 and May 2018 were investigated. Overall survival (OS) was calculated from the time of diagnosis until the day of death. The impact of suggested variables on survival was evaluated using the univariate and multivariate analyses. Results There were 41 children with non–brain stem high-grade glioma (NBSHGG). The mean OS of patients was 21.24 ± 10.16 months. The extent of resection (p = 0.002, hazard ratio [HR] = 4.84), the grade of the tumor (p = 0.017, HR = 4.36), and temozolomide (TMZ) therapy (p = 0.038, HR = 3.57) were the independent predictors of OS in children with NBSHGG. Age, gender, tumor location, and size of tumor were not associated with the survival of these patients. Conclusion HGGs are uncommon pediatric tumors with an aggressive nature and a poor prognosis. Our results revealed that in NBSHGG cases, children with maximal safe tumor resection and children that received temozolomide therapy as well as children with grade III of the tumor had higher survival.

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“…Medical doctors increasingly use prediction models to make estimations on patient's prognosis or diagnosis. Pediatric neurosurgical prediction models are mathematical equations using child-related risk factors-e.g., gender, age, type of hydrocephalus-to calculate the probability of an outcome of interest for that particular child such as cerebrospinal fluid (CSF) diversion revision at 6 months, survival after brain tumor resection, or postoperative cerebellar mutism [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Application of clinical prediction modeling in pediatric neurosurgery: a case study

et al. 2021

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There has been an increasing interest in articles reporting on clinical prediction models in pediatric neurosurgery. Clinical prediction models are mathematical equations that combine patient-related risk factors for the estimation of an individual’s risk of an outcome. If used sensibly, these evidence-based tools may help pediatric neurosurgeons in medical decision-making processes. Furthermore, they may help to communicate anticipated future events of diseases to children and their parents and facilitate shared decision-making accordingly. A basic understanding of this methodology is incumbent when developing or applying a prediction model. This paper addresses this methodology tailored to pediatric neurosurgery. For illustration, we use original pediatric data from our institution to illustrate this methodology with a case study. The developed model is however not externally validated, and clinical impact has not been assessed; therefore, the model cannot be recommended for clinical use in its current form.

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Risk stratification of pediatric high-grade glioma: a newly proposed prognostic score

Cited by 6 publications

References 17 publications

Machine learning classifiers for predicting 3-year progression-free survival and overall survival in patients with gliomas after surgery

Machine learning classifiers for predicting 3-year progression-free survival and overall survival in patients with gliomas after surgery

Pediatric Non–Brain Stem High-Grade Glioma: A Single-Center Experience

Application of clinical prediction modeling in pediatric neurosurgery: a case study

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