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Cillekens, Jos M.; Beliën, Jeroen A.M.; Valk, Paul van der; Faes, T.J.C.; Diest, Paul J. van; Broeckaert, Mark A. M.; Kralendonk, J. H.; Kamphorst, Wouter

doi:10.1023/a:1006496328729

Cited by 14 publications

(2 citation statements)

References 40 publications

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“…A precise prediction of the prognosis depends on an accurate pathology diagnosis. Compared to glioblastoma (GBM), low-grade gliomas (LGGs; WHO II) have greater chemotherapy sensitivity and a better post-therapy prognosis (Cillekens et al, 2000; Cavaliere et al, 2005; Wang and Jiang, 2013).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations

Wang

Yao

et al. 2019

Front. Neurosci.

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Gliomas are the most common primary malignant brain tumors in adults. Accurate grading is crucial as therapeutic strategies are often disparate for different grades and may influence patient prognosis. This study aims to provide an automated glioma grading platform on the basis of machine learning models. In this paper, we investigate contributions of multi-parameters from multimodal data including imaging parameters or features from the Whole Slide images (WSI) and the proliferation marker Ki-67 for automated brain tumor grading. For each WSI, we extract both visual parameters such as morphology parameters and sub-visual parameters including first-order and second-order features. On the basis of machine learning models, our platform classifies gliomas into grades II, III, and IV. Furthermore, we quantitatively interpret and reveal the important parameters contributing to grading with the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. The quantitative analysis and explanation may assist clinicians to better understand the disease and accordingly to choose optimal treatments for improving clinical outcomes. The performance of our grading model was evaluated with cross-validation, which randomly divided the patients into non-overlapping training and testing sets and repeatedly validated the model on the different testing sets. The primary results indicated that this modular platform approach achieved the highest grading accuracy of 0.90 ± 0.04 with support vector machine (SVM) algorithm, with grading accuracies of 0.91 ± 0.08, 0.90 ± 0.08, and 0.90 ± 0.07 for grade II, III, and IV gliomas, respectively.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations

Wang

Yao

et al. 2019

Front. Neurosci.

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“…Aggregation of GFAP is most typically associated with Alexander's disease, where the so-called Rosenthal fibres have major effects on the physiology of the cell (Hagemann 2022). Rosenthal fibres can sometimes also be observed in glioma, where they are mostly associated with low-grade tumours (Cillekens et al 2000). It remains to be investigated how these aggregates occur and what the consequences are for the (migration induced) stress response of glioma cells.…”

Section: Meta-analysis and Judgement Of Bias And Applicabilitymentioning

confidence: 99%

A grasp on GFAP in Glioma

Asperen¹

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Gliomas are the most common primary brain tumours. Their highly invasive character and the heterogeneity of active oncogenic pathways within single tumours complicate the development of curative therapies and cause poor patient prognosis. Glioma cells express the intermediate filament protein glial fibrillary acidic protein (GFAP), and the level of its alternative splice variant GFAPδ, relative to its canonical splice variant GFAPα, is higher in grade IV compared to lower grade and lower malignant glioma.In this study we show that a high GFAPδ/α ratio induces the expression of the dual specificity phosphatase 4 (DUSP4) in focal adhesions. By focussing on pathways upand down-stream of DUSP4 that are involved in the cell-extracellular matrix interaction we show that a high GFAPδ/α ratio equips glioma cells to better invade the brain. This study supports the hypothesis that glioma cells with a high GFAPδ/α ratio are highly invasive and more malignant cells, thus making GFAP alternative splicing a potential therapeutic target.

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Atypical focal MRI lesions in a case of juvenile Alexander's disease

Probst

Hagel

Weisz

et al. 2002

Annals of Neurology

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We present a juvenile case of Alexander's disease with atypical focal magnetic resonance imaging-detected lesions and elevated levels of lactate in cerebrospinal fluid. The diagnosis was based on the neuropathological finding of a diffuse accumulation of Rosenthal fibers within the brain and the spinal cord. The diagnosis was confirmed by detection of a mutation in exon 1 at nucleotide position 249 of glial fibrillary acidic protein cDNA, a finding previously reported in cases of infantile Alexander's disease.

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Cited by 14 publications

References 40 publications

Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations

Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations

A grasp on GFAP in Glioma

Atypical focal MRI lesions in a case of juvenile Alexander's disease

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