Local image variance of 7 Tesla SWI is a new technique for preoperative characterization of diffusely infiltrating gliomas: correlation with tumour grade and IDH1 mutational status

Gräβner, G.; Kiesel, Barbara; Wöhrer, Adelheid; Millesi, Matthias; Wurzer, Aygül; Göd, Sabine; Mallouhi, Ammar; Knosp, Engelbert; Marosi, Christine; Trattnig, Siegfried; Wolfsberger, Stefan; Preusser, Matthias; Widhalm, Georg

doi:10.1007/s00330-016-4451-y

Cited by 24 publications

(20 citation statements)

References 48 publications

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“…33 DWI acquisitions involving three or more b-values were used to model the non-Gaussian diffusion properties of brain tissue, and they have identified abnormalities in normal-appearing white matter that are proposed to be biomarkers of tumor infiltration. 34 Tissue validation, however, is still pending. Magnetic resonance spectroscopy (MRS) is a noninvasive way of measuring the metabolic changes associated with increased cellular proliferation and has biopsy-proven validation linking some of the spectra changes to tumor infiltration.…”

Section: Extent Of Resection: Definition and Measurementmentioning

confidence: 99%

Importance and Evidence of Extent of Resection in Glioblastoma

Wykes

Zisakis

Irimia

et al. 2020

J Neurol Surg A Cent Eur Neurosurg

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Maximal safe resection is an essential part of the multidisciplinary care of patients with glioblastoma. A growing body of data shows that gross total resection is an independent prognostic factor associated with improved clinical outcome. The relationship between extent of glioblastoma (GB) resection and clinical benefit depends critically on the balance between cytoreduction and avoiding neurologic morbidity. The definition of the extent of tumor resection, how this is best measured pre- and postoperatively, and its relation to volume of residual tumor is still discussed. We review the literature supporting extent of resection in GB, highlighting the importance of a standardized definition and measurement of extent of resection to allow greater collaboration in research projects and trials. Recent developments in neurosurgical techniques and technologies focused on maximizing extent of resection and safety are discussed.

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Section: Extent Of Resection: Definition and Measurementmentioning

confidence: 99%

Importance and Evidence of Extent of Resection in Glioblastoma

Wykes

Zisakis

Irimia

et al. 2020

J Neurol Surg A Cent Eur Neurosurg

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“…Previously, researchers have utilized non-invasive structural and functional preoperative neuroimaging techniques to 1) search for molecular biomarkers of gliomas (Kickingereder et al 2016a;Itakura et al 2015;Grabner et al 2016), 2) model tumor growth dynamics (Stensjøen et al 2015), and 3) differentiate glioma subtypes (Collet et al 2015). These studies have directly or indirectly contributed to survival time prediction.…”

Section: Introductionmentioning

confidence: 99%

Overall survival time prediction for high-grade glioma patients based on large-scale brain functional networks

Liu

Zhang

et al. 2018

Brain Imaging and Behavior

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High-grade glioma (HGG) is a lethal cancer with poor outcome. Accurate preoperative overall survival (OS) time prediction for HGG patients is crucial for treatment planning. Traditional presurgical and noninvasive OS prediction studies have used radiomics features at the local lesion area based on the magnetic resonance images (MRI). However, the highly complex lesion MRI appearance may have large individual variability, which could impede accurate individualized OS prediction. In this paper, we propose a novel concept, namely brain connectomics-based OS prediction. It is based on presurgical resting-state functional MRI (rs-fMRI) and the non-local, large-scale brain functional networks where the global and systemic prognostic features rather than the local lesion appearance are used to predict OS. We propose that the connectomics features could capture tumor-induced network-level alterations that are associated with prognosis. We construct both low-order (by means of sparse representation with regional rs-fMRI signals) and high-order functional connectivity (FC) networks (characterizing more complex multi-regional relationship by synchronized dynamics FC time courses). Then, we conduct a graph-theoretic analysis on both networks for a jointly, machine-learning-based individualized OS prediction. Based on a preliminary dataset (N = 34 with bad OS, mean OS, ~400 days; N = 34 with good OS, mean OS, ~1030 days), we achieve a promising OS prediction accuracy (86.8%) on separating the individuals with bad OS from those with good OS. However, if using only conventionally derived descriptive features (e.g., age and tumor characteristics), the accuracy is low (63.2%). Our study highlights the importance of the rs-fMRI and brain functional connectomics for treatment planning.

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“…SWI sequences provide information on venous vasculature, hemorrhage, iron deposits, and very small vascular structures within the central nervous system (including brain tumors) [4,[15][16][17]. Fractal geometry has been demonstrated to offer appropriate tools to quantify irregular-shaped biological objects, including microvascular and SWI patterns [8], that can be used in automated algorithms for counting microbleeds [18], quantitative susceptibility mapping [19], percentage-wise quantification of intratumoral-susceptibility signals [20], local image variance [21] and other quantitative methodologies [16]. The fractal dimension, the most used parameter in fractal geometry, has been shown as a reliable numerical index to objectively quantify geometrical complexity of microvascular patterns in brain tumors [22].…”

Section: Discussionmentioning

confidence: 99%

Advanced computational and statistical multiparametric analysis of Susceptibility-Weighted Imaging to characterize gliomas and brain metastases

Ieva

Russo

Reste³

et al. 2020

Preprint

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contributed equally to this work.Abstract. Susceptibility-weighted imaging (SWI) is a technique useful for evaluation of the internal structures of brain tumors, including microvasculature and microbleeds. Intratumoral patterns of magnetic susceptibility can be quantified by means of fractal analysis. Here, we propose a radiomics methodological pipeline to merge advanced fractal-based computational modelling with statistical analysis to objectively characterize the fingerprint of gliomas and brain metastases. Forty-seven patients with glioma (grades II-IV, according to the WHO 2016 classification system) and fourteen with brain metastases underwent 3 Tesla MRI using a SWI protocol. All images underwent computational analysis aimed to quantify three Euclidean parameters (related to tumor and SWI volume) and five fractal-based parameters (related to the pixel distribution and geometrical complexity of the SWI patterns). Principal components analysis, linear and quadratic discriminant analysis, K-nearest neighbor and support vector machine methods were used to discriminate between tumor types. The combination of parameters offered an objective evaluation of the SWI pattern in gliomas and brain metastases. The model accurately predicted 88% of glioblastoma, according to the quantification of intratumoral SWI features, failing to discriminate the other types. SWI is not normally used to classify brain tumors, however fractal-based multi-parametric computational analysis can be used to characterize intratumoral SWI patterns to objectively quantify tumors-related features. Specific parameters still have to be identified to provide completely automatic computerized differential diagnosis.

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Local image variance of 7 Tesla SWI is a new technique for preoperative characterization of diffusely infiltrating gliomas: correlation with tumour grade and IDH1 mutational status

Cited by 24 publications

References 48 publications

Importance and Evidence of Extent of Resection in Glioblastoma

Importance and Evidence of Extent of Resection in Glioblastoma

Overall survival time prediction for high-grade glioma patients based on large-scale brain functional networks

Advanced computational and statistical multiparametric analysis of Susceptibility-Weighted Imaging to characterize gliomas and brain metastases

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