Multimodal MRI characteristics of the glioblastoma infiltration beyond contrast enhancement

Yan, Jiun-Lin; Li, Chao; Boonzaier, Natalie R.; Fountain, Daniel M; Larkin, Timothy J.; Matys, Tomasz; Hoorn, Anouk van der; Price, Stephen J.

doi:10.1177/1756286419844664

Cited by 28 publications

(30 citation statements)

References 32 publications

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“…MRI Data acquisition and imaging processing. Detailed MRI data acquisition and imaging processing protocols were as previous described 16,17 . In brief, all preoperative MRI data was obtained from the same 3.0 Tesla Siemens MR Magnetron system (Siemens Healthcare, Munich, Germany) with a standard 12-channel head coil.…”

Section: Methodsmentioning

confidence: 99%

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A Neural Network Approach to Identify the Peritumoral Invasive Areas in Glioblastoma Patients by Using MR Radiomics

Yan

Hoorn

et al. 2020

Sci Rep

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the challenge in the treatment of glioblastoma is the failure to identify the cancer invasive area outside the contrast-enhancing tumour which leads to the high local progression rate. Our study aims to identify its progression from the preoperative MR radiomics. 57 newly diagnosed cerebral glioblastoma patients were included. All patients received 5-aminolevulinic acid (5-ALA) fluorescence guidance surgery and postoperative temozolomide concomitant chemoradiotherapy. Preoperative 3 T MRI data including structure MR, perfusion MR, and DTI were obtained. Voxel-based radiomics features extracted from 37 patients were used in the convolutional neural network to train and as internal validation. Another 20 patients of the cohort were tested blindly as external validation. Our results showed that the peritumoural progression areas had higher signal intensity in FLAIR (p = 0.02), rCBV (p = 0.038), and T1C (p = 0.0004), and lower intensity in ADC (p = 0.029) and DTI-p (p = 0.001) compared to non-progression area. The identification of the peritumoural progression area was done by using a supervised convolutional neural network. There was an overall accuracy of 92.6% in the training set and 78.5% in the validation set. Multimodal MR radiomics can demonstrate distinct characteristics in areas of potential progression on preoperative MRI.

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Section: Methodsmentioning

confidence: 99%

“…Perfusion MR was processed using NordicICE (NordicNeuroLab AS, Bergen, Norway) and maps of rCBV were created following contrast agent leakage correction. Spectroscopy data was processed using LCModel 19 and the MRS result in each ROIs were obtained by using a previous described 3D voxel-wise approach 17 .…”

Section: Methodsmentioning

confidence: 99%

A Neural Network Approach to Identify the Peritumoral Invasive Areas in Glioblastoma Patients by Using MR Radiomics

Yan

Hoorn

et al. 2020

Sci Rep

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“…The Cancer Genome Atlas (TCGA) and Rembrandt). In recent years, however, several studies have started to gain molecular information about tumor cells at the edge located outside the area of contrast enhancement [8][9][10]. Likewise, through the collection of subcortical edge tissues from the non-eloquent deep white matter during supra-total resection during awake procedures, we have molecularly characterized the edge-and core-located glioblastoma cells.…”

Section: Introductionmentioning

confidence: 99%

Endothelial-secreted Endocan protein acts as a PDGFR alpha ligand and regulates vascularity, radioresistance, and regional phenotype in glioblastoma

Bastola

Ghochani³

et al. 2020

Preprint

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Glioblastoma is a lethal brain cancer with active infiltration of tumor cells at the periphery where the vascular supply is enriched to create its own niche. Given that tumor recurrence is predominantly local, most of the seeds for lethal recurrence are hidden at the tumor edge and are surgically inaccessible. Here, we found that the spatial identity of the edge-located glioblastoma cells is, in the tested models, constructed by inter-cellular signals derived from the soluble factor endocan (protein product of Esm1) that is secreted by tumor-associated vascular endothelial (VE) cells. Injection of two distinct mouse glioblastoma models into Esm1 knockout (KO) mice resulted in tumors that failed to form typical edge lesions, resulting in the formation of compact core-only lesions. In sharp contrast, tumors derived in WT (Esm1-intact) mice harbored both tumor core and edge structures. Despite these obvious phenotypic differences, the aggressiveness of these two tumor types was indistinguishable in vivo. Surprisingly, regardless of the host system, co-injection of mixture of these two tumor subpopulations gave rise tumors with much worse survival, indicating that the accumulating tumor cell heterogeneity contributes to elevate malignancy. Mechanistically, endocan competes with PDGF to bind and activate PDGFRα in human glioblastoma cells, accompanied with the upregulation of the Myc transcriptional activity via alterations in its promoter chromatin structure. One of the mainstays of glioblastoma treatment, radiation, induces endocan secretion from VE cells, which promotes the radioresistance of the edge-located glioblastoma cells, allowing for the persistence of the edge structure. Collectively, these data suggest that intra-tumoral spatial heterogeneity is initiated by the VE cell-derived endocan signals through PDGFRα to construct tumor edge-to-core (E-to-C) architecture to cause lethal tumor recurrence.

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“…However, there is no shortage of studies showing adverse results [ 12 , 47 , 57 , 64 ]. A lower number of studies investigated metrics arising from different approaches, such as four-component histogram analysis, fDM maps analysis, radiomics analysis and DTI isotropic and anisotropic components [ 24 , 43 , 49 , 51 , 68 , 74 , 80 , 82 , 83 ].…”

Section: Discussionmentioning

confidence: 99%

Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics—A Systematic Review

Brancato

Nuzzo

Tramontano

et al. 2020

Cancers

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Despite advances in surgical and medical treatment of glioblastoma (GBM), the medium survival is about 15 months and varies significantly, with occasional longer survivors and individuals whose tumours show a significant response to therapy with respect to others. Diffusion MRI can provide a quantitative assessment of the intratumoral heterogeneity of GBM infiltration, which is of clinical significance for targeted surgery and therapy, and aimed at improving GBM patient survival. So, the aim of this systematic review is to assess the role of diffusion MRI metrics in predicting survival of patients with GBM. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a systematic literature search was performed to identify original articles since 2010 that evaluated the association of diffusion MRI metrics with overall survival (OS) and progression-free survival (PFS). The quality of the included studies was evaluated using the QUIPS tool. A total of 52 articles were selected. The most examined metrics were associated with the standard Diffusion Weighted Imaging (DWI) (34 studies) and Diffusion Tensor Imaging (DTI) models (17 studies). Our findings showed that quantitative diffusion MRI metrics provide useful information for predicting survival outcomes in GBM patients, mainly in combination with other clinical and multimodality imaging parameters.

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Multimodal MRI characteristics of the glioblastoma infiltration beyond contrast enhancement

Cited by 28 publications

References 32 publications

A Neural Network Approach to Identify the Peritumoral Invasive Areas in Glioblastoma Patients by Using MR Radiomics

A Neural Network Approach to Identify the Peritumoral Invasive Areas in Glioblastoma Patients by Using MR Radiomics

Endothelial-secreted Endocan protein acts as a PDGFR alpha ligand and regulates vascularity, radioresistance, and regional phenotype in glioblastoma

Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics—A Systematic Review

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