ArticleJournal of neuro-oncology2015
Classification of tumor area using combined DCE and DSC MRI in patients with glioblastoma.
Article in Journal of neuro-oncology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Article
- The Role of Multimodal Imaging in Differentiating Vasogenic from Infiltrative Edema: A Systematic Review.The Indian journal of radiology & imaging · 2023Review
- Article
- Conventional and advanced imaging throughout the cycle of care of gliomas.Neurosurgical review · 2021Review
- Perfusion weighted imaging using combined gradient/spin echo EPIK: Brain tumour applications in hybrid MR-PET.Human brain mapping · 2021Article
- Article
- Machine-learning based classification of glioblastoma using delta-radiomic features derived from dynamic susceptibility contrast enhanced magnetic resonance images: Introduction.Quantitative imaging in medicine and surgery · 2019Article
- Conventional and advanced magnetic resonance imaging in patients with high-grade glioma.The quarterly journal of nuclear medicine and molecular imaging : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology (IAR), [and] Section of the Society of... · 2018Review
- Computer-assisted brain tumor type discrimination using magnetic resonance imaging features.Biomedical engineering letters · 2018Review
- Is more better? The impact of extended adjuvant temozolomide in newly diagnosed glioblastoma: a secondary analysis of EORTC and NRG Oncology/RTOG.Neuro-oncology · 2017Article
- Diffusion Weighted/Tensor Imaging, Functional MRI and Perfusion Weighted Imaging in Glioblastoma-Foundations and Future.Frontiers in neurology · 2017Review
- Clinical Applications of Contrast-Enhanced Perfusion MRI Techniques in Gliomas: Recent Advances and Current Challenges.Contrast media & molecular imaging · 2017Review
- Antiangiogenic Effect of Bevacizumab: Application of Arterial Spin-Labeling Perfusion MR Imaging in a Rat Glioblastoma Model.AJNR. American journal of neuroradiology · 2016Article
- Physiologic MRI for assessment of response to therapy and prognosis in glioblastoma.Neuro-oncology · 2016Review
- Alterations of the Blood-Brain Barrier and Regional Perfusion in Tumor Development: MRI Insights from a Rat C6 Glioma Model.PloS one · 2016Article
- Early biomarkers from dynamic contrast-enhanced magnetic resonance imaging to predict the response to antiangiogenic therapy in high-grade gliomas.Neuroradiology · 2015Article
- Towards the Personalized Treatment of Glioblastoma: Integrating Patient-Specific Clinical Data in a Continuous Mechanical Model.PloS one · 2015Article
- Magnetic resonance image-based brain tumour segmentation methods: A systematic review.Digital healthReview
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study proposes an automatic method for identification and quantification of different tissue components: the non-enhanced infiltrative tumor, vasogenic edema and enhanced tumor areas, at the subject level, in patients with glioblastoma (GB) based on dynamic contrast enhancement (DCE) and dynamic susceptibility contrast (DSC) MRI. Nineteen MR data sets, obtained from 12 patients with GB, were included. Seven patients were scanned before and 8 weeks following bevacizumab initiation. Segmentation of the tumor area was performed based on the temporal data of DCE and DSC at the group-level using k-means algorithm, and further at the subject-level using support vector machines algorithm. The obtained components were associated to different tissues types based on their temporal characteristics, calculated perfusion and permeability values and MR-spectroscopy. The method enabled the segmentation of the tumor area into the enhancing permeable component; the non-enhancing hypoperfused component, associated with vasogenic edema; and the non-enhancing hyperperfused component, associated with infiltrative tumor. Good agreement was obtained between the group-level, unsupervised and subject-level, supervised classification results, with significant correlation (r = 0.93, p < 0.001) and average symmetric root-mean-square surface distance of 2.5 ± 5.1 mm. Longitudinal changes in the volumes of the three components were assessed alongside therapy. Tumor area segmentation using DCE and DSC can be used to differentiate between vasogenic edema and infiltrative tumors in patients with GB, which is of major clinical importance in therapy response assessment.
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