Automated differentiation of glioblastomas from intracranial metastases using 3T MR spectroscopic and perfusion data

Tsolaki, Evangelia; Svolos, Patricia; Kousi, Evanthia; Kapsalaki, Eftychia; Fountas, Konstantinos; Theodorou, K.; Tsougos, Ioannis

doi:10.1007/s11548-012-0808-0

Cited by 43 publications

(42 citation statements)

References 37 publications

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“…These approaches have already been used to assess the histological nature of the adjacent tumor [28][29][30] or the WHO grade of glial tumors [31,32]. Recently, diffusion tensor imaging sequences were used to assess PBZ infiltration and showed that vasogenic edema and tumor-infiltrated edema are characterized by distinct patterns in imaging data [33].…”

Section: Discussionmentioning

confidence: 99%

Characterizing the peritumoral brain zone in glioblastoma: a multidisciplinary analysis

et al. 2015

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Glioblastoma (GB) is the most frequent and aggressive type of primary brain tumor. Recurrences are mostly located at the margin of the resection cavity in the peritumoral brain zone (PBZ). Although it is widely believed that infiltrative tumor cells in this zone are responsible for GB recurrence, few studies have examined this zone. In this study, we analyzed PBZ left after surgery with a variety of techniques including radiology, histopathology, flow cytometry, genomic, transcriptomic, proteomic, and primary cell cultures. The resulting PBZ profiles were compared with those of the GB tumor zone and normal brain samples to identify characteristics specific to the PBZ. We found that tumor cell infiltration detected by standard histological analysis was present in almost one third of PBZ taken from an area that was considered normal both on standard MRI and by the neurosurgeon under an operating microscope. The panel of techniques used in this study show that the PBZ, similar to the tumor zone itself, is characterized by substantial inter-patient heterogeneity, which makes it difficult to identify representative markers. Nevertheless, we identified specific alterations in the PBZ such as the presence of selected tumor clones and stromal cells with tumorigenic and angiogenic properties. The study of GB-PBZ is a growing field of interest and this region needs to be characterized further. This will facilitate the development of new, targeted therapies for patients with GB and the development of approaches to refine the per-operative evaluation of the PBZ to optimize the surgical resection of the tumor.

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Section: Discussionmentioning

confidence: 99%

Characterizing the peritumoral brain zone in glioblastoma: a multidisciplinary analysis

et al. 2015

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“…Recent studies have reported that pattern recognition techniques may be used as an automated computer analysis tool, in order to aid differential diagnosis [14,16,53,54]. The use of such techniques allows the manipulation and evaluation of a large amount of quantitative data during clinical practice.…”

Section: Roc Analysis -Classificationmentioning

confidence: 99%

“…Recently, a number of studies have investigated whether pattern recognition techniques can be used as an automated computer analysis tool, in order to aid decision making [14][15][16]. These techniques provide the mathematical and computational mechanisms to take advantage of the available biological knowledge and data gathered from a clinical problem [17].…”

Section: Introductionmentioning

confidence: 99%

“…These techniques provide the mathematical and computational mechanisms to take advantage of the available biological knowledge and data gathered from a clinical problem [17]. Support vector machines (SVM) have been reported as a promising classification method in tumor characterization and grading [16,[18][19][20][21]. Furthermore, a variety of classification features, morphological (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques

Svolos

Tsolaki

Kapsalaki

et al. 2013

Magnetic Resonance Imaging

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“…Taking into account the complex structure of the clinical data and the difficulty of brain tumor discrimination due to their intrinsic heterogeneity, the research community has shifted towards the application of machine learning algorithms, in order to assign different tissue types to specific patterns. Several studies have previously investigated the differentiation of brain tumors in adults based on machine learning techniques [16][17][18][19][20] , as well as the discrimination of pediatric brain tumors [21,22] . By importing and utilizing these intelligent techniques in a clinical decision support system (CDSS), several advanced MRI techniques may become a part of the clinical routine in order to resolve demanding diagnostic problems.…”

Section: Even Ifmentioning

confidence: 99%

Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques

Tsolaki

Kousi

Svolos

et al. 2014

WJR

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In recent years, advanced magnetic resonance imaging (MRI) techniques, such as magnetic resonance spectroscopy, diffusion weighted imaging, diffusion tensor imaging and perfusion weighted imaging have been used in order to resolve demanding diagnostic problems such as brain tumor characterization and grading, as these techniques offer a more detailed and noninvasive evaluation of the area under study. In the last decade a great effort has been made to import and utilize intelligent systems in the so-called clinical decision support systems (CDSS) for automatic processing, classification, evaluation and representation of MRI data in order for advanced MRI techniques to become a part of the clinical routine, since the amount of data from the aforementioned techniques has gradually increased. Hence, the purpose of the current review article is two-fold. The first is to review and evaluate the progress that has been made towards the utilization of CDSS based on data from advanced MRI techniques. The second is to analyze and propose the future work that has to be done, based on the existing problems and challenges, especially taking into account the new imaging techniques and parameters that can be introduced into intelligent systems to significantly improve their diagnostic specificity and clinical application.

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Automated differentiation of glioblastomas from intracranial metastases using 3T MR spectroscopic and perfusion data

Abstract: The application of pattern recognition techniques using 3T MR-based perfusion and metabolic features may provide incremental diagnostic value in the differentiation of common intraaxial brain tumors, such as glioblastoma versus metastasis.

Cited by 43 publications

References 37 publications

Characterizing the peritumoral brain zone in glioblastoma: a multidisciplinary analysis

Characterizing the peritumoral brain zone in glioblastoma: a multidisciplinary analysis

Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques

Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques

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