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

Svolos, Patricia; Tsolaki, Evangelia; Kapsalaki, Eftychia Z.; Theodorou, K.; Fountas, Kostas N.; Fezoulidis, Ioannis; Tsougos, Ioannis

doi:10.1016/j.mri.2013.06.010

Cited by 76 publications

(67 citation statements)

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“…Our study builds on previous efforts to define pattern recognition models for MR imaging characterization of patients with glioma (2)(3)(4)(5)24,25) and leaves us with two key advances in knowledge. First, the hemodynamic features of highly aggressive and malignant gliomas that lead to patient death within 6 months are distinctly different from those in patients with less malignant tumors and overall survival beyond 6 months.…”

Section: Discussionmentioning

confidence: 99%

“…T o answer the call for improved reproducibility and standardization in clinical imaging (1), as well as the increasing complexity of cancer imaging protocols, machine learning methods have been introduced as a computer-aided diagnostic tool in glioma characterization for reduced operator measurement error (2)(3)(4)(5). According to the assumption that high malignancy is reflected by increased vascular growth and tortuosity (6,7), glioma grading or survival associations can be assessed by using the quotient of relative cerebral blood volume (rCBV) values in tumor hot spots and reference tissue or wholetumor rCBV distribution analysis (8)(9)(10)(11)(12).…”

Section: Discussionmentioning

confidence: 99%

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A Generic Support Vector Machine Model for Preoperative Glioma Survival Associations

Emblem¹,

Pinho²,

Zöllner³

et al. 2015

Radiology

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Generic Support Vector Machine Model for Preoperative Glioma Survival Associations

Emblem¹,

Pinho²,

Zöllner³

et al. 2015

Radiology

View full text Add to dashboard Cite

show abstract

“…Other investigators have used MR perfusion to differentiate solitary MET from GBM either by taking advantage of the existing differences in hemodynamic curve analysis and utilizing the parameters of peak height and percentage signal recovery [4] or by using dynamic susceptibility contrast (DSC)-derived relative cerebral blood volume (rCBV) to exploit the differences in tumor vascularity and angiogenesis between the two [13,14]. More recently, researchers have investigated the differences between GBM and MET utilizing a combination of these techniques [15,16].…”

Section: Introductionmentioning

confidence: 99%

Differentiation of solitary brain metastasis from glioblastoma multiforme: a predictive multiparametric approach using combined MR diffusion and perfusion

et al. 2015

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“…2c), time to peak (Fig. 2f), cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT) [8]. MR spectroscopy (MRS) is a technique that capitalizes relative differences in metabolic activity of diseased and normal brain tissue.…”

Section: Advanced Physiologic Imaging Methodsmentioning

confidence: 99%

The evolving role of neurological imaging in neuro-oncology

et al. 2014

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Neuroimaging has played a critical role in the management of patients with neurological disease, since the first ventriculogram was performed in 1918 by Walter Dandy (Mezger et al. Langenbecks Arch Surg 398(4):501-514, 2013). Over the last century, technology has evolved significantly, and within the last decade, the role of imaging in the management of patients with neuro-oncologic disease has shifted from a tool for gross identification of intracranial pathology, to an integral part of real-time neurological surgery. Current neurological imaging provides detailed information about anatomical structure, neurological function, and metabolic and metabolism-important characteristics that help clinicians and surgeons non-invasively manage patients with brain tumors. It is valuable to review the evolution of neurological imaging over the past several decades, focusing on its role in the management of patients with intracranial tumors. Novel neuro-imaging tools and developing technology with the potential to further transform clinical practice will be discussed, as will the key role neurological imaging plays in neurosurgical planning and intraoperative navigation. With increasingly complex imaging modalities creating growing amounts of raw data, validation of techniques, data analysis, and integrating various pieces of imaging data into individual patient management plans, remain significant challenges for clinicians. We thus suggest mechanisms that might ultimately allow for evidence based integration of imaging in the management of patients with neuro-oncologic disease.

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

Cited by 76 publications

References 50 publications

A Generic Support Vector Machine Model for Preoperative Glioma Survival Associations

A Generic Support Vector Machine Model for Preoperative Glioma Survival Associations

Differentiation of solitary brain metastasis from glioblastoma multiforme: a predictive multiparametric approach using combined MR diffusion and perfusion

The evolving role of neurological imaging in neuro-oncology

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