Multimodal MR imaging model to predict tumor infiltration in patients with gliomas

Durst, Christopher R.; Raghavan, Prashant; Shaffrey, Mark E.; Schiff, David; Lopes, M. Beatriz; Sheehan, Jason P.; Tustison, Nicholas J.; Patrie, James T.; Xin, Wenjun; Elias, W. Jeff; Liu, Kenneth C.; Helm, Greg; Cupino, Alan; Wintermark, Max

doi:10.1007/s00234-013-1308-9

Cited by 26 publications

(32 citation statements)

References 38 publications

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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%

A Generic Support Vector Machine Model for Preoperative Glioma Survival Associations

Emblem¹,

Pinho²,

Zöllner³

et al. 2015

Radiology

100

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Section: Discussionmentioning

confidence: 99%

A Generic Support Vector Machine Model for Preoperative Glioma Survival Associations

Emblem¹,

Pinho²,

Zöllner³

et al. 2015

Radiology

100

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show abstract

“…[16][17][18] There have been sporadic attempts to study the role of quantitative MRI methods, including DSC-MRI, DWI/ DTI, IVIM, and T 2 -relaxometry for characterizing infiltrating glioma regions through biopsy validation. [18][19][20] Hence, we sought to address: 1) the roles of individual MRIderived parameters in differentiation of the three subregions, ie, active tumor (AT), infiltrative edema (IE), and normal tissue (NT), from each other; 2) the relationship between MRI-derived parameters with histopathological cellular density; and 3) the best multiparametric combination of MRIderived features for discrimination of AT, IE, and NT through a quantitative methodology.…”

mentioning

confidence: 99%

Characterization of active and infiltrative tumorous subregions from normal tissue in brain gliomas using multiparametric MRI

Kazerooni

Nabil

Zadeh

et al. 2018

Magnetic Resonance Imaging

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Background Targeted localized biopsies and treatments for diffuse gliomas rely on accurate identification of tissue subregions, for which current MRI techniques lack specificity. Purpose To explore the complementary and competitive roles of a variety of conventional and quantitative MRI methods for distinguishing subregions of brain gliomas. Study Type Prospective. Population Fifty-one tissue specimens were collected using image-guided localized biopsy surgery from 10 patients with newly diagnosed gliomas. Field Strength/Sequence Conventional and quantitative MR images consisting of pre- and postcontrast T1w, T2w, T2-FLAIR, T2-relaxometry, DWI, DTI, IVIM, and DSC-MRI were acquired preoperatively at 3T. Assessment Biopsy specimens were histopathologically attributed to glioma tissue subregion categories of active tumor (AT), infiltrative edema (IE), and normal tissue (NT) subregions. For each tissue sample, a feature vector comprising 15 MRI-based parameters was derived from preoperative images and assessed by a machine learning algorithm to determine the best multiparametric feature combination for characterizing the tissue subregions. Statistical Tests For discrimination of AT, IE, and NT subregions, a one-way analysis of variance (ANOVA) test and for pairwise tissue subregion differentiation, Tukey honest significant difference, and Games-Howell tests were applied (P < 0.05). Cross-validated feature selection and classification methods were implemented for identification of accurate multiparametric MRI parameter combination. Results After exclusion of 17 tissue specimens, 34 samples (AT = 6, IE = 20, and NT = 8) were considered for analysis. Highest accuracies and statistically significant differences for discrimination of IE from NT and AT from NT were observed for diffusion-based parameters (AUCs >90%), and the perfusion-derived parameter as the most accurate feature in distinguishing IE from AT. A combination of “CBV, MD, T2_ISO, FLAIR” parameters showed high diagnostic performance for identification of the three subregions (AUC ~90%). Data Conclusion Integration of a few quantitative along with conventional MRI parameters may provide a potential multiparametric imaging biomarker for predicting the histopathologically proven glioma tissue subregions. Level of Evidence 2 Technical Efficacy Stage 3

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“…These advanced diffusion techniques are currently used notably in clinical research and will likely enter clinical routine in the next years [30][31][32].…”

Section: Diffusion Imagingmentioning

confidence: 99%

State-of-the-art MRI techniques in neuroradiology: principles, pitfalls, and clinical applications

et al. 2015

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This article reviews the most relevant state-of-the-art magnetic resonance (MR) techniques, which are clinically available to investigate brain diseases. MR acquisition techniques addressed include notably diffusion imaging (diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI)) as well as perfusion imaging (dynamic susceptibility contrast (DSC), arterial spin labeling (ASL), and dynamic contrast enhanced (DCE)). The underlying models used to process these images are described, as well as the theoretic underpinnings of quantitative diffusion and perfusion MR imaging-based methods. The technical requirements and how they may help to understand, classify, or follow-up neurological pathologies are briefly summarized. Techniques, principles, advantages but also intrinsic limitations, typical artifacts, and alternative solutions developed to overcome them are discussed. In this article, we also review routinely available three-dimensional (3D) techniques in neuro MRI, including state-of-the-art and emerging angiography sequences, and briefly introduce more recently proposed 3D quantitative neuro-anatomy sequences, and new technology, such as multi-slice and multi-transmit imaging.

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Multimodal MR imaging model to predict tumor infiltration in patients with gliomas

Abstract: A multi-input, single output imaging model may predict the extent of glioma invasion with significant correlation with histopathology.

Cited by 26 publications

References 38 publications

A Generic Support Vector Machine Model for Preoperative Glioma Survival Associations

A Generic Support Vector Machine Model for Preoperative Glioma Survival Associations

Characterization of active and infiltrative tumorous subregions from normal tissue in brain gliomas using multiparametric MRI

State-of-the-art MRI techniques in neuroradiology: principles, pitfalls, and clinical applications

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