Multimodal MRI profiling of focal cortical dysplasia type II

Hong, Seok‐Jun; Bernhardt, Boris C.; Caldairou, Benoît; Hall, Jeffery A.; Guiot, Marie Christine; Schrader, Dewi; Bernasconi, Andrea

doi:10.1212/wnl.0000000000003632

Cited by 79 publications

(62 citation statements)

References 39 publications

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“…Due to their ability to probe tissue biophysical properties in vivo , they have the potential to bridge the gap between radiological assessment and ex vivo histology . For this reason, we did not apply the profiling analysis to FLAIR and T1w images, which have already been characterized . Although these clinical images provide good contrast in some FCD lesions, the intensity is not quantitative, thereby limiting their specificity to microstructural tissue properties …”

Section: Discussionmentioning

confidence: 99%

“…The DTI‐based metrics FA and MD cannot differentiate between the contributions to signal changes of fiber density/orientation dispersion and diffusion across intracellular and extracellular compartments . This lack of specificity hampers the neurobiological interpretation and is a confounder in the identification of pathophysiological phenomena in FCD because similar signal variation can result from pathological changes or normal white matter structure …”

Section: Introductionmentioning

confidence: 99%

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MRI profiling of focal cortical dysplasia using multi‐compartment diffusion models

et al. 2020

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Objective Focal cortical dysplasia (FCD) lesion detection and subtyping remain challenging on conventional MRI. New diffusion models such as the spherical mean technique (SMT) and neurite orientation dispersion and density imaging (NODDI) provide measurements that potentially produce more specific maps of abnormal tissue microstructure. This study aims to assess the SMT and NODDI maps for computational and radiological lesion characterization compared to standard fractional anisotropy (FA) and mean diffusivity (MD). Methods SMT, NODDI, FA, and MD maps were calculated for 33 pediatric patients with suspected FCD (18 histologically confirmed). Two neuroradiologists scored lesion visibility on clinical images and diffusion maps. Signal profile changes within lesions and homologous regions were quantified using a surface‐based approach. Diffusion parameter changes at multiple cortical depths were statistically compared between FCD type IIa and type IIb. Results Compared to fluid‐attenuated inversion recovery (FLAIR) or T1‐weighted imaging, lesions conspicuity on NODDI intracellular volume fraction (ICVF) maps was better/equal/worse in 5/14/14 patients, respectively, while on SMT intra‐neurite volume fraction (INVF) in 3/3/27. Compared to FA or MD, lesion conspicuity on the ICVF was better/equal/worse in 27/4/2, while on the INVF in 20/7/6. Quantitative signal profiling demonstrated significant ICVF and INVF reductions in the lesions, whereas SMT microscopic mean, radial, and axial diffusivities were significantly increased. FCD type IIb exhibited greater changes than FCD type IIa. No changes were detected on FA or MD profiles. Significance FCD lesion‐specific signal changes were found in ICVF and INVF but not in FA and MD maps. ICVF and INVF showed greater contrast than FLAIR in some cases and had consistent signal changes specific to FCD, suggesting that they could improve current presurgical pediatric epilepsy imaging protocols and can provide features useful for automated lesion detection.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MRI profiling of focal cortical dysplasia using multi‐compartment diffusion models

et al. 2020

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“…73 Surface-based methods improve intersubject anatomical correspondence and allow for multivariate analysis of MRI contrasts and features to unveil latent tissue properties not readily identified on a single modality. 89 …”

Section: Texture Analysismentioning

confidence: 99%

Recommendations for the use of structural magnetic resonance imaging in the care of patients with epilepsy: A consensus report from the International League Against Epilepsy Neuroimaging Task Force

et al. 2019

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Structural magnetic resonance imaging (MRI) is of fundamental importance to the diagnosis and treatment of epilepsy, particularly when surgery is being considered. Despite previous recommendations and guidelines, practices for the use of MRI are variable worldwide and may not harness the full potential of recent technological advances for the benefit of people with epilepsy. The International League Against Epilepsy Diagnostic Methods Commission has thus charged the 2013‐2017 Neuroimaging Task Force to develop a set of recommendations addressing the following questions: (1) Who should have an MRI? (2) What are the minimum requirements for an MRI epilepsy protocol? (3) How should magnetic resonance (MR) images be evaluated? (4) How to optimize lesion detection? These recommendations target clinicians in established epilepsy centers and neurologists in general/district hospitals. They endorse routine structural imaging in new onset generalized and focal epilepsy alike and describe the range of situations when detailed assessment is indicated. The Neuroimaging Task Force identified a set of sequences, with three‐dimensional acquisitions at its core, the harmonized neuroimaging of epilepsy structural sequences—HARNESS‐MRI protocol. As these sequences are available on most MR scanners, the HARNESS‐MRI protocol is generalizable, regardless of the clinical setting and country. The Neuroimaging Task Force also endorses the use of computer‐aided image postprocessing methods to provide an objective account of an individual's brain anatomy and pathology. By discussing the breadth and depth of scope of MRI, this report emphasizes the unique role of this noninvasive investigation in the care of people with epilepsy.

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“…26 Performance quality was evaluated across combinations of (1) total accuracy of the prediction (specificity in the bilateral hippocampus of the healthy control group and sensitivity in the affected hippocampus of the HS patient group as well as in the HS patient subgroups [MRI-positive and MRI-negative patients]), (2) predictive value of global features (assessment of the area under curve [AUC] for each outcome), and (3) robustness of the model. SVM incorporates several advantageous properties to reduce overfitting and deliver F I G U R E 1 Schematic outline of the present study.…”

Section: Machine Learning Modelmentioning

confidence: 99%

Automated detection of hippocampal sclerosis using clinically empirical and radiomics features

Liu

Sun

et al. 2019

Epilepsia

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Objective: Temporal lobe epilepsy is a common form of epilepsy that might be amenable to surgery. However, magnetic resonance imaging (MRI)-negative hippocampal sclerosis (HS) can hamper early diagnosis and surgical intervention for patients in clinical practice, resulting in disease progression. Our aim was to automatically detect and evaluate the structural alterations of HS. Methods: Eighty patients with pharmacoresistant epilepsy and histologically proven HS and 80 healthy controls were included in the study. Two automated classifiers relying on clinically empirical and radiomics features were developed to detect HS. Cross-validation was implemented on all participants, and specificity was assessed in the 80 controls. The performance, robustness, and clinical utility of the model were also evaluated. Structural analysis was performed to investigate the morphological abnormalities of HS. Results: The computational model based on clinical empirical features showed excellent performance, with an area under the curve (AUC) of 0.981 in the primary cohort and 0.993 in the validation cohort. One of the features, gray-white matter boundary blurring in the temporal pole, exhibited the highest weight in model performance.Another model based on radiomics features also showed satisfactory performance, with AUC of 0.997 in the primary cohort and 0.978 in the validation cohort. In particular, the model improved the detection rate of MRI-negative HS to 96.0%. The novel feature of cortical folding complexity of the temporal pole not only played a crucial role in the classifier but also had significant correlation with disease duration. |MO et al.

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Multimodal MRI profiling of focal cortical dysplasia type II

Cited by 79 publications

References 39 publications

MRI profiling of focal cortical dysplasia using multi‐compartment diffusion models

MRI profiling of focal cortical dysplasia using multi‐compartment diffusion models

Recommendations for the use of structural magnetic resonance imaging in the care of patients with epilepsy: A consensus report from the International League Against Epilepsy Neuroimaging Task Force

Automated detection of hippocampal sclerosis using clinically empirical and radiomics features

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