Predictive value of preoperative statistical parametric mapping of regional glucose metabolism in mesial temporal lobe epilepsy with hippocampal sclerosis

Kojan, Martin; Doležalová, Irena; Koriťáková, Eva; Mareček, Radek; Řehák, Zdeněk; Hermanová, Markéta; Brázdil, Milan; Rektor, Ivan

doi:10.1016/j.yebeh.2017.11.014

Cited by 17 publications

(22 citation statements)

References 34 publications

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“… 20 Previous studies showed that radiology reports and the epilepsy surgery conference consensus differ 31% of the time in FDG PET studies. 21 In this study, the accuracy of visual analysis by 3 physicians was 75.3%. Moreover, previous studies showed that the accuracy of FDG PET by visual analysis in overall intractable epilepsy is 85% (86% in patients with good outcomes, 58% in patients who need invasive studies), 22 and the accuracy in MTLE patients is 64%.…”

Section: Discussionmentioning

confidence: 55%

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Machine Learning Quantitative Analysis of FDG PET Images of Medial Temporal Lobe Epilepsy Patients

Shih¹,

Lee²,

Yu³

et al. 2022

Clin Nucl Med

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Purpose: 18 F-FDG PET is widely used in epilepsy surgery. We established a robust quantitative algorithm for the lateralization of epileptogenic foci and examined the value of machine learning of 18 F-FDG PET data in medial temporal lobe epilepsy (MTLE) patients. Patients and Methods: We retrospectively reviewed patients who underwent surgery for MTLE. Three clinicians identified the side of MTLE epileptogenesis by visual inspection. The surgical side was set as the epileptogenic side. Two parcellation paradigms and corresponding atlases (Automated Anatomical Labeling and FreeSurfer aparc + aseg) were used to extract the normalized PET uptake of the regions of interest (ROIs). The lateralization index of the MTLE-associated regions in either hemisphere was calculated. The lateralization indices of each ROI were subjected for machine learning to establish the model for classifying the side of MTLE epileptogenesis. Result: Ninety-three patients were enrolled for training and validation, and another 11 patients were used for testing. The hit rate of lateralization by visual analysis was 75.3%. Among the 23 patients whose MTLE side of epileptogenesis was incorrectly determined or for whom no conclusion was reached by visual analysis, the Automated Anatomical Labeling and aparc + aseg parcellated the associated ROIs on the correctly lateralized MTLE side in 100.0% and 82.6%. In the testing set, lateralization accuracy was 100% in the 2 paradigms. Conclusions: Visual analysis of 18 F-FDG PET to lateralize MTLE epileptogenesis showed a lower hit rate compared with machine-assisted interpretation. While reviewing 18 F-FDG PET images of MTLE patients, considering the regions associated with MTLE resulted in better performance than limiting analysis to hippocampal regions.

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Section: Discussionmentioning

confidence: 55%

“…Pertinently, glucose hypometabolism has been demonstrated in regions of the ipsilateral temporal and extratemporal regions, including the amygdalohippocampal complex, parahippocampal gyrus, temporal pole, orbitofrontal cortex, insula, posterior and anterior cingulate gyrus, and thalamus. 21 …”

Section: Discussionmentioning

confidence: 99%

Machine Learning Quantitative Analysis of FDG PET Images of Medial Temporal Lobe Epilepsy Patients

Shih¹,

Lee²,

Yu³

et al. 2022

Clin Nucl Med

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“…First, we did not find any significant sex or MTLE laterality effects on brain metabolism in patients with MTLE (Supplementary Results), probably due to our limited sample size. To increase statistical power and reduce type I errors, we used a “flip‐and‐combine” strategy following previous studies (Akman et al., 2010; Guedj et al., 2015; Kojan et al., 2018; Nelissen et al., 2006) and identified common characteristics across the patients with MTLE regardless of their HS laterality. Nonetheless, considering the importance to recognize these effects, future studies with larger sample size are encouraged to examine the influence of sex and MTLE laterality on metabolic patterns in MTLE.…”

Section: Discussionmentioning

confidence: 99%

Characterizing the hyper‐ and hypometabolism in temporal lobe epilepsy using multivariate machine learning

Yang

Gong

et al. 2021

J of Neuroscience Research

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Mesial temporal lobe epilepsy (MTLE) is the most common type of focal epilepsy, presenting both structural and metabolic abnormalities in the ipsilateral mesial temporal lobe. While it has been demonstrated that the metabolic abnormalities in MTLE actually extend beyond the epileptogenic zone, how such multidimensional information is associated with the diagnosis of MTLE remains to be tested. Here, we explore the whole‐brain metabolic patterns in 23 patients with MTLE and 24 healthy controls using [18F]fluorodeoxyglucose PET imaging. Based on a multivariate machine learning approach, we demonstrate that the brain metabolic patterns can discriminate patients with MTLE from controls with a superior accuracy (>95%). Importantly, voxels showing the most extreme contributing weights to the classification (i.e., the most important regional predictors) distribute across both hemispheres, involving both ipsilateral negative weights over the anterior part of lateral and medial temporal lobe, posterior insula, and lateral orbital frontal gyrus, and contralateral positive weights over the anterior frontal lobe, temporal lobe, and lingual gyrus. Through region‐of‐interest analyses, we verify that in patients with MTLE, the negatively weighted regions are hypometabolic, and the positively weighted regions are hypermetabolic, compared to controls. Interestingly, despite that both hypo‐ and hypermetabolism have mutually contributed to our model, they may reflect different pathological and/or compensative responses. For instance, patients with earlier age at epilepsy onset present greater hypometabolism in the ipsilateral inferior temporal gyrus, while we find no evidence of such association with hypermetabolism. In summary, quantitative models utilizing multidimensional brain metabolic information may provide additional assistance to presurgical workups in TLE.

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“…Allerdings ist dies nicht bei allen Patienten der Fall, zudem kann das Areal des Hypometabolismus über den eigentlichen Fokus hinausgehen. Falls der Hypometabolismus konkordant zur Hypothese des Anfallsursprungs sein sollte, kann von einer besseren Wahrscheinlichkeit für eine postoperative Anfallsfreiheit ausgegangen werden, was bei Temporallappenepilepsien mit Hippocampussklerose gezeigt werden konnte [73,74]. Auch das Ausmaß des Hypometabolismus und die entsprechend ausgiebige Resektion der hypometabolen Areale scheinen relevant zu sein.…”

Section: Positronen-emissions-tomografie (Pet)unclassified

Bildgebung bei Epilepsie

Marquetand¹,

Bender²,

Focke³

2019

Nervenheilkunde

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ZUSAMMENFASSUNGIn der Diagnostik der Epilepsien sind bildgebende Verfahren ein wesentlicher Bestandteil. Neben bildgebender Notfalldiagnostik und verschiedener MRT-Protokolle nach erstmaligem epileptischem Anfall werden häufige epileptogene Läsionen und deren bildgebende Charakteristika dargestellt. Darüber hinaus werden bildgebende Verfahren (VBM, DTI, Quellenlokalisation, nuklearmedizinische Verfahren, etc.) erläutert und deren Nutzen in der prächirurgischen Epilepsiediagnostik aufgezeigt.In diesem Übersichtsartikel wollen wir einen Überblick über das wachsende Feld der bildgebenden Verfahren bei Epilepsie und klinisch relevante neue Entwicklungen geben.

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Predictive value of preoperative statistical parametric mapping of regional glucose metabolism in mesial temporal lobe epilepsy with hippocampal sclerosis

Cited by 17 publications

References 34 publications

Machine Learning Quantitative Analysis of FDG PET Images of Medial Temporal Lobe Epilepsy Patients

Machine Learning Quantitative Analysis of FDG PET Images of Medial Temporal Lobe Epilepsy Patients

Characterizing the hyper‐ and hypometabolism in temporal lobe epilepsy using multivariate machine learning

Bildgebung bei Epilepsie

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