Advanced [18F]FDG and [11C]flumazenil PET analysis for individual outcome prediction after temporal lobe epilepsy surgery for hippocampal sclerosis

Njiwa, J. Yankam; Gray, Katherine R.; Costes, Nicolas; Mauguı̀ere, François; Ryvlin, Philippe; Hammers, Alexander

doi:10.1016/j.nicl.2014.11.013

Cited by 33 publications

(24 citation statements)

References 43 publications

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“…Only one study has previously used machine learning on PET data to predict surgical outcome in epilepsy 28 . Using RF classifiers on 16 surgical patients, it reported a 62.5% accuracy using FDG‐PET, and a 87.5% accuracy using a [11C]flumazenil (FMZ) PET tracer.…”

Section: Discussionmentioning

confidence: 99%

Machine learning approaches for imaging‐based prognostication of the outcome of surgery for mesial temporal lobe epilepsy

et al. 2022

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Objectives Around 30% of patients undergoing surgical resection for drug‐resistant mesial temporal lobe epilepsy (MTLE) do not obtain seizure freedom. Success of anterior temporal lobe resection (ATLR) critically depends on the careful selection of surgical candidates, aiming at optimizing seizure freedom while minimizing postoperative morbidity. Structural MRI and FDG‐PET neuroimaging are routinely used in presurgical assessment and guide the decision to proceed to surgery. In this study, we evaluate the potential of machine learning techniques applied to standard presurgical MRI and PET imaging features to provide enhanced prognostic value relative to current practice. Methods Eighty two patients with drug resistant MTLE were scanned with FDG‐PET pre‐surgery and T1‐weighted MRI pre‐ and postsurgery. From these images the following features of interest were derived: volume of temporal lobe (TL) hypometabolism, % of extratemporal hypometabolism, presence of contralateral TL hypometabolism, presence of hippocampal sclerosis, laterality of seizure onset volume of tissue resected and % of temporal lobe hypometabolism resected. These measures were used as predictor variables in logistic regression, support vector machines, random forests and artificial neural networks. Results In the study cohort, 24 of 82 (28.3%) who underwent an ATLR for drug‐resistant MTLE did not achieve Engel Class I (i.e., free of disabling seizures) outcome at a minimum of 2 years of postoperative follow‐up. We found that machine learning approaches were able to predict up to 73% of the 24 ATLR surgical patients who did not achieve a Class I outcome, at the expense of incorrect prediction for up to 31% of patients who did achieve a Class I outcome. Overall accuracies ranged from 70% to 80%, with an area under the receiver operating characteristic curve (AUC) of .75–.81. We additionally found that information regarding overall extent of both total and significantly hypometabolic tissue resected was crucial to predictive performance, with AUC dropping to .59–.62 using presurgical information alone. Incorporating the laterality of seizure onset and the choice of machine learning algorithm did not significantly change predictive performance. Significance Collectively, these results indicate that "acceptable" to "good" patient‐specific prognostication for drug‐resistant MTLE surgery is feasible with machine learning approaches utilizing commonly collected imaging modalities, but that information on the surgical resection region is critical for optimal prognostication.

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Section: Discussionmentioning

confidence: 99%

Machine learning approaches for imaging‐based prognostication of the outcome of surgery for mesial temporal lobe epilepsy

et al. 2022

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“…Machine learning techniques have also been utilized in epilepsy research for automated epilepsy diagnosis [14], [15], [16], seizure lateralization [17], [7], [18], [19], differentiation between mesial and neocortical temporal lobe epilepsies [20], analysis of electrophysiology-hemodynamics connectivity in epileptic patients [21], [22], and in several studies to predict postsurgical seizure freedom [23], [24], [25], [26], [27], [28], [29], [30], [31]. …”

Section: Introductionmentioning

confidence: 99%

Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy

Memarian¹,

Kim²,

Dewar³

et al. 2015

Computers in Biology and Medicine

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Background This study sought to predict postsurgical seizure freedom from pre-operative diagnostic test results and clinical information using a rapid automated approach, based on supervised learning methods in patients with drug-resistant focal seizures suspected to begin in temporal lobe. Method We applied machine learning, specifically a combination of mutual information-based feature selection and supervised learning classifiers on multimodal data, to predict surgery outcome retrospectively in 20 presurgical patients (13 female; mean age±SD, in years 33±9.7 for females, and 35.3±9.4 for males) who were diagnosed with mesial temporal lobe epilepsy (MTLE) and subsequently underwent standard anteromesial temporal lobectomy. The main advantage of the present work over previous studies is the inclusion of the extent of ipsilateral neocortical gray matter atrophy and spatiotemporal properties of depth electrode-recorded seizures as training features for individual patient surgery planning. Results A maximum relevance minimum redundancy (mRMR) feature selector identified the following features as the most informative predictors of postsurgical seizure freedom in this study's sample of patients: family history of epilepsy, ictal EEG onset pattern (positive correlation with seizure freedom), MRI-based gray matter thickness reduction in the hemisphere ipsilateral to seizure onset, proportion of seizures that first appeared in ipsilateral amygdala to total seizures, age, epilepsy duration, delay in the spread of ipsilateral ictal discharges from site of onset, gender, and number of electrode contacts at seizure onset (negative correlation with seizure freedom). Using these features in combination with a least square support vector machine (LS-SVM) classifier compared to other commonly used classifiers resulted in very high surgical outcome prediction accuracy (95%). Conclusions Supervised machine learning using multimodal compared to unimodal data accurately predicted postsurgical outcome in patients with atypical MTLE.

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“…These approaches have been widely used in understanding the abnormalities in both neurological and psychiatric disorders (Arbabshirani et al., 2017), such as Alzheimer's disease (Dai et al., 2012; Liu et al., 2014), depression (Zeng et al., 2012), schizophrenia (Sun et al., 2009), and developmental dyslexia (Cui et al., 2016). However, they are rarely applied to explore the multidimensional abnormalities of brain metabolism in MTLE (Guedj et al., 2015; Pustina et al., 2015; Yankam Njiwa et al., 2015).…”

Section: Introductionmentioning

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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Advanced [18F]FDG and [11C]flumazenil PET analysis for individual outcome prediction after temporal lobe epilepsy surgery for hippocampal sclerosis

Cited by 33 publications

References 43 publications

Machine learning approaches for imaging‐based prognostication of the outcome of surgery for mesial temporal lobe epilepsy

Machine learning approaches for imaging‐based prognostication of the outcome of surgery for mesial temporal lobe epilepsy

Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy

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

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