Distinguishing type II focal cortical dysplasias from normal cortex: A novel normative modeling approach

Snyder, Kathryn; Whitehead, Emily; Theodore, William H.; Zaghloul, Kareem A.; Inati, Souheil; Inati, Sara K.

doi:10.1016/j.nicl.2021.102565

Cited by 9 publications

(5 citation statements)

References 39 publications

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“…1 MRI-negative patients represent a major diagnostic challenge. 2 Currently, benchmark automated detection methods fail in 20% to 40% of patients, [3][4][5][6] particularly those with subtle FCD, and suffer from high false-positive (FP) rates. 7 Conversely, deep neural networks outperform state-of-the-art methods at disease detection (see elsewhere 8,9 for review).…”

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confidence: 99%

Multicenter Validation of a Deep Learning Detection Algorithm for Focal Cortical Dysplasia

et al. 2021

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Objective.To test the hypothesis that a multicenter-validated computer deep learning algorithm detects MRI-negative focal cortical dysplasia (FCD).Methods.We used clinically-acquired 3D T1-weighted and 3D FLAIR MRI of 148 patients (median age, 23 years [range, 2-55]; 47% female) with histologically-verified FCD at nine centers to train a deep convolutional neural network (CNN) classifier. Images were initially deemed as MRI-negative in 51% of cases, in whom intracranial EEG determined the focus. For risk stratification, the CNN incorporated Bayesian uncertainty estimation as a measure of confidence. To evaluate performance, detection maps were compared to expert FCD manual labels. Sensitivity was tested in an independent cohort of 23 FCD cases (13±10 years). Applying the algorithm to 42 healthy and 89 temporal lobe epilepsy disease controls tested specificity.Results.Overall sensitivity was 93% (137/148 FCD detected) using a leave-one-site-out cross-validation, with an average of six false positives per patient. Sensitivity in MRI-negative FCD was 85%. In 73% of patients, the FCD was among the clusters with the highest confidence; in half it ranked the highest. Sensitivity in the independent cohort was 83% (19/23; average of five false positives per patient). Specificity was 89% in healthy and disease controls.Conclusions.This first multicenter-validated deep learning detection algorithm yields the highest sensitivity to date in MRI-negative FCD. By pairing predictions with risk stratification this classifier may assist clinicians to adjust hypotheses relative to other tests, increasing diagnostic confidence. Moreover, generalizability across age and MRI hardware makes this approach ideal for pre-surgical evaluation of MRI-negative epilepsy.Classification of evidence.This study provides Class III evidence that deep learning on multimodal MRI accurately identifies FCD in epilepsy patients initially diagnosed as MRI-negative.

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confidence: 99%

Multicenter Validation of a Deep Learning Detection Algorithm for Focal Cortical Dysplasia

et al. 2021

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“…Over the last decades, several surface-based algorithms have been developed for fully automated detection of FCD type II [52][53][54][55][56][57][58][59][60]; the addition of FLAIR has increased sensitivity for the identification of smaller lesions [55]. Importantly, careful preprocessing, including manual corrections of tissue segmentation and surface extraction, have delivered high-fidelity FCD features [61].…”

Section: Lesion Detectionmentioning

confidence: 99%

“…Conversely, suboptimal processing of surface-based data may lead to poor performance, even in cases with MRI visible lesions [62]. Admittedly, current benchmark automated detection algorithms fail in 20–40% of patients [55, 56, 59, 63], particularly those with subtle FCD type II, and suffer from relatively high false-positive rates [60]; also, the limited set of features designed by human experts may not capture the full complexity of pathology. Alternatively, in recent years, deep learning has shown high detection performance relative to conventional methods [64, 65].…”

Section: Lesion Detectionmentioning

confidence: 99%

The Role of MRI in the Treatment of Drug-Resistant Focal Epilepsy

Bernasconi

2022

Eur Neurol

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Background: Epilepsy is a prevalent chronic condition affecting about 50 million people worldwide. A third of patients with focal epilepsy suffer from seizures unresponsive to medication. Uncontrolled seizures damage the brain, are associated with cognitive decline, and have negative impact on well-being. For these patients, the surgical resection of the brain region that gives rise to seizures is the most effective treatment. Summary: Magnetic resonance imaging (MRI) plays a central role in detecting epileptogenic brain lesions. In this review, we critically discuss advances in neuroimaging acquisition, analytical post-acquisition techniques, and machine leaning methods for the detection of epileptogenic lesions, prediction of clinical outcomes, and identification of disease subtypes. Key Message: MRI is a mandatory investigation for diagnosis and treatment of epilepsy, particularly when surgery is being considered. Continuous progress in imaging techniques, combined with machine learning, will continue to push the boundaries of lesion visibility and provide increasingly precise predictors of clinical outcomes. Current efforts aiming at strengthening the competences of epileptologists in neuroimaging will ultimately reduce the need for invasive diagnostics.

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“…Snyder et al implemented a new method to describe and detect FCD lesions [ 23 ]. First, they created a model based on a rotationally invariant and multi-contrast 3D local feature implementation that describes the normal variability of the cortex in healthy subjects.…”

Section: Methodsmentioning

confidence: 99%

Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review

Jiménez-Murillo,

Castro-Ospina,

Duque-Muñoz

et al. 2023

Sensors

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Focal cortical dysplasia (FCD) is a congenital brain malformation that is closely associated with epilepsy. Early and accurate diagnosis is essential for effectively treating and managing FCD. Magnetic resonance imaging (MRI)—one of the most commonly used non-invasive neuroimaging methods for evaluating the structure of the brain—is often implemented along with automatic methods to diagnose FCD. In this review, we define three categories for FCD identification based on MRI: visual, semi-automatic, and fully automatic methods. By conducting a systematic review following the PRISMA statement, we identified 65 relevant papers that have contributed to our understanding of automatic FCD identification techniques. The results of this review present a comprehensive overview of the current state-of-the-art in the field of automatic FCD identification and highlight the progress made and challenges ahead in developing reliable, efficient methods for automatic FCD diagnosis using MRI images. Future developments in this area will most likely lead to the integration of these automatic identification tools into medical image-viewing software, providing neurologists and radiologists with enhanced diagnostic capabilities. Moreover, new MRI sequences and higher-field-strength scanners will offer improved resolution and anatomical detail for precise FCD characterization. This review summarizes the current state of automatic FCD identification, thereby contributing to a deeper understanding and the advancement of FCD diagnosis and management.

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Distinguishing type II focal cortical dysplasias from normal cortex: A novel normative modeling approach

Cited by 9 publications

References 39 publications

Multicenter Validation of a Deep Learning Detection Algorithm for Focal Cortical Dysplasia

Multicenter Validation of a Deep Learning Detection Algorithm for Focal Cortical Dysplasia

The Role of MRI in the Treatment of Drug-Resistant Focal Epilepsy

Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review

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