1. External validation of automated focal cortical dysplasia detection using morphometric analysis
- Author
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Christian E. Elger, Hans-Jürgen Huppertz, Jan Wagner, Bernhard Oehl, Bernd Weber, Judith Kröll-Seger, Tobias Breyer, Hartmut Vatter, Elke Hattingen, Rainer Surges, Albert J. Becker, Bastian David, Jörg Wellmer, Fabiane Schuch, Friedrich G. Woermann, Theodor Rüber, Wim Van Paesschen, and Horst Urbach
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Adult ,Male ,0301 basic medicine ,Drug Resistant Epilepsy ,Adolescent ,Clinical Neurology ,Young Adult ,03 medical and health sciences ,Imaging, Three-Dimensional ,0302 clinical medicine ,medicine ,Humans ,Generalizability theory ,Child ,Aged ,Retrospective Studies ,validation ,Science & Technology ,Training set ,medicine.diagnostic_test ,business.industry ,External validation ,Infant ,Magnetic resonance imaging ,Middle Aged ,Cortical dysplasia ,medicine.disease ,Magnetic Resonance Imaging ,Malformations of Cortical Development ,Data set ,030104 developmental biology ,Visual detection ,Neurology ,Morphometric analysis ,Child, Preschool ,MAP ,epilepsy ,Female ,Neural Networks, Computer ,Neurosciences & Neurology ,Neurology (clinical) ,business ,Nuclear medicine ,Life Sciences & Biomedicine ,artificial neural network ,lesion localization ,030217 neurology & neurosurgery ,MRI - Abstract
OBJECTIVE: Focal cortical dysplasias (FCDs) are a common cause of drug-resistant focal epilepsy but frequently remain undetected by conventional magnetic resonance imaging (MRI) assessment. The visual detection can be facilitated by morphometric analysis of T1-weighted images, for example, using the Morphometric Analysis Program (v2018; MAP18), which was introduced in 2005, independently validated for its clinical benefits, and successfully integrated in standard presurgical workflows of numerous epilepsy centers worldwide. Here we aimed to develop an artificial neural network (ANN) classifier for robust automated detection of FCDs based on these morphometric maps and probe its generalization performance in a large, independent data set. METHODS: In this retrospective study, we created a feed-forward ANN for FCD detection based on the morphometric output maps of MAP18. The ANN was trained and cross-validated on 113 patients (62 female, mean age ± SD =29.5 ± 13.6 years) with manually segmented FCDs and 362 healthy controls (161 female, mean age ± SD =30.2 ± 9.6 years) acquired on 13 different scanners. In addition, we validated the performance of the trained ANN on an independent, unseen data set of 60 FCD patients (28 female, mean age ± SD =30 ± 15.26 years) and 70 healthy controls (42 females, mean age ± SD = 40.0 ± 12.54 years). RESULTS: In the cross-validation, the ANN achieved a sensitivity of 87.4% at a specificity of 85.4% on the training data set. On the independent validation data set, our method still reached a sensitivity of 81.0% at a comparably high specificity of 84.3%. SIGNIFICANCE: Our method shows a robust automated detection of FCDs and performance generalizability, largely independent of scanning site or MR-sequence parameters. Taken together with the minimal input requirements of a standard T1 image, our approach constitutes a clinically viable and useful tool in the presurgical diagnostic routine for drug-resistant focal epilepsy. ispartof: EPILEPSIA vol:62 issue:4 pages:1005-1021 ispartof: location:United States status: published
- Published
- 2021
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