Distinguishing Focal Cortical Dysplasia From Glioneuronal Tumors in Patients With Epilepsy by Machine Learning
Open Access
- 24 November 2020
- journal article
- research article
- Published by Frontiers Media SA in Frontiers in Neurology
Abstract
Purpose: We are aiming to build a supervised machine learning-based classifier, in order to preoperatively distinguish focal cortical dysplasia (FCD) from glioneuronal tumors (GNTs) in patients with epilepsy. Methods: This retrospective study was comprised of 96 patients who underwent epilepsy surgery, with the final neuropathologic diagnosis of either an FCD or GNTs. Seven classical machine learning algorithms (i.e., Random Forest, SVM, Decision Tree, Logistic Regression, XGBoost, LightGBM, and CatBoost) were employed and trained by our dataset to get the classification model. Ten features [i.e., Gender, Past history, Age at seizure onset, Course of disease, Seizure type, Seizure frequency, Scalp EEG biomarkers, MRI features, Lesion location, Number of antiepileptic drug (AEDs)] were analyzed in our study. Results: We enrolled 56 patients with FCD and 40 patients with GNTs, which included 29 with gangliogliomas (GGs) and 11 with dysembryoplasic neuroepithelial tumors (DNTs). Our study demonstrated that the Random Forest-based machine learning model offered the best predictive performance on distinguishing the diagnosis of FCD from GNTs, with an F1-score of 0.9180 and AUC value of 0.9340. Furthermore, the most discriminative factor between FCD and GNTs was the feature “age at seizure onset” with the Chi-square value of 1,213.0, suggesting that patients who had a younger age at seizure onset were more likely to be diagnosed as FCD. Conclusion: The Random Forest-based machine learning classifier can accurately differentiate FCD from GNTs in patients with epilepsy before surgery. This might lead to improved clinician confidence in appropriate surgical planning and treatment outcomes.Keywords
Funding Information
- National Natural Science Foundation of China
This publication has 32 references indexed in Scilit:
- Factors associated with seizure freedom in the surgical resection of glioneuronal tumorsEpilepsia, 2011
- The clinicopathologic spectrum of focal cortical dysplasias: A consensus classification proposed by an ad hoc Task Force of the ILAE Diagnostic Methods Commission1Epilepsia, 2010
- Performance assessment for EEG-based neonatal seizure detectorsClinical Neurophysiology, 2010
- Neuropathological Findings in Intractable Epilepsy: 435 Chinese CasesBrain Pathology, 2010
- Evaluation of Focal Cortical Dysplasia and Mixed Neuronal and Glial Tumors in Pediatric Epilepsy Patients Using18F-FDG and11C-Methionine PETJournal of Nuclear Medicine, 2010
- Revised terminology and concepts for organization of seizures and epilepsies: Report of the ILAE Commission on Classification and Terminology, 2005–2009Epilepsia, 2010
- Predictors of surgical outcome and pathologic considerations in focal cortical dysplasiaNeurology, 2009
- Different presurgical characteristics and seizure outcomes in children with focal cortical dysplasia type I or IIEpilepsia, 2009
- The 2007 WHO Classification of Tumours of the Central Nervous SystemActa Neuropathologica, 2007
- Support vector machinesIEEE Intelligent Systems and their Applications, 1998