Distinguishing Focal Cortical Dysplasia From Glioneuronal Tumors in Patients With Epilepsy by Machine Learning

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.

Leveraging large, real-world data through machine-learning to increase efficiency in robotic-assisted total knee arthroplasty

“…The algorithm relied on gradient boosting, a stepwise approach, where sets of decision trees were created at each step with a smaller error (e.g., smaller difference between predicted and actual operative time in the training data set) compared to the existing trees. Catboost was selected, since it handles categorical predictors (e.g., “male” vs “female”) and has already been successfully applied to medical data [14, 20] and specifically in the orthopaedic field [10, 15]. The second was tabNet [3], a new algorithm recently developed by Google Cloud AI (pytorch‐tabNet 3.1.1 implementation).…”

Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

“…The output of a logistic regression model is a probability value that falls into a 0-1 range, but with the use of a classification cut-off (i.e., probability of 0.5), logistic regression can be used for classification tasks (Pustina et al, 2015;Peter et al, 2018). Logistic regression has been widely used a binary classifier in epilepsy studies (Ahmed et al, 2015;Mahmoudi et al, 2018;Guo et al, 2020).…”

“…More recent studies tend to use combined data from multimodal imaging, whereas earlier studies used only T1WI (Table 3). Differential diagnoses such as FCD type I vs. II and FCD vs. tumor were also reported (Hong et al, 2016;Guo et al, 2020).…”