Evaluation of brain FDG PET images in temporal lobe epilepsy for lateralization of epileptogenic focus using data mining methods

Abstract

Background/aim: In temporal lobe epilepsy (TLE), brain positron emission tomography (PET) performed with F-18 fluorodeoxyglucose (FDG) is commonly used for lateralization of the epileptogenic temporal lobe. In this study, we aimed to evaluate the success of quantitative analysis of brain FDG PET images using data mining methods in the lateralization of the epileptogenic temporal lobe. Materials and methods: Presurgical interictal brain FDG PET images of 49 adult mesial TLE patients with a minimum of 2 years of postsurgical follow-up and Engel I outcomes were retrospectively analyzed. Asymmetry indices were calculated from PET images from the mesial temporal lobe and its contiguous structures. The J48 and the logistic model tree (LMT) data mining algorithms were used to find classification rules for the lateralization of the epileptogenic temporal lobe. The classification results obtained by these rules were compared with the physicians' visual readings and the findings of single-patient statistical parametric mapping (SPM) analyses in a test set of 18 patients. An additional 5-fold cross-validation was applied to the data to overcome the limitation of a relatively small sample size. Results: In the lateralization of 18 patients in the test set, J48 and LMT methods were successful in 16 (89%) and 17 (94%) patients, respectively. The visual consensus readings were correct in all patients and SPM results were correct in 16 patients. The 5-fold crossvalidation method resulted in a mean correct lateralization ratio of 96% (47/49) for the LMT algorithm. This ratio was 88% (43 / 49) for the J48 algorithm. Conclusion: Lateralization of the epileptogenic temporal lobe with data mining methods using regional metabolic asymmetry values obtained from interictal brain FDG PET images in mesial TLE patients is highly accurate. The application of data mining can contribute to the reader in the process of visual evaluation of FDG PET images of the brain.

Nuclear imaging for localization and surgical outcome prediction in epilepsy: A review of latest discoveries and future perspectives

“…Several studies using supervised or unsupervised machine learning classification algorithms, such as decision tree (DT), logistic regression (LR), logistic model tree (LMT), random forest (RF), multivariate pattern analysis (MPA), support vector machine (SVM), multilayer perceptron (MLP), artificial neural network, convolutional neural network (CNN), and XGBoost, were published. Machine learning using FDG-PET with classification algorithms by MLP (127,128), LR (129), LMT (130), SVM (131), MPA (132), CNN (133), and combined several methods (134) were used for the lateralization of TLE. Machine learning classification algorithms by DT, LR, RF, SVM, and XGBoost using FDG-PET and/or MRI were used for the detection of focal cortical dysplasia (135,136).…”