Accurate lateralization and classification of MRI-negative 18F-FDG-PET-positive temporal lobe epilepsy using double inversion recovery and machine-learning. (October 2021)

• Record Type: Journal Article
• Authors: Beheshti, Iman; Sone, Daichi; Maikusa, Norihide; Kimura, Yukio; Shigemoto, Yoko; Sato, Noriko; Matsuda, Hiroshi
• Is Part Of: Computers in biology and medicine. Volume 137(2021)
• Publication Date: 2021-10
• Subjects: Biomarker -- Double inversion recovery -- Prediction -- Support vector machine -- Temporal lobe epilepsy -- MRI-negative focal epilepsy; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104805
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Objective: The main objective of this study was to determine the ability of double inversion recovery (DIR) data coupled with machine-learning algorithms to distinguish normal individuals from epileptic subjects and to identify the laterality of the focus side in MRI-negative, PET-positive temporal lobe epilepsy (TLE) patients. Materials and methods: We used whole-brain DIR data as the input features with which to train a linear support-vector machine model in 63 participants who underwent high-resolution structural MRI and DIR scans. The subjects included 20 left TLE patients, 19 right TLE patients, and 24 healthy controls (HCs). Results: Using the DIR data, we achieved a robust accuracy of 87.30% for discriminating among the left TLE, right TLE, and HC groups as well as 84.61%, 97.72%, and 93.02% prediction accuracies for distinguishing left TLE from right TLE, HC from right TLE, and HC from left TLE, respectively. Interpretation: Our experimental results suggest that DIR data coupled with machine-learning algorithms provide a promising approach to identifying MRI-negative TLE patients, especially when fluorodeoxyglucose-PET is not available. Highlights: We assessed the utility of DIR data for identifying MRI-negative epilepsy. Using DIR data, we achieved a prediction accuracy of 87% for discriminating among the three groups. DIR data can be viewed as a sensitive biomarker for MRI-negative TLE classification tasks.