Computers in biology and medicine · 2021
Accurate lateralization and classification of MRI-negative 18F-FDG-PET-positive temporal lobe epilepsy using double inversion recovery and machine-learning.
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Beheshti Iman, Sone Daichi, Maikusa Norihide, Kimura Yukio, Shigemoto Yoko, Sato Noriko, Matsuda Hiroshi
Аннотация
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.
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.