脳腫瘍に関連する低悪性度てんかんの手術成績を予測する脳接続性
Wenjie Ming1, Yang Zheng2, Qi Lian3
1Epilepsy Center, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Department of Neurology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Objective:
Low-grade epilepsy-associated neuroepithelial tumors (LEATs) often cause drug-resistant epilepsy. Despite complete resection of these lesions, approximately 20% of patients continue to experience suboptimal seizure control. This study aims to investigate the predictive value of quantitative features in determining the surgical outcomes for LEAT patients.
Methods:
We retrospectively analyzed 44 temporal LEAT patients who underwent gross-total lesionectomy. EEG features, including power spectral density (PSD) and weighted phase lag index (wPLI), were compared between patients with good (Engel I) and poor (Engel II-IV) outcomes. Significant EEG features were identified through these analyses. Domain Adversarial Neural Network (DANN) was employed to assess the predictive value of these features for surgical outcomes.
Results:
No significant PSD differences were found, but patients with good outcomes had higher alpha-band wPLI (p = 0.008). LEATnet, predicted outcomes with an AUC of 0.81and correctly classified 8 of 11 patients in the independent validation cohort.
Conclusions:
Alpha-band functional connectivity is a key predictor of surgical outcomes in LEAT patients.
Significance:
EEG-based connectivity analysis may improve prognostic accuracy and aid clinical decision-making in LEAT epilepsy.
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