International journal of surgery (London, England) · 2025
Individualized structural network deviations predict surgical outcome in mesial temporal lobe epilepsy: a multicenter validation study.
СтатьяМногоцентровое исследованиеВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Feng Li, Han Honghao, Mo Jiajie, Huang Yongwen, Huang Kailing, Zhou Chunyao, Wang Xuyang, Zhang Junquan, Yang Zhiquan, Liu Dingyang, Zhang Kai, Chen Huafu, Liu Qiangqiang, Li Rong
Аннотация
BACKGROUND: Surgical resection is an effective treatment for medically refractory mesial temporal lobe epilepsy (mTLE), however, more than one-third of patients fail to achieve seizure freedom after surgery. This study aimed to evaluate preoperative individual morphometric network characteristics and develop a machine learning model to predict surgical outcome in mTLE.
METHODS: This multicenter, retrospective study included 189 mTLE patients who underwent unilateral temporal lobectomy and 78 normal controls between February 2018 and June 2023. Postoperative seizure outcomes were categorized as seizure-free (SF, n = 125) or non-seizure-free (NSF, n = 64) at a minimum of 1-year follow-up. The preoperative individualized structural covariance network (iSCN) derived from T1-weighted MRI was constructed for each patient by calculating deviations from the control-based reference distribution, and further divided into the surgery network and the surgically spared network using a standard resection mask by merging each patient's individual lacuna. Regional features were selected separately from bilateral, ipsilateral, and contralateral iSCN abnormalities to train support vector machine models, validated in two independent external datasets.
RESULTS: NSF patients showed greater iSCN deviations from the normative distribution in the surgically spared network compared to SF patients ( P = 0.02). These deviations were widely distributed in the contralateral functional modules ( P < 0.05, false discovery rate corrected). Seizure outcome was optimally predicted by the contralateral iSCN features, with an accuracy of 82% ( P < 0.05, permutation test) and an area under the receiver operating characteristic curve (AUC) of 0.81, with the default mode and frontoparietal areas contributing most. External validation in two independent cohorts showed accuracy of 80% and 88%, with AUC of 0.80 and 0.82, respectively, emphasizing the generalizability of the model.
CONCLUSION: This study provides reliable personalized structural biomarkers for predicting surgical outcome in mTLE and has the potential to assist tailored surgical treatment strategies.
METHODS: This multicenter, retrospective study included 189 mTLE patients who underwent unilateral temporal lobectomy and 78 normal controls between February 2018 and June 2023. Postoperative seizure outcomes were categorized as seizure-free (SF, n = 125) or non-seizure-free (NSF, n = 64) at a minimum of 1-year follow-up. The preoperative individualized structural covariance network (iSCN) derived from T1-weighted MRI was constructed for each patient by calculating deviations from the control-based reference distribution, and further divided into the surgery network and the surgically spared network using a standard resection mask by merging each patient's individual lacuna. Regional features were selected separately from bilateral, ipsilateral, and contralateral iSCN abnormalities to train support vector machine models, validated in two independent external datasets.
RESULTS: NSF patients showed greater iSCN deviations from the normative distribution in the surgically spared network compared to SF patients ( P = 0.02). These deviations were widely distributed in the contralateral functional modules ( P < 0.05, false discovery rate corrected). Seizure outcome was optimally predicted by the contralateral iSCN features, with an accuracy of 82% ( P < 0.05, permutation test) and an area under the receiver operating characteristic curve (AUC) of 0.81, with the default mode and frontoparietal areas contributing most. External validation in two independent cohorts showed accuracy of 80% and 88%, with AUC of 0.80 and 0.82, respectively, emphasizing the generalizability of the model.
CONCLUSION: This study provides reliable personalized structural biomarkers for predicting surgical outcome in mTLE and has the potential to assist tailored surgical treatment strategies.