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Updated: Sep 17, 2025

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Individualized structural network deviations predict surgical outcome in mesial temporal lobe epilepsy: a multicenter

Li Feng1,2, Honghao Han3,4, Jiajie Mo5

  • 1Department of Neurology, Xiangya Hospital, Central South University, Changsha, P.R. China.

International Journal of Surgery (London, England)
|July 3, 2025
PubMed
Summary

Predicting surgical success in mesial temporal lobe epilepsy (mTLE) is crucial. Machine learning models using preoperative individualized structural covariance networks (iSCN) can accurately forecast seizure freedom after surgery.

Keywords:
individualizedmachine learningmesial temporal lobe epilepsystructural networksurgical outcome

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Machine Learning

Background:

  • Surgical resection is a key treatment for medically refractory mesial temporal lobe epilepsy (mTLE).
  • Over one-third of patients do not achieve seizure freedom post-surgery.
  • Predicting surgical outcomes remains a challenge.

Purpose of the Study:

  • To evaluate preoperative individual morphometric network characteristics in mTLE patients.
  • To develop a machine learning model for predicting surgical outcomes in mTLE.
  • To identify reliable biomarkers for personalized surgical treatment.

Main Methods:

  • A multicentre, retrospective study of 189 mTLE patients and 78 controls.
  • Construction of preoperative individualized structural covariance networks (iSCN) from T1-weighted MRI.
  • Support vector machine models trained on iSCN features, validated in external datasets.

Main Results:

  • Non-seizure-free (NSF) patients showed greater iSCN deviations in the surgically spared network.
  • Contralateral iSCN features optimally predicted seizure outcome (82% accuracy, AUC 0.81).
  • External validation confirmed model generalizability (80-88% accuracy, AUC 0.80-0.82).

Conclusions:

  • Personalized structural biomarkers can reliably predict surgical outcomes in mTLE.
  • Machine learning models utilizing iSCN show promise for predicting surgical success.
  • Findings support tailored surgical treatment strategies for mTLE.