Objective:We assessed pre-operative structural brain networks and clinical characteristics of patients with drug resistant temporal lobe epilepsy (TLE) to identify correlates of post-surgical seizure recurrences.Methods:We examined data from 51 TLE patients who underwent anterior temporal lobe resection (ATLR) and 29 healthy controls. For each patient, using the pre-operative structural, diffusion, and post-operative structural MRI, we generated two networks: ‘pre-surgery’ network and ‘surgically-spared’ network. Standardising these networks with respect to controls, we determined the number of abnormal nodes before surgery and expected to be spared by surgery. We incorporated these 2 abnormality measures and 13 commonly acquired clinical data from each patient in a robust machine learning framework to estimate patient-specific chances of seizures persisting after surgery.Results:Patients with more abnormal nodes had lower chance of complete seizure freedom at 1 year and even if seizure-free at 1 year, were more likely to relapse within five years. The number of abnormal nodes was greater and their locations more widespread in the surgically-spared networks of poor outcome patients than in good outcome patients. We achieved 0.84±0.06 AUC and 0.89±0.09 specificity in predicting unsuccessful seizure outcomes (ILAE3-5) as opposed to complete seizure freedom (ILAE1) at 1 year. Moreover, the model-predicted likelihood of seizure relapse was significantly correlated with the grade of surgical outcome at year-one and associated with relapses up-to five years post-surgery.Conclusion:Node abnormality offers a personalised non-invasive marker, that can be combined with clinical data, to better estimate the chances of seizure freedom at 1 year, and subsequent relapse up to 5 years after ATLR.Classification of evidence:This study provides Class II evidence that node abnormality predicts post-surgical seizure recurrence.