Objective This study aimed to determine whether quantitative volumetric measures from MRI and metabolic indices from FDG-PET can independently or jointly predict surgical outcome in patients with drug-resistant temporal lobe epilepsy (TLE), particularly when accounting for the well-established prognostic role of hippocampal sclerosis (HS) co-pathology. Methods We retrospectively reviewed patients who underwent temporal lobe resection for drug-resistant epilepsy, comprising 80 with MRI data, 77 with FDG-PET data, and a subset of 42 with both modalities available (fusion cohort). Quantitative asymmetry indices for temporal lobe subregions were derived using the AAL atlas. To assess whether imaging features contributed prognostic information beyond HS status and clinical covariates, we employed hierarchical logistic regression. Data-driven predictive performance was further evaluated through machine learning models using nested leave-one-out cross-validation with permutation testing. Results Across all cohorts, HS co-pathology consistently emerged as the strongest predictor of favorable outcome (MRI cohort: OR=18.4, P<0.001; PET cohort: OR=42.0, P<0.001). When examined individually, neither MRI-derived nor PET-derived quantitative features added significantly to the predictive model beyond HS (MRI: P=0.085; PET: P=0.386). By contrast, combining both modalities in the fusion cohort yielded a significant incremental contribution over HS and clinical variables (likelihood ratio test, P=0.009), with the AUC rising from 0.778 to 0.963. Of particular interest, amygdala volumetric asymmetry on MRI was identified as an independent predictor not previously reported (OR=83.7, P=0.041). Machine learning approaches yielded only modest discrimination (fusion cohort AUC=0.692, P=0.075) and did not outperform the hypothesis-driven statistical framework. Conclusion Integrating MRI volumetric and FDG-PET metabolic data offers meaningful prognostic value that extends beyond what HS status alone can provide. Amygdala asymmetry on MRI represents a novel independent predictor warranting further validation. Our findings favor comprehensive multimodal presurgical workup over reliance on a single imaging modality and suggest that, for surgical outcome prediction in TLE, hypothesis-driven analytical approaches may hold advantages over purely data-driven machine learning strategies.
VNS can achieve a stable reduction in seizure frequency across different DRE populations, with a well-documented time-dependent cumulative therapeutic effect, while complete seizure freedom is rarely achieved in patients. Compared with low-intensity stimulation, high-intensity stimulation significantly increased the probability of achieving ≥50% seizure reduction (RR=1.73). For pediatric DRE, systematic reviews and meta-analyses revealed that approximately 55% of patients achieved significant seizure reduction (≥50%), and the overall response rate reached around 68% in DRE cases with genetic etiologies. Real-world studies indicated that the cumulative rate of ≥12-month complete seizure freedom was approximately 11%, whereas the cumulative rate of ≥12-month freedom from tonic-clonic seizures reached up to 54.9%; moreover, seizure-free status was closely correlated with improved quality of life. In terms of safety profile, common stimulation-related adverse events included voice alteration/hoarseness, cough, and dyspnea, most of which were tolerable. Summaries of long-term clinical experience showed that the incidence of surgery-related complications was approximately 8.6%, and that of hardware-related complications was around 3.7%. Infection, hematoma, vocal cord paralysis, and lead malfunction were the key events requiring priority prevention and control.VNS is an important neuromodulatory treatment option for DRE, especially for patients who are ineligible for resective surgery or have persistent seizures after surgery, with a favorable overall tolerability profile. However, its ability to achieve long-term complete seizure freedom remains limited. Standardized long-term follow-up and individualized programming (including closed-loop strategies and parameter optimization) are expected to further enhance clinical benefits. Future research should focus on stratification by etiology/network phenotype, standardization of core outcome measures, external validation of translatable predictive biomarkers, as well as prospective controlled studies on key programming strategies and combined treatment pathways.