Predictive Model Identifies Best Patients for Minimally Invasive Epilepsy Surgery

S., with about one-third not responding effectively to medications. For these individuals, surgery to remove or disable the part of the brain responsible for seizures can be a viable treatment, though predicting which patients will become seizure-free has been challenging. Now, a new scoring system may help doctors better predict which patients are likely to be free of seizures after undergoing minimally invasive epilepsy surgery.

Researchers from Rutgers Health (New Brunswick, NJ, USA) and other institutions have developed a predictive model aimed at improving access to surgical treatment for epilepsy. Their model, based on data from 101 patients who underwent stereotactic laser amygdalohippocampotomy (SLAH)—a minimally invasive procedure using laser interstitial thermal therapy (LITT) to target and disable a small region of the brain’s temporal lobe—identifies eight clinical factors linked to a higher likelihood of becoming seizure-free post-surgery. These factors include patient history, MRI abnormalities, lesions, and febrile seizures. Instead of using complex statistical models, the team created a straightforward scoring system by assigning one point for each positive factor, which outperformed other predictive models, including those based solely on MRI findings or more elaborate analyses.

The findings, published in Annals of Clinical and Translational Neurology, show that patients with a score of 6 or higher on the 8-point scale had a 70% to 80% chance of becoming seizure-free after SLAH—comparable to success rates of traditional open surgery. Patients with lower scores experienced progressively reduced chances of achieving seizure freedom. Interestingly, even those without clear MRI evidence of scarring in the temporal lobe—long considered a key indicator for surgical success—could still benefit from SLAH if they had several other positive factors. This approach could help broaden the availability of surgical treatment for epilepsy, which remains underutilized. Many patients are reluctant to pursue invasive brain surgery due to concerns about cognitive side effects, but the less invasive SLAH procedure might be more attractive, especially with clearer predictions about the likelihood of success.

Although this predictive model could be used to guide clinical decisions, the researchers acknowledge it requires further validation on additional patient outcome data beyond their initial study group. The scoring system also does not account for all potential factors that might affect surgical outcomes, such as the distribution of abnormal brain activity across hemispheres or specific seizure types. Despite these limitations, the researchers believe this model marks a significant advancement in personalizing epilepsy treatment. By offering more refined predictions of surgical outcomes, this tool may enable more patients with drug-resistant epilepsy to achieve relief through minimally invasive procedures. As research progresses, the model may be enhanced by incorporating more detailed data, such as seizure characteristics and neuropsychological profiles, potentially leading to even more accurate predictions and improved patient care.

"We've pried open the therapeutic window with this minimally invasive approach," said Robert Gross, senior author of the study and chair of the Department of Neurosurgery at the Rutgers Robert Wood Johnson and New Jersey Medical School. "The concordance of multiple clinical data points better predicts seizure freedom after SLAH than any one data point alone."