  Seizure recurrence is an important concern after an initial unprovoked
seizure; without drug treatment, it occurs within 2 years in 40-50% of cases.
The decision to treat currently relies on predictors of seizure recurrence risk
that are inaccurate, resulting in unnecessary, possibly harmful, treatment in
some patients and potentially preventable seizures in others. Because of the
link between brain lesions and seizure recurrence, we developed a recurrence
prediction tool using machine learning and clinical 3T brain MRI. We developed
NeuroMorphix, a feature construction approach based on MRI brain anatomy. Each
of seven NeuroMorphix features measures the absolute or relative difference
between corresponding regions in each cerebral hemisphere. FreeSurfer was used
to segment brain regions and to generate values for morphometric parameters (8
for each cortical region and 5 for each subcortical region). The parameters
were then mapped to whole brain NeuroMorphix features, yielding a total of 91
features per subject. Features were generated for a first seizure patient
cohort (n = 169) categorised into seizure recurrence and non-recurrence
subgroups. State-of-the-art classification algorithms were trained and tested
using NeuroMorphix features to predict seizure recurrence. Classification
models using the top 5 features, ranked by sequential forward selection,
demonstrated excellent performance in predicting seizure recurrence, with area
under the ROC curve of 88-93%, accuracy of 83-89%, and F1 score of 83-90%.
Highly ranked features aligned with structural alterations known to be
associated with epilepsy. This study highlights the potential for targeted,
data-driven approaches to aid clinical decision-making in brain disorders.
