Key words: focal cortical dysplasia; MR morphometry; artificial intelligence; machine learning; pharmacoresistant epilepsy; multimodal MRI.
- Abstract
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Focal cortical dysplasia (FCD) is a leading cause of pharmacoresistant epilepsy. Up to 30–50% of cases remain MRI-negative on routine visual assessment. MR morphometry improves dysplasia detection; however, conventional approaches are limited to the analysis of T1-weighted images and only two features (gray–white matter blurring and cortical thickness abnormalities).
The aim of the study was to compare the diagnostic efficiency of the conventional T1-based two-feature algorithm and a multimodal multi-feature algorithm incorporating machine learning.
Materials and Methods. The study included four patients with histologically verified FCD (2 patients with type I and 2 patients with type IIB according to the Blümcke classification). All patients underwent MRI using the HARNESS protocol. MR morphometry was performed using two independent algorithms.
The conventional method included FreeSurfer-based assessment of 2 features (the analysis of cortical thickening and gray–white matter boundary blurring) on T1-weighted images.
The multimodal morphometric algorithm incorporated T1/T2-weighted images and FLAIR, and included the following features: gray–white matter transition abnormalities on T1, T2, and T2-FLAIR images (Blurring_T1/T2/FLAIR); detection of local T2- and FLAIR-hyperintense signal abnormalities (CR_T2/FLAIR); assessment of FLAIR intensity distribution heterogeneity to identify blurred boundaries or the transmantle sign (Variance); measurement of signal irregularity on FLAIR images to detect the regions of increased contrast (Entropy). Machine learning models integrating all extracted features were used to generate final FCD prediction maps (MLP and XGBoost).
Results. In two cases, where FCD had typical T1-signs (cortical thickening or well-defined demarcation impairment), both algorithms demonstrated equal diagnostic performance. In the third case, the classical approach failed to detect pathology, since only T1-weighted images were analyzed, whereas the multimodal approach enabled to identify dysplastic areas (only T2/FLAIR images showed the changes). In the fourth case, the conventional algorithm detected a single lesion, while the multimodal algorithm identified an additional second lesion, which influenced the surgical treatment strategy.
Conclusion. Multimodal MR morphometry with artificial intelligence outperforms the conventional approach in complex cases (MRI-negative and multifocal lesions) and should be considered an important supplementary technique in preoperative planning.
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