Automated detection of cortical dysplasia type II in MRI-negative epilepsy

Abstract

Objective: To detect automatically focal cortical dysplasia (FCD) type II in patients with extratemporal epilepsy initially diagnosed as MRI-negative on routine inspection of 1.5 and 3.0T scans.

Methods: We implemented an automated classifier relying on surface-based features of FCD morphology and intensity, taking advantage of their covariance. The method was tested on 19 patients (15 with histologically confirmed FCD) scanned at 3.0T, and cross-validated using a leave-one-out strategy. We assessed specificity in 24 healthy controls and 11 disease controls with temporal lobe epilepsy. Cross-dataset classification performance was evaluated in 20 healthy controls and 14 patients with histologically verified FCD examined at 1.5T.

Results: Sensitivity was 74%, with 100% specificity (i.e., no lesions detected in healthy or disease controls). In 50% of cases, a single cluster colocalized with the FCD lesion, while in the remaining cases a median of 1 extralesional cluster was found. Applying the classifier (trained on 3.0T data) to the 1.5T dataset yielded comparable performance (sensitivity 71%, specificity 95%).

Conclusion: In patients initially diagnosed as MRI-negative, our fully automated multivariate approach offered a substantial gain in sensitivity over standard radiologic assessment. The proposed method showed generalizability across cohorts, scanners, and field strengths. Machine learning may assist presurgical decision-making by facilitating hypothesis formulation about the epileptogenic zone.

Classification of evidence: This study provides Class II evidence that automated machine learning of MRI patterns accurately identifies FCD among patients with extratemporal epilepsy initially diagnosed as MRI-negative.

Figure 1. Flow diagram of study design and results

*Classifier trained on 3.0T MRI of patients with histologically proven focal cortical dysplasia (FCD). Engel Ia = completely seizure-free in Engel classification; FN = false-negatives; FP = false-positives; TLE-HS = temporal lobe epilepsy with histologically proven hippocampal sclerosis; TN = true-negatives; TP = true-positives.

Figure 2. Examples of automated focal cortical dysplasia type II detection

The axial T1-weighted MRI sections show the region containing the focal cortical dysplasia (FCD) (dashed square). The magnified panel displays the manually segmented FCD label (dotted green line) and its volume; the label is projected onto the surface template. In these 3 examples, the vertex-wise classification identified several putative lesions (red), whereas the subsequent cluster-wise classification discarded all false-positives except the cluster colocalizing with the manual label (blue). The case number refers to that listed in table 1.

Figure 3. Extralesional findings

Results of the automated classification are projected onto the patient's cortical surface (case 1; see table 1 for details). Z scores of sulcal depth, thickness, and gradient for the lesional (blue) and extralesional (red) clusters are indicated. While the lesional cluster colocalizing with the manually segmented focal cortical dysplasia label (dotted green line on axial T1-weighted MRI) exhibits abnormalities evenly distributed across the different features, the extralesional cluster is mainly characterized by increased sulcal depth. Visual MRI inspection in this region (dashed white circle, frontal operculum) does not reveal any obvious anomaly besides altered sulcal arrangement.