Surfaced‐based detection of focal cortical dysplasia using magnetic resonance fingerprinting and machine learning

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Abstract
Objective: This study was undertaken to develop a framework for focal cortical dysplasia (FCD) detection using surface‐based morphometric (SBM) analysis and machine learning (ML) applied to three‐dimensional (3D) magnetic resonance fingerprinting (MRF). Methods: We included 114 subjects (44 patients with medically intractable focal epilepsy and FCD, 70 healthy controls [HCs]). All subjects underwent high‐resolution 3‐T MRF scans generating T1 and T2 maps. All patients had clinical T1‐weighted (T1w) images; 35 also had 3D fluid‐attenuated inversion recovery (FLAIR). A 3D region of interest (ROI) was manually created for each lesion. All maps/images and lesion ROIs were registered to T1w images. Surface‐based features were extracted following the Multi‐center Epilepsy Lesion Detection pipeline. Features were normalized using intrasubject, interhemispheric, and intersubject z ‐scoring. A two‐stage ML approach was applied: a vertexwise neural network classifier for lesional versus normal vertices using T1w/MRF/FLAIR features, followed by a clusterwise Random Undersampling Boosting classifier to suppress false positives (FPs) based on cluster size, prediction probabilities, and feature statistics. Leave‐one‐out cross‐validation was performed at both stages. Results: Using T1w features, sensitivity was 70.4% with 11.6 FP clusters/patient and 4.1 in HCs. Adding MRF reduced FPs to 6.6 clusters/patient and 1.5 in HCs, with 68.2% sensitivity. Combining T1w, MRF, and FLAIR achieved 71.4% sensitivity, with 4.7 FPs/patient and 1.1 in HCs. Detection probabilities were significantly higher for true positive clusters than FPs ( p < .001). Type II showed higher detection rates than non‐type II. Magnetic resonance imaging (MRI)‐positive patients showed higher detection rates and fewer FPs than MRI‐negative patients. Seizure‐free patients demonstrated higher detection rates than non‐seizure‐free patients. Subtyping accuracy was 80.8% for non‐type II versus type II, and 68.4% for IIa versus IIb, although limited by small sample size. The transmantle sign was present in 61.5% of IIb and 40% of IIa cases. Significance: We developed an ML framework for FCD detection integrating SBM with clinical MRI and MRF. Advances include improved FP control and enhanced subtyping; selected model outputs may provide indicators of detection confidence and seizure outcome.
Funding Information
  • National Institute of Neurological Disorders and Stroke (2R01 NS109439)
  • National Institute of Neurological Disorders and Stroke (R01 NS109439)
  • Rosetrees Trust
  • Wellcome Trust

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