Automatic Detection of Focal Cortical Dysplasia Type II in MRI: Is the Application of Surface-Based Morphometry and Machine Learning Promising?

Abstract

Background and ObjectivesFocal cortical dysplasia (FCD) is a type of malformations of cortical development and one of the leading causes of drug-resistant epilepsy. Postoperative results improve the diagnosis of lesions on structural MRIs. Advances in quantitative algorithms have increased the identification of FCD lesions. However, due to significant differences in size, shape, and location of the lesion in different patients and a big deal of time for the objective diagnosis of lesion as well as the dependence of individual interpretation, sensitive approaches are required to address the challenge of lesion diagnosis. In this research, a FCD computer-aided diagnostic system to improve existing methods is presented.MethodsMagnetic resonance imaging (MRI) data were collected from 58 participants (30 with histologically confirmed FCD type II and 28 without a record of any neurological prognosis). Morphological and intensity-based features were calculated for each cortical surface and inserted into an artificial neural network. Statistical examinations evaluated classifier efficiency.ResultsNeural network evaluation metrics—sensitivity, specificity, and accuracy—were 96.7, 100, and 98.6%, respectively. Furthermore, the accuracy of the classifier for the detection of the lobe and hemisphere of the brain, where the FCD lesion is located, was 84.2 and 77.3%, respectively.ConclusionAnalyzing surface-based features by automated machine learning can give a quantitative and objective diagnosis of FCD lesions in presurgical assessment and improve postsurgical outcomes.

An Assessment of the Pathological Classification and Postoperative Outcome of Focal Cortical Dysplasia by Simultaneous Hybrid PET/MRI

“…There is an agreement that the type of FCD determines whether the lesion can be seen on MRI: MRI can show type II lesions as long as there is a sufficiently clear image and postprocessing is completed [ 3 ]. One study found that automatic machine learning analysis of surface-based features could provide a quantitative and objective diagnosis of type II FCD lesions during preoperative evaluation and improve postoperative outcomes [ 32 ]. In contrast, cortical cell density in FCD I patients changed only when the tissue was disordered, which was difficult to detect on MRI [ 20 ].…”

Artificial intelligence for the detection of focal cortical dysplasia: Challenges in translating algorithms into clinical practice

“…The output is thus generated on the voxel level, 24,46,[71][72][73] vertex level, 13,18,35,40,70 or patch level. 16,34,69,74 Figure 2 shows examples of the outputs from Multi-centre Epilepsy Lesion Detection (MELD), 18 Morphometric Analysis Program v2018 (MAP18), 24 and deepFCD 16 models.…”

Detecting Cortical Thickness Changes in Epileptogenic Lesions Using Machine Learning

“…ML methods can provide a major FCD diagnosis outcome as a presurgical assessment for drug-resistant epilepsy patients. Zohera et al [ 45 ] applied artificial neural networks after extracting morphological and intensity-based features. They used a total of 58 patients: 30 with verified FCD type II and 28 adults as healthy controls.…”