Automatic analysis of integrated magnetic resonance and positron emission tomography images improves the accuracy of detection of focal cortical dysplasia type IIb lesions

We aimed to develop an efficient and objective pre‐evaluation method to identify the precise location of a focal cortical dysplasia lesion before surgical resection to reduce medication use and decrease the post‐operative frequency of seizure attacks. We developed a novel machine learning‐based appr...

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Bibliographic Details
Published in:	The European journal of neuroscience Vol. 53; no. 9; pp. 3231 - 3241
Main Authors:	Lin, Yaoyun, Mo, Jiajie, Jin, Huiwen, Cao, Xueliang, Zhao, Yang, Wu, Changjun, Zhang, Kai, Hu, Wenhan, Lin, Zhiguo
Format:	Journal Article
Language:	English
Published:	France Wiley Subscription Services, Inc 01-05-2021
Subjects:	Chi-square test Dysplasia FCD Learning algorithms Lesions machine learning Magnetic resonance imaging MRI PET Positron emission tomography quantitative Statistical analysis machine learning MRI FCD quantitative PET
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Summary:	We aimed to develop an efficient and objective pre‐evaluation method to identify the precise location of a focal cortical dysplasia lesion before surgical resection to reduce medication use and decrease the post‐operative frequency of seizure attacks. We developed a novel machine learning‐based approach using cortical surface‐based features by integrating MRI and metabolic PET to identify focal cortical dysplasia lesions. Significant surface‐based features of 22 patients with histopathologically proven FCD IIb lesions were extracted from PET and MRI images using FreeSurfer. We modified significant parameters, trained and tested the XGBoost model using these surface‐based features, and made predictions. We detected lesions in all 20 patients using the XGBoost model, with an accuracy of 91%. We used one‐way chi‐squared test to test the null hypothesis that the population proportion was 50% (p = 0.0001), indicating that our classification of the algorithm was statistically significant. The sensitivity, specificity, and false‐positive rates were 93%, 91%, and 9%, respectively. We developed an objective, quantitative XGBoost classifier that combined MRI and PET imaging features to locate focal cortical dysplasia. This automated method yielded better outcomes than conventional visual analysis and single modality quantitative analysis for surgical pre‐evaluation, especially in subtle or visually unidentifiable FCD lesions. This time‐efficient method would also help doctors identify otherwise overlooked details. We developed a novel machine learning‐based approach using cortical surface‐based features by integrating MRI and metabolic PET to identify focal cortical dysplasia lesions. We selected the XGBoost model, modified significant parameters, trained, and tested the model using these surface‐based features, and made predictions. This automated method yielded better outcomes than the conventional visual and single modality quantitative analysis methods for surgical pre‐evaluation, especially in subtle or visually unidentifiable FCD lesions.
Bibliography:	Edited by Yoland Smith Wu, Zhang and Hu have the same contributions. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0953-816X 1460-9568
DOI:	10.1111/ejn.15185