AUTHOR=Liao Kai , Wu Huanhua , Jiang Yuanfang , Dong Chenchen , Zhou Hailing , Wu Biao , Tang Yongjin , Gong Jian , Ye Weijian , Hu Youzhu , Guo Qiang , Xu Hao 

TITLE=Machine learning techniques based on 18F-FDG PET radiomics features of temporal regions for the classification of temporal lobe epilepsy patients from healthy controls

JOURNAL=Frontiers in Neurology

VOLUME=15

YEAR=2024

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2024.1377538

DOI=10.3389/fneur.2024.1377538

ISSN=1664-2295

ABSTRACT=<sec><title>Background</title><p>This study aimed to investigate the clinical application of <sup>18</sup>F-FDG PET radiomics features for temporal lobe epilepsy and to create PET radiomics-based machine learning models for differentiating temporal lobe epilepsy (TLE) patients from healthy controls.</p></sec><sec><title>Methods</title><p>A total of 347 subjects who underwent <sup>18</sup>F-FDG PET scans from March 2014 to January 2020 (234 TLE patients: 25.50 ± 8.89 years, 141 male patients and 93 female patients; and 113 controls: 27.59 ± 6.94 years, 48 male individuals and 65 female individuals) were allocated to the training (<italic>n</italic> = 248) and test (<italic>n</italic> = 99) sets. All 3D PET images were registered to the Montreal Neurological Institute template. PyRadiomics was used to extract radiomics features from the temporal regions segmented according to the Automated Anatomical Labeling (AAL) atlas. The least absolute shrinkage and selection operator (LASSO) and Boruta algorithms were applied to select the radiomics features significantly associated with TLE. Eleven machine-learning algorithms were used to establish models and to select the best model in the training set.</p></sec><sec><title>Results</title><p>The final radiomics features (<italic>n</italic> = 7) used for model training were selected through the combinations of the LASSO and the Boruta algorithms with cross-validation. All data were randomly divided into a training set (<italic>n</italic> = 248) and a testing set (<italic>n</italic> = 99). Among 11 machine-learning algorithms, the logistic regression (AUC 0.984, F1-Score 0.959) model performed the best in the training set. Then, we deployed the corresponding online website version (<ext-link ext-link-type="uri" xlink:href="https://wane199.shinyapps.io/TLE_Classification/" xmlns:xlink="http://www.w3.org/1999/xlink">https://wane199.shinyapps.io/TLE_Classification/</ext-link>), showing the details of the LR model for convenience. The AUCs of the tuned logistic regression model in the training and test sets were 0.981 and 0.957, respectively. Furthermore, the calibration curves demonstrated satisfactory alignment (visually assessed) for identifying the TLE patients.</p></sec><sec><title>Conclusion</title><p>The radiomics model from temporal regions can be a potential method for distinguishing TLE. Machine learning-based diagnosis of TLE from preoperative FDG PET images could serve as a useful preoperative diagnostic tool.</p></sec>