Abstract:
BACKGROUND:Accurately diagnosing schizophrenia is still challenging due to the lack of validated biomarkers. Here, we aimed to investigate whether radiomic features in bilateral hippocampal subfields from magnetic resonance images (MRIs) can differentiate patients with schizophrenia from healthy controls (HCs). METHODS:A total of 152 participants with MRI (86 schizophrenia and 66 HCs) were allocated to training (n = 106) and test (n = 46) sets. Radiomic features (n = 642) from the bilateral hippocampal subfields processed with automatic segmentation techniques were extracted from T1-weighted MRIs. After feature selection, various combinations of classifiers (logistic regression, extra-trees, AdaBoost, XGBoost, or support vector machine) and subsampling were trained. The performance of the classifier was validated in the test set by determining the area under the curve (AUC). Furthermore, the association between selected radiomic features and clinical symptoms in schizophrenia was assessed. RESULTS:Thirty radiomic features were identified to differentiate participants with schizophrenia from HCs. In the training set, the AUC exhibited poor to good performance (range: 0.683-0.861). The best performing radiomics model in the test set was achieved by the mutual information feature selection and logistic regression with an AUC, accuracy, sensitivity, and specificity of 0.821 (95% confidence interval 0.681-0.961), 82.1%, 76.9%, and 70%, respectively. Greater maximum values in the left cornu ammonis 1-3 subfield were associated with a higher severity of positive symptoms and general psychopathology in participants with schizophrenia. CONCLUSION:Radiomic features from hippocampal subfields may be useful biomarkers for identifying schizophrenia.
journal_name
Schizophr Resjournal_title
Schizophrenia researchauthors
Park YW,Choi D,Lee J,Ahn SS,Lee SK,Lee SH,Bang Mdoi
10.1016/j.schres.2020.09.009subject
Has Abstractpub_date
2020-09-01 00:00:00pages
337-344eissn
0920-9964issn
1573-2509pii
S0920-9964(20)30467-9journal_volume
223pub_type
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