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Radiomics features of hippocampal regions in magnetic resonance imaging can differentiate medial temporal lobe epilepsy patients from healthy controls.


ABSTRACT: To investigative whether radiomics features in bilateral hippocampi from MRI can identify temporal lobe epilepsy (TLE). A total of 131 subjects with MRI (66 TLE patients [35 right and 31 left TLE] and 65 healthy controls [HC]) were allocated to training (n?=?90) and test (n?=?41) sets. Radiomics features (n?=?186) from the bilateral hippocampi were extracted from T1-weighted images. After feature selection, machine learning models were trained. The performance of the classifier was validated in the test set to differentiate TLE from HC and ipsilateral TLE from HC. Identical processes were performed to differentiate right TLE from HC (training set, n?=?69; test set; n?=?31) and left TLE from HC (training set, n?=?66; test set, n?=?30). The best-performing model for identifying TLE showed an AUC, accuracy, sensitivity, and specificity of 0.848, 84.8%, 76.2%, and 75.0% in the test set, respectively. The best-performing radiomics models for identifying right TLE and left TLE subgroups showed AUCs of 0.845 and 0.840 in the test set, respectively. In addition, multiple radiomics features significantly correlated with neuropsychological test scores (false discovery rate-corrected p-values?

SUBMITTER: Park YW 

PROVIDER: S-EPMC7658973 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Radiomics features of hippocampal regions in magnetic resonance imaging can differentiate medial temporal lobe epilepsy patients from healthy controls.

Park Yae Won YW   Choi Yun Seo YS   Kim Song E SE   Choi Dongmin D   Han Kyunghwa K   Kim Hwiyoung H   Ahn Sung Soo SS   Kim Sol-Ah SA   Kim Hyeon Jin HJ   Lee Seung-Koo SK   Lee Hyang Woon HW  

Scientific reports 20201111 1


To investigative whether radiomics features in bilateral hippocampi from MRI can identify temporal lobe epilepsy (TLE). A total of 131 subjects with MRI (66 TLE patients [35 right and 31 left TLE] and 65 healthy controls [HC]) were allocated to training (n = 90) and test (n = 41) sets. Radiomics features (n = 186) from the bilateral hippocampi were extracted from T1-weighted images. After feature selection, machine learning models were trained. The performance of the classifier was validated in  ...[more]

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