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MRI-Based Radiomics Approach for Differentiating Juvenile Myoclonic Epilepsy from Epilepsy with Generalized Tonic-Clonic Seizures Alone.
Sim, Yongsik; Lee, Seung-Koo; Chu, Min Kyung; Kim, Won-Joo; Heo, Kyoung; Kim, Kyung Min; Sohn, Beomseok.
Afiliação
  • Sim Y; Department of Radiology and Research, Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.
  • Lee SK; Department of Radiology and Research, Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea.
  • Chu MK; Department of Neurology, Epilepsy Research Institute, Yonsei University College of Medicine, Seoul, Korea.
  • Kim WJ; Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
  • Heo K; Department of Neurology, Epilepsy Research Institute, Yonsei University College of Medicine, Seoul, Korea.
  • Kim KM; Department of Neurology, Epilepsy Research Institute, Yonsei University College of Medicine, Seoul, Korea.
  • Sohn B; Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
J Magn Reson Imaging ; 2023 Oct 10.
Article em En | MEDLINE | ID: mdl-37814782
ABSTRACT

BACKGROUND:

The clinical presentation of juvenile myoclonic epilepsy (JME) and epilepsy with generalized tonic-clonic seizures alone (GTCA) is similar, and MRI scans are often perceptually normal in both conditions making them challenging to differentiate.

PURPOSE:

To develop and validate an MRI-based radiomics model to accurately diagnose JME and GTCA, as well as to classify prognostic groups. STUDY TYPE Retrospective. POPULATION 164 patients (127 with JME and 37 with GTCA) patients (age 24.0 ± 9.6; 50% male), divided into training (n = 114) and test (n = 50) sets in a 73 ratio with the same proportion of JME and GTCA patients kept in both sets. FIELD STRENGTH/SEQUENCE 3T; 3D T1-weighted spoiled gradient-echo. ASSESSMENT A total of 17 region-of-interest in the brain were identified as having clinical evidence of association with JME and GTCA, from where 1581 radiomics features were extracted for each subject. Forty-eight machine-learning combinations of oversampling, feature selection, and classification algorithms were explored to develop an optimal radiomics model. The performance of the best radiomics models for diagnosis and for classification of the favorable outcome group were evaluated in the test set. STATISTICAL TESTS Model performance measured using area under the curve (AUC) of receiver operating characteristic (ROC) curve. Shapley additive explanations (SHAP) analysis to estimate the contribution of each radiomics feature.

RESULTS:

The AUC (95% confidence interval) of the best radiomics models for diagnosis and for classification of favorable outcome group were 0.767 (0.591-0.943) and 0.717 (0.563-0.871), respectively. SHAP analysis revealed that the first-order and textural features of the caudate, cerebral white matter, thalamus proper, and putamen had the highest importance in the best radiomics model.

CONCLUSION:

The proposed MRI-based radiomics model demonstrated the potential to diagnose JME and GTCA, as well as to classify prognostic groups. MRI regions associated with JME, such as the basal ganglia, thalamus, and cerebral white matter, appeared to be important for constructing radiomics models. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY Stage 3.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: J Magn Reson Imaging Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: J Magn Reson Imaging Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2023 Tipo de documento: Article