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Dynamic contrast-enhanced MRI radiomics model predicts epidermal growth factor receptor amplification in glioblastoma, IDH-wildtype.
Sohn, Beomseok; Park, Kisung; Ahn, Sung Soo; Park, Yae Won; Choi, Seung Hong; Kang, Seok-Gu; Kim, Se Hoon; Chang, Jong Hee; Lee, Seung-Koo.
Afiliación
  • Sohn B; Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea.
  • Park K; Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
  • Ahn SS; Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea.
  • Park YW; Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, South Korea.
  • Choi SH; Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea. SUNGSOO@yuhs.ac.
  • Kang SG; Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea.
  • Kim SH; Department of Radiology, Seoul National University Hospital, Seoul, South Korea.
  • Chang JH; Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea.
  • Lee SK; Department of Pathology, Yonsei University College of Medicine, Seoul, South Korea.
J Neurooncol ; 164(2): 341-351, 2023 Sep.
Article en En | MEDLINE | ID: mdl-37689596
ABSTRACT

PURPOSE:

To develop and validate a dynamic contrast-enhanced (DCE) MRI-based radiomics model to predict epidermal growth factor receptor (EGFR) amplification in patients with glioblastoma, isocitrate dehydrogenase (IDH) wildtype.

METHODS:

Patients with pathologically confirmed glioblastoma, IDH wildtype, from January 2015 to December 2020, with an EGFR amplification status, were included. Patients who did not undergo DCE or conventional brain MRI were excluded. Patients were categorized into training and test sets by a ratio of 73. DCE MRI data were used to generate volume transfer constant (Ktrans) and extracellular volume fraction (Ve) maps. Ktrans, Ve, and conventional MRI were then used to extract the radiomics features, from which the prediction models for EGFR amplification status were developed and validated.

RESULTS:

A total of 190 patients (mean age, 59.9; male, 55.3%), divided into training (n = 133) and test (n = 57) sets, were enrolled. In the test set, the radiomics model using the Ktrans map exhibited the highest area under the receiver operating characteristic curve (AUROC), 0.80 (95% confidence interval [CI], 0.65-0.95). The AUROC for the Ve map-based and conventional MRI-based models were 0.74 (95% CI, 0.58-0.90) and 0.76 (95% CI, 0.61-0.91).

CONCLUSION:

The DCE MRI-based radiomics model that predicts EGFR amplification in glioblastoma, IDH wildtype, was developed and validated. The MRI-based radiomics model using the Ktrans map has higher AUROC than conventional MRI.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas / Glioblastoma Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans / Male / Middle aged Idioma: En Revista: J Neurooncol Año: 2023 Tipo del documento: Article País de afiliación: Corea del Sur

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas / Glioblastoma Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans / Male / Middle aged Idioma: En Revista: J Neurooncol Año: 2023 Tipo del documento: Article País de afiliación: Corea del Sur