Dynamic contrast-enhanced MRI radiomics model predicts epidermal growth factor receptor amplification in glioblastoma, IDH-wildtype

Beomseok Sohn, Kisung Park, Sung Soo Ahn, Yae Won Park, Seung Hong Choi, Seok Gu Kang, Se Hoon Kim, Jong Hee Chang, Seung Koo Lee

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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 7:3. 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.

Original languageEnglish
Pages (from-to)341-351
Number of pages11
JournalJournal of Neuro-Oncology
Volume164
Issue number2
DOIs
Publication statusPublished - 2023 Sept

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Oncology
  • Neurology
  • Clinical Neurology
  • Cancer Research

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