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XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma.
Le, Nguyen Quoc Khanh; Do, Duyen Thi; Chiu, Fang-Ying; Yapp, Edward Kien Yee; Yeh, Hui-Yuan; Chen, Cheng-Yu.
Afiliação
  • Le NQK; Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei City 106, Taiwan.
  • Do DT; Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei City 106, Taiwan.
  • Chiu FY; Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City 70000, Vietnam.
  • Yapp EKY; Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei City 106, Taiwan.
  • Yeh HY; Singapore Institute of Manufacturing Technology, 2 Fusionopolis Way, #08-04, Innovis, Singapore 138634, Singapore.
  • Chen CY; Medical Humanities Research Cluster, School of Humanities, Nanyang Technological University, 48 Nanyang Ave, Singapore 639798, Singapore.
J Pers Med ; 10(3)2020 Sep 15.
Article em En | MEDLINE | ID: mdl-32942564
ABSTRACT
Approximately 96% of patients with glioblastomas (GBM) have IDH1 wildtype GBMs, characterized by extremely poor prognosis, partly due to resistance to standard temozolomide treatment. O6-Methylguanine-DNA methyltransferase (MGMT) promoter methylation status is a crucial prognostic biomarker for alkylating chemotherapy resistance in patients with GBM. However, MGMT methylation status identification methods, where the tumor tissue is often undersampled, are time consuming and expensive. Currently, presurgical noninvasive imaging methods are used to identify biomarkers to predict MGMT methylation status. We evaluated a novel radiomics-based eXtreme Gradient Boosting (XGBoost) model to identify MGMT promoter methylation status in patients with IDH1 wildtype GBM. This retrospective study enrolled 53 patients with pathologically proven GBM and tested MGMT methylation and IDH1 status. Radiomics features were extracted from multimodality MRI and tested by F-score analysis to identify important features to improve our model. We identified nine radiomics features that reached an area under the curve of 0.896, which outperformed other classifiers reported previously. These features could be important biomarkers for identifying MGMT methylation status in IDH1 wildtype GBM. The combination of radiomics feature extraction and F-core feature selection significantly improved the performance of the XGBoost model, which may have implications for patient stratification and therapeutic strategy in GBM.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Pers Med Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Taiwan

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Pers Med Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Taiwan