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Open AccessArticle

XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma

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Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei City 106, Taiwan
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Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei City 106, Taiwan
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Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City 70000, Vietnam
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Singapore Institute of Manufacturing Technology, 2 Fusionopolis Way, #08-04, Innovis, Singapore 138634, Singapore
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Medical Humanities Research Cluster, School of Humanities, Nanyang Technological University, 48 Nanyang Ave, Singapore 639798, Singapore
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Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
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Department of Medical Imaging, Taipei Medical University Hospital, Taipei 11031, Taiwan
*
Authors to whom correspondence should be addressed.
J. Pers. Med. 2020, 10(3), 128; https://doi.org/10.3390/jpm10030128
Received: 22 August 2020 / Revised: 3 September 2020 / Accepted: 9 September 2020 / Published: 15 September 2020
(This article belongs to the Special Issue Biomedical Imaging and Cancers)
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. View Full-Text
Keywords: radiogenomics; glioblastoma; IDH1 wildtype; O6-methylguanine-DNA methyltransferase; XGBoost; machine learning; F-score feature selection; molecular subtype; concomitant adjuvant temozolomide; noninvasive imaging biomarker radiogenomics; glioblastoma; IDH1 wildtype; O6-methylguanine-DNA methyltransferase; XGBoost; machine learning; F-score feature selection; molecular subtype; concomitant adjuvant temozolomide; noninvasive imaging biomarker
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MDPI and ACS Style

Le, N.Q.K.; Do, D.T.; Chiu, F.-Y.; Yapp, E.K.Y.; Yeh, H.-Y.; Chen, C.-Y. XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma. J. Pers. Med. 2020, 10, 128.

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