Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145898
Title: XGBoost improves classification of MGMT promoter methylation status in IDH1 wildtype glioblastoma
Authors: Le, Nguyen Quoc Khanh
Do, Duyen Thi
Chiu, Fang-Ying
Yapp, Edward Kien Yee
Yeh, Hui-Yuan
Chen, Cheng-Yu
Keywords: Science::Medicine
Issue Date: 2020
Source: Le, N. Q. K., Do, D. T., Chiu, F.-Y., Yapp, E. K. Y., Yeh, H.-Y., & Chen, C.-Y. (2020). XGBoost improves classification of MGMT promoter methylation status in IDH1 wildtype glioblastoma. Journal of Personalized Medicine, 10(3), 128-. doi:10.3390/jpm10030128
Journal: Journal of Personalized Medicine
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.
URI: https://hdl.handle.net/10356/145898
ISSN: 2075-4426
DOI: 10.3390/jpm10030128
Rights: © 2020 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
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