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

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 alky...

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Autores principales: Le, Nguyen Quoc Khanh, Do, Duyen Thi, Chiu, Fang-Ying, Yapp, Edward Kien Yee, Yeh, Hui-Yuan, Chen, Cheng-Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7563334/
https://www.ncbi.nlm.nih.gov/pubmed/32942564
http://dx.doi.org/10.3390/jpm10030128
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author Le, Nguyen Quoc Khanh
Do, Duyen Thi
Chiu, Fang-Ying
Yapp, Edward Kien Yee
Yeh, Hui-Yuan
Chen, Cheng-Yu
author_facet Le, Nguyen Quoc Khanh
Do, Duyen Thi
Chiu, Fang-Ying
Yapp, Edward Kien Yee
Yeh, Hui-Yuan
Chen, Cheng-Yu
author_sort Le, Nguyen Quoc Khanh
collection PubMed
description 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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spelling pubmed-75633342020-10-27 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 J Pers Med Article 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. MDPI 2020-09-15 /pmc/articles/PMC7563334/ /pubmed/32942564 http://dx.doi.org/10.3390/jpm10030128 Text en © 2020 by 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/).
spellingShingle Article
Le, Nguyen Quoc Khanh
Do, Duyen Thi
Chiu, Fang-Ying
Yapp, Edward Kien Yee
Yeh, Hui-Yuan
Chen, Cheng-Yu
XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma
title XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma
title_full XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma
title_fullStr XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma
title_full_unstemmed XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma
title_short XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma
title_sort xgboost improves classification of mgmt promoter methylation status in idh1 wildtype glioblastoma
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7563334/
https://www.ncbi.nlm.nih.gov/pubmed/32942564
http://dx.doi.org/10.3390/jpm10030128
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