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

被引:74
|
作者
Le, Nguyen Quoc Khanh [1 ,2 ]
Do, Duyen Thi [3 ]
Chiu, Fang-Ying [2 ]
Yapp, Edward Kien Yee [4 ]
Yeh, Hui-Yuan [5 ]
Chen, Cheng-Yu [1 ,2 ,6 ,7 ]
机构
[1] Taipei Med Univ, Coll Med, Profess Master Program Artificial Intelligence Me, Taipei 106, Taiwan
[2] Taipei Med Univ, Res Ctr Artificial Intelligence Med, Taipei 106, Taiwan
[3] Ton Duc Thang Univ, Fac Appl Sci, Ho Chi Minh City 70000, Vietnam
[4] Singapore Inst Mfg Technol, 2 Fusionopolis Way,08-04, Singapore 138634, Singapore
[5] Nanyang Technol Univ, Sch Humanities, Med Humanities Res Cluster, 48 Nanyang Ave, Singapore 639798, Singapore
[6] Taipei Med Univ, Coll Med, Sch Med, Dept Radiol, Taipei 11031, Taiwan
[7] Taipei Med Univ Hosp, Dept Med Imaging, Taipei 11031, Taiwan
来源
JOURNAL OF PERSONALIZED MEDICINE | 2020年 / 10卷 / 03期
关键词
radiogenomics; glioblastoma; IDH1; wildtype; O6-methylguanine-DNA methyltransferase; XGBoost; machine learning; F-score feature selection; molecular subtype; concomitant adjuvant temozolomide; noninvasive imaging biomarker; POTENTIAL BIOMARKER; RADIOMICS SIGNATURE; PREDICTION; TUMOR; SEGMENTATION; GLIOMA;
D O I
10.3390/jpm10030128
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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.
引用
收藏
页码:1 / 13
页数:13
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