Suppr超能文献

XGBoost算法改善了异柠檬酸脱氢酶1(IDH1)野生型胶质母细胞瘤中O6-甲基鸟嘌呤-DNA甲基转移酶(MGMT)启动子甲基化状态的分类。

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

作者信息

Le Nguyen Quoc Khanh, Do Duyen Thi, Chiu Fang-Ying, Yapp Edward Kien Yee, Yeh Hui-Yuan, Chen Cheng-Yu

机构信息

Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei City 106, Taiwan.

Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei City 106, Taiwan.

出版信息

J Pers Med. 2020 Sep 15;10(3):128. doi: 10.3390/jpm10030128.

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.

摘要

约96%的胶质母细胞瘤(GBM)患者患有异柠檬酸脱氢酶1(IDH1)野生型GBM,其预后极差,部分原因是对标准替莫唑胺治疗耐药。O6-甲基鸟嘌呤-DNA甲基转移酶(MGMT)启动子甲基化状态是GBM患者对烷化剂化疗耐药的关键预后生物标志物。然而,MGMT甲基化状态的鉴定方法需要对肿瘤组织进行采样,既耗时又昂贵。目前,术前非侵入性成像方法用于识别生物标志物以预测MGMT甲基化状态。我们评估了一种基于影像组学的新型极端梯度提升(XGBoost)模型,以识别IDH1野生型GBM患者的MGMT启动子甲基化状态。这项回顾性研究纳入了53例经病理证实的GBM患者,并检测了MGMT甲基化和IDH1状态。从多模态磁共振成像(MRI)中提取影像组学特征,并通过F分数分析进行测试,以识别重要特征来改进我们的模型。我们确定了九个影像组学特征,其曲线下面积达到0.896,优于先前报道的其他分类器。这些特征可能是识别IDH1野生型GBM中MGMT甲基化状态的重要生物标志物。影像组学特征提取和F核心特征选择的结合显著提高了XGBoost模型的性能,这可能对GBM患者的分层和治疗策略具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ca1f/7563334/7f0e9380d2d7/jpm-10-00128-g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验