Multiregional radiomics phenotypes at MR imaging predict MGMT promoter methylation in Glioblastoma
文献类型：会议论文
| 作者 | Hongmin Bai; Zhi-Cheng Li; Yinsheng Chen; Qiuchang Sun; Qihua Li; Lei Liu; Ronghui Luo; Chaofeng Liang |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | 布拉格 |
| 英文摘要 | This study aimed to build a reliable radiomics model from magnetic resonance imaging (MRI) for pretreatment prediction of MGMT methylation status in Glioblastoma. High-throughput radiomics features were automatically extracted from multiparametric MRI, including location features, geometry features, intensity features and texture features. A machine learning method was used to select a minimal set of all-relevant features. Based on these selected features, a radiomics model were built by using a random forest classifier for MGMT methylation prediction from a primary cohort (133 patients) and tested on an independent validation cohort (60 patients). Predictive models combing radiomics features and clinical factors were built and evaluated. The radiomics model with 6 all-relevant features allowed pretreatment prediction of MGMT methylation (AUC = 0.88, accuracy = 80%). Combing clinical factors with radiomics features did not benefit the prediction performance. The proposed radiomics model could provide a tool to guide preoperative patient care and made a step forward radiomics-based precision medicine for GBM patients. |
| URL标识 | 查看原文 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14587]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 推荐引用方式 GB/T 7714 | Hongmin Bai,Zhi-Cheng Li,Yinsheng Chen,et al. Multiregional radiomics phenotypes at MR imaging predict MGMT promoter methylation in Glioblastoma[C]. 见:. 布拉格. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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