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Radiogenomic analysis of PTEN mutation in glioblastoma using preoperative multi-parametric magnetic resonance imaging

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收录情况： ◇ SCIE

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机构： [1]Beijing Neurosurgical Institute, Capital Medical University, Beijing, China [2]Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 6 Tiantanxili, Beijing 100050, China [3]Institute of Automation, Chinese Academy of Sciences, Beijing, China [4]Neurological Imaging Center, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China [5]Chinese Glioma Genome Atlas Network (CGGA) and Asian Glioma Genome Atlas Network (AGGA), Beijing, China [6]China National Clinical Research Center for Neurological Diseases, Beijing, China

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关键词： Glioblastoma Machine learning Phosphatase and tensin homolog (PTEN) Radiogenomics

摘要：

Purpose: PTEN mutation status is a pivotal biomarker for glioblastoma. This study aimed to establish a radiomic signature to predict PTEN mutation status in patients with glioblastoma, and to investigate the genetic background behind this radiomic signature. Methods: In this study, a total of 862 radiomic features were extracted from each patient. The training (n = 69) and validation (n = 40) sets were retrospectively collected from the Cancer Genome Atlas and the Chinese Glioma Genome Atlas, respectively. The minimum redundancy maximum relevance (mRMR) algorithm was used to select the best predictive features of PTEN status. A machine learning model was then built with the selected features using a support vector machine classifier. The predictive performance of each selected feature and the complete model were evaluated via the area under the curve from receiver operating characteristic analysis in both the training and validation sets. The genetic background underlying the radiomic signature was determined using radiogenomic analysis. Results: Six features were selected using the mRMR algorithm, including two features derived from contrast-enhanced images and four features derived from T2-weighted images. The predictive performance of the machine learning model for the training and validation sets were 0.925 and 0.787, respectively, which were better than the individual features. Radiogenomics analysis revealed that the PTEN-associated biological processes could be described using the radiomic signature. Conclusion: These results show that radiomic features derived from preoperative MRI can predict PTEN mutation status in glioblastoma patients, thus providing a novel noninvasive imaging biomarker. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

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出版当年[2018]版：

大类 | 3 区医学

小类 | 3 区核医学 4 区临床神经病学 4 区神经成像

最新[2023]版：

大类 | 3 区医学

小类 | 3 区临床神经病学 3 区神经成像 3 区核医学

JCR分区：

出版当年[2017]版：

Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Q3 CLINICAL NEUROLOGY Q3 NEUROIMAGING

最新[2023]版：

Q2 CLINICAL NEUROLOGY Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Q2 NEUROIMAGING

影响因子： 2.4 最新[2023版] 2.6 最新五年平均 2.346 出版当年[2017版] 2.533 出版当年五年平均 2.093 出版前一年[2016版] 2.504 出版后一年[2018版]

第一作者：

第一作者机构： [1]Beijing Neurosurgical Institute, Capital Medical University, Beijing, China

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通讯作者：

通讯机构： [2]Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, 6 Tiantanxili, Beijing 100050, China

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