Magnetic resonance imaging (MRI)-based intratumoral and peritumoral radiomics for prognosis prediction in glioma patients
文献类型：期刊论文
| 作者 | Gao, M.1; Cheng, J.2,3; Qiu, A.4,5; Zhao, D.6; Wang, J.2; Liu, J.1,7 |
| 刊名 | CLINICAL RADIOLOGY
 |
| 出版日期 | 2024-11-01 |
| 卷号 | 79期号:11页码:e1383-e1393 |
| ISSN号 | 0009-9260 |
| DOI | 10.1016/j.crad.2024.08.005 |
| 英文摘要 | AIM: The purpose of this study was to identify robust radiological features from intratumoral and peritumoral regions, evaluate MRI protocols, and machine learning methods for overall survival stratification of glioma patients, and explore the relationship between radiological features and the tumour microenvironment. MATERIAL AND METHODS: A retrospective analysis was conducted on 163 glioma patients, divided into a training set (n=113) and a testing set (n=50). For each patient, 2135 features were extracted from clinical MRI. Feature selection was performed using the Minimum Redundancy Maximum Relevance method and the Random Forest (RF) algorithm. Prognostic factors were assessed using the Cox proportional hazards model. Four machine learning models (RF, Logistic Regression, Support Vector Machine, and XGBoost) were trained on clinical and radiological features from tumour and peritumoral regions. Model evaluations on the testing set used receiver operating characteristic curves. RESULTS: Among the 163 patients, 96 had an overall survival (OS) of less than three years postsurgery, while 67 had an OS of more than three years. Univariate Cox regression in the validation set indicated that age (p=0.0 03) and tumour grade (p<0.001) were positively associated with the risk of death within three years postsurgery. The final predictive model incorporated 13 radiological and 7 clinical features. The RF model, combining intratumor and peritumor radiomics, achieved the best predictive performance (AUC = 0.91; ACC = 0.86), outperforming single-region models. |
| 资助项目 | Graduate School-Enterprise Joint Innovation Program of Central South Uni-versity[2023XQLH142] ; National Natural Science Foundation of China[62302119] ; Research Project of Postgraduate Education and Teaching Reform of Central South University[2021JGB147] ; Key R&D Project of Science and Technology Department of Hunan Province[2022SK2047] |
| WOS研究方向 | Radiology, Nuclear Medicine & Medical Imaging |
| 语种 | 英语 |
| WOS记录号 | WOS:001338893400001 |
| 出版者 | W B SAUNDERS CO LTD |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/39501]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Zhao, D.; Wang, J.; Liu, J. |
| 作者单位 | 1.Cent South Univ, Dept Radiol, Xiangya Hosp 2, Changsha, Peoples R China 2.Cent South Univ, Sch Comp Sci & Engn, Hunan Prov Key Lab Bioinformat, Changsha, Peoples R China 3.Inst Guizhou Aerosp Measuring & Testing Technol, Guiyang, Peoples R China 4.Johns Hopkins Univ, Dept Biomed Engn, Baltimore, MD USA 5.Hong Kong Polytech Univ, Dept Hlth Technol & Informat, Hong Kong, Peoples R China 6.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 7.Dept Radiol Qual Control Ctr, Changsha, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, M.,Cheng, J.,Qiu, A.,et al. Magnetic resonance imaging (MRI)-based intratumoral and peritumoral radiomics for prognosis prediction in glioma patients[J]. CLINICAL RADIOLOGY,2024,79(11):e1383-e1393. |
| APA | Gao, M.,Cheng, J.,Qiu, A.,Zhao, D.,Wang, J.,&Liu, J..(2024).Magnetic resonance imaging (MRI)-based intratumoral and peritumoral radiomics for prognosis prediction in glioma patients.CLINICAL RADIOLOGY,79(11),e1383-e1393. |
| MLA | Gao, M.,et al."Magnetic resonance imaging (MRI)-based intratumoral and peritumoral radiomics for prognosis prediction in glioma patients".CLINICAL RADIOLOGY 79.11(2024):e1383-e1393. |
入库方式： OAI收割
来源：计算技术研究所
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