机构：[a]Department of Neuro-oncology, Neurosurgery Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China重点科室诊疗科室神经外科神经外科首都医科大学附属天坛医院[b]Beijing Neurosurgical Institute, Capital Medical University, Beijing, China研究所北京市神经外科研究所首都医科大学附属天坛医院[c]Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China重点科室诊疗科室神经外科神经外科首都医科大学附属天坛医院[d]Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China医技科室核医学科首都医科大学附属天坛医院[e]Department of Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China重点科室医技科室研究所放射科放射科北京市神经外科研究所首都医科大学附属天坛医院[f]Department of Neurosurgery, The 309th Hospital of Chinese People's Liberation Army, Beijing, China
This study aimed to identify the optimal radiomic machine-learning classifier for differentiating glioblastoma (GBM) from solitary brain metastases (MET) preoperatively. Four hundred and twelve patients with solitary brain tumors (242 GBM and 170 solitary brain MET) were divided into training (n = 227) and test (n = 185) cohorts. Radiomic features extraction was performed with PyRadiomics software. In the training cohort, twelve feature selection methods and seven classification methods were evaluated to construct favorable radiomic machine-learning classifiers. The performance of the classifiers was evaluated using the mean area under the curve (AUC) and relative standard deviation in percentile (RSD). In the training cohort, thirteen classifiers had favorable predictive performances (AUC >= 0.95 and RSD <= 6). In the test cohort, receiver operating characteristic (ROC) curve analysis revealed that support vector machines (SVM) + least absolute shrinkage and selection operator (LASSO) (AUC, 0.90) classifiers had the highest prediction efficacy. Furthermore, the clinical performance of the best classifier was superior to neuroradiologists in accuracy, sensitivity, and specificity. In conclusion, employing radiomic machine-learning technology could help neuroradiologist in differentiating GBM from solitary brain MET preoperatively.
基金：
National Natural Science Foundation of ChinaNational Natural Science Foundation of China [81773208]; Key laboratory of functional and clinical translational medicine, Fujian province university [JNYLC1808]; Beijing Nova ProgramBeijing Municipal Science & Technology Commission [Z16110004916082]
第一作者机构：[a]Department of Neuro-oncology, Neurosurgery Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
共同第一作者：
通讯作者：
通讯机构：[a]Department of Neuro-oncology, Neurosurgery Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China[*1]Beijing Tiantan Hospital, Department of Neurosurgery, 6 Tiantanxili, Beijing, 100050, China.[*2]Beijing Tiantan Hospital, Capital Medical University, 6 Tiantanxili, Beijing, 100050, China.
推荐引用方式(GB/T 7714)：
Zenghui Qian,Yiming Li,Yongzhi Wang,et al.Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers[J].CANCER LETTERS.2019,451:128-135.doi:10.1016/j.canlet.2019.02.054.
APA：
Zenghui Qian,Yiming Li,Yongzhi Wang,Lianwang Li,Runting Li...&Wenbin Li.(2019).Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers.CANCER LETTERS,451,
MLA：
Zenghui Qian,et al."Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers".CANCER LETTERS 451.(2019):128-135
医学