Editorial for 'The nomogram of MRI-based radiomics with complementary visual features by machine learning improves stratification of glioblastoma patients: A multicenter study'

Wang Jinnan

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Editorial for 'The nomogram of MRI-based radiomics with complementary visual features by machine learning improves stratification of glioblastoma patients: A multicenter study'

机译：编辑为“基于MRI的辐射瘤的ROM图，通过机器学习具有互补的视觉特征，提高了胶质母细胞瘤患者的分层：多中心的研究”

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来源
《Journal of magnetic resonance imaging: JMRI》 |2021年第2期|共2页
作者
Wang Jinnan;
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作者单位

Univ Washington Dept Radiol Seattle WA 98195 USA;

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正文语种 eng
中图分类诊断学;
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4. The Nomogram of MRI-based Radiomics with Complementary Visual Features by Machine Learning Improves Stratification of Glioblastoma Patients: A Multicenter Study [J] . Xu Yuyun, He Xiaodong, Li Yumei, Journal of magnetic resonance imaging: JMRI . 2021,第2期

机译：通过机器学习的MRI基射频学基于互补的辐射瘤的墨迹改善了胶质母细胞瘤患者的分层：多中心研究
5. Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T 1 -weighted Contrast-enhanced Imaging [J] . Ying-Zhi Sun, Lin-Feng Yan, Yu Han, BMC Medical Imaging . 2021,第1期

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机译：基于机器学习的辐射瘤，临床和语义特征分析，用于预测胶质母细胞瘤患者的整体存活和MGMT启动子甲基化状态
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Editorial for 'The nomogram of MRI-based radiomics with complementary visual features by machine learning improves stratification of glioblastoma patients: A multicenter study'

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