Noninvasive Prediction of IDH1 Mutation and ATRX Expression Loss in Low‐Grade Gliomas Using Multiparametric MR Radiomic Features
暂无分享,去创建一个
Hongbing Lu | Hong Chen | Xi Zhang | Yan Ren | Wenting Rui | Haopeng Pang | T. Qiu | Jing Wang | Qian Xie | T. Jin | Hua Zhang | Yong Zhang | Zhenwei Yao | Junhai Zhang | Xiaoyuan Feng | Hong Chen
[1] R. Barnard,et al. The classification of tumours of the central nervous system. , 1982, Neuropathology and applied neurobiology.
[2] Ron Kohavi,et al. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.
[3] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[4] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[5] Alain Rakotomamonjy,et al. Variable Selection Using SVM-based Criteria , 2003, J. Mach. Learn. Res..
[6] B. Scheithauer,et al. The 2007 WHO classification of tumours of the central nervous system , 2007, Acta Neuropathologica.
[7] O. Chinot,et al. Absence of IDH mutation identifies a novel radiologic and molecular subtype of WHO grade II gliomas with dismal prognosis , 2010, Acta Neuropathologica.
[8] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[9] J. Peltier,et al. Perfusion MRI as a Neurosurgical Tool for Improved Targeting in Stereotactic Tumor Biopsies , 2012, Stereotactic and Functional Neurosurgery.
[10] P. A. Futreal,et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. , 2012, The New England journal of medicine.
[11] Patrick Granton,et al. Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.
[12] V. P. Collins,et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics , 2013, Proceedings of the National Academy of Sciences.
[13] D. Bihan. Apparent Diffusion Coefficient and Beyond: What Diffusion MR Imaging Can Tell Us about Tissue Structure , 2013 .
[14] D. Le Bihan. Apparent diffusion coefficient and beyond: what diffusion MR imaging can tell us about tissue structure. , 2013, Radiology.
[15] Zhengrong Liang,et al. Volumetric texture features from higher-order images for diagnosis of colon lesions via CT colonography , 2014, International Journal of Computer Assisted Radiology and Surgery.
[16] S. Choi,et al. Evaluation of the microenvironmental heterogeneity in high-grade gliomas with IDH1/2 gene mutation using histogram analysis of diffusion-weighted imaging and dynamic-susceptibility contrast perfusion imaging , 2014, Journal of Neuro-Oncology.
[17] S. Li,et al. Anatomical localization of isocitrate dehydrogenase 1 mutation: a voxel‐based radiographic study of 146 low‐grade gliomas , 2015, European journal of neurology.
[18] G. Zaharchuk,et al. Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: A consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. , 2015, Magnetic resonance in medicine.
[19] D. Geng,et al. Combination of diffusion tensor imaging and conventional MRI correlates with isocitrate dehydrogenase 1/2 mutations but not 1p/19q genotyping in oligodendroglial tumours , 2016, European Radiology.
[20] Joseph O. Deasy,et al. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images , 2015, Proceedings of the National Academy of Sciences.
[21] S. Heiland,et al. IDH mutation status is associated with a distinct hypoxia/angiogenesis transcriptome signature which is non-invasively predictable with rCBV imaging in human glioma , 2015, Scientific Reports.
[22] Steven J. M. Jones,et al. Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas. , 2015, The New England journal of medicine.
[23] A. Olar,et al. Molecular Markers in Low-Grade Glioma-Toward Tumor Reclassification. , 2015, Seminars in radiation oncology.
[24] H. Aburatani,et al. Utility of ATRX immunohistochemistry in diagnosis of adult diffuse gliomas , 2016, Histopathology.
[25] Yuanyuan Wang,et al. Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma , 2017, European Radiology.
[26] G. Reifenberger,et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary , 2016, Acta Neuropathologica.
[27] B. Yin,et al. Detecting isocitrate dehydrogenase gene mutations in oligodendroglial tumors using diffusion tensor imaging metrics and their correlations with proliferation and microvascular density , 2016, Journal of Magnetic Resonance Imaging.
[28] Yanqi Huang,et al. Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[29] Paul Kinahan,et al. Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.
[30] Karra A. Jones,et al. Imaging correlates for the 2016 update on WHO classification of grade II/III gliomas: implications for IDH, 1p/19q and ATRX status , 2017, Journal of Neuro-Oncology.
[31] T. Maehara,et al. ATRX status correlates with 11 C-methionine uptake in WHO grade II and III gliomas with IDH1 mutations , 2017, Brain Tumor Pathology.
[32] Yuanyuan Wang,et al. Anatomical location differences between mutated and wild-type isocitrate dehydrogenase 1 in low-grade gliomas , 2017, The International journal of neuroscience.
[33] G. Reifenberger,et al. European Association for Neuro-Oncology (EANO) guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas. , 2017, The Lancet. Oncology.
[34] Jennie W. Taylor,et al. Adult infiltrating gliomas with WHO 2016 integrated diagnosis: additional prognostic roles of ATRX and TERT , 2017, Acta Neuropathologica.
[35] P. V. van Zijl,et al. Predicting IDH mutation status in grade II gliomas using amide proton transfer‐weighted (APTw) MRI , 2017, Magnetic resonance in medicine.
[36] Wei Cao,et al. Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma , 2017, Scientific Reports.
[37] S. Kothari,et al. Role of exponential apparent diffusion coefficient in characterizing breast lesions by 3.0 Tesla diffusion-weighted magnetic resonance imaging , 2017, Indian Journal of Radiology and Imaging.
[38] Raymond Y Huang,et al. Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas , 2017, Neuro-oncology.
[39] Baojuan Li,et al. Radiomics Strategy for Molecular Subtype Stratification of Lower‐Grade Glioma: Detecting IDH and TP53 Mutations Based on Multimodal MRI , 2018, Journal of magnetic resonance imaging : JMRI.
[40] Jong-Hee Chang,et al. Prediction of IDH1-Mutation and 1p/19q-Codeletion Status Using Preoperative MR Imaging Phenotypes in Lower Grade Gliomas , 2018, American Journal of Neuroradiology.