Radiomics-based evaluation and possible characterization of dynamic contrast enhanced (DCE) perfusion derived different sub-regions of Glioblastoma
Tài liệu tham khảo
Omuro, 2013, Glioblastoma and Other Malignant Gliomas: A Clinical Review, JAMA, 310, 1842, 10.1001/jama.2013.280319
Lin, 2013, Glioma-related edema: New insight into molecular mechanisms and their clinical implications, Chin. J. Cancer, 32, 49, 10.5732/cjc.012.10242
Ghodasara, 2020, Quantifying Perfusion Properties with DCE-MRI Using a Dictionary Matching Approach, Sci. Rep., 10, 10.1038/s41598-020-66985-9
Singh, 2009, Improved bolus arrival time and arterial input function estimation for tracer kinetic analysis in DCE-MRI, J. Magn. Reson. Imaging, 29, 166, 10.1002/jmri.21624
Khalifa, 2014, Models and methods for analyzing DCE-MRI: A review, Med. Phys., 41, 124301, 10.1118/1.4898202
Sahoo, 2013, Subcompartmentalization of extracellular extravascular space (EES) into permeability and leaky space with local arterial input function (AIF) results in improved discrimination between high- and low-grade glioma using dynamic contrast-enhanced (DCE) MRI, J. Magn. Reson. Imaging, 38, spcone, 10.1002/jmri.24404
Sahoo, 2016, Comparison of actual with default hematocrit value in dynamic contrast enhanced MR perfusion quantification in grading of human glioma, Magn. Reson. Imaging, 34, 1071, 10.1016/j.mri.2016.05.004
Sahoo, 2017, Diagnostic accuracy of automatic normalization of CBV in glioma grading using T1- weighted DCE-MRI, Magn. Reson. Imaging, 44, 32, 10.1016/j.mri.2017.08.003
Vallatos, 2019, Quantitative histopathologic assessment of perfusion MRI as a marker of glioblastoma cell infiltration in and beyond the peritumoral edema region, J. Magn. Reson. Imaging, 50, 529, 10.1002/jmri.26580
Louis, 2021, The 2021 WHO Classification of Tumors of the Central Nervous System: a summary, Neuro Oncol., 23, 1231, 10.1093/neuonc/noab106
Sengupta, 2019, Glioma grading using a machine-learning framework based on optimized features obtained from T1 perfusion MRI and volumes of tumor components, J. Magn. Reson. Imaging, 50, 1295, 10.1002/jmri.26704
Raza, 2002, Necrosis and glioblastoma: A friend or a foe? A review and a hypothesis, Neurosurgery, 51, 2, 10.1097/00006123-200207000-00002
Esquenazi, 2017, Critical Care Management of Cerebral Edema in Brain Tumors, J. Intensive Care Med., 32, 15, 10.1177/0885066615619618
Blystad, 2017, Quantitative MRI for analysis of peritumoral edema in malignant gliomas, PLoS One, 12, 1, 10.1371/journal.pone.0177135
Liu, 2013, Pre-operative peritumoral edema and survival rate in glioblastoma multiforme, Onkologie(Czech Republic), 36, 679
Sengupta, 2018, On differentiation between vasogenic edema and non-enhancing tumor in high-grade glioma patients using a support vector machine classifier based upon pre and post-surgery MRI images, Eur. J. Radiol., 106, 199, 10.1016/j.ejrad.2018.07.018
Liang, 2018, Diagnostic Values of DCE-MRI and DSC-MRI for Differentiation Between High-grade and Low-grade Gliomas: A Comprehensive Meta-analysis, Acad. Radiol., 25, 338, 10.1016/j.acra.2017.10.001
You, 2018, Differentiation of high-grade from low-grade astrocytoma: improvement in diagnostic accuracy and reliability of pharmacokinetic parameters from DCE MR imaging by using arterial input functions obtained from DSC MR imaging, Radiology, 286, 981, 10.1148/radiol.2017170764
Haller, 2016, Arterial spin labeling perfusion of the brain: Emerging clinical applications, Radiology, 281, 337, 10.1148/radiol.2016150789
Bhattacharjee, 2020, Quantitative vs. semiquantitative assessment of intratumoral susceptibility signals in patients with different grades of glioma, J. Magn. Reson. Imaging, 51, 225, 10.1002/jmri.26786
Kocher, 2020, Applications of radiomics and machine learning for radiotherapy of malignant brain tumors, Strahlenther. Onkol., 196, 856, 10.1007/s00066-020-01626-8
Zhang, 2017, Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features, Oncotarget, 8, 47816, 10.18632/oncotarget.18001
Cho H ho, Lee S hak, Kim J, Park H. Classification of the glioma grading using radiomics analysis. PeerJ. 2018;2018(11):1-17. doi:10.7717/peerj.5982.
Chen, 2019, Radiomics-based machine learning in differentiation between glioblastoma and metastatic brain tumors, Frontiers Oncology., 9(AUG):1–7
McKenney, 2022, Radiomic Analysis to Predict Histopathologically Confirmed Pseudoprogression in Glioblastoma Patients, Advances in Radiation Oncology. Published online, 100916
Chen, 2020, Combining Dynamic Contrast-Enhanced Magnetic Resonance Imaging and Apparent Diffusion Coefficient Maps for a Radiomics Nomogram to Predict Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients, J Comput Assist Tomogr., 44, 275, 10.1097/RCT.0000000000000978
Li, 2021, Intratumoral and Peritumoral Radiomics Based on Functional Parametric Maps from Breast DCE-MRI for Prediction of HER-2 and Ki-67 Status, J. Magn. Reson. Imaging, 54, 703, 10.1002/jmri.27651
Elshafeey, 2019, Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma, Nat. Commun., 10, 10.1038/s41467-019-11007-0
Singh, 2007, “Quantification of physiological and hemodynamic indices using T1 dynamic contrast-enhanced MRI in intracranial mass lesions.” Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for, Magn. Reson. Med., 26, 871
Li, 2014, Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study, BMC Bioinf., 15, 10.1186/1471-2105-15-291
Chiu, 2021, A multiparametric MRI-based radiomics analysis to efficiently classify tumor subregions of glioblastoma: A pilot study in machine learning, J. Clin. Med., 10, 2030, 10.3390/jcm10092030
Rathore, 2018, Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning, J. Med. Imag., 5, 10.1117/1.JMI.5.2.021219
Gooya, 2012, GLISTR: glioma image segmentation and registration, IEEE Trans. Med. Imag., 31, 1941, 10.1109/TMI.2012.2210558
Li, 2018, Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: a multicentre study, Eur. Radiol., 28, 3640, 10.1007/s00330-017-5302-1
Chaddad, 2020, Deep radiomic analysis to predict gleason score in prostate cancer, IEEE Access, 8, 167767, 10.1109/ACCESS.2020.3023902