Radiomics-based evaluation and possible characterization of dynamic contrast enhanced (DCE) perfusion derived different sub-regions of Glioblastoma

European Journal of Radiology - Tập 159 - Trang 110655 - 2023
Suhail Parvaze1, Rupsa Bhattacharjee2, Anup Singh3, Sunita Ahlawat4, Rana Patir5, Sandeep Vaishya5, Tejas J. Shah1, Rakesh K. Gupta6
1Philips Innovation Campus, Bangalore, India
2Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States
3Medical Image and Signal Processing Lab, CBME, Indian Institute of Technology, Delhi, India
4SRL Diagnostics, Fortis Memorial Research Institute, Gurugram, India
5Department of Neurosurgery, Fortis Memorial Research Institute, Gurugram, India
6Department of Radiology and Imaging, Fortis Memorial Research Institute, Gurugram, India

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