Tian Xie1, Xiao Chen1, Jingqin Fang1, Houyi Kang1, Wei Xue1, Haipeng Tong1, Peng Cao2, Sumei Wang3, Yizeng Yang4, Weiguo Zhang1,5. 1. Department of Radiology, Institute of Surgery Research, Daping Hospital, Third Military Medical University, Chongqing, China. 2. GE HealthCare (China), Pudong, Shanghai, China. 3. Department of Radiology, Division of Neuroradiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA. 4. Department of Medicine, Gastroenterology Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. 5. Chongqing Clinical Research Center of Imaging and Nuclear Medicine, Chongqing, China.
Abstract
BACKGROUND: Presurgical glioma grading by dynamic contrast-enhanced MRI (DCE-MRI) has unresolved issues. PURPOSE: The aim of this study was to investigate the ability of textural features derived from pharmacokinetic model-based or model-free parameter maps of DCE-MRI in discriminating between different grades of gliomas, and their correlation with pathological index. STUDY TYPE: Retrospective. SUBJECTS: Forty-two adults with brain gliomas. FIELD STRENGTH/SEQUENCE: 3.0T, including conventional anatomic sequences and DCE-MRI sequences (variable flip angle T1-weighted imaging and three-dimensional gradient echo volumetric imaging). ASSESSMENT: Regions of interest on the cross-sectional images with maximal tumor lesion. Five commonly used textural features, including Energy, Entropy, Inertia, Correlation, and Inverse Difference Moment (IDM), were generated. RESULTS: All textural features of model-free parameters (initial area under curve [IAUC], maximal signal intensity [Max SI], maximal up-slope [Max Slope]) could effectively differentiate between grade II (n = 15), grade III (n = 13), and grade IV (n = 14) gliomas (P < 0.05). Two textural features, Entropy and IDM, of four DCE-MRI parameters, including Max SI, Max Slope (model-free parameters), vp (Extended Tofts), and vp (Patlak) could differentiate grade III and IV gliomas (P < 0.01) in four measurements. Both Entropy and IDM of Patlak-based Ktrans and vp could differentiate grade II (n = 15) from III (n = 13) gliomas (P < 0.01) in four measurements. No textural features of any DCE-MRI parameter maps could discriminate between subtypes of grade II and III gliomas (P < 0.05). Both Entropy and IDM of Extended Tofts- and Patlak-based vp showed highest area under curve in discriminating between grade III and IV gliomas. However, intraclass correlation coefficient (ICC) of these features revealed relatively lower inter-observer agreement. No significant correlation was found between microvascular density and textural features, compared with a moderate correlation found between cellular proliferation index and those features. DATA CONCLUSION: Textural features of DCE-MRI parameter maps displayed a good ability in glioma grading. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1099-1111.
BACKGROUND: Presurgical glioma grading by dynamic contrast-enhanced MRI (DCE-MRI) has unresolved issues. PURPOSE: The aim of this study was to investigate the ability of textural features derived from pharmacokinetic model-based or model-free parameter maps of DCE-MRI in discriminating between different grades of gliomas, and their correlation with pathological index. STUDY TYPE: Retrospective. SUBJECTS: Forty-two adults with brain gliomas. FIELD STRENGTH/SEQUENCE: 3.0T, including conventional anatomic sequences and DCE-MRI sequences (variable flip angle T1-weighted imaging and three-dimensional gradient echo volumetric imaging). ASSESSMENT: Regions of interest on the cross-sectional images with maximal tumor lesion. Five commonly used textural features, including Energy, Entropy, Inertia, Correlation, and Inverse Difference Moment (IDM), were generated. RESULTS: All textural features of model-free parameters (initial area under curve [IAUC], maximal signal intensity [Max SI], maximal up-slope [Max Slope]) could effectively differentiate between grade II (n = 15), grade III (n = 13), and grade IV (n = 14) gliomas (P < 0.05). Two textural features, Entropy and IDM, of four DCE-MRI parameters, including Max SI, Max Slope (model-free parameters), vp (Extended Tofts), and vp (Patlak) could differentiate grade III and IV gliomas (P < 0.01) in four measurements. Both Entropy and IDM of Patlak-based Ktrans and vp could differentiate grade II (n = 15) from III (n = 13) gliomas (P < 0.01) in four measurements. No textural features of any DCE-MRI parameter maps could discriminate between subtypes of grade II and III gliomas (P < 0.05). Both Entropy and IDM of Extended Tofts- and Patlak-based vp showed highest area under curve in discriminating between grade III and IV gliomas. However, intraclass correlation coefficient (ICC) of these features revealed relatively lower inter-observer agreement. No significant correlation was found between microvascular density and textural features, compared with a moderate correlation found between cellular proliferation index and those features. DATA CONCLUSION: Textural features of DCE-MRI parameter maps displayed a good ability in glioma grading. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1099-1111.
Authors: José Gerardo Suárez-García; Javier Miguel Hernández-López; Eduardo Moreno-Barbosa; Benito de Celis-Alonso Journal: PLoS One Date: 2020-05-15 Impact factor: 3.240