Zhiye Chen1,2, Peng Zhou3,4, Bin Lv5, Mengqi Liu1,2, Yan Wang1, Yulin Wang1, Xin Lou1, Qiuping Gui6, Huiguang He7,8,9, Lin Ma10. 1. Department of Radiology, Chinese PLA General Hospital, Fuxing Road 28, Beijing, 100853, China. 2. Department of Radiology, Hainan Branch of Chinese PLA General Hospital, Sanya, 572013, China. 3. Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. 4. University of Chinese Academy of Sciences , Beijing, 100190, China. 5. Academy of Telecommunication Research of MIIT, Beijing, 100083, China. 6. Department of Pathology, Chinese PLA General Hospital, Beijing, 100853, China. 7. Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. huiguang.he@ia.ac.cn. 8. University of Chinese Academy of Sciences , Beijing, 100190, China. huiguang.he@ia.ac.cn. 9. Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, 100190, China. huiguang.he@ia.ac.cn. 10. Department of Radiology, Chinese PLA General Hospital, Fuxing Road 28, Beijing, 100853, China. cjr.malin@vip.163.com.
Abstract
OBJECTIVES: To retrospectively evaluate the diagnostic value of high-frequency power (HFP) compared with the minimum apparent diffusion coefficient (MinADC) in the prediction of neuroepithelial tumour grading. METHODS: Diffusion-weighted imaging (DWI) data were acquired on 115 patients by a 3.0-T MRI system, which included b0 images and b1000 images over the whole brain in each patient. The HFP values and MinADC values were calculated by an in-house script written on the MATLAB platform. RESULTS: There was a significant difference among each group excluding grade I (G1) vs. grade II (G2) (P = 0.309) for HFP and among each group for MinADC. ROC analysis showed a higher discriminative accuracy between low-grade glioma (LGG) and high-grade glioma (HGG) for HFP with area under the curve (AUC) value 1 compared with that for MinADC with AUC 0.83 ± 0.04 and also demonstrated a higher discriminative ability among the G1-grade IV (G4) group for HFP compared with that for MinADC except G1 vs. G2. CONCLUSIONS: HFP could provide a simple and effective optimal tool for the prediction of neuroepithelial tumour grading based on diffusion-weighted images in routine clinical practice. KEY POINTS: • HFP shows positive correlation with neuroepithelial tumour grading. • HFP presents a good diagnostic efficacy for LGG and HGG. • HFP is helpful in the selection of brain tumour boundary.
OBJECTIVES: To retrospectively evaluate the diagnostic value of high-frequency power (HFP) compared with the minimum apparent diffusion coefficient (MinADC) in the prediction of neuroepithelial tumour grading. METHODS: Diffusion-weighted imaging (DWI) data were acquired on 115 patients by a 3.0-T MRI system, which included b0 images and b1000 images over the whole brain in each patient. The HFP values and MinADC values were calculated by an in-house script written on the MATLAB platform. RESULTS: There was a significant difference among each group excluding grade I (G1) vs. grade II (G2) (P = 0.309) for HFP and among each group for MinADC. ROC analysis showed a higher discriminative accuracy between low-grade glioma (LGG) and high-grade glioma (HGG) for HFP with area under the curve (AUC) value 1 compared with that for MinADC with AUC 0.83 ± 0.04 and also demonstrated a higher discriminative ability among the G1-grade IV (G4) group for HFP compared with that for MinADC except G1 vs. G2. CONCLUSIONS: HFP could provide a simple and effective optimal tool for the prediction of neuroepithelial tumour grading based on diffusion-weighted images in routine clinical practice. KEY POINTS: • HFP shows positive correlation with neuroepithelial tumour grading. • HFP presents a good diagnostic efficacy for LGG and HGG. • HFP is helpful in the selection of brain tumour boundary.
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