Changliang Su1, Shihui Li2, Xiaowei Chen3, Chengxia Liu2, Mehran Shaghaghi4, Jingjing Jiang2, Shun Zhang2, Yuanyuan Qin2, Kejia Cai4. 1. Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China. 2. Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. 3. Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. 4. Department of Radiology, the University of Illinois at Chicago, Chicago, Illinois, USA.
Abstract
BACKGROUND: The non-invasive characterization of glioma metabolites would greatly assist the management of glioma patients in the clinical setting. This study investigated the applicability of intra-subject inter-metabolite correlation analyses for differentiating glioma malignancy and proliferation. METHODS: A total of 17 negative controls (NCs), 39 low-grade gliomas (LGGs) patients, and 25 high-grade gliomas (HGGs) subjects were included in this retrospective study. Amide proton transfer (APT) and magnetization transfer contrast (MTC) imaging contrasts, as well as total choline/total creatine (tCho/tCr) and total N-acetylaspartate/total creatine (tNAA/tCr) ratios quantified from magnetic resonance spectroscopic imaging (MRSI) were co-registered voxel-wise and used to produce three intra-subject inter-metabolite correlation coefficients (IMCCs), namely, RAPT vs . MTC, RAPT vs . tCho/tCr, and RMTC vs . tNAA/tCr. The correlation between the IMCCs and tumor grade and Ki-67 labeling index (LI) for tumor proliferation were explored. The differences in the IMCCs between the three groups were compared with one-way analysis of variance (ANOVA). Finally, regression analysis was used to build a combined model with multiple IMCCs to improve the diagnostic performance for tumor grades based on receiver operator characteristic curves. RESULTS: Compared with the NCs, gliomas showed stronger inter-metabolic correlations. RAPT vs . MTC was significantly different among the three groups (NC vs. LGGs vs. HGGs: -0.18±0.38 vs. -0.40±0.34 vs. -0.70±0.29, P<0.0001). No significant differences were detected in RMTC vs . tNAA/tCr among the three groups. RAPT vs . MTC and RAPT vs . tCho/tCr correlated significantly with tumor grade (R=-0.41, P=0.001 and R=0.448, P<0.001, respectively). However, only RAPT vs . MTC was mildly correlated with Ki-67 (R=-0.33, P=0.02). RAPT vs . MTC and RAPT vs . tCho/tCr achieved areas under the curve (AUCs) of 0.754 and 0.71, respectively, for differentiating NCs from gliomas; and 0.77 and 0.78, respectively, for differentiating LGGs from HGGs. The combined multi-IMCCs model improved the correlation with the Ki-67 LI (R=0.46, P=0.0008) and the tumor-grade stratification with AUC increased to 0.85 (sensitivity: 80.0%, specificity: 79.5%). CONCLUSIONS: This study demonstrated that glioma patients showed stronger inter-metabolite correlations than control subjects, and the IMCCs were significantly correlated with glioma grade and proliferation. The multi-IMCCs combined model further improved the performance of clinical diagnosis. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.
BACKGROUND: The non-invasive characterization of glioma metabolites would greatly assist the management of glioma patients in the clinical setting. This study investigated the applicability of intra-subject inter-metabolite correlation analyses for differentiating glioma malignancy and proliferation. METHODS: A total of 17 negative controls (NCs), 39 low-grade gliomas (LGGs) patients, and 25 high-grade gliomas (HGGs) subjects were included in this retrospective study. Amide proton transfer (APT) and magnetization transfer contrast (MTC) imaging contrasts, as well as total choline/total creatine (tCho/tCr) and total N-acetylaspartate/total creatine (tNAA/tCr) ratios quantified from magnetic resonance spectroscopic imaging (MRSI) were co-registered voxel-wise and used to produce three intra-subject inter-metabolite correlation coefficients (IMCCs), namely, RAPT vs . MTC, RAPT vs . tCho/tCr, and RMTC vs . tNAA/tCr. The correlation between the IMCCs and tumor grade and Ki-67 labeling index (LI) for tumor proliferation were explored. The differences in the IMCCs between the three groups were compared with one-way analysis of variance (ANOVA). Finally, regression analysis was used to build a combined model with multiple IMCCs to improve the diagnostic performance for tumor grades based on receiver operator characteristic curves. RESULTS: Compared with the NCs, gliomas showed stronger inter-metabolic correlations. RAPT vs . MTC was significantly different among the three groups (NC vs. LGGs vs. HGGs: -0.18±0.38 vs. -0.40±0.34 vs. -0.70±0.29, P<0.0001). No significant differences were detected in RMTC vs . tNAA/tCr among the three groups. RAPT vs . MTC and RAPT vs . tCho/tCr correlated significantly with tumor grade (R=-0.41, P=0.001 and R=0.448, P<0.001, respectively). However, only RAPT vs . MTC was mildly correlated with Ki-67 (R=-0.33, P=0.02). RAPT vs . MTC and RAPT vs . tCho/tCr achieved areas under the curve (AUCs) of 0.754 and 0.71, respectively, for differentiating NCs from gliomas; and 0.77 and 0.78, respectively, for differentiating LGGs from HGGs. The combined multi-IMCCs model improved the correlation with the Ki-67 LI (R=0.46, P=0.0008) and the tumor-grade stratification with AUC increased to 0.85 (sensitivity: 80.0%, specificity: 79.5%). CONCLUSIONS: This study demonstrated that glioma patients showed stronger inter-metabolite correlations than control subjects, and the IMCCs were significantly correlated with glioma grade and proliferation. The multi-IMCCs combined model further improved the performance of clinical diagnosis. 2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Entities:
Keywords:
Magnetic resonance imaging (MRI); brain tumor; cell proliferation; magnetization transfer contrast imaging (MTC imaging); tumor grading
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