• Jie Zhang, Dong-Xiao Zhuang, Cheng-Jun Yao, Ching-Po Lin, Tian-Liang Wang, Zhi-Yong Qin, and Jin-Song Wu.
• Glioma Surgery Division, Department of Neurologic Surgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai;
• J. Neurosurg. 2016 Jun 1; 124 (6): 1585-93.

AbstractOBJECT The extent of resection is one of the most essential factors that influence the outcomes of glioma resection. However, conventional structural imaging has failed to accurately delineate glioma margins because of tumor cell infiltration. Three-dimensional proton MR spectroscopy ((1)H-MRS) can provide metabolic information and has been used in preoperative tumor differentiation, grading, and radiotherapy planning. Resection based on glioma metabolism information may provide for a more extensive resection and yield better outcomes for glioma patients. In this study, the authors attempt to integrate 3D (1)H-MRS into neuronavigation and assess the feasibility and validity of metabolically based glioma resection. METHODS Choline (Cho)-N-acetylaspartate (NAA) index (CNI) maps were calculated and integrated into neuronavigation. The CNI thresholds were quantitatively analyzed and compared with structural MRI studies. Glioma resections were performed under 3D (1)H-MRS guidance. Volumetric analyses were performed for metabolic and structural images from a low-grade glioma (LGG) group and high-grade glioma (HGG) group. Magnetic resonance imaging and neurological assessments were performed immediately after surgery and 1 year after tumor resection. RESULTS Fifteen eligible patients with primary cerebral gliomas were included in this study. Three-dimensional (1)H-MRS maps were successfully coregistered with structural images and integrated into navigational system. Volumetric analyses showed that the differences between the metabolic volumes with different CNI thresholds were statistically significant (p < 0.05). For the LGG group, the differences between the structural and the metabolic volumes with CNI thresholds of 0.5 and 1.5 were statistically significant (p = 0.0005 and 0.0129, respectively). For the HGG group, the differences between the structural and metabolic volumes with CNI thresholds of 0.5 and 1.0 were statistically significant (p = 0.0027 and 0.0497, respectively). All patients showed no tumor progression at the 1-year follow-up. CONCLUSIONS This study integrated 3D MRS maps and intraoperative navigation for glioma margin delineation. Optimum CNI thresholds were applied for both LGGs and HGGs to achieve resection. The results indicated that 3D (1)H-MRS can be integrated with structural imaging to provide better outcomes for glioma resection.

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