Research On Differential Diagnosis Between Low-and High-Grade Glioma And Combinded DTI Metrics Classification Model Using IGNS

Glioma, the most common primary malignant tumors of the central nervous system, can be considered second to none in the incidence of intracranial tumors. In recent years, the clinical diagnosis of gliomas, treatment strategies and basic research have a great deal of development. The intracranial tumor is from considering as just the restricted area of the surgery in the early to taking simple surgical excision; Treatment strategies are also from total removal on the basis of protecting critical brain function areas to nowdays multimodal treatment including surgical resection, radiotherapy, and chemotherapy. With the rapid rise of computer science, digital image standards and medical imaging, especially magnetic resonance imaging from simple anatomical structure to functional imaging, computer-aided navigation and positioning system, image-guidance neurosurgery[1] and other emerging areas of research on glial gradually in-depth understanding of tumor, preoperative imaging diagnosis, surgical approach successfully, the precision of radiotherapy and chemotherapy techniques and basic research have been greatly improved.Although the multimodal treatment of gliomas in recent years has made great progress, relative to the low-grade gliomas, the prognosis of patients with high-grade gliomas remains poor[2-31and most of their survival time is less than1year. Treatment strategies including surgical resection, radiotherapy, and chemotherapy for these two grades are very different. Therefore, it is crucial to discriminate accurately between high-grade and low-grade gliomas preoperatively.To date, glioma grading is still limited to subjective observation of both neurosurgeons and neuroradiologist without accurate quantitative analysis methods. Therefore, we studied diffusion tensor characteristics of high-grade and low-grade gliomas based on various areas of the tumor.OBJECTIVE:To ascertain whether diffusion tensor imaging (DTI) metrics including tensor shape measures such as planar and spherical isotropy coefficients (CP and CS) can be used to distinguish high-grade from low-grade gliomas. METHODS:Twenty-five patients with histologically proved World Health Organization (WHO) grade I-IV gliomas (10low-grade and15high-grade) were included in this study. Contrast-enhanced Tl-weighted images, non-diffusion weighted b=0(b0) images, fractional anisotropy (FA), apparent diffusion coefficient (ADC), CS and CP maps were co-registered and each lesion was divided into two regions of interest (ROI):enhancing and immediate peritumoral edema (edema adjacent to tumor). Univariate and multivariate logistic regression analyses were applied to determine the best classification model.RESULTS:There was a statistically significant difference in the multivariate logistic regression analysis. The best logistic regression model for classification combined three parameters (CS, FA and CP) from the peritumoral part (p=0.024), resulting in86%sensitivity,80%specificity and area under the curve of0.81.CONCLUSION:Our observations revealed that combined DTI metrics can function in effect as a non-invasive measure to distinguish between low-grade and high-grade gliomas.