Analysis of DWI in the classification of glioma pathology and its therapeutic application in clinical surgery: a case-control study

Glioma is a common primary craniocerebral malignant tumor, due to the lack of specificity of imaging examination and clinical manifestations, its diagnostic accuracy is relatively low, which may result in misdiagnosis and missed diagnosis. The apparent diffusion coefficient (ADC) in magnetic resonance diffusion weighted imaging (DWI) can reflect the histological characteristics of gliomas, which can be widely applied to classify gliomas and evaluate the extent of metastasis of glioma. The present study aimed to assess the clinical value of magnetic resonance DWI in the pathological grading of glioma and its therapeutic application in clinical surgery.This article retrospectively analyzed the clinical data of 41 patients with glioma confirmed by surgical pathology results from January 1, 2019 to March 31, 2020 in the People's Hospital of Gaozhou. Among them, 16 patients had low-grade gliomas [World Health Organization (WHO) grade I-II] and 25 patients had high-grade gliomas (WHO grade III-IV). They were subjected to conventional T1WI and T2WI plain scans, along with DWI and enhanced scans before surgery. The ADC values of the glioma parenchyma, the peritumoral edema area, the surrounding white matter, and the contralateral normal white matter were measured. We selected some tumor tissues for pathological analysis as well, and conducted pathological grading according to WHO grading standards.We compared and evaluated the ADC values of the observed areas for low-grade gliomas and high-grade gliomas. The ADC values of low-grade gliomas in the tumor parenchyma, peritumoral edema, and white matter around the edema area were significantly lower than those of high-grade gliomas, and the differences were statistically significant (P<0.05). The difference in ADC values of normal white matter between the two groups of patients was not statistically significant (P=0.125).DWI has prognostic predictive value in the preoperative differential diagnosis and pathological classification of gliomas. This advanced technology can verify the extent of glioma infiltration in the surrounding brain tissue. It can help clinicians formulate a safer and more effective therapeutic strategy by providing accurate information on prognostic evaluation before the successful surgical intervention of gliomas.