Looking through the imaging perspective: the importance of imaging necrosis in glioma diagnosis and prognostic prediction - single centre experience.

BACKGROUND

The aim of the study was to investigate the diagnostic value of imaging necrosis (Im) in grading, predict the genotype and prognosis of gliomas, and further assess tumor necrosis by dynamic contrast-enhanced MR perfusion imaging (DCE-MRI).

PATIENTS AND METHODS

We retrospectively included 150 patients (104 males, mean age: 46 years old) pathologically proved as adult diffuse gliomas and all diagnosis was based on the 2021 WHO central nervous system (CNS) classification. The pathological necrosis (Pa) and gene mutation information were collected. All patients underwent conventional and DCE-MRI examinations and had been followed until May 31, 2021. The Im was determined by two experienced neuroradiologists. DCE-MRI derived metric maps have been post-processed, and the mean value of each metric in the tumor parenchyma, peritumoral and contralateral area were recorded.

RESULTS

There was a strong degree of inter-observer agreement in defining Im (Kappa = 0.668, p < 0.001) and a strong degree of agreement between Im and Pa (Kappa = 0.767, p < 0.001). Compared to low-grade gliomas, high-grade gliomas had more Im (85.37%, p < 0.001), and Im significantly increased with the grade of gliomas increasing. And Im was significantly more identified in -wildtype, -non-codeletion, and -homozygous-deletion gliomas. Using multivariate Cox regression analysis, Im was an independent and unfavorable prognosis factor (Hazard Ratio = 2.113, p = 0.046) in gliomas. Additionally, extravascular extracellular volume fraction () in tumor parenchyma derived from DCE-MRI demonstrated the highest diagnostic efficiency in identifying Pa and Im with high specificity (83.3% and 91.9%, respectively).

CONCLUSIONS

Im can provide supplementary evidence beyond Pa in grading, predicting the genotype and prognosis of gliomas, and in tumor parenchyma can help to predict tumor necrosis with high specificity.