Preoperative Apparent Diffusion Coefficient of Peritumoral Lesion Associate with Recurrence in Patients with Glioblastoma

Association of ADC of hyperintense lesions on FLAIR images with TERT promoter mutation status in glioblastoma IDH wild type

Background

Although mutations in telomerase reverse transcriptase (TERT) promoter (TERTp) are the most common alterations in glioblastoma (GBM), predicting TERTp mutation status by preoperative imaging is difficult. We determined whether tumour-surrounding hyperintense lesions on fluid-attenuated inversion recovery (FLAIR) were superior to those of contrast-enhanced lesions (CELs) in assessing TERTp mutation status using magnetic resonance imaging (MRI).

Methods

This retrospective study included 114 consecutive patients with primary isocitrate dehydrogenase (IDH)-wild-type GBM. The apparent diffusion coefficient (ADC) and volume of CELs and FLAIR hyperintense lesions (FHLs) were determined, and the correlation between MRI features and TERTp mutation status was analyzed. In a subset of cases, FHLs were histopathologically analyzed to determine the correlation between tumor cell density and ADC.

Results

TERTp mutations were present in 77 (67.5%) patients. The minimum ADC of FHLs was significantly lower in the TERTp-mutant group than in the TERTp-wild-type group (mean, 958.9 × 10-3 and 1092.1 × 10-3 mm2/s, respectively, P < 0.01). However, other MRI features, such as CEL and FHL volumes, minimum ADC of CELs, and FHL/CEL ratio, were not significantly different between the two groups. Histopathologic analysis indicated high tumor cell density in FHLs with low ADC.

Conclusion

The ADC of FHLs was significantly lower in IDH-wild-type GBM with TERTp mutations, suggesting that determining the ADC of FHLs on preoperative MRI might be helpful in predicting TERTp mutation status and surgical planning.

Added value of dynamic contrast-enhanced MR imaging in deep learning-based prediction of local recurrence in grade 4 adult-type diffuse gliomas patients

Local recurrences in patients with grade 4 adult-type diffuse gliomas mostly occur within residual non-enhancing T2 hyperintensity areas after surgical resection. Unfortunately, it is challenging to distinguish non-enhancing tumors from edema in the non-enhancing T2 hyperintensity areas using conventional MRI alone. Quantitative DCE MRI parameters such as Ktrans and Ve convey permeability information of glioblastomas that cannot be provided by conventional MRI. We used the publicly available nnU-Net to train a deep learning model that incorporated both conventional and DCE MRI to detect the subtle difference in vessel leakiness due to neoangiogenesis between the non-recurrence area and the local recurrence area, which contains a higher proportion of high-grade glioma cells. We found that the addition of Ve doubled the sensitivity while nonsignificantly decreasing the specificity for prediction of local recurrence in glioblastomas, which implies that the combined model may result in fewer missed cases of local recurrence. The deep learning model predictive of local recurrence may enable risk-adapted radiotherapy planning in patients with grade 4 adult-type diffuse gliomas.