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• In this cohort, the cumulative incidence of psPD increases from 13% in the first year to 28% at 10 years. • psPD is asymptomatic in 83% of patients. • 1p/19q codeletion and biopsy are associated with an increased risk of psPD. • When analyzed as a time-dependent covariate, psPD is not associated with overall survival. • An uniform definition and analysis method for psPD in diffuse glioma is needed for comparisons between photon - and proton beam radiotherapy. The interpretation of new enhancing lesions after radiotherapy for diffuse glioma remains a clinical challenge. We sought to characterize and classify new contrast enhancing lesions in a historical multicenter cohort of patients with IDH mutated grade 2 diffuse glioma treated with photon therapy. We reviewed all follow-up MRI's of all patients treated with radiotherapy for histologically confirmed, IDH mutated diffuse grade 2 glioma between 1–1-2007 and 31–12-2018 in two tertiary referral centers. Disease progression (PD) was defined in accordance with the RANO criteria for progressive disease in low grade glioma. Pseudoprogression (psPD) was defined as any transient contrast-enhancing lesion between the end of radiotherapy and PD, or any new contrast-enhancing lesion that remained stable over a period of 12 months in patients who did not exhibit PD. A total of 860 MRI's of 106 patients were reviewed. psPD was identified in 24 patients (23%) on 76 MRI's. The cumulative incidence of psPD was 13% at 1 year, 22% at 5 years, and 28% at 10 years. The mean of the observed maximal volume of psPD was 2.4 cc. The median Dmin in psPD lesions was 50.1 Gy. The presence of an 1p/19q codeletion was associated with an increased risk of psPD (subhazard ratio 2.34, p = 0.048). psPD was asymptomatic in 83% of patients. The cumulative incidence of psPD in grade 2 diffuse glioma increases over time. Consensus regarding event definition and statistical analysis is needed for comparisons between series investigating psPD. [ABSTRACT FROM AUTHOR]

Quantitative MR imaging is becoming more feasible to be used in clinical work since new approaches have been proposed in order to substantially accelerate the acquisition and due to the possibility of synthetically deriving weighted images from the parametric maps. However, their applicability has to be thoroughly validated in order to be included in clinical practice. In this pilot study, we acquired Magnetic Resonance Image Compilation scans to obtain T1, T2 and PD maps in 14 glioma patients. Abnormal tissue was segmented based on conventional images and using a deep learning segmentation technique to define regions of interest (ROIs). The quantitative T1, T2 and PD values inside ROIs were analyzed using the mean, the standard deviation, the skewness and the kurtosis and compared to the quantitative T1, T2 and PD values found in normal white matter. We found significant differences in pre-contrast T1 and T2 values between abnormal tissue and healthy tissue, as well as between T1w-enhancing and non-enhancing regions. ROC analysis was used to evaluate the potential of quantitative T1 and T2 values for voxel-wise classification of abnormal/normal tissue (AUC = 0.95) and of T1w enhancement/non-enhancement (AUC = 0.85). A cross-validated ROC analysis found high sensitivity (73%) and specificity (73%) with AUCs up to 0.68 on the a priori distinction between abnormal tissue with and without T1w-enhancement. These results suggest that normal tissue, abnormal tissue, and tissue with T1w-enhancement are distinguishable by their pre-contrast quantitative values but further investigation is needed. [ABSTRACT FROM AUTHOR]