Patterns, predictors and prognostic relevance of high-grade hematotoxicity after temozolomide or temozolomide-lomustine in the CeTeG/NOA-09 trial.

PURPOSE: In the randomized phase III trial CeTeG/NOA-09, temozolomide (TMZ)/lomustine (CCNU) combination therapy was superior to TMZ in newly diagnosed MGMT methylated glioblastoma, albeit reporting more frequent hematotoxicity. Here, we analyze high grade hematotoxicity and its prognostic relevance in the trial population. METHODS: Descriptive and comparative analysis of hematotoxicity adverse events ≥ grade 3 (HAE) according to the Common Terminology of Clinical Adverse Events, version 4.0 was performed. The association of HAE with survival was assessed in a landmark analysis. Logistic regression analysis was performed to predict HAE during the concomitant phase of chemotherapy. RESULTS: HAE occurred in 36.4% and 28.6% of patients under CCNU/TMZ and TMZ treatment, respectively. The median onset of the first HAE was during concomitant chemotherapy (i.e. first CCNU/TMZ course or daily TMZ therapy), and 42.9% of patients with HAE receiving further courses experienced repeat HAE. Median HAE duration was similar between treatment arms (CCNU/TMZ 11.5; TMZ 13 days). Chemotherapy was more often discontinued due to HAE in CCNU/TMZ than in TMZ (19.7 vs. 6.3%, p = 0.036). The occurrence of HAE was not associated with survival differences (p = 0.76). Regression analysis confirmed older age (OR 1.08) and female sex (OR 2.47), but not treatment arm, as predictors of HAE. CONCLUSION: Older age and female sex are associated with higher incidence of HAE. Although occurrence of HAE was not associated with shorter survival, reliable prediction of patients at risk might be beneficial to allow optimal management of therapy and allocation of supportive measures. TRIAL REGISTRATION: NCT01149109.

Fully automated analysis combining [18F]-FET-PET and multiparametric MRI including DSC perfusion and APTw imaging: a promising tool for objective evaluation of glioma progression.

PURPOSE: To evaluate diagnostic accuracy of fully automated analysis of multimodal imaging data using [18F]-FET-PET and MRI (including amide proton transfer-weighted (APTw) imaging and dynamic-susceptibility-contrast (DSC) perfusion) in differentiation of tumor progression from treatment-related changes in patients with glioma. MATERIAL AND METHODS: At suspected tumor progression, MRI and [18F]-FET-PET data as part of a retrospective analysis of an observational cohort of 66 patients/74 scans (51 glioblastoma and 23 lower-grade-glioma, 8 patients included at two different time points) were automatically segmented into necrosis, FLAIR-hyperintense, and contrast-enhancing areas using an ensemble of deep learning algorithms. In parallel, previous MR exam was processed in a similar way to subtract preexisting tumor areas and focus on progressive tumor only. Within these progressive areas, intensity statistics were automatically extracted from [18F]-FET-PET, APTw, and DSC-derived cerebral-blood-volume (CBV) maps and used to train a Random Forest classifier with threefold cross-validation. To evaluate contribution of the imaging modalities to the classifier's performance, impurity-based importance measures were collected. Classifier performance was compared with radiology reports and interdisciplinary tumor board assessments. RESULTS: In 57/74 cases (77%), tumor progression was confirmed histopathologically (39 cases) or via follow-up imaging (18 cases), while remaining 17 cases were diagnosed as treatment-related changes. The classification accuracy of the Random Forest classifier was 0.86, 95% CI 0.77-0.93 (sensitivity 0.91, 95% CI 0.81-0.97; specificity 0.71, 95% CI 0.44-0.9), significantly above the no-information rate of 0.77 (p = 0.03), and higher compared to an accuracy of 0.82 for MRI (95% CI 0.72-0.9), 0.81 for [18F]-FET-PET (95% CI 0.7-0.89), and 0.81 for expert consensus (95% CI 0.7-0.89), although these differences were not statistically significant (p > 0.1 for all comparisons, McNemar test). [18F]-FET-PET hot-spot volume was single-most important variable, with relevant contribution from all imaging modalities. CONCLUSION: Automated, joint image analysis of [18F]-FET-PET and advanced MR imaging techniques APTw and DSC perfusion is a promising tool for objective response assessment in gliomas.