Wietse Geens

The determination of prognoses for patients with recurrent glioblastoma remains challenging. This study aimed to evaluate the prognostic value of static O-(2-¹⁸F-fluoroethyl)-l-tyrosine (¹⁸F-FET) PET parameters in patients with recurrent glioblastoma. Methods: We retrospectively evaluated patients treated in 3 institutional clinical trials examining vascular endothelial growth factor inhibition, immune checkpoint inhibition, or their combination in recurrent glioblastoma. Patients with a baseline ¹⁸F-FET PET were included in the analysis and stratified by treatment group. Prognostic value was evaluated using univariate Kaplan-Meier and multivariate Cox regression analyses, with receiver-operating-characteristic analysis to identify optimal thresholds. Results: Both univariate and multivariate analysis revealed that patients with a larger baseline metabolic tumor volume (MTV) and higher mean tumor-to-background ratio (TBR_mean) had an increased risk of death independent of treatment (MTV per 10 mL: hazard ratio, 1.06; 95% CI, 1.01-1.12; P = 0.023; TBR_mean: hazard ratio, 1.91; 95% CI, 1.14-3.21; P = 0.014). Receiver-operating-characteristic analysis showed that an MTV and TBR_mean of more than 27.94 cm³ and 2.15, respectively, identified patients with worse overall survival in our patient population. Conclusion: Pretreatment MTV and TBR_mean had a significant prognostic value in patients with recurrent glioblastoma, independent of treatment, and could be useful to stratify and select patients for future clinical trials.

Positron emission tomography (PET) with a [18F]fluoroethyl)-L-tyrosine ([18F]FET) tracer is of growing importance in the management of glioblastoma for the estimation of tumor extent and extraction of diagnostic and prognostic parameters. Robust and accurate glioblastoma segmentation methods are essential to maximize the benefits of this imaging modality. Given the importance of setting the foreground threshold during manual tumor delineation, this study investigates the added value of incorporating such prior knowledge to guide the automated segmentation and improve performance. Two segmentation networks were trained based on the nnU-Net guidelines: one with the [18F]FET PET image as sole input, and one with an additional input channel for the threshold map. For the latter, we investigate the benefit of manually obtained thresholds and explore automated prediction and generation of such maps. A fully automated pipeline was constructed by selecting the best performing threshold prediction approach and cascading this with the tumor segmentation model. The proposed two-channel network shows increased performance with guidance of threshold maps originating from the same reader whose ground-truth tumor label the prediction is compared to (DSC = 0.901). When threshold maps were generated by a different reader, performance reverted to levels comparable to the one-channel network and inter-reader variability. The proposed full pipeline achieves results on par with current state of the art (DSC = 0.807). Incorporating a threshold map can significantly improve tumor segmentation performance when it aligns well with the ground-truth label. However, the current inability to reliably reproduce these maps—both manually and automatically—or the ground-truth tumor labels, restricts the achievable accuracy for automated glioblastoma segmentation on [18F]FET PET, highlighting the need for more consistent definitions of such ground-truth delineations.

BACKGROUND AND OBJECTIVES: External ventricular drain (EVD) placement is often performed freehand, a technique subpar to accurate yet impractical image-guided methods, yielding optimal placement in only 70%. The aim of this study was to address shortcomings in EVD placement and image guidance technologies by implementing high-accuracy augmented reality (AR) guidance. METHODS: We conducted a prospective clinical pilot study to assess feasibility, safety, and clinical performance of EVD placement using a standalone AR headset equipped with high-accuracy inside-out infrared tracking and software addressing EVD placement. Placement quality was reported using a newly defined extended modified Kakarla scale, and dichotomized into clinically relevant outcome parameters. Results were compared with a nonconcurrent freehand control group using one-sided Fisher exact tests. RESULTS: Eleven AR-guided EVD placements were performed, achieving functional placement in all cases on the first attempt, vs 7 (64%) in the control group (P = .045); successful placement in 9 (82%) vs 5 (45%); optimal in 8 (73%) vs 3 (27%) (P = .043); suboptimal in 2 (18%) vs 5 (45%); and failed in 0 vs 1 (9%). No AR-guided placements required revision, whereas the freehand group had a 36% reintervention rate (P = .045). Procedure-related complications occurred in 2 AR-guided cases (18%), vs 5 (45%) freehand (all post-reintervention). CONCLUSION: This study presents the first clinical use case of EVD placement using high-accuracy AR guidance contained in a standalone head-worn navigation system. Safe and reliable outcomes using a validated pipeline were demonstrated, eliminating stick-and-poke attempts and resulting in improved quality, increased single attempt success rates, and reduced revision and complication rates. Based on these results, a multicenter randomized controlled trial will be initiated.

Purpose: Automated glioblastoma segmentation from magnetic resonance imaging is generally performed on a four-modality input, including T1, contrast T1, T2 and FLAIR. We hypothesize that information redundancy is present within these image combinations, which can possibly reduce a model’s performance. Moreover, for clinical applications, the risk of encountering missing data rises as the number of required input modalities increases. Therefore, this study aimed to explore the relevance and influence of the different modalities used for MRI-based glioblastoma segmentation. Methods: After the training of multiple segmentation models based on nnU-Net and SwinUNETR architectures, differing only in their amount and combinations of input modalities, each model was evaluated with regard to segmentation accuracy and epistemic uncertainty. Results: Results show that T1CE-based segmentation (for enhanced tumor and tumor core) and T1CE-FLAIR-based segmentation (for whole tumor and overall segmentation) can reach segmentation accuracies comparable to the full-input version. Notably, the highest segmentation accuracy for nnU-Net was found for a three-input configuration of T1CE-FLAIR-T1, suggesting the confounding effect of redundant input modalities. The SwinUNETR architecture appears to suffer less from this, where said three-input and the full-input model yielded statistically equal results. Conclusion: The T1CE-FLAIR-based model can therefore be considered as a minimal-input alternative to the full-input configuration. Addition of modalities beyond this does not statistically improve and can even deteriorate accuracy, but does lower the segmentation uncertainty.

Purpose After glioblastoma (GB) recurrence, prognosis is very cumbersome. Therefore, health-related quality of life (HRQoL) and neurocognitive functioning (NCF) have become important endpoints in clinical trials when evaluating novel treatments. We aimed to evaluate the HRQoL and NCF in patients with recurrent glioblastoma (rGB) treated with a combination of surgical intervention (reoperation or biopsy) and intracerebral immune checkpoint inhibition. Methods Patients who participated in the trial (N = 23), at a single-center university hospital were included. Data were collected using 3 patient-reported outcome measures (EORTC-QLQ-C30, EORTC-QLQ-BN20, and HADS) and computerized NCF testing. In the responder group, baseline values were compared to results at a 6-month follow-up. Additionally, exploratory analyses compared baseline HRQoL and NCF between responders and non-responders. Results There were five responders and 18 non-responders. When comparing the mean and individual baseline with follow-up results for the responders, we observed overall a stable to slight clinically relevant improvement of HRQoL in multiple subsets of the questionnaires while maintaining a stable NCF. One patient deteriorated on anxiety and depression symptoms from baseline to follow-up. Conclusions In patients that responded to intracerebral immunotherapy in our institutional trial, HRQoL and NCF remained stable over time, suggesting that no detrimental effect on cognitive function or quality of life may be expected with this treatment approach. Furthermore, there seems to be an overall tendency for responders to score better on HRQoL and NCF than non-responders at baseline.

Supplementary Figure 1. Workflow employed for software-assisted quantification of immunohistochemistry through the marking of p-ERK+ cells.

Supplementary Figure 3. Association of p-ERK with survival in glioblastoma patients treated with immune checkpoint blockade.

Supplementary Figure 2. Survival outcomes of high-grade gliomas in evaluable patients