Reitman, Zachary J.

Abstract Background Multi-modality therapy has improved survival for many patients with brain metastases. However, neurocognitive sequelae remain a challenge. While advances in radiotherapy have reduced toxicity, the potential interaction with targeted therapy and immunotherapy and the consequent impact on neurocognitive outcomes remain poorly defined. We appraised the current evidence on neurocognitive outcomes in the era of modern combined-modality therapy. Methods This systematic review adhered to PRISMA guidelines and included clinical trials reporting neurocognitive outcomes in patients with brain metastases receiving radiotherapy ± concurrent systemic therapies. Studies published through December 31, 2024 were identified through PubMed/Medline, EMBASE, and Web of Science using pre-defined MeSH term-based search strings. Results 39 studies from 1997-2024 involving 6,617 patients met inclusion criteria (n = 27 whole-brain radiotherapy [WBRT]; n = 12 radiosurgery), including six studies evaluating combined-modality therapy in 1,161 patients. Baseline cognitive evaluation was performed inconsistently, but neurocognitive deficits were frequently observed prior to treatment. Most randomized trials reported improved cognitive outcomes and quality of life with advanced radiotherapy techniques (e.g., hippocampal avoidance, radiosurgery) vs WBRT. Among 20 randomized trials comparing radiotherapy with or without a concurrent investigational systemic therapy, only four included neurocognitive assessments. There was no evidence of cognitive sequelae with combined-modality therapy, though evaluable trials were limited due to inconsistent reporting of neurocognitive functioning. Conclusion As survival improves in patients with brain metastases, preserved cognition is an important outcome measure. The lack of neurocognitive endpoints in pivotal brain metastasis trials represents a critical evidence gap. The interaction between radiotherapy and novel systemic therapies, and their combined effects on neurocognition, remain unknown. To address this, we recommend 1) integrating sensitive, pragmatic, and tailored neurocognitive assessments into oncology trials to quantify additive toxicity, 2) early and systematically incorporating neuropsychology into care pathways, 3) expanding radiation dose-sparing and radioprotective strategies, and 4) evaluating the utility of cognitive rehabilitation programs.

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTS-PEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps.

Abstract DNA damage response (DDR) mechanisms are critical to maintenance of overall genomic stability, and their dysfunction can contribute to oncogenesis. Significant advances in our understanding of DDR pathways have raised the possibility of developing therapies that exploit these processes. In this expert-driven consensus review, we examine mechanisms of response to DNA damage, progress in development of DDR inhibitors in IDH-wild-type glioblastoma and IDH-mutant gliomas, and other important considerations such as biomarker development, preclinical models, combination therapies, mechanisms of resistance and clinical trial design considerations.