Faculty Bibliography

The paradigm of isocitrate dehydrogenase (IDH)-wildtype glioblastoma is rapidly evolving, reflecting clinical, pathological, and imaging advancements. Thus, it remains challenging for radiologists, even those who are dedicated to neuro-oncology imaging, to keep pace with this rapidly progressing field and provide useful and updated information to clinicians. Based on current knowledge, radiologists can play a significant role in managing patients with IDH-wildtype glioblastoma by providing accurate preoperative diagnosis as well as preoperative and postoperative treatment planning including accurate delineation of the residual tumor. Through active communication with clinicians, extending far beyond the confines of the radiology reading room, radiologists can impact clinical decision making. This Part 1 review provides an overview about the neuropathological diagnosis of glioblastoma to understand the past, present, and upcoming revisions of the World Health Organization classification. The imaging findings that are noteworthy for radiologists while communicating with clinicians on preoperative and immediate postoperative imaging of IDH-wildtype glioblastomas will be summarized.

OBJECTIVE:The objective was to comprehensively investigate the clinical, molecular, and imaging characteristics and outcomes of H3 K27-altered diffuse midline glioma (DMG) in adults. METHODS:Retrospective chart and imaging reviews were performed in 111 adult patients with H3 K27-altered DMG from two tertiary institutions. Clinical, molecular, imaging, and survival characteristics were analyzed. Characteristics were compared between adult and 365 pediatric patients from a previous multicenter meta-analysis dataset. Cox analyses were performed to determine predictors of overall survival (OS) in adult patients. RESULTS:The median (range) age of adult patients was 40 (18-75) years, and 64 males and 47 females were included. Adults had a higher male proportion (57.7% vs 45.3%, p = 0.023), lower proportion of histological grade 4 (41.4% vs 74.0%, p < 0.001), and different tumor locations (p < 0.001) compared with pediatric patients; adults commonly showed a thalamus location (41.5%) followed by the spinal cord (27.0%), whereas pediatric patients predominantly showed a pons location (64.9%). The OS of adults was longer than that of pediatric patients (30.3 vs 12.0 months, p < 0.001, log-rank test). Older age at diagnosis (HR 0.96, p = 0.001), histologically lower grade (HR 0.25, p = 0.003), and gross-total resection of nonenhancing tumor (HR 0.15, p = 0.003) were independent favorable prognostic factors. CONCLUSIONS:Adult patients with H3 K27-altered DMG showed distinct clinical, histological, and imaging characteristics compared to pediatric counterparts, with a significantly better prognosis. The authors' results suggest that aggressive surgery should be pursued when deemed feasible for better survival outcomes.

Owing to recent advancements in various postoperative treatment modalities, such as radiation, chemotherapy, antiangiogenic treatment, and immunotherapy, the radiological and clinical assessment of patients with isocitrate dehydrogenase-wildtype glioblastoma using post-treatment imaging has become increasingly challenging. This review highlights the challenges in differentiating treatment-related changes such as pseudoprogression, radiation necrosis, and pseudoresponse from true tumor progression and aims to serve as a guideline for efficient communication with clinicians for optimal management of patients with post-treatment imaging.

PURPOSE/OBJECTIVE:The purpose of this study was to investigate an extended self-adapting nnU-Net framework for detecting and segmenting brain metastases (BM) on magnetic resonance imaging (MRI). METHODS AND MATERIALS/METHODS:Six different nnU-Net systems with adaptive data sampling, adaptive Dice loss, or different patch/batch sizes were trained and tested for detecting and segmenting intraparenchymal BM with a size ≥2 mm on 3 Dimensional (3D) post-Gd T1-weighted MRI volumes using 2092 patients from 7 institutions (1712, 195, and 185 patients for training, validation, and testing, respectively). Gross tumor volumes of BM delineated by physicians for stereotactic radiosurgery were collected retrospectively and curated at each institute. Additional centralized data curation was carried out to create gross tumor volumes of uncontoured BM by 2 radiologists to improve the accuracy of ground truth. The training data set was augmented with synthetic BMs of 1025 MRI volumes using a 3D generative pipeline. BM detection was evaluated by lesion-level sensitivity and false-positive (FP) rate. BM segmentation was assessed by lesion-level Dice similarity coefficient, 95-percentile Hausdorff distance, and average Hausdorff distance (HD). The performances were assessed across different BM sizes. Additional testing was performed using a second data set of 206 patients. RESULTS:. Mean values of Dice similarity coefficient, 95-percentile Hausdorff distance, and average HD of all detected BMs were 0.758, 1.45, and 0.23 mm, respectively. Performances on the second testing data set achieved a sensitivity of 0.907 at an FP rate of 0.57 ± 0.85 for all BM sizes, and an average HD of 0.33 mm for all detected BM. CONCLUSIONS:Our proposed extension of the self-configuring nnU-Net framework substantially improved small BM detection sensitivity while maintaining a controlled FP rate. Clinical utility of the extended nnU-Net model for assisting early BM detection and stereotactic radiosurgery planning will be investigated.

BACKGROUND:Glioblastoma is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification. METHODS:We developed a highly reproducible, personalized prognostication and clinical subgrouping system using machine learning (ML) on routine clinical data, MRI, and molecular measures from 2,838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]). RESULTS:The ML model stratified patients into distinct prognostic subgroups with HRs between subgroups I-II and I-III of 1.62 (95%CI: 1.43-1.84, p<0.001) and 3.48 (95%CI: 2.94-4.11, p<0.001), respectively. Analysis of imaging features revealed several tumor properties contributing unique prognostic value, supporting the feasibility of a generalizable prognostic classification system in a diverse cohort. CONCLUSIONS:Our ML model demonstrates extensive reproducibility and online accessibility, utilizing routine imaging data rather than complex imaging protocols. This platform offers a unique approach for personalized patient management and clinical trial stratification in glioblastoma.

Technological advancements have enabled the extended investigation, development, and application of computational approaches in various domains, including health care. A burgeoning number of diagnostic, predictive, prognostic, and monitoring biomarkers are continuously being explored to improve clinical decision making in neuro-oncology. These advancements describe the increasing incorporation of artificial intelligence (AI) algorithms, including the use of radiomics. However, the broad applicability and clinical translation of AI are restricted by concerns about generalisability, reproducibility, scalability, and validation. This Policy Review intends to serve as the leading resource of recommendations for the standardisation and good clinical practice of AI approaches in health care, particularly in neuro-oncology. To this end, we investigate the repeatability, reproducibility, and stability of AI in response assessment in neuro-oncology in studies on factors affecting such computational approaches, and in publicly available open-source data and computational software tools facilitating these goals. The pathway for standardisation and validation of these approaches is discussed with the view of trustworthy AI enabling the next generation of clinical trials. We conclude with an outlook on the future of AI-enabled neuro-oncology.

The development, application, and benchmarking of artificial intelligence (AI) tools to improve diagnosis, prognostication, and therapy in neuro-oncology are increasing at a rapid pace. This Policy Review provides an overview and critical assessment of the work to date in this field, focusing on diagnostic AI models of key genomic markers, predictive AI models of response before and after therapy, and differentiation of true disease progression from treatment-related changes, which is a considerable challenge based on current clinical care in neuro-oncology. Furthermore, promising future directions, including the use of AI for automated response assessment in neuro-oncology, are discussed.

BACKGROUND:The incidence of leptomeningeal metastases (LM) has been reported diversely. This study aimed to investigate the incidence, risk factors, and prognosis of LM in patients with IDH-wildtype glioblastoma. METHODS:A total of 828 patients with IDH-wildtype glioblastoma were enrolled between 2005 and 2022. Baseline preoperative MRI including post-contrast fluid-attenuated inversion recovery (FLAIR) was used for LM diagnosis. Qualitative and quantitative features, including distance between tumor and subventricular zone (SVZ) and tumor volume by automatic segmentation of the lateral ventricles and tumor, were assessed. Logistic analysis of LM development was performed using clinical, molecular, and imaging data. Survival analysis was performed. RESULTS:The incidence of LM was 11.4%. MGMTp unmethylation (odds ratio [OR] = 1.92, P = 0.014), shorter distance between tumor and SVZ (OR = 0.94, P = 0.010), and larger contrast-enhancing tumor volume (OR = 1.02, P < 0.001) were significantly associated with LM. The overall survival (OS) was significantly shorter in patients with LM than in those without (log-rank test; P < 0.001), with median OS of 12.2 and 18.5 months, respectively. Presence of LM remained an independent prognostic factor for OS in IDH-wildtype glioblastoma (hazard ratio = 1.42, P = 0.011), along with other clinical, molecular, imaging, and surgical prognostic factors. CONCLUSION/CONCLUSIONS:The incidence of LM is high in patients with IDH-wildtype glioblastoma, and aggressive molecular and imaging factors are correlated with LM development. The prognostic significance of LM based on post-contrast FLAIR imaging suggests acknowledgement of post-contrast FLAIR as a reliable diagnostic tool for clinicians.

BACKGROUND AND PURPOSE/OBJECTIVE:-WT), grade 4. MATERIALS AND METHODS/METHODS:-WT with comprehensive information on tumor parameters was acquired from the Radiomics Signatures for Precision Oncology in Glioblastoma consortium. Data imputation was performed for missing values. Sex-based differences in tumor parameters, such as age, molecular parameters, preoperative Karnofsky performance score (KPS), tumor volumes, epicenter, and laterality were assessed through nonparametric tests. Spatial atlases were generated by using preoperative MRI maps to visualize tumor characteristics. Survival time analysis was performed through log-rank tests and Cox proportional hazard analyses. RESULTS:in men, FDR = 0.0001). The right temporal region was the most common tumor epicenter in the overall population. Right as well as left temporal lobes were more frequently involved in men. There were no sex-specific differences in survival outcomes and mortality ratios. Higher age, unmethylated O6-methylguanine-DNA-methyltransferase promoter and undergoing subtotal resection increased the mortality risk in both men and women. CONCLUSIONS:Our study demonstrates significant sex-based differences in clinical and radiologic tumor parameters of patients with glioblastoma. Sex is not an independent prognostic factor for survival outcomes and the tumor parameters influencing patient outcomes are identical for men and women.