Treatment decisions for patients with presumed glioblastoma are based on tumor characteristics available from a preoperative MR scan. Tumor characteristics, including volume, location, and resectability, are often estimated or manually delineated. This process is time consuming and subjective. Hence, comparison across cohorts, trials, or registries are subject to assessment bias. In this study, we propose a standardized Glioblastoma Surgery Imaging Reporting and Data System (GSI-RADS) based on an automated method of tumor segmentation that provides standard reports on tumor features that are potentially relevant for glioblastoma surgery. As clinical validation, we determine the agreement in extracted tumor features between the automated method and the current standard of manual segmentations from routine clinical MR scans before treatment. In an observational consecutive cohort of 1596 adult patients with a first time surgery of a glioblastoma from 13 institutions, we segmented gadolinium-enhanced tumor parts both by a human rater and by an automated algorithm. Tumor features were extracted from segmentations of both methods and compared to assess differences, concordance, and equivalence. The laterality, contralateral infiltration, and the laterality indices were in excellent agreement. The native and normalized tumor volumes had excellent agreement, consistency, and equivalence. Multifocality, but not the number of foci, had good agreement and equivalence. The location profiles of cortical and subcortical structures were in excellent agreement. The expected residual tumor volumes and resectability indices had excellent agreement, consistency, and equivalence. Tumor probability maps were in good agreement. In conclusion, automated segmentations are in excellent agreement with manual segmentations and practically equivalent regarding tumor features that are potentially relevant for neurosurgical purposes. Standard GSI-RADS reports can be generated by open access software.

For patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude eyeballing or manually. The standardized GSI-RADS aims to provide neurosurgeons with automatic tumor segmentations to extract tumor features rapidly and objectively. In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSI-RADS, and determined their speed. Two recent neural network architectures were considered for the segmentation task: nnU-Net and AGU-Net. Two preprocessing schemes were introduced to investigate the tradeoff between performance and processing speed. A summarized description of the tumor feature extraction and standardized reporting process is included. The trained architectures for automatic segmentation and the code for computing the standardized report are distributed as open-source and as open-access software. Validation studies were performed on a dataset of 1594 gadolinium-enhanced T1-weighted MRI volumes from 13 hospitals and 293 T1-weighted MRI volumes from the BraTS challenge. The glioblastoma tumor core segmentation reached a Dice score slightly below 90%, a patientwise F1-score close to 99%, and a 95th percentile Hausdorff distance slightly below $4.0$ mm on average with either architecture and the heavy preprocessing scheme. A patient MRI volume can be segmented in less than one minute, and a standardized report can be generated in up to five minutes. The proposed GSI-RADS software showed robust performance on a large collection of MRI volumes from various hospitals and generated results within a reasonable runtime.

Purpose: Many patients with a malignant (i.e., grade II-IV) glioma are of working age, yet they are rarely included in “cancer and work” studies. Here, we explored (1) the work-related experiences and unmet needs of patients with a malignant glioma and (2) the experiences and needs of relevant healthcare and occupational (health) professionals (“professionals”) in providing work-related support to this patient group. Methods: Individual semi-structured interviews were held with patients with a malignant glioma who were of working age and had an employment contract at diagnosis, and relevant professionals. Interviews were transcribed verbatim and analysed thematically. Results: Patients (n = 22) were on average 46 ± 13 years of age (64% male) and diagnosed with a grade II (n = 12), III (n = 4), or IV glioma (n = 6). Professionals (n = 16) had on average 15 ± 9 years of relevant work experience with the patient group. Four themes emerged from the data: (1) having a malignant glioma: experienced consequences on work ability, (2) communicating about the consequences of a malignant glioma at work, (3) distilling the right approach: generic or tailored work-related support, and (4) accessibility of work-related support. Conclusions: Glioma-specific consequences on patients’ work ability necessitate better communication between, and tailored guidance for, patients, relevant professionals, and the workplace. Suggestions for improvement, e.g., the periodic use of comprehensive neuropsychological assessments, are provided in the article. Implications for cancer survivors: Patients with a malignant glioma would benefit from tailored and proactive outreach about work-related issues bv relevant professionals.

Purpose: This registry-based study aims to examine the variation in biopsy and resection indications for glioblastoma patients across Dutch hospitals and to identify patient- and hospital-related factors associated with the surgical treatment. Methods: Data from all 7443 adults with first-time glioblastoma surgery at 12 hospitals were obtained from the prospective population-based Quality Registry Neurosurgery in the Netherlands between 2011 and 2021. Patients were stratified by either biopsy or resection. We analyzed variation in American Association of Anesthesiologist (ASA) classification, Karnofsky Performance Score (KPS), gender and age distribution between the different centers. Between-hospital variation in biopsy percentage was analyzed using a funnel plot. Logistic regression was used to identify associated patient- and hospital-related factors. Results: In total, 32% of the newly diagnosed glioblastoma patients underwent a biopsy, with wide variations between the different centers (23–56%). Patients-related variables such as higher age or ASA classification and lower KPS were significantly associated with the indication for biopsy. After correction for these factors, between-hospital variation persisted, with two institutes performing more biopsies than expected and one less than expected. Median overall survival was 12.5 months (95% CI 12.2–12.9) in the resection group and 5.6 months (95% CI 5.1–6) in the biopsy group, with wide variations between the different centers. Conclusion: A substantial between-hospital variation in biopsy percentages was found. Patient factors (age, ASA classification and KPS) but also hospital factors (such as academic setting) impact surgical decisions. Variation persisted also after correction for potential confounders, indicating that other factors play a role in decision-making.