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.

BACKGROUND AND OBJECTIVES: Patients with newly diagnosed lower-grade glioma (World Health Organization grade II and III) are typically of working age. However, work resumption after surgical resection is uncertain, possibly due to loss of capacity from resection of tumor-infiltrated brain regions. Therefore, we explore the association between work resumption and the resection location in addition to other patient, tumor, and treatment characteristics. METHODS: This retrospective cohort consisted of adults undergoing first-time resection for lower-grade glioma between 2011 and 2020 in hospitals in France, Germany, and the Netherlands. Employment was evaluated at baseline and within 1 year after surgery to determine work resumption. The association between work resumption and patient, tumor, and treatment characteristics was analyzed using logistic regression. Resection cavities were segmented from postoperative MRI scans, registered to standard brain space and related to gray nuclei, cortical networks, and white matter tracts using atlas parcellations. To identify brain regions potentially involved with work resumption, the association between work resumption and resection location was analyzed using Bayesian hurdle regression. The identified regions and characteristics were jointly analyzed in their association with work resumption using multiple logistic regression. RESULTS: Of 207 patients, 181 (87%) were employed at baseline. Of these employed patients, 111 (61%) had resumed work at follow-up. Male sex, younger age, and larger extent of resection were independent significant predictors of work resumption. Resection location was not associated with work resumption. CONCLUSION: Almost two-thirds of patients resumed work 1 year after surgery. Work resumption was associated with patient characteristics (male sex and younger age) and extent of resection, but not with resection location.