ArticleCancers2021
Glioblastoma Surgery Imaging-Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated Segmentations.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- A unified FLAIR hyperintensity segmentation model for various CNS tumor types and acquisition time points.Scientific reports · 2026Article
- Review
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- Automatic Brain Tumor Segmentation in 2D Intra-Operative Ultrasound Images Using Magnetic Resonance Imaging Tumor Annotations.Journal of imaging · 2025Article
- Structured reporting of gliomas based on VASARI criteria to improve report content and consistency.BMC medical imaging · 2025Article
- Sequential immunotherapy and bevacizumab treatments in glioblastoma multiforme: A case series and review of the literature.Oncology letters · 2025Article
- Patient selection and outcome in low-grade glioma surgery.Frontiers in oncology · 2025Article
- Glioma lateralization: Focus on the anatomical localization and the distribution of molecular alterations (Review).Oncology reports · 2024Review
- Deep Learning for MRI Segmentation and Molecular Subtyping in Glioblastoma: Critical Aspects from an Emerging Field.Biomedicines · 2024Review
- Augmented surgical decision-making for glioblastoma: integrating AI tools into education and practice.Frontiers in neurology · 2024Article
- Standardized evaluation of the extent of resection in glioblastoma with automated early post-operative segmentation.Frontiers in radiology · 2024Article
- Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.Scientific reports · 2023Article
- Deep learning-based algorithm for postoperative glioblastoma MRI segmentation: a promising new tool for tumor burden assessment.Brain informatics · 2023Article
- Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting.Scientific reports · 2023Article
- The clinical characteristics and outcomes of incidentally discovered glioblastoma.Journal of neuro-oncology · 2022Article
- Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting.Frontiers in neurology · 2022Article
- Glioblastoma Surgery Imaging-Reporting and Data System: Validation and Performance of the Automated Segmentation Task.Cancers · 2021Article
- Lower-Grade Gliomas: An Epidemiological Voxel-Based Analysis of Location and Proximity to Eloquent Regions.Frontiers in oncology · 2021Article
- Estimating overall survival of glioblastoma patients using clinical variables, tumor size, and location.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
29 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.