Glioblastoma surgery imaging—reporting and data system: Standardized reporting of tumor volume, location, and resectability based on automated segmentations
Kommers, Ivar; Bouget, David; Pedersen, André; Eijgelaar, Roelant S.; Ardon, Hilko; Barkhof, Frederik; Bello, Lorenzo; Berger, Mitchel S.; Nibali, Marco Conti; Furtner, Julia; Fyllingen, Even H.; Hervey-Jumper, Shawn; Idema, Albert J.S.; Kiesel, Barbara; Kloet, Alfred; Mandonnet, Emmanuel; Müller, Domenique M.J.; Robe, Pierre A.; Rossi, Marco; Sagberg, Lisa M.; Sciortino, Tommaso; van den Brink, Wimar A.; Wagemakers, Michiel; Widhalm, Georg; Witte, Marnix G.; Zwinderman, Aeilko H.; Reinertsen, Ingerid; Solheim, Ole; De Witt Hamer, Philip C.
(2021) Cancers, volume 13, issue 12
(Article)
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.
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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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Keywords: Computer-assisted image processing, Glioblastoma, Machine learning, Magnetic resonance imaging, Neuroimaging, Neurosurgical procedures, computer-assisted image processing, neurosurgical procedures, magnetic resonance imaging, glioblastoma, machine learning, neuroimaging, Oncology, Cancer Research, Journal Article
ISSN: 2072-6694
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
Note: Funding Information: Funding: This research was supported by an unrestricted grant of Stichting Hanarth fonds, “Machine learning for better neurosurgical decisions in patients with glioblastoma”; a grant for public-private partnerships (Amsterdam UMC PPP-grant) sponsored by the Dutch government (Ministry of Economic Affairs) through the Rijksdienst voor Ondernemend Nederland (RVO) and Topsector Life Sciences and Health (LSH), “Picturing predictions for patients with brain tumors”; a grant from the Innovative Medical Devices Initiative program, project number 10-10400-96-14003; The Netherlands Organisation for Scientific Research (NWO), 2020.027; a grant from the Dutch Cancer Society, VU2014-7113; the Anita Veldman foundation, CCA2018-2-17; and the Norwegian National Advisory Unit for Ultrasound and Image Guided Therapy. Funding Information: Acknowledgments: BrainLab® has generously provided us with their proprietary neuronavigational software, which was used for the manual segmentation. This work was in part conducted on the Dutch national e-infrastructure with the support of SURF Cooperative and the Translational Research IT (TraIT) project, an initiative from the Center for Translational Molecular Medicine (CTMM). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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