Glioblastoma Surgery Imaging—Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated Segmentations

Autor: Lisa Millgård Sagberg, Marnix G. Witte, Domenique M J Müller, Frederik Barkhof, Marco Rossi, Wimar A. van den Brink, André Pedersen, Hilko Ardon, Pierre A. Robe, Ole Solheim, Ivar Kommers, Philip C. De Witt Hamer, Michiel Wagemakers, Georg Widhalm, Shawn L. Hervey-Jumper, Mitchel S. Berger, Aeilko H. Zwinderman, Roelant S Eijgelaar, Alfred Kloet, David Bouget, Albert J S Idema, Barbara Kiesel, Tommaso Sciortino, Even Hovig Fyllingen, Julia Furtner, Lorenzo Bello, Ingerid Reinertsen, Emmanuel Mandonnet, Marco Conti Nibali
Přispěvatelé: Epidemiology and Data Science, APH - Methodology, Neurosurgery, Radiology and nuclear medicine, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Neuroinfection & -inflammation, CCA - Imaging and biomarkers, CCA - Cancer Treatment and quality of life, Amsterdam Neuroscience - Systems & Network Neuroscience
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Cancer Research
medicine.medical_specialty
Artificial intelligence
RESECTION
computer-assisted image processing
Concordance
Article
Neurosurgical Procedures
030218 nuclear medicine & medical imaging
CLINICAL TARGET VOLUME
03 medical and health sciences
0302 clinical medicine
Neuroimaging
Consistency (statistics)
medicine
Clinicial decision support
magnetic resonance imaging
DIAGNOSTIC-ACCURACY
Equivalence (measure theory)
RC254-282
Neurokirurgiske / nevrokirurgiske prosedyrer
neuroimaging
medicine.diagnostic_test
business.industry
glioblastoma
EXTENT
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Magnetic resonance imaging
CARE
medicine.disease
Radiology and diagnostic imaging: 763 [VDP]
neurosurgical procedures
Surgery
Radiologi og bildediagnostikk: 763 [VDP]
machine learning
Oncology
Kunstig intelligens
030220 oncology & carcinogenesis
AGREEMENT
PATTERNS
SURVIVAL
Klinisk beslutningsstøtte
GLIOMA
Observational study
business
HUMAN CEREBRAL-CORTEX
Volume (compression)
Glioblastoma
Zdroj: Cancers, 13(12):2854. MDPI AG
Cancers, Vol 13, Iss 2854, p 2854 (2021)
Cancers
Cancers, 13(12):2854. Multidisciplinary Digital Publishing Institute (MDPI)
Volume 13
Issue 12
Kommers, I, Bouget, D, Pedersen, A, Eijgelaar, R S, Ardon, H, Barkhof, F, Bello, L, Berger, M S, Nibali, M C, Furtner, J, Fyllingen, E H, Hervey-Jumper, S, Idema, A J S, Kiesel, B, Kloet, A, Mandonnet, E, Müller, D M J, Robe, P A, Rossi, M, Sagberg, L M, Sciortino, T, van den Brink, W A, Wagemakers, M, Widhalm, G, Witte, M G, Zwinderman, A H, Reinertsen, I, Solheim, O & de Witt Hamer, P C 2021, ' Glioblastoma surgery imaging—reporting and data system: Standardized reporting of tumor volume, location, and resectability based on automated segmentations ', Cancers, vol. 13, no. 12, 2854 . https://doi.org/10.3390/cancers13122854
ISSN: 2072-6694
DOI: 10.3390/cancers13122854
Popis: Simple Summary Neurosurgical decisions for patients with glioblastoma depend on tumor characteristics in the preoperative MR scan. Currently, this is based on subjective estimates or manual tumor delineation in the absence of a standard for reporting. We compared tumor features of 1596 patients from 13 institutions extracted from manual segmentations by a human rater and from automated segmentations generated by a machine learning model. The automated segmentations were in excellent agreement with manual segmentations and are practically equivalent regarding tumor features that are potentially relevant for neurosurgical purposes. Standard reports can be generated by open access software, enabling comparison between surgical cohorts, multicenter trials, and patient registries. 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.
Databáze: OpenAIRE
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