Glioblastoma Surgery Imaging—Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated Segmentations
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/13/12/2854 |
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| author | Ivar Kommers David Bouget André Pedersen Roelant S. Eijgelaar Hilko Ardon Frederik Barkhof Lorenzo Bello Mitchel S. Berger Marco Conti Nibali Julia Furtner Even H. Fyllingen Shawn Hervey-Jumper Albert J. S. Idema Barbara Kiesel Alfred Kloet Emmanuel Mandonnet Domenique M. J. Müller Pierre A. Robe Marco Rossi Lisa M. Sagberg Tommaso Sciortino Wimar A. van den Brink Michiel Wagemakers Georg Widhalm Marnix G. Witte Aeilko H. Zwinderman Ingerid Reinertsen Ole Solheim Philip C. De Witt Hamer |
| author_facet | Ivar Kommers David Bouget André Pedersen Roelant S. Eijgelaar Hilko Ardon Frederik Barkhof Lorenzo Bello Mitchel S. Berger Marco Conti Nibali Julia Furtner Even H. Fyllingen Shawn Hervey-Jumper Albert J. S. Idema Barbara Kiesel Alfred Kloet Emmanuel Mandonnet Domenique M. J. Müller Pierre A. Robe Marco Rossi Lisa M. Sagberg Tommaso Sciortino Wimar A. van den Brink Michiel Wagemakers Georg Widhalm Marnix G. Witte Aeilko H. Zwinderman Ingerid Reinertsen Ole Solheim Philip C. De Witt Hamer |
| author_sort | Ivar Kommers |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T10:37:31Z |
| format | Article |
| id | doaj.art-d847d1b403a34084bc9a3ddb259a76a1 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-10T10:37:31Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-d847d1b403a34084bc9a3ddb259a76a12023-11-21T23:11:45ZengMDPI AGCancers2072-66942021-06-011312285410.3390/cancers13122854Glioblastoma Surgery Imaging—Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated SegmentationsIvar Kommers0David Bouget1André Pedersen2Roelant S. Eijgelaar3Hilko Ardon4Frederik Barkhof5Lorenzo Bello6Mitchel S. Berger7Marco Conti Nibali8Julia Furtner9Even H. Fyllingen10Shawn Hervey-Jumper11Albert J. S. Idema12Barbara Kiesel13Alfred Kloet14Emmanuel Mandonnet15Domenique M. J. Müller16Pierre A. Robe17Marco Rossi18Lisa M. Sagberg19Tommaso Sciortino20Wimar A. van den Brink21Michiel Wagemakers22Georg Widhalm23Marnix G. Witte24Aeilko H. Zwinderman25Ingerid Reinertsen26Ole Solheim27Philip C. De Witt Hamer28Department of Neurosurgery, Amsterdam University Medical Centers, Vrije Universiteit, 1081 HV Amsterdam, The NetherlandsDepartment of Health Research, SINTEF Digital, NO-7465 Trondheim, NorwayDepartment of Health Research, SINTEF Digital, NO-7465 Trondheim, NorwayDepartment of Neurosurgery, Amsterdam University Medical Centers, Vrije Universiteit, 1081 HV Amsterdam, The NetherlandsDepartment of Neurosurgery, Twee Steden Hospital, 5042 AD Tilburg, The NetherlandsDepartment of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Vrije Universiteit, 1081 HV Amsterdam, The NetherlandsNeurosurgical Oncology Unit, Department of Oncology and Hemato-Oncology, Humanitas Research Hospital, Università Degli Studi di Milano, 20122 Milano, ItalyDepartment of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USANeurosurgical Oncology Unit, Department of Oncology and Hemato-Oncology, Humanitas Research Hospital, Università Degli Studi di Milano, 20122 Milano, ItalyDepartment of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, 1090 Wien, AustriaDepartment of Circulation and Medical Imaging, Norwegian University of Science and Technology, NO-7491 Trondheim, NorwayDepartment of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USADepartment of Neurosurgery, Northwest Clinics, 1815 JD Alkmaar, The NetherlandsDepartment of Neurosurgery, Medical University Vienna, 1090 Wien, AustriaDepartment of Neurosurgery, Haaglanden Medical Center, 2512 VA The Hague, The NetherlandsDepartment of Neurological Surgery, Hôpital Lariboisière, 75010 Paris, FranceDepartment of Neurosurgery, Amsterdam University Medical Centers, Vrije Universiteit, 1081 HV Amsterdam, The NetherlandsDepartment of Neurology and Neurosurgery, University Medical Center Utrecht, 3584 CX Utrecht, The NetherlandsNeurosurgical Oncology Unit, Department of Oncology and Hemato-Oncology, Humanitas Research Hospital, Università Degli Studi di Milano, 20122 Milano, ItalyDepartment of Neurosurgery, St. Olav’s Hospital, Trondheim University Hospital, NO-7030 Trondheim, NorwayNeurosurgical Oncology Unit, Department of Oncology and Hemato-Oncology, Humanitas Research Hospital, Università Degli Studi di Milano, 20122 Milano, ItalyDepartment of Neurosurgery, Isala, 8025 AB Zwolle, The NetherlandsDepartment of Neurosurgery, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The NetherlandsDepartment of Neurosurgery, Medical University Vienna, 1090 Wien, AustriaDepartment of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The NetherlandsDepartment of Clinical Epidemiology and Biostatistics, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The NetherlandsDepartment of Health Research, SINTEF Digital, NO-7465 Trondheim, NorwayDepartment of Neurosurgery, St. Olav’s Hospital, Trondheim University Hospital, NO-7030 Trondheim, NorwayDepartment of Neurosurgery, Amsterdam University Medical Centers, Vrije Universiteit, 1081 HV Amsterdam, The NetherlandsTreatment 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.https://www.mdpi.com/2072-6694/13/12/2854glioblastomamagnetic resonance imagingneuroimagingcomputer-assisted image processingmachine learningneurosurgical procedures |
| spellingShingle | Ivar Kommers David Bouget André Pedersen Roelant S. Eijgelaar Hilko Ardon Frederik Barkhof Lorenzo Bello Mitchel S. Berger Marco Conti Nibali Julia Furtner Even H. Fyllingen Shawn Hervey-Jumper Albert J. S. Idema Barbara Kiesel Alfred Kloet Emmanuel Mandonnet Domenique M. J. Müller Pierre A. Robe Marco Rossi Lisa M. Sagberg Tommaso Sciortino Wimar A. van den Brink Michiel Wagemakers Georg Widhalm Marnix G. Witte Aeilko H. Zwinderman Ingerid Reinertsen Ole Solheim Philip C. De Witt Hamer Glioblastoma Surgery Imaging—Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated Segmentations Cancers glioblastoma magnetic resonance imaging neuroimaging computer-assisted image processing machine learning neurosurgical procedures |
| title | Glioblastoma Surgery Imaging—Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated Segmentations |
| title_full | Glioblastoma Surgery Imaging—Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated Segmentations |
| title_fullStr | Glioblastoma Surgery Imaging—Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated Segmentations |
| title_full_unstemmed | Glioblastoma Surgery Imaging—Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated Segmentations |
| title_short | Glioblastoma Surgery Imaging—Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated Segmentations |
| title_sort | glioblastoma surgery imaging reporting and data system standardized reporting of tumor volume location and resectability based on automated segmentations |
| topic | glioblastoma magnetic resonance imaging neuroimaging computer-assisted image processing machine learning neurosurgical procedures |
| url | https://www.mdpi.com/2072-6694/13/12/2854 |
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