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

Cancers

Julia Furtner¹  Even H. Fyllingen²  Aeilko H. Zwinderman³  André Pedersen⁴  David Bouget⁴  Ingerid Reinertsen⁴  Emmanuel Mandonnet⁵  Mitchel S. Berger⁶  Shawn Hervey-Jumper⁶  Pierre A. Robe⁷  Roelant S. Eijgelaar⁸  Domenique M. J. Müller⁸  Ivar Kommers⁸  Philip C. De Witt Hamer⁸  Alfred Kloet⁹  Wimar A. van den Brink^1,10  Barbara Kiesel^1,11  Georg Widhalm^1,11  Albert J. S. Idema^1,12  Lisa M. Sagberg^1,13  Ole Solheim^1,13  Hilko Ardon^1,14  Michiel Wagemakers^1,15  Marnix G. Witte^1,16  Frederik Barkhof^1,17  Tommaso Sciortino^1,18  Lorenzo Bello^1,18  Marco Conti Nibali^1,18  Marco Rossi^1,18 

[1] Department of Biomedical Imaging and Image-Guided Therapy, Medical University Vienna, 1090 Wien, Austria;Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway;Department of Clinical Epidemiology and Biostatistics, Amsterdam University Medical Centers, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands;Department of Health Research, SINTEF Digital, NO-7465 Trondheim, Norway;Department of Neurological Surgery, Hôpital Lariboisière, 75010 Paris, France;Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA;Department of Neurology and Neurosurgery, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands;Department of Neurosurgery, Amsterdam University Medical Centers, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands;Department of Neurosurgery, Haaglanden Medical Center, 2512 VA The Hague, The Netherlands;Department of Neurosurgery, Isala, 8025 AB Zwolle, The Netherlands;Department of Neurosurgery, Medical University Vienna, 1090 Wien, Austria;Department of Neurosurgery, Northwest Clinics, 1815 JD Alkmaar, The Netherlands;Department of Neurosurgery, St. Olav’s Hospital, Trondheim University Hospital, NO-7030 Trondheim, Norway;Department of Neurosurgery, Twee Steden Hospital, 5042 AD Tilburg, The Netherlands;Department of Neurosurgery, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands;Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands;Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands;Neurosurgical Oncology Unit, Department of Oncology and Hemato-Oncology, Humanitas Research Hospital, Università Degli Studi di Milano, 20122 Milano, Italy;

关键词: glioblastoma;    magnetic resonance imaging;    neuroimaging;    computer-assisted image processing;    machine learning;    neurosurgical procedures;   

DOI  :  10.3390/cancers13122854

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown