Glioblastoma Surgery Imaging-Reporting and Data System: Validation and Performance of the Automated Segmentation Task
| Autor: | Marco Rossi, Lisa Millgård Sagberg, Ole Solheim, Frederik Barkhof, Wimar A. van den Brink, Ingerid Reinertsen, Mitchel S. Berger, Emmanuel Mandonnet, Shawn L. Hervey-Jumper, Lorenzo Bello, Aeilko H. Zwinderman, Marco Conti Nibali, David Bouget, Barbara Kiesel, André Pedersen, Philip C. De Witt Hamer, Pierre A. Robe, Georg Widhalm, Ivar Kommers, Roelant S Eijgelaar, Albert J S Idema, Tommaso Sciortino, Alfred Kloet, Michiel Wagemakers, Julia Furtner, Even Hovig Fyllingen, Marnix G. Witte, Hilko Ardon, Domenique M J Müller |
|---|---|
| Přispěvatelé: | 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, Epidemiology and Data Science, APH - Methodology |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Cancer Research
Percentile neuroimaging 3D segmentation Artificial neural network computer-assisted image processing Computer science business.industry Deep learning Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION glioblastoma deep learning Neoplasms. Tumors. Oncology. Including cancer and carcinogens Pattern recognition Article Hausdorff distance Software Oncology magnetic resonance imaging Preprocessor Segmentation Artificial intelligence business RC254-282 |
| Zdroj: | Cancers Cancers, 13(18):4674. Multidisciplinary Digital Publishing Institute (MDPI) Cancers, Vol 13, Iss 4674, p 4674 (2021) Volume 13 Issue 18 Bouget, D, Eijgelaar, R S, Pedersen, A, Kommers, I, 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, De Witt Hamer, P C & Solheim, O 2021, ' Glioblastoma Surgery Imaging-Reporting and Data System : Validation and Performance of the Automated Segmentation Task ', Cancers, vol. 13, no. 18, 4674 . https://doi.org/10.3390/cancers13184674 Cancers, 13(18):4674. MDPI AG |
| ISSN: | 2072-6694 |
| DOI: | 10.3390/cancers13184674 |
| Popis: | Simple Summary Neurosurgical decisions for patients with glioblastoma depend on visual inspection of a preoperative MR scan to determine the tumor characteristics. To avoid subjective estimates and manual tumor delineation, automatic methods and standard reporting are necessary. We compared and extensively assessed the performances of two deep learning architectures on the task of automatic tumor segmentation. A total of 1887 patients from 14 institutions, manually delineated by a human rater, were compared to automated segmentations generated by neural networks. The automated segmentations were in excellent agreement with the manual segmentations, and external validity, as well as generalizability were demonstrated. Together with automatic tumor feature computation and standardized reporting, our Glioblastoma Surgery Imaging Reporting And Data System (GSI-RADS) exhibited the potential for more accurate data-driven clinical decisions. The trained models and software are open-source and open-access, enabling comparisons among surgical cohorts, multicenter trials, and patient registries. Abstract For patients with presumed glioblastoma, essential tumor characteristics are determined from preoperative MR images to optimize the treatment strategy. This procedure is time-consuming and subjective, if performed by crude eyeballing or manually. The standardized GSI-RADS aims to provide neurosurgeons with automatic tumor segmentations to extract tumor features rapidly and objectively. In this study, we improved automatic tumor segmentation and compared the agreement with manual raters, describe the technical details of the different components of GSI-RADS, and determined their speed. Two recent neural network architectures were considered for the segmentation task: nnU-Net and AGU-Net. Two preprocessing schemes were introduced to investigate the tradeoff between performance and processing speed. A summarized description of the tumor feature extraction and standardized reporting process is included. The trained architectures for automatic segmentation and the code for computing the standardized report are distributed as open-source and as open-access software. Validation studies were performed on a dataset of 1594 gadolinium-enhanced T1-weighted MRI volumes from 13 hospitals and 293 T1-weighted MRI volumes from the BraTS challenge. The glioblastoma tumor core segmentation reached a Dice score slightly below 90%, a patientwise F1-score close to 99%, and a 95th percentile Hausdorff distance slightly below 4.0 mm on average with either architecture and the heavy preprocessing scheme. A patient MRI volume can be segmented in less than one minute, and a standardized report can be generated in up to five minutes. The proposed GSI-RADS software showed robust performance on a large collection of MRI volumes from various hospitals and generated results within a reasonable runtime. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|