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1دورية أكاديمية
المؤلفون: 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., Muller 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.
المساهمون: I. Kommer, D. Bouget, A. Pedersen, R.S. Eijgelaar, H. Ardon, F. Barkhof, L. Bello, M.S. Berger, M.C. Nibali, J. Furtner, E.H. Fyllingen, S. Hervey-Jumper, A.J.S. Idema, B. Kiesel, A. Kloet, E. Mandonnet, D.M.J. Muller, P.A. Robe, M. Rossi, L.M. Sagberg, T. Sciortino, W.A. van den Brink, M. Wagemaker, G. Widhalm, M.G. Witte, A.H. Zwinderman, I. Reinertsen, O. Solheim, P.C. De Witt Hamer
مصطلحات موضوعية: Computer-assisted image processing, Glioblastoma, Machine learning, Magnetic resonance imaging, Neuroimaging, Neurosurgical procedures, Settore MED/27 - Neurochirurgia
الوصف: 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.
العلاقة: info:eu-repo/semantics/altIdentifier/pmid/34201021; info:eu-repo/semantics/altIdentifier/wos/WOS:000665882300001; volume:13; issue:12; firstpage:1; lastpage:23; numberofpages:23; journal:CANCERS; http://hdl.handle.net/2434/883412Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85107462533
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2دورية أكاديمية
المؤلفون: Muller D. M. J., Robe P. A., Ardon H., Barkhof F., Bello L., Berger M. S., Bouwknegt W., Van den Brink W. A., Nibali M. C., Eijgelaar R. S., Furtner J., Han S. J., Hervey-Jumper S. L., Idema A. J. S., Kiesel B., Kloet A., De Munck J. C., Rossi M., Sciortino T., Peter Vandertop W., Visser M., Wagemakers M., Widhalm G., Witte M. G., Zwinderman A. H., De Witt Hamer P. C.
المساهمون: D.M.J. Muller, P.A. Robe, H. Ardon, F. Barkhof, L. Bello, M.S. Berger, W. Bouwknegt, W.A. Van den Brink, M.C. Nibali, R.S. Eijgelaar, J. Furtner, S.J. Han, S.L. Hervey-Jumper, A.J.S. Idema, B. Kiesel, A. Kloet, J.C. De Munck, M. Rossi, T. Sciortino, W. Peter Vandertop, M. Visser, M. Wagemaker, G. Widhalm, M.G. Witte, A.H. Zwinderman, P.C. De Witt Hamer
مصطلحات موضوعية: Extent of resection, Glioma, Neurosurgery, Oncology, Reproducibility of result, Residual volume, Adult, Aged, Biopsy, Brain Mapping, Brain Neoplasm, Female, Glioblastoma, Human, Kaplan-Meier Estimate, Karnofsky Performance Statu, Male, Middle Aged, Neoplasm, Residual, Neurosurgical Procedure, Probability, ROC Curve, Survival Analysi, Treatment Outcome, Settore MED/27 - Neurochirurgia
الوصف: OBJECTIVE Decisions in glioblastoma surgery are often guided by presumed eloquence of the tumor location. The authors introduce the "expected residual tumor volume" (eRV) and the "expected resectability index" (eRI) based on previous decisions aggregated in resection probability maps. The diagnostic accuracy of eRV and eRI to predict biopsy decisions, resectability, functional outcome, and survival was determined. METHODS Consecutive patients with first-time glioblastoma surgery in 2012-2013 were included from 12 hospitals. The eRV was calculated from the preoperative MR images of each patient using a resection probability map, and the eRI was derived from the tumor volume. As reference, Sawaya's tumor location eloquence grades (EGs) were classified. Resectability was measured as observed extent of resection (EOR) and residual volume, and functional outcome as change in Karnofsky Performance Scale score. Receiver operating characteristic curves and multivariable logistic regression were applied. RESULTS Of 915 patients, 674 (74%) underwent a resection with a median EOR of 97%, functional improvement in 71 (8%), functional decline in 78 (9%), and median survival of 12.8 months. The eRI and eRV identified biopsies and EORs of at least 80%, 90%, or 98% better than EG. The eRV and eRI predicted observed residual volumes under 10, 5, and 1 ml better than EG. The eRV, eRI, and EG had low diagnostic accuracy for functional outcome changes. Higher eRV and lower eRI were strongly associated with shorter survival, independent of known prognostic factors. CONCLUSIONS The eRV and eRI predict biopsy decisions, resectability, and survival better than eloquence grading and may be useful preoperative indices to support surgical decisions.
العلاقة: info:eu-repo/semantics/altIdentifier/pmid/32244208; info:eu-repo/semantics/altIdentifier/wos/WOS:000646501900001; volume:134; issue:4; firstpage:1091; lastpage:1101; numberofpages:11; journal:JOURNAL OF NEUROSURGERY; http://hdl.handle.net/2434/904218Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85098687263
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3دورية أكاديمية
المؤلفون: 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., Muller 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., Hamer P. C. D. W., Solheim O.
المساهمون: D. Bouget, R.S. Eijgelaar, A. Pedersen, I. Kommer, H. Ardon, F. Barkhof, L. Bello, M.S. Berger, M.C. Nibali, J. Furtner, E.H. Fyllingen, S. Hervey-Jumper, A.J.S. Idema, B. Kiesel, A. Kloet, E. Mandonnet, D.M.J. Muller, P.A. Robe, M. Rossi, L.M. Sagberg, T. Sciortino, W.A. Van den Brink, M. Wagemaker, G. Widhalm, M.G. Witte, A.H. Zwinderman, I. Reinertsen, P.C.D.W. Hamer, O. Solheim
مصطلحات موضوعية: 3D segmentation, Computer-assisted image processing, Deep learning, Glioblastoma, Magnetic resonance imaging, Neuroimaging, Settore MED/27 - Neurochirurgia
الوصف: 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 GSIRADS, 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.
العلاقة: info:eu-repo/semantics/altIdentifier/pmid/34572900; info:eu-repo/semantics/altIdentifier/wos/WOS:000699070100001; volume:13; issue:18; firstpage:1; lastpage:23; numberofpages:23; journal:CANCERS; http://hdl.handle.net/2434/883410Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85115054009
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المؤلفون: Bouget, D., Pedersen, A., Jakola, Asgeir Store, Kavouridis, V., Emblem, K. E., Eijgelaar, R. S., Kommers, I., Ardon, H., Barkhof, F., Bello, L., Berger, M. S., Nibali, M. C., Furtner, J., Hervey-Jumper, S., Idema, A. J. S., Kiesel, B., Kloet, A., Mandonnet, E., Muller, D. M. J., Robe, P. A., Rossi, M., Sciortino, T., van den Brink, W. A., Wagemakers, M., Widhalm, G., Witte, M. G., Zwinderman, A. H., Hamer, P. D. C., Solheim, O., Reinertsen, I.
المصدر: Frontiers in Neurology. 13
مصطلحات موضوعية: Cancer and Oncology, Cancer och onkologi, metastasis, meningioma, glioma, RADS, MRI, deep learning, 3D, segmentation, open-source software, central-nervous-system, world-health-organization, classification, epidemiology, performance, images, Neurosciences & Neurology
الوصف: For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16-54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5-15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports.
الوصول الحر: https://gup.ub.gu.se/publication/318209Test
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