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1تقرير
المؤلفون: Helland, Ragnhild Holden, Ferles, Alexandros, Pedersen, André, Kommers, Ivar, Ardon, Hilko, Barkhof, Frederik, Bello, Lorenzo, Berger, Mitchel S., Dunås, Tora, Nibali, Marco Conti, Furtner, Julia, Hervey-Jumper, Shawn, Idema, Albert J. S., Kiesel, Barbara, Tewari, Rishi Nandoe, Mandonnet, Emmanuel, Müller, Domenique M. J., Robe, Pierre A., Rossi, Marco, Sagberg, Lisa M., Sciortino, Tommaso, Aalders, Tom, Wagemakers, Michiel, Widhalm, Georg, Witte, Marnix G., Zwinderman, Aeilko H., Majewska, Paulina L., Jakola, Asgeir S., Solheim, Ole, Hamer, Philip C. De Witt, Reinertsen, Ingerid, Eijgelaar, Roelant S., Bouget, David
مصطلحات موضوعية: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, I.4.6, J.3
الوصف: Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61\% Dice score, and the best classification performance was about 80\% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.
Comment: 13 pages, 4 figures, 4 tablesالوصول الحر: http://arxiv.org/abs/2304.08881Test
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2تقرير
المؤلفون: Bouget, D., Pedersen, A., Jakola, A. S., 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., Müller, D. M. J., Robe, P. A., Rossi, M., Sciortino, T., Brink, W. Van den, Wagemakers, M., Widhalm, G., Witte, M. G., Zwinderman, A. H., Hamer, P. C. De Witt, Solheim, O., Reinertsen, I.
المصدر: Frontiers in Neurology, Sec. Applied Neuroimaging, Volume 13, 2022
مصطلحات موضوعية: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, I.4.6, J.3
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 represents a major hurdle. In this study, we investigate glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4000 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%. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16 to 54 seconds depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5 to 15 minutes 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, 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.
Comment: 20 pages, 5 figures, 10 tablesالوصول الحر: http://arxiv.org/abs/2204.14199Test
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3دورية أكاديمية
المؤلفون: Pemberton, Hugh G., Wu, Jiaming, Kommers, Ivar, Müller, Domenique M. J., Hu, Yipeng, Goodkin, Olivia, Vos, Sjoerd B., Bisdas, Sotirios, Robe, Pierre A., Ardon, Hilko, Bello, Lorenzo, Rossi, Marco, Sciortino, Tommaso, Nibali, Marco Conti, Berger, Mitchel S., Hervey-Jumper, Shawn L., Bouwknegt, Wim, van den Brink, Wimar A., Furtner, Julia, Han, Seunggu J., Idema, Albert J. S., Kiesel, Barbara, Widhalm, Georg, Kloet, Alfred, Wagemakers, Michiel, Zwinderman, Aeilko H., Krieg, Sandro M., Mandonnet, Emmanuel, Prados, Ferran, de Witt Hamer, Philip, Barkhof, Frederik, Eijgelaar, Roelant S.
المصدر: Pemberton , H G , Wu , J , Kommers , I , Müller , D M J , Hu , Y , Goodkin , O , Vos , S B , Bisdas , S , Robe , P A , Ardon , H , Bello , L , Rossi , M , Sciortino , T , Nibali , M C , Berger , M S , Hervey-Jumper , S L , Bouwknegt , W , van den Brink , W A , Furtner , J , Han , S J , Idema , A J S , Kiesel ....
الوصف: This study tests the generalisability of three Brain Tumor Segmentation (BraTS) challenge models using a multi-center dataset of varying image quality and incomplete MRI datasets. In this retrospective study, DeepMedic, no-new-Unet (nn-Unet), and NVIDIA-net (nv-Net) were trained and tested using manual segmentations from preoperative MRI of glioblastoma (GBM) and low-grade gliomas (LGG) from the BraTS 2021 dataset (1251 in total), in addition to 275 GBM and 205 LGG acquired clinically across 12 hospitals worldwide. Data was split into 80% training, 5% validation, and 15% internal test data. An additional external test-set of 158 GBM and 69 LGG was used to assess generalisability to other hospitals’ data. All models’ median Dice similarity coefficient (DSC) for both test sets were within, or higher than, previously reported human inter-rater agreement (range of 0.74–0.85). For both test sets, nn-Unet achieved the highest DSC (internal = 0.86, external = 0.93) and the lowest Hausdorff distances (10.07, 13.87 mm, respectively) for all tumor classes (p < 0.001). By applying Sparsified training, missing MRI sequences did not statistically affect the performance. nn-Unet achieves accurate segmentations in clinical settings even in the presence of incomplete MRI datasets. This facilitates future clinical adoption of automated glioma segmentation, which could help inform treatment planning and glioma monitoring.
الإتاحة: https://doi.org/10.1038/s41598-023-44794-0Test
https://research.vumc.nl/en/publications/0f8ce6db-8527-46e5-ac52-ce80d62e108cTest
http://www.scopus.com/inward/record.url?scp=85175837438&partnerID=8YFLogxKTest -
4دورية أكاديمية
المؤلفون: Kommers, Ivar, Bouget, David, Pedersen, André, Eijgelaar, Roelant S., Ardon, Hilko, Barkhof, Frederik, Bello, Lorenzo, Berger, Mitchel S., Nibali, Marco Conti, Furtner, Julia, Fyllingen, Even H., Hervey-Jumper, Shawn, Idema, Albert J. S., Kiesel, Barbara, Kloet, Alfred, Mandonnet, Emmanuel, Müller, Domenique M. J., Robe, Pierre A., Rossi, Marco, Sagberg, Lisa M., Sciortino, Tommaso, van den Brink, Wimar A., Wagemakers, Michiel, Widhalm, Georg, Witte, Marnix G., Zwinderman, Aeilko H., Reinertsen, Ingerid, Solheim, Ole, de Witt Hamer, Philip C.
المصدر: 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 ....
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.
الإتاحة: https://doi.org/10.3390/cancers13122854Test
https://research.vumc.nl/en/publications/6627c074-8fc5-48fd-8bcd-b3bae4a102e7Test
http://www.scopus.com/inward/record.url?scp=85107462533&partnerID=8YFLogxKTest -
5دورية أكاديمية
المؤلفون: Müller, Domenique M J, De Swart, Merijn E, Ardon, Hilko, Barkhof, Frederik, Bello, Lorenzo, Berger, Mitchel S, Bouwknegt, Wim, Van den Brink, Wimar A, Conti Nibali, Marco, Eijgelaar, Roelant S, Furtner, Julia, Han, Seunggu J, Hervey-Jumper, Shawn, Idema, Albert J S, Kiesel, Barbara, Kloet, Alfred, Mandonnet, Emmanuel, Robe, Pierre A J T, Rossi, Marco, Sciortino, Tommaso, Vandertop, W Peter, Visser, Martin, Wagemakers, Michiel, Widhalm, Georg, Witte, Marnix G, De Witt Hamer, Philip C
المصدر: Müller , D M J , De Swart , M E , Ardon , H , Barkhof , F , Bello , L , Berger , M S , Bouwknegt , W , Van den Brink , W A , Conti Nibali , M , Eijgelaar , R S , Furtner , J , Han , S J , Hervey-Jumper , S , Idema , A J S , Kiesel , B , Kloet , A , Mandonnet , E , Robe , P A J T , Rossi , M , Sciortino , T , Vandertop , W ....
الوصف: Background: The impact of time-to-surgery on clinical outcome for patients with glioblastoma has not been determined. Any delay in treatment is perceived as detrimental, but guidelines do not specify acceptable timings. In this study, we relate the time to glioblastoma surgery with the extent of resection and residual tumor volume, performance change, and survival, and we explore the identification of patients for urgent surgery. Methods: Adults with first-time surgery in 2012-2013 treated by 12 neuro-oncological teams were included in this study. We defined time-to-surgery as the number of days between the diagnostic MR scan and surgery. The relation between time-to-surgery and patient and tumor characteristics was explored in time-to-event analysis and proportional hazard models. Outcome according to time-to-surgery was analyzed by volumetric measurements, changes in performance status, and survival analysis with patient and tumor characteristics as modifiers. Results: Included were 1033 patients of whom 729 had a resection and 304 a biopsy. The overall median time-to-surgery was 13 days. Surgery was within 3 days for 235 (23%) patients, and within a month for 889 (86%). The median volumetric doubling time was 22 days. Lower performance status (hazard ratio [HR] 0.942, 95% confidence interval [CI] 0.893-0.994) and larger tumor volume (HR 1.012, 95% CI 1.010-1.014) were independently associated with a shorter time-to-surgery. Extent of resection, residual tumor volume, postoperative performance change, and overall survival were not associated with time-to-surgery. Conclusions: With current decision-making for urgent surgery in selected patients with glioblastoma and surgery typically within 1 month, we found equal extent of resection, residual tumor volume, performance status, and survival after longer times-to-surgery.
الإتاحة: https://doi.org/10.1093/noajnl/vdab053Test
https://research.vumc.nl/en/publications/1c981ba9-0c65-400e-9275-67108c65629cTest -
6دورية أكاديمية
المؤلفون: Bouget, David, Eijgelaar, Roelant S, Pedersen, André, Kommers, Ivar, Ardon, Hilko, Barkhof, Frederik, Bello, Lorenzo, Berger, Mitchel S, Nibali, Marco Conti, Furtner, Julia, Fyllingen, Even Hovig, Hervey-Jumper, Shawn, Idema, Albert J S, Kiesel, Barbara, Kloet, Alfred, Mandonnet, Emmanuel, Müller, Domenique M J, Robe, Pierre A, Rossi, Marco, Sagberg, Lisa M, Sciortino, Tommaso, Van den Brink, Wimar A, Wagemakers, Michiel, Widhalm, Georg, Witte, Marnix G, Zwinderman, Aeilko H, Reinertsen, Ingerid, De Witt Hamer, Philip C, Solheim, Ole
المصدر: 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 ....
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 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.
الإتاحة: https://doi.org/10.3390/cancers13184674Test
https://research.vumc.nl/en/publications/c58a1a0a-4309-4f16-907b-9e97c6b50743Test
http://www.scopus.com/inward/record.url?scp=85115054009&partnerID=8YFLogxKTest -
7دورية أكاديمية
المؤلفون: Müller, Domenique M. J., Robe, Pierre A., Ardon, Hilko, Barkhof, Frederik, Bello, Lorenzo, Berger, Mitchel S., Bouwknegt, Wim, van den Brink, Wimar A., Nibali, Marco Conti, Eijgelaar, Roelant S., Furtner, Julia, Han, Seunggu J., Hervey-Jumper, Shawn L., Idema, Albert J. S., Kiesel, Barbara, Kloet, Alfred, de Munck, Jan C., Rossi, Marco, Sciortino, Tommaso, Peter Vandertop, W., Visser, Martin, Wagemakers, Michiel, Widhalm, Georg, Witte, Marnix G., Zwinderman, Aeilko H., de Witt Hamer, Philip C.
المصدر: Müller , 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 ....
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.
الإتاحة: https://doi.org/10.3171/2020.1.JNS193049Test
https://research.vumc.nl/en/publications/a4fc7b5b-17ff-4509-83f8-c665d053c88dTest
http://www.scopus.com/inward/record.url?scp=85098687263&partnerID=8YFLogxKTest -
8دورية أكاديمية
المؤلفون: de Witt Hamer, Philip C., Ho, Vincent K. Y., Zwinderman, Aeilko H., Ackermans, Linda, Ardon, Hilko, Boomstra, Sytske, Bouwknegt, Wim, van den Brink, Wimar A., Dirven, Clemens M., van der Gaag, Niels A., van der Veer, Olivier, Idema, Albert J. S., Kloet, Alfred, Koopmans, Jan, ter Laan, Mark, Verstegen, Marco J. T., Wagemakers, Michiel, Robe, Pierre A. J. T.
المصدر: de Witt Hamer , P C , Ho , V K Y , Zwinderman , A H , Ackermans , L , Ardon , H , Boomstra , S , Bouwknegt , W , van den Brink , W A , Dirven , C M , van der Gaag , N A , van der Veer , O , Idema , A J S , Kloet , A , Koopmans , J , ter Laan , M , Verstegen , M J T , Wagemakers , M , Robe , P A J T & on behalf of the ....
الوصف: Purpose: Standards for surgical decisions are unavailable, hence treatment decisions can be personalized, but also introduce variation in treatment and outcome. National registrations seek to monitor healthcare quality. The goal of the study is to measure between-hospital variation in risk-standardized survival outcome after glioblastoma surgery and to explore the association between survival and hospital characteristics in conjunction with patient-related risk factors. Methods: Data of 2,409 adults with first-time glioblastoma surgery at 14 hospitals were obtained from a comprehensive, prospective population-based Quality Registry Neuro Surgery in The Netherlands between 2011 and 2014. We compared the observed survival with patient-specific risk-standardized expected early (30-day) mortality and late (2-year) survival, based on age, performance, and treatment year. We analyzed funnel plots, logistic regression and proportional hazards models. Results: Overall 30-day mortality was 5.2% and overall 2-year survival was 13.5%. Median survival varied between 4.8 and 14.9 months among hospitals, and biopsy percentages ranged between 16 and 73%. One hospital had lower than expected early mortality, and four hospitals had lower than expected late survival. Higher case volume was related with lower early mortality (P = 0.031). Patient-related risk factors (lower age; better performance; more recent years of treatment) were significantly associated with longer overall survival. Of the hospital characteristics, longer overall survival was associated with lower biopsy percentage (HR 2.09, 1.34–3.26, P = 0.001), and not with academic setting, nor with case volume. Conclusions: Hospitals vary more in late survival than early mortality after glioblastoma surgery. Widely varying biopsy percentages indicate treatment variation. Patient-related factors have a stronger association with overall survival than hospital-related factors.
الإتاحة: https://doi.org/10.1007/s11060-019-03229-5Test
https://research.vumc.nl/en/publications/f6bb338e-c3c7-49fa-b274-fd4077a46104Test
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85068134263&origin=inwardTest
https://www.ncbi.nlm.nih.gov/pubmed/31236819Test -
9دورية أكاديمية
المؤلفون: Hamer, Philip C. De Witt, Ho, Vincent K. Y., Zwinderman, Aeilko H., Ackermans, Linda, Ardon, Hilko, Boomstra, Sytske, Bouwknegt, Wim, van den Brink, Wimar A., Dirven, Clemens M., van der Gaag, Niels A., van der Veer, Olivier, Idema, Albert J. S., Kloet, Alfred, Koopmans, Jan, ter Laan, Mark, Verstegen, Marco J. T., Wagemakers, Michiel, Robe, Pierre A. J. T.
المصدر: Hamer , P C D W , Ho , V K Y , Zwinderman , A H , Ackermans , L , Ardon , H , Boomstra , S , Bouwknegt , W , van den Brink , W A , Dirven , C M , van der Gaag , N A , van der Veer , O , Idema , A J S , Kloet , A , Koopmans , J , ter Laan , M , Verstegen , M J T , Wagemakers , M , Robe , P A J T & Quality Registry Neuro Surgery ....
مصطلحات موضوعية: Glioblastoma, Neurosurgical procedures, Quality of health care, Outcome assessment, Mortality, Survival, NEWLY-DIAGNOSED GLIOBLASTOMA, UNITED-STATES, TEMOZOLOMIDE ERA, ELDERLY-PATIENTS, PHASE-III, CARE, PATTERNS, RESECTION, OUTCOMES, GLIOMA
الوصف: Purpose Standards for surgical decisions are unavailable, hence treatment decisions can be personalized, but also introduce variation in treatment and outcome. National registrations seek to monitor healthcare quality. The goal of the study is to measure between-hospital variation in risk-standardized survival outcome after glioblastoma surgery and to explore the association between survival and hospital characteristics in conjunction with patient-related risk factors. Methods Data of 2,409 adults with first-time glioblastoma surgery at 14 hospitals were obtained from a comprehensive, prospective population-based Quality Registry Neuro Surgery in The Netherlands between 2011 and 2014. We compared the observed survival with patient-specific risk-standardized expected early (30-day) mortality and late (2-year) survival, based on age, performance, and treatment year. We analyzed funnel plots, logistic regression and proportional hazards models. Results Overall 30-day mortality was 5.2% and overall 2-year survival was 13.5%. Median survival varied between 4.8 and 14.9 months among hospitals, and biopsy percentages ranged between 16 and 73%. One hospital had lower than expected early mortality, and four hospitals had lower than expected late survival. Higher case volume was related with lower early mortality (P = 0.031). Patient-related risk factors (lower age; better performance; more recent years of treatment) were significantly associated with longer overall survival. Of the hospital characteristics, longer overall survival was associated with lower biopsy percentage (HR 2.09, 1.34-3.26, P = 0.001), and not with academic setting, nor with case volume. Conclusions Hospitals vary more in late survival than early mortality after glioblastoma surgery. Widely varying biopsy percentages indicate treatment variation. Patient-related factors have a stronger association with overall survival than hospital-related factors.
الإتاحة: https://doi.org/10.1007/s11060-019-03229-5Test
https://cris.maastrichtuniversity.nl/en/publications/430fc53f-8c54-4a8c-89fa-2b1361128615Test -
10دورية أكاديمية
المؤلفون: Müller, Domenique M. J., Robe, Pierre A., Ardon, Hilko, Barkhof, Frederik, Bello, Lorenzo, Berger, Mitchel S., Bouwknegt, Wim, van den Brink, Wimar A., Conti Nibali, Marco, Eijgelaar, Roelant S., Furtner, Julia, Han, Seunggu J., Hervey-Jumper, Shawn L., Idema, Albert J. S., Kiesel, Barbara, Kloet, Alfred, Mandonnet, Emmanuel, de Munck, Jan C., Rossi, Marco, Sciortino, Tommaso, Vandertop, W. Peter, Visser, Martin, Wagemakers, Michiel, Widhalm, Georg, Witte, Marnix G., Zwinderman, Aeilko H., de Witt Hamer, Philip C.
المصدر: Müller , D M J , Robe , P A , Ardon , H , Barkhof , F , Bello , L , Berger , M S , Bouwknegt , W , van den Brink , W A , Conti Nibali , M , Eijgelaar , R S , Furtner , J , Han , S J , Hervey-Jumper , S L , Idema , A J S , Kiesel , B , Kloet , A , Mandonnet , E , de Munck , J C , Rossi , M , Sciortino , T , Vandertop , W P ....
الوصف: OBJECTIVE: The aim of glioblastoma surgery is to maximize the extent of resection while preserving functional integrity. Standards are lacking for surgical decision-making, and previous studies indicate treatment variations. These shortcomings reflect the need to evaluate larger populations from different care teams. In this study, the authors used probability maps to quantify and compare surgical decision-making throughout the brain by 12 neurosurgical teams for patients with glioblastoma. METHODS: The study included all adult patients who underwent first-time glioblastoma surgery in 2012-2013 and were treated by 1 of the 12 participating neurosurgical teams. Voxel-wise probability maps of tumor location, biopsy, and resection were constructed for each team to identify and compare patient treatment variations. Brain regions with different biopsy and resection results between teams were identified and analyzed for patient functional outcome and survival. RESULTS: The study cohort consisted of 1087 patients, of whom 363 underwent a biopsy and 724 a resection. Biopsy and resection decisions were generally comparable between teams, providing benchmarks for probability maps of resections and biopsies for glioblastoma. Differences in biopsy rates were identified for the right superior frontal gyrus and indicated variation in biopsy decisions. Differences in resection rates were identified for the left superior parietal lobule, indicating variations in resection decisions. CONCLUSIONS: Probability maps of glioblastoma surgery enabled capture of clinical practice decisions and indicated that teams generally agreed on which region to biopsy or to resect. However, treatment variations reflecting clinical dilemmas were observed and pinpointed by using the probability maps, which could therefore be useful for quality-of-care discussions between surgical teams for patients with glioblastoma.
الإتاحة: https://doi.org/10.3171/2020.11.JNS202897Test
https://research.vumc.nl/en/publications/09e3d500-fc34-4236-9a49-9e89cd2bb042Test
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85123389719&origin=inwardTest
https://www.ncbi.nlm.nih.gov/pubmed/34243150Test
