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Foltyn-Dumitru M, Schell M, Rastogi A, Sahm F, Kessler T, Wick W, Bendszus M, Brugnara G, Vollmuth P. Impact of signal intensity normalization of MRI on the generalizability of radiomic-based prediction of molecular glioma subtypes. Eur Radiol 2024; 34:2782-2790. [PMID: 37672053 PMCID: PMC10957611 DOI: 10.1007/s00330-023-10034-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/09/2023] [Accepted: 06/16/2023] [Indexed: 09/07/2023]
Abstract
OBJECTIVES Radiomic features have demonstrated encouraging results for non-invasive detection of molecular biomarkers, but the lack of guidelines for pre-processing MRI-data has led to poor generalizability. Here, we assessed the influence of different MRI-intensity normalization techniques on the performance of radiomics-based models for predicting molecular glioma subtypes. METHODS Preoperative MRI-data from n = 615 patients with newly diagnosed glioma and known isocitrate dehydrogenase (IDH) and 1p/19q status were pre-processed using four different methods: no normalization (naive), N4 bias field correction (N4), N4 followed by either WhiteStripe (N4/WS), or z-score normalization (N4/z-score). A total of 377 Image-Biomarker-Standardisation-Initiative-compliant radiomic features were extracted from each normalized data, and 9 different machine-learning algorithms were trained for multiclass prediction of molecular glioma subtypes (IDH-mutant 1p/19q codeleted vs. IDH-mutant 1p/19q non-codeleted vs. IDH wild type). External testing was performed in public glioma datasets from UCSF (n = 410) and TCGA (n = 160). RESULTS Support vector machine yielded the best performance with macro-average AUCs of 0.84 (naive), 0.84 (N4), 0.87 (N4/WS), and 0.87 (N4/z-score) in the internal test set. Both N4/WS and z-score outperformed the other approaches in the external UCSF and TCGA test sets with macro-average AUCs ranging from 0.85 to 0.87, replicating the performance of the internal test set, in contrast to macro-average AUCs ranging from 0.19 to 0.45 for naive and 0.26 to 0.52 for N4 alone. CONCLUSION Intensity normalization of MRI data is essential for the generalizability of radiomic-based machine-learning models. Specifically, both N4/WS and N4/z-score approaches allow to preserve the high model performance, yielding generalizable performance when applying the developed radiomic-based machine-learning model in an external heterogeneous, multi-institutional setting. CLINICAL RELEVANCE STATEMENT Intensity normalization such as N4/WS or N4/z-score can be used to develop reliable radiomics-based machine learning models from heterogeneous multicentre MRI datasets and provide non-invasive prediction of glioma subtypes. KEY POINTS • MRI-intensity normalization increases the stability of radiomics-based models and leads to better generalizability. • Intensity normalization did not appear relevant when the developed model was applied to homogeneous data from the same institution. • Radiomic-based machine learning algorithms are a promising approach for simultaneous classification of IDH and 1p/19q status of glioma.
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Affiliation(s)
- Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Aditya Rastogi
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Tobias Kessler
- Department of Neurology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Wolfgang Wick
- Department of Neurology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, DE, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany.
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, DE, Germany.
- Division of Medical Image Computing (MIC), German Cancer Research Center (DFKZ), Heidelberg, Germany.
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Rastogi A, Brugnara G, Foltyn-Dumitru M, Mahmutoglu MA, Preetha CJ, Kobler E, Pflüger I, Schell M, Deike-Hofmann K, Kessler T, van den Bent MJ, Idbaih A, Platten M, Brandes AA, Nabors B, Stupp R, Bernhardt D, Debus J, Abdollahi A, Gorlia T, Tonn JC, Weller M, Maier-Hein KH, Radbruch A, Wick W, Bendszus M, Meredig H, Kurz FT, Vollmuth P. Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study. Lancet Oncol 2024; 25:400-410. [PMID: 38423052 DOI: 10.1016/s1470-2045(23)00641-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/20/2023] [Accepted: 12/07/2023] [Indexed: 03/02/2024]
Abstract
BACKGROUND The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. METHODS In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. FINDINGS In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 across different acceleration rates, with 0·92 (95% CI 0·92-0·93) for 10-times acceleration (R=10). The 10-times undersampled dCNN-reconstructed MRI yielded excellent agreement with original MRI when assessing volumes of contrast-enhancing tumour (median DICE for spatial agreement of 0·89 [95% CI 0·88 to 0·89]; median volume difference of 0·01 cm3 [95% CI 0·00 to 0·03] equalling 0·21%; p=0·0036 for equivalence) or non-enhancing tumour or oedema (median DICE of 0·94 [95% CI 0·94 to 0·95]; median volume difference of -0·79 cm3 [95% CI -0·87 to -0·72] equalling -1·77%; p=0·023 for equivalence) in the EORTC-26101 test dataset. Automated volumetric tumour response assessment in the EORTC-26101 test dataset yielded an identical median time to progression of 4·27 months (95% CI 4·14 to 4·57) when using 10-times-undersampled dCNN-reconstructed or original MRI (log-rank p=0·80) and agreement in the time to progression in 374 (95·2%) of 393 patients with data. The dCNN generalised well to the fastMRI brain dataset, with significant improvements in the median SSIM when using multi-coil compared with single-coil k-space data (p<0·0001). INTERPRETATION Deep-learning-based reconstruction of undersampled MRI allows for a substantial reduction of scan times, with a 10-times acceleration demonstrating excellent image quality while preserving the accuracy of derived imaging biomarkers for the assessment of oncological treatment response. Our developments are available as open source software and hold considerable promise for increasing the accessibility to MRI, pending further prospective validation. FUNDING Deutsche Forschungsgemeinschaft (German Research Foundation) and an Else Kröner Clinician Scientist Endowed Professorship by the Else Kröner Fresenius Foundation.
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Affiliation(s)
- Aditya Rastogi
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martha Foltyn-Dumitru
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Mustafa Ahmed Mahmutoglu
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Chandrakanth J Preetha
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Erich Kobler
- Department of Neuroradiology, University Medical Center Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Irada Pflüger
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Marianne Schell
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Katerina Deike-Hofmann
- Department of Neuroradiology, University Medical Center Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany; German Center for Neurodegenerative Diseases, Bonn, Germany
| | - Tobias Kessler
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany; Clinical Cooperation Unit Neurooncology, German Cancer Consortium within German Cancer Research Center, Heidelberg, Germany
| | | | - Ahmed Idbaih
- Assistance Publique-Hôpitaux de Paris, Service de Neurologie 1, Hôpital Pitié-Salpêtrière, Sorbonne Université, Paris, France
| | - Michael Platten
- Department of Neurology, Medical Faculty Mannheim, Mannheim Center for Translational Neuroscience, University of Heidelberg, Mannheim, Germany; Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Consortium within German Cancer Research Center, Heidelberg, Germany
| | - Alba A Brandes
- Department of Medical Oncology, Azienda UnitàSanitaria Locale of Bologna, Bologna, Italy
| | - Burt Nabors
- Department of Neurology, Division of Neuro-Oncology, University of Alabama at Birmingham, Birmingham, AL, USA; O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Roger Stupp
- Lou and Jean Malnati Brain Tumor Institute, Robert H Lurie Comprehensive Cancer Center, Northwestern Medicine and Northwestern University, Chicago, USA; Department of Neurological Surgery, Northwestern Medicine and Northwestern University, Chicago, USA; Department of Neurology, Northwestern Medicine and Northwestern University, Chicago, USA
| | - Denise Bernhardt
- Department of Radiation Oncology, School of Medicine and Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Institute of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Ion-Beam Therapy Center, Heidelberg University Hospital, Heidelberg, Germany
| | - Amir Abdollahi
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Institute of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Ion-Beam Therapy Center, Heidelberg University Hospital, Heidelberg, Germany
| | - Thierry Gorlia
- European Organization for Research and Treatment of Cancer, Brussels, Belgium
| | - Jörg-Christian Tonn
- Department of Neurosurgery, Ludwig-Maximilians-University, Munich, Germany; German Cancer Consortium within German Center Research Center, partner site Munich, Germany
| | - Michael Weller
- Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Klaus H Maier-Hein
- Medical Image Computing, German Cancer Research Center, Heidelberg, Germany; Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Alexander Radbruch
- Department of Neuroradiology, University Medical Center Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Wolfgang Wick
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany; Clinical Cooperation Unit Neurooncology, German Cancer Consortium within German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Hagen Meredig
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Felix T Kurz
- Division of Diagnostic and Interventional Neuroradiology, Geneva University Hospitals, Geneva, Switzerland; Department of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Philipp Vollmuth
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Neuroradiology, University Medical Center Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany; Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.
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Alzaid H, Simon JJ, Brugnara G, Vollmuth P, Bendszus M, Friederich HC. Hypothalamic subregion alterations in anorexia nervosa and obesity: Association with appetite-regulating hormone levels. Int J Eat Disord 2024; 57:581-592. [PMID: 38243035 DOI: 10.1002/eat.24137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 12/25/2023] [Accepted: 12/27/2023] [Indexed: 01/21/2024]
Abstract
OBJECTIVE Anorexia nervosa (AN) and obesity are weight-related disorders with imbalances in energy homeostasis that may be due to hormonal dysregulation. Given the importance of the hypothalamus in hormonal regulation, we aimed to identify morphometric alterations to hypothalamic subregions linked to these conditions and their connection to appetite-regulating hormones. METHODS Structural magnetic resonance imaging (MRI) was obtained from 78 patients with AN, 27 individuals with obesity and 100 normal-weight healthy controls. Leptin, ghrelin, and insulin blood levels were measured in a subsample of each group. An automated segmentation method was used to segment the hypothalamus and its subregions. Volumes of the hypothalamus and its subregions were compared between groups, and correlational analysis was employed to assess the relationship between morphometric measurements and appetite-regulating hormone levels. RESULTS While accounting for total brain volume, patients with AN displayed a smaller volume in the inferior-tubular subregion (ITS). Conversely, obesity was associated with a larger volume in the anterior-superior, ITS, posterior subregions (PS), and entire hypothalamus. There were no significant volumetric differences between AN subtypes. Leptin correlated positively with PS volume, whereas ghrelin correlated negatively with the whole hypothalamus volume in the entire cohort. However, appetite-regulating hormone levels did not mediate the effects of body mass index on volumetric measures. CONCLUSION Our results indicate the importance of regional structural hypothalamic alterations in AN and obesity, extending beyond global changes to brain volume. Furthermore, these alterations may be linked to changes in hormonal appetite regulation. However, given the small sample size in our correlation analysis, further analyses in a larger sample size are warranted. PUBLIC SIGNIFICANCE Using an automated segmentation method to investigate morphometric alterations of hypothalamic subregions in AN and obesity, this study provides valuable insights into the complex interplay between hypothalamic alterations, hormonal appetite regulation, and body weight, highlighting the need for further research to uncover underlying mechanisms.
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Affiliation(s)
- Haidar Alzaid
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Joe J Simon
- Department of General Internal Medicine and Psychosomatics, University Hospital Heidelberg, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Hans-Christoph Friederich
- Department of General Internal Medicine and Psychosomatics, University Hospital Heidelberg, Heidelberg, Germany
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Brugnara G, Engel A, Jesser J, Ringleb PA, Purrucker J, Möhlenbruch MA, Bendszus M, Neuberger U. Cortical atrophy on baseline computed tomography imaging predicts clinical outcome in patients undergoing endovascular treatment for acute ischemic stroke. Eur Radiol 2024; 34:1358-1366. [PMID: 37581657 PMCID: PMC10853300 DOI: 10.1007/s00330-023-10107-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 06/05/2023] [Accepted: 07/01/2023] [Indexed: 08/16/2023]
Abstract
OBJECTIVE Multiple variables beyond the extent of recanalization can impact the clinical outcome after acute ischemic stroke due to large vessel occlusions. Here, we assessed the influence of small vessel disease and cortical atrophy on clinical outcome using native cranial computed tomography (NCCT) in a large single-center cohort. METHODS A total of 1103 consecutive patients who underwent endovascular treatment (EVT) due to occlusion of the middle cerebral artery territory were included. NCCT data were visually assessed for established markers of age-related white matter changes (ARWMC) and brain atrophy. All images were evaluated separately by two readers to assess the inter-observer variability. Regression and machine learning models were built to determine the predictive relevance of ARWMC and atrophy in the presence of important baseline clinical and imaging metrics. RESULTS Patients with favorable outcome presented lower values for all measured metrics of pre-existing brain deterioration (p < 0.001). Both ARWMC (p < 0.05) and cortical atrophy (p < 0.001) were independent predictors of clinical outcome at 90 days when controlled for confounders in both regression analyses and led to a minor improvement of prediction accuracy in machine learning models (p < 0.001), with atrophy among the top-5 predictors. CONCLUSION NCCT-based cortical atrophy and ARWMC scores on NCCT were strong and independent predictors of clinical outcome after EVT. CLINICAL RELEVANCE STATEMENT Visual assessment of cortical atrophy and age-related white matter changes on CT could improve the prediction of clinical outcome after thrombectomy in machine learning models which may be integrated into existing clinical routines and facilitate patient selection. KEY POINTS • Cortical atrophy and age-related white matter changes were quantified using CT-based visual scores. • Atrophy and age-related white matter change scores independently predicted clinical outcome after mechanical thrombectomy and improved machine learning-based prediction models. • Both scores could easily be integrated into existing clinical routines and prediction models.
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Affiliation(s)
- Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- Division of Computational Neuroimaging, Heidelberg University Hospital, Heidelberg, Germany
| | - Adrian Engel
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- Department of Neurosurgery, Essen University Hospital, Essen, Germany
| | - Jessica Jesser
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | | | - Jan Purrucker
- Department of Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | - Markus A Möhlenbruch
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Ulf Neuberger
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany.
- Division of Computational Neuroimaging, Heidelberg University Hospital, Heidelberg, Germany.
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Foltyn-Dumitru M, Schell M, Sahm F, Kessler T, Wick W, Bendszus M, Rastogi A, Brugnara G, Vollmuth P. Advancing noninvasive glioma classification with diffusion radiomics: Exploring the impact of signal intensity normalization. Neurooncol Adv 2024; 6:vdae043. [PMID: 38596719 PMCID: PMC11003539 DOI: 10.1093/noajnl/vdae043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024] Open
Abstract
Background This study investigates the influence of diffusion-weighted Magnetic Resonance Imaging (DWI-MRI) on radiomic-based prediction of glioma types according to molecular status and assesses the impact of DWI intensity normalization on model generalizability. Methods Radiomic features, compliant with image biomarker standardization initiative standards, were extracted from preoperative MRI of 549 patients with diffuse glioma, known IDH, and 1p19q-status. Anatomical sequences (T1, T1c, T2, FLAIR) underwent N4-Bias Field Correction (N4) and WhiteStripe normalization (N4/WS). Apparent diffusion coefficient (ADC) maps were normalized using N4 or N4/z-score. Nine machine-learning algorithms were trained for multiclass prediction of glioma types (IDH-mutant 1p/19q codeleted, IDH-mutant 1p/19q non-codeleted, IDH-wild type). Four approaches were compared: Anatomical, anatomical + ADC naive, anatomical + ADC N4, and anatomical + ADC N4/z-score. The University of California San Francisco (UCSF)-glioma dataset (n = 409) was used for external validation. Results Naïve-Bayes algorithms yielded overall the best performance on the internal test set. Adding ADC radiomics significantly improved AUC from 0.79 to 0.86 (P = .011) for the IDH-wild-type subgroup, but not for the other 2 glioma subgroups (P > .05). In the external UCSF dataset, the addition of ADC radiomics yielded a significantly higher AUC for the IDH-wild-type subgroup (P ≤ .001): 0.80 (N4/WS anatomical alone), 0.81 (anatomical + ADC naive), 0.81 (anatomical + ADC N4), and 0.88 (anatomical + ADC N4/z-score) as well as for the IDH-mutant 1p/19q non-codeleted subgroup (P < .012 each). Conclusions ADC radiomics can enhance the performance of conventional MRI-based radiomic models, particularly for IDH-wild-type glioma. The benefit of intensity normalization of ADC maps depends on the type and context of the used data.
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Affiliation(s)
- Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tobias Kessler
- Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wolfgang Wick
- Department of Neurology and Neurooncology Program, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Aditya Rastogi
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Radiology & Clinical AI, Department of Neuroradiology, Bonn University Hospital, Bonn, Germany
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Mahmutoglu MA, Preetha CJ, Meredig H, Tonn JC, Weller M, Wick W, Bendszus M, Brugnara G, Vollmuth P. Deep Learning-based Identification of Brain MRI Sequences Using a Model Trained on Large Multicentric Study Cohorts. Radiol Artif Intell 2024; 6:e230095. [PMID: 38166331 PMCID: PMC10831512 DOI: 10.1148/ryai.230095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 09/30/2023] [Accepted: 10/30/2023] [Indexed: 01/04/2024]
Abstract
Purpose To develop a fully automated device- and sequence-independent convolutional neural network (CNN) for reliable and high-throughput labeling of heterogeneous, unstructured MRI data. Materials and Methods Retrospective, multicentric brain MRI data (2179 patients with glioblastoma, 8544 examinations, 63 327 sequences) from 249 hospitals and 29 scanner types were used to develop a network based on ResNet-18 architecture to differentiate nine MRI sequence types, including T1-weighted, postcontrast T1-weighted, T2-weighted, fluid-attenuated inversion recovery, susceptibility-weighted, apparent diffusion coefficient, diffusion-weighted (low and high b value), and gradient-recalled echo T2*-weighted and dynamic susceptibility contrast-related images. The two-dimensional-midsection images from each sequence were allocated to training or validation (approximately 80%) and testing (approximately 20%) using a stratified split to ensure balanced groups across institutions, patients, and MRI sequence types. The prediction accuracy was quantified for each sequence type, and subgroup comparison of model performance was performed using χ2 tests. Results On the test set, the overall accuracy of the CNN (ResNet-18) ensemble model among all sequence types was 97.9% (95% CI: 97.6, 98.1), ranging from 84.2% for susceptibility-weighted images (95% CI: 81.8, 86.6) to 99.8% for T2-weighted images (95% CI: 99.7, 99.9). The ResNet-18 model achieved significantly better accuracy compared with ResNet-50 despite its simpler architecture (97.9% vs 97.1%; P ≤ .001). The accuracy of the ResNet-18 model was not affected by the presence versus absence of tumor on the two-dimensional-midsection images for any sequence type (P > .05). Conclusion The developed CNN (www.github.com/neuroAI-HD/HD-SEQ-ID) reliably differentiates nine types of MRI sequences within multicenter and large-scale population neuroimaging data and may enhance the speed, accuracy, and efficiency of clinical and research neuroradiologic workflows. Keywords: MR-Imaging, Neural Networks, CNS, Brain/Brain Stem, Computer Applications-General (Informatics), Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2023.
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Affiliation(s)
- Mustafa Ahmed Mahmutoglu
- From the Department of Neuroradiology (M.A.M., C.J.P., H.M., M.B., G.B., P.V.), Department of Neuroradiology, Division for Computational Neuroimaging (M.A.M., C.J.P., H.M., G.B., P.V.), and Department of Neurology (W.W.), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital Munich LMU, Munich, Germany (J.C.T.); and Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland (M.W.)
| | - Chandrakanth Jayachandran Preetha
- From the Department of Neuroradiology (M.A.M., C.J.P., H.M., M.B., G.B., P.V.), Department of Neuroradiology, Division for Computational Neuroimaging (M.A.M., C.J.P., H.M., G.B., P.V.), and Department of Neurology (W.W.), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital Munich LMU, Munich, Germany (J.C.T.); and Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland (M.W.)
| | - Hagen Meredig
- From the Department of Neuroradiology (M.A.M., C.J.P., H.M., M.B., G.B., P.V.), Department of Neuroradiology, Division for Computational Neuroimaging (M.A.M., C.J.P., H.M., G.B., P.V.), and Department of Neurology (W.W.), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital Munich LMU, Munich, Germany (J.C.T.); and Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland (M.W.)
| | - Joerg-Christian Tonn
- From the Department of Neuroradiology (M.A.M., C.J.P., H.M., M.B., G.B., P.V.), Department of Neuroradiology, Division for Computational Neuroimaging (M.A.M., C.J.P., H.M., G.B., P.V.), and Department of Neurology (W.W.), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital Munich LMU, Munich, Germany (J.C.T.); and Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland (M.W.)
| | - Michael Weller
- From the Department of Neuroradiology (M.A.M., C.J.P., H.M., M.B., G.B., P.V.), Department of Neuroradiology, Division for Computational Neuroimaging (M.A.M., C.J.P., H.M., G.B., P.V.), and Department of Neurology (W.W.), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital Munich LMU, Munich, Germany (J.C.T.); and Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland (M.W.)
| | - Wolfgang Wick
- From the Department of Neuroradiology (M.A.M., C.J.P., H.M., M.B., G.B., P.V.), Department of Neuroradiology, Division for Computational Neuroimaging (M.A.M., C.J.P., H.M., G.B., P.V.), and Department of Neurology (W.W.), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital Munich LMU, Munich, Germany (J.C.T.); and Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland (M.W.)
| | - Martin Bendszus
- From the Department of Neuroradiology (M.A.M., C.J.P., H.M., M.B., G.B., P.V.), Department of Neuroradiology, Division for Computational Neuroimaging (M.A.M., C.J.P., H.M., G.B., P.V.), and Department of Neurology (W.W.), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital Munich LMU, Munich, Germany (J.C.T.); and Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland (M.W.)
| | - Gianluca Brugnara
- From the Department of Neuroradiology (M.A.M., C.J.P., H.M., M.B., G.B., P.V.), Department of Neuroradiology, Division for Computational Neuroimaging (M.A.M., C.J.P., H.M., G.B., P.V.), and Department of Neurology (W.W.), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital Munich LMU, Munich, Germany (J.C.T.); and Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland (M.W.)
| | - Philipp Vollmuth
- From the Department of Neuroradiology (M.A.M., C.J.P., H.M., M.B., G.B., P.V.), Department of Neuroradiology, Division for Computational Neuroimaging (M.A.M., C.J.P., H.M., G.B., P.V.), and Department of Neurology (W.W.), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany; Department of Neurosurgery, University Hospital Munich LMU, Munich, Germany (J.C.T.); and Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland (M.W.)
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7
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Brugnara G, Mihalicz P, Herweh C, Schönenberger S, Purrucker J, Nagel S, Ringleb PA, Bendszus M, Möhlenbruch MA, Neuberger U. Clinical value of automated volumetric quantification of early ischemic tissue changes on non-contrast CT. J Neurointerv Surg 2023; 15:e178-e183. [PMID: 36175015 DOI: 10.1136/jnis-2022-019400] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 09/16/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Quantitative and automated volumetric evaluation of early ischemic changes on non-contrast CT (NCCT) has recently been proposed as a new tool to improve prognostic performance in patients undergoing endovascular therapy (EVT) for acute ischemic stroke (AIS). We aimed to test its clinical value compared with the Alberta Stroke Program Early CT Score (ASPECTS) in a large single-institutional patient cohort. METHODS A total of 1103 patients with AIS due to large vessel occlusion in the M1 or proximal M2 segments who underwent NCCT and EVT between January 2013 and November 2019 were retrospectively enrolled. Acute ischemic volumes (AIV) and ASPECTS were generated from the baseline NCCT through e-ASPECTS (Brainomix). Correlations were tested using Spearman's coefficient. The predictive capabilities of AIV for a favorable outcome (modified Rankin Scale score at 90 days ≤2) were tested using multivariable logistic regression as well as machine-learning models. Performance of the models was assessed using receiver operating characteristic (ROC) curves and differences were tested using DeLong's test. RESULTS Patients with a favorable outcome had a significantly lower AIV (median 12.0 mL (IQR 5.7-21.7) vs 18.8 mL (IQR 9.4-33.9), p<0.001). AIV was highly correlated with ASPECTS (rho=0.78, p<0.001) and weakly correlated with the National Institutes of Health Stroke Scale score at baseline (rho=0.22, p<0.001), and was an independent predictor of an unfavorable clinical outcome (adjusted OR 0.97, 95% CI 0.96 to 0.98). No significant difference was found between machine-learning models using either AIV or ASPECTS or both metrics for predicting a good clinical outcome (p>0.05). CONCLUSION AIV is an independent predictor of clinical outcome and presented a non-inferior performance compared with ASPECTS, without clear advantages for prognostic modelling.
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Affiliation(s)
- Gianluca Brugnara
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
- Section of Computational Neuroimaging, University Hospital Heidelberg, Heidelberg, Germany
| | - Peter Mihalicz
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Christian Herweh
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | | | - Jan Purrucker
- Neurology, University Hospital Heidelberg, Heidelberg, Baden-Württemberg, Germany
| | - Simon Nagel
- Neurology, University Hospital Heidelberg, Heidelberg, Baden-Württemberg, Germany
- Department of Neurology, Städtisches Klinikum Ludwigshafen, Ludwigshafen, Germany
| | - Peter Arthur Ringleb
- Neurology, University Hospital Heidelberg, Heidelberg, Baden-Württemberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Markus A Möhlenbruch
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Ulf Neuberger
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
- Section of Computational Neuroimaging, Heidelberg University Hospital, Heidelberg, Germany
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8
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Brugnara G, Baumgartner M, Scholze ED, Deike-Hofmann K, Kades K, Scherer J, Denner S, Meredig H, Rastogi A, Mahmutoglu MA, Ulfert C, Neuberger U, Schönenberger S, Schlamp K, Bendella Z, Pinetz T, Schmeel C, Wick W, Ringleb PA, Floca R, Möhlenbruch M, Radbruch A, Bendszus M, Maier-Hein K, Vollmuth P. Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke. Nat Commun 2023; 14:4938. [PMID: 37582829 PMCID: PMC10427649 DOI: 10.1038/s41467-023-40564-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 08/01/2023] [Indexed: 08/17/2023] Open
Abstract
Swift diagnosis and treatment play a decisive role in the clinical outcome of patients with acute ischemic stroke (AIS), and computer-aided diagnosis (CAD) systems can accelerate the underlying diagnostic processes. Here, we developed an artificial neural network (ANN) which allows automated detection of abnormal vessel findings without any a-priori restrictions and in <2 minutes. Pseudo-prospective external validation was performed in consecutive patients with suspected AIS from 4 different hospitals during a 6-month timeframe and demonstrated high sensitivity (≥87%) and negative predictive value (≥93%). Benchmarking against two CE- and FDA-approved software solutions showed significantly higher performance for our ANN with improvements of 25-45% for sensitivity and 4-11% for NPV (p ≤ 0.003 each). We provide an imaging platform ( https://stroke.neuroAI-HD.org ) for online processing of medical imaging data with the developed ANN, including provisions for data crowdsourcing, which will allow continuous refinements and serve as a blueprint to build robust and generalizable AI algorithms.
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Affiliation(s)
- Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Michael Baumgartner
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz Imaging, Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
| | - Edwin David Scholze
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Katerina Deike-Hofmann
- Department of Neuroradiology, Bonn University Hospital, Bonn, Germany
- Clinical Neuroimaging Group, German Center for Neurodegenerative Diseases, DZNE, Bonn, Germany
| | - Klaus Kades
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
| | - Jonas Scherer
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Stefan Denner
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine, University of Heidelberg, Heidelberg, Germany
| | - Hagen Meredig
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Aditya Rastogi
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Mustafa Ahmed Mahmutoglu
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Christian Ulfert
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Ulf Neuberger
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Kai Schlamp
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University of Heidelberg, Heidelberg, Germany
| | - Zeynep Bendella
- Department of Neuroradiology, Bonn University Hospital, Bonn, Germany
| | - Thomas Pinetz
- Institute for Applied Mathematics, University of Bonn, Bonn, Germany
| | - Carsten Schmeel
- Department of Neuroradiology, Bonn University Hospital, Bonn, Germany
- Clinical Neuroimaging Group, German Center for Neurodegenerative Diseases, DZNE, Bonn, Germany
| | - Wolfgang Wick
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Peter A Ringleb
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Ralf Floca
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany
| | - Markus Möhlenbruch
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Alexander Radbruch
- Department of Neuroradiology, Bonn University Hospital, Bonn, Germany
- Clinical Neuroimaging Group, German Center for Neurodegenerative Diseases, DZNE, Bonn, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Pattern Analysis and Learning Group, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
- Division for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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9
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Vollmuth P, Foltyn M, Huang RY, Galldiks N, Petersen J, Isensee F, van den Bent MJ, Barkhof F, Park JE, Park YW, Ahn SS, Brugnara G, Meredig H, Jain R, Smits M, Pope WB, Maier-Hein K, Weller M, Wen PY, Wick W, Bendszus M. Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: An international multi-reader study. Neuro Oncol 2023; 25:533-543. [PMID: 35917833 PMCID: PMC10013635 DOI: 10.1093/neuonc/noac189] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND To assess whether artificial intelligence (AI)-based decision support allows more reproducible and standardized assessment of treatment response on MRI in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden using the Response Assessment in Neuro-Oncology (RANO) criteria. METHODS A series of 30 patients (15 lower-grade gliomas, 15 glioblastoma) with availability of consecutive MRI scans was selected. The time to progression (TTP) on MRI was separately evaluated for each patient by 15 investigators over two rounds. In the first round the TTP was evaluated based on the RANO criteria, whereas in the second round the TTP was evaluated by incorporating additional information from AI-enhanced MRI sequences depicting the longitudinal changes in tumor volumes. The agreement of the TTP measurements between investigators was evaluated using concordance correlation coefficients (CCC) with confidence intervals (CI) and P-values obtained using bootstrap resampling. RESULTS The CCC of TTP-measurements between investigators was 0.77 (95% CI = 0.69,0.88) with RANO alone and increased to 0.91 (95% CI = 0.82,0.95) with AI-based decision support (P = .005). This effect was significantly greater (P = .008) for patients with lower-grade gliomas (CCC = 0.70 [95% CI = 0.56,0.85] without vs. 0.90 [95% CI = 0.76,0.95] with AI-based decision support) as compared to glioblastoma (CCC = 0.83 [95% CI = 0.75,0.92] without vs. 0.86 [95% CI = 0.78,0.93] with AI-based decision support). Investigators with less years of experience judged the AI-based decision as more helpful (P = .02). CONCLUSIONS AI-based decision support has the potential to yield more reproducible and standardized assessment of treatment response in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden, particularly in patients with lower-grade gliomas. A fully-functional version of this AI-based processing pipeline is provided as open-source (https://github.com/NeuroAI-HD/HD-GLIO-XNAT).
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Affiliation(s)
- Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martha Foltyn
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Norbert Galldiks
- Department of Neurology, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany.,Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany.,Center for Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany
| | - Jens Petersen
- Department of Medical Image Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Fabian Isensee
- Department of Medical Image Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.,Institutes of Neurology & Centre for Medical Image Computing, University College London, London, UK
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Centre, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Yae Won Park
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Sung Soo Ahn
- Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Hagen Meredig
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Rajan Jain
- Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Whitney B Pope
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Klaus Maier-Hein
- Department of Medical Image Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Weller
- Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Patrick Y Wen
- Center for Neuro-oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Wolfgang Wick
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany.,Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
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10
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Leone R, Meredig H, Foltyn-Dumitru M, Sahm F, Hamelmann S, Kurz F, Kessler T, Bonekamp D, Schlemmer HP, Hansen MB, Wick W, Bendszus M, Vollmuth P, Brugnara G. Assessing the added value of ADC, CBV and radiomic MR features for differentiation of pseudoprogression vs. true tumor progression in patients with glioblastoma. Neurooncol Adv 2023; 5:vdad016. [PMID: 36968291 PMCID: PMC10034916 DOI: 10.1093/noajnl/vdad016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023] Open
Abstract
Abstract
Background
Pseudoprogression (PsPD) is a major diagnostic challenge in the follow-up of patients with glioblastoma (GB) after chemo-radiotherapy (CRT). Conventional imaging signs and parameters derived from diffusion and perfusion-MRI have yet to prove their reliability in clinical practice for an accurate differential diagnosis. Here, we tested these parameters and combined them with radiomic features (RFs), clinical data, and MGMT-promoter methylation-status using machine- and deep-learning models to distinguish PsPD from PD.
Methods
In a single-center analysis, 105 patients with GB who developed a suspected imaging PsPD in the first 7 months after standard-CRT were identified retrospectively. Imaging data included standard MRI anatomical sequences, apparent diffusion coefficient (ADC) and normalized relative cerebral blood volume (nrCBV) maps. Median values (ADC, nrCBV) and RFs (all sequences) were calculated from deep-learning-based tumor segmentations. Generalized linear models with LASSO feature-selection and deep-learning models were built integrating clinical data, MGMT-methylation-status, median ADC and nrCBV values and RFs.
Results
A model based on clinical data and MGMT-methylation-status yielded an AUC=0.69 (95%CI 0.55-0.83) for detecting PsPD, and the addition of median ADC and nrCBV values resulted in a non-significant increase in performance (AUC=0.71,95%CI 0.57-0.85, p=0.416). Combining clinical/MGMT information with RFs derived from ADC, nrCBV and from all available sequences both resulted in significantly (both p<0.005) lower model performances, with AUC=0.52 (0.38-0.66) and AUC=0.54 (0.40-0.68), respectively. Deep-learning imaging models resulted in AUCs≤0.56.
Conclusion
Currently available imaging biomarkers could not reliably differentiate PsPD from true tumor progression in patients with glioblastoma; larger collaborative efforts are needed to build more reliable models.
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Affiliation(s)
- Riccardo Leone
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg, Germany
- Division for Computational Neuroimaging, Heidelberg University Hospital , Heidelberg, Germany
| | - Hagen Meredig
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg, Germany
- Division for Computational Neuroimaging, Heidelberg University Hospital , Heidelberg, Germany
| | - Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg, Germany
- Division for Computational Neuroimaging, Heidelberg University Hospital , Heidelberg, Germany
| | - Felix Sahm
- Department of Pathology, Heidelberg University Hospital , Heidelberg, Germany
| | - Stefan Hamelmann
- Department of Pathology, Heidelberg University Hospital , Heidelberg, Germany
| | - Felix Kurz
- Department of Radiology, German Cancer Research Center (DKFZ) , Heidelberg, Germany
| | - Tobias Kessler
- Department of Neurology, Heidelberg University Hospital , Heidelberg, Germany
| | - David Bonekamp
- Department of Radiology, German Cancer Research Center (DKFZ) , Heidelberg, Germany
| | | | - Mikkel Bo Hansen
- Center for Functionally Integrative Neuroscience (CFIN), University of Aarhus , Aarhus, Denmark
| | - Wolfgang Wick
- Department of Neurology, Heidelberg University Hospital , Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg, Germany
- Division for Computational Neuroimaging, Heidelberg University Hospital , Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg, Germany
- Division for Computational Neuroimaging, Heidelberg University Hospital , Heidelberg, Germany
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11
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Turco V, Pfleiderer K, Hunger J, Horvat NK, Karimian-Jazi K, Schregel K, Fischer M, Brugnara G, Jähne K, Sturm V, Streibel Y, Nguyen D, Altamura S, Agardy DA, Soni SS, Alsasa A, Bunse T, Schlesner M, Muckenthaler MU, Weissleder R, Wick W, Heiland S, Vollmuth P, Bendszus M, Rodell CB, Breckwoldt MO, Platten M. T cell-independent eradication of experimental glioma by intravenous TLR7/8-agonist-loaded nanoparticles. Nat Commun 2023; 14:771. [PMID: 36774352 PMCID: PMC9922247 DOI: 10.1038/s41467-023-36321-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 01/24/2023] [Indexed: 02/13/2023] Open
Abstract
Glioblastoma, the most common and aggressive primary brain tumor type, is considered an immunologically "cold" tumor with sparse infiltration by adaptive immune cells. Immunosuppressive tumor-associated myeloid cells are drivers of tumor progression. Therefore, targeting and reprogramming intratumoral myeloid cells is an appealing therapeutic strategy. Here, we investigate a β-cyclodextrin nanoparticle (CDNP) formulation encapsulating the Toll-like receptor 7 and 8 (TLR7/8) agonist R848 (CDNP-R848) to reprogram myeloid cells in the glioma microenvironment. We show that intravenous monotherapy with CDNP-R848 induces regression of established syngeneic experimental glioma, resulting in increased survival rates compared with unloaded CDNP controls. Mechanistically, CDNP-R848 treatment reshapes the immunosuppressive tumor microenvironment and orchestrates tumor clearing by pro-inflammatory tumor-associated myeloid cells, independently of T cells and NK cells. Using serial magnetic resonance imaging, we identify a radiomic signature in response to CDNP-R848 treatment and ultrasmall superparamagnetic iron oxide (USPIO) imaging reveals that immunosuppressive macrophage recruitment is reduced by CDNP-R848. In conclusion, CDNP-R848 induces tumor regression in experimental glioma by targeting blood-borne macrophages without requiring adaptive immunity.
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Affiliation(s)
- Verena Turco
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.,Department of Neurology, Medical Faculty Mannheim, Mannheim Center for Translational Neurosciences, Heidelberg University, Theodor-Kutzer-Ufer 1-3, Mannheim, Germany.,Neuroradiology Department, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Kira Pfleiderer
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.,Neuroradiology Department, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Jessica Hunger
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.,Neuroradiology Department, University Hospital Heidelberg, 69120, Heidelberg, Germany.,Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Natalie K Horvat
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.,Department of Pediatric Oncology, Hematology and Immunology, University Hospital, Heidelberg, Germany.,Molecular Medicine Partnership Unit (MMPU), Heidelberg University, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Kianush Karimian-Jazi
- Neuroradiology Department, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Katharina Schregel
- Neuroradiology Department, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Manuel Fischer
- Neuroradiology Department, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Gianluca Brugnara
- Neuroradiology Department, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Kristine Jähne
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.,Department of Neurology, Medical Faculty Mannheim, Mannheim Center for Translational Neurosciences, Heidelberg University, Theodor-Kutzer-Ufer 1-3, Mannheim, Germany
| | - Volker Sturm
- Neuroradiology Department, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Yannik Streibel
- Neuroradiology Department, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Duy Nguyen
- Junior Research Group Bioinformatics and Omics Data Analytics, DKFZ, Heidelberg, Germany
| | - Sandro Altamura
- Department of Pediatric Oncology, Hematology and Immunology, University Hospital, Heidelberg, Germany.,Molecular Medicine Partnership Unit (MMPU), Heidelberg University, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Dennis A Agardy
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.,Department of Neurology, Medical Faculty Mannheim, Mannheim Center for Translational Neurosciences, Heidelberg University, Theodor-Kutzer-Ufer 1-3, Mannheim, Germany.,Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Shreya S Soni
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, 19104, USA
| | - Abdulrahman Alsasa
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, 19104, USA
| | - Theresa Bunse
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany.,Department of Neurology, Medical Faculty Mannheim, Mannheim Center for Translational Neurosciences, Heidelberg University, Theodor-Kutzer-Ufer 1-3, Mannheim, Germany
| | - Matthias Schlesner
- Junior Research Group Bioinformatics and Omics Data Analytics, DKFZ, Heidelberg, Germany.,Biomedical Informatics, Data Mining and Data Analytics, Faculty of Applied Computer Science and Medical Faculty, University of Augsburg, Augsburg, Germany
| | - Martina U Muckenthaler
- Department of Pediatric Oncology, Hematology and Immunology, University Hospital, Heidelberg, Germany.,Molecular Medicine Partnership Unit (MMPU), Heidelberg University, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Ralph Weissleder
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, 02114, USA.,Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Wolfgang Wick
- Clinical Cooperation Unit Neurooncology, DKTK within DKFZ, Heidelberg, Germany.,Department of Neurology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital, Heidelberg, Germany
| | - Sabine Heiland
- Neuroradiology Department, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Philipp Vollmuth
- Neuroradiology Department, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Martin Bendszus
- Neuroradiology Department, University Hospital Heidelberg, 69120, Heidelberg, Germany
| | - Christopher B Rodell
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, 19104, USA
| | - Michael O Breckwoldt
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany. .,Neuroradiology Department, University Hospital Heidelberg, 69120, Heidelberg, Germany.
| | - Michael Platten
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany. .,Department of Neurology, Medical Faculty Mannheim, Mannheim Center for Translational Neurosciences, Heidelberg University, Theodor-Kutzer-Ufer 1-3, Mannheim, Germany.
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Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, Bakas S. Author Correction: Federated learning enables big data for rare cancer boundary detection. Nat Commun 2023; 14:436. [PMID: 36702828 PMCID: PMC9879935 DOI: 10.1038/s41467-023-36188-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Satyam Ghodasara
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Felix Sahm
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Zenk
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Evan Calabrese
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey Rudie
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Soonmee Cha
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Manali Jadhav
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Umang Pandey
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - John Garrett
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Matthew Larson
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Robert Jeraj
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Stuart Currie
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Russell Frood
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Kavi Fatania
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Josep Puig
- Department of Radiology (IDI), Girona Biomedical Research Institute (IdIBGi), Josep Trueta University Hospital, Girona, Spain
| | - Johannes Trenkler
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Josef Pichler
- Department of Neurooncology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Georg Necker
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Andreas Haunschmidt
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Stephan Meckel
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
- Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Spencer Liem
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Gregory S Alexander
- Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA
| | - Joseph Lombardo
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joshua D Palmer
- Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Craig K Jones
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Meirui Jiang
- The Chinese University of Hong Kong, Hong Kong, China
| | - Tiffany Y So
- The Chinese University of Hong Kong, Hong Kong, China
| | - Cheng Chen
- The Chinese University of Hong Kong, Hong Kong, China
| | | | - Qi Dou
- The Chinese University of Hong Kong, Hong Kong, China
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Filip Lux
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Jan Michálek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Tereza Kopřivová
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
- Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Václav Vybíhal
- Department of Neurosurgery, Faculty of Medicine, Masaryk University, Brno, and University Hospital and Czech Republic, Brno, Czech Republic
| | - Michael A Vogelbaum
- Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - J Ross Mitchell
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Joaquim Farinhas
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Marco C Pinho
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Divya Reddy
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - James Holcomb
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Talia Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Minh-Son To
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
- Division of Surgery and Perioperative Medicine, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sargam Bhardwaj
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Chee Chong
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Marc Agzarian
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | | | - Bernardo C A Teixeira
- Instituto de Neurologia de Curitiba, Curitiba, Paraná, Brazil
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Flávia Sprenger
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - David Menotti
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Diego R Lucio
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Matthew Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Derrick Murcia
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Eric Fu
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Rourke Haas
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - John Thompson
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - David Ryan Ormond
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, OH, USA
| | - Andrew E Sloan
- Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Vachan Vadmal
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristin Waite
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
| | - Rivka R Colen
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linmin Pei
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat Ak
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashok Srinivasan
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - J Rajiv Bapuraj
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Ota Yoshiaki
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Toshio Moritani
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Sevcan Turk
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Snehal Prabhudesai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fanny Morón
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Mandel
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Konstantinos Kamnitsas
- Department of Computing, Imperial College London, London, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Luke V M Dixon
- Department of Radiology, Imperial College NHS Healthcare Trust, London, UK
| | - Matthew Williams
- Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, UK
| | - Peter Zampakis
- Department of NeuroRadiology, University of Patras, Patras, Greece
| | | | - Panagiotis Tsiganos
- Clinical Radiology Laboratory, Department of Medicine, University of Patras, Patras, Greece
| | - Sotiris Alexiou
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Ilias Haliassos
- Department of Neuro-Oncology, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | | | | | | | | | | | | | - Sung Soo Ahn
- Yonsei University College of Medicine, Seoul, Korea
| | - Bing Luo
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Laila Poisson
- Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | | | - Ruchika Verma
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
- Case Western Reserve University, Cleveland, OH, USA
| | - Rohan Bareja
- Case Western Reserve University, Cleveland, OH, USA
| | - Ipsa Yadav
- Case Western Reserve University, Cleveland, OH, USA
| | | | - Neeraj Kumar
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Sebastian R van der Voort
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Ahmed Alafandi
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Fatih Incekara
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Maarten M J Wijnenga
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Georgios Kapsas
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Joost W Schouten
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Hendrikus J Dubbink
- Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Arnaud J P E Vincent
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Martin J van den Bent
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Pim J French
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sonam Sharma
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tzu-Chi Tseng
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saba Adabi
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Olivier Keunen
- Translation Radiomics, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Ann-Christin Hau
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - David Fortin
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Martin Lepage
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Nuclear Medicine and Radiobiology, Sherbrooke Molecular Imaging Centre, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Bennett Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Silky Chotai
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Akshitkumar Mistry
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Reid C Thompson
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anousheh Sayah
- Division of Neuroradiology & Neurointerventional Radiology, Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Camelia Bencheqroun
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anas Belouali
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Haris Shuaib
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Carmen Dragos
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
| | | | | | | | | | - Shady Gamal
- University of Cairo School of Medicine, Giza, Egypt
| | | | | | | | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Jihye Yun
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, UK
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Sean Benson
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
| | - Jonas Teuwen
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | | | - William Escobar
- Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
- Universidad del Valle, Cali, Colombia
| | | | - Jose Bernal
- Universidad del Valle, Cali, Colombia
- The University of Edinburgh, Edinburgh, UK
| | | | - Joseph Choi
- Department of Industrial and Systems Engineering, University of Iowa, Iowa, USA
| | - Stephen Baek
- Department of Industrial and Systems Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Yusung Kim
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Heba Ismael
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Bryan Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | | | - Hongwei Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sampurna Shrestha
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Kartik M Mani
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Radiation Oncology, Stony Brook University, Stony Brook, NY, USA
| | - David Payne
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Enrique Pelaez
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | - Francis Loayza
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | | | | | | | - Franco Vera
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Elvis Ríos
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Eduardo López
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Sergio A Velastin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Godwin Ogbole
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mayowa Soneye
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | | | - Babatunde Osobu
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mustapha Shu'aibu
- Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
| | - Adeleye Dorcas
- Department of Radiology, Obafemi Awolowo University Ile-Ife, Ile-Ife, Osun, Nigeria
| | - Farouk Dako
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amber L Simpson
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Mohammad Hamghalam
- School of Computing, Queen's University, Kingston, ON, Canada
- Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Anh Tran
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Danielle Cutler
- The Faculty of Arts & Sciences, Queen's University, Kingston, ON, Canada
| | - Fabio Y Moraes
- Department of Oncology, Queen's University, Kingston, ON, Canada
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - James Gimpel
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Deepak Kattil Veettil
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Kendall Schmidt
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Brian Bialecki
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Sailaja Marella
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Cynthia Price
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Lisa Cimino
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Charles Apgar
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Jill S Barnholtz-Sloan
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), National Institute of Health, Bethesda, MD, USA
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos C, Sako C, Ghodasara S, Bilello M, Mohan S, Vollmuth P, Brugnara G, Preetha CJ, Sahm F, Maier-Hein K, Zenk M, Bendszus M, Wick W, Calabrese E, Rudie J, Villanueva-Meyer J, Cha S, Ingalhalikar M, Jadhav M, Pandey U, Saini J, Garrett J, Larson M, Jeraj R, Currie S, Frood R, Fatania K, Huang RY, Chang K, Balaña C, Capellades J, Puig J, Trenkler J, Pichler J, Necker G, Haunschmidt A, Meckel S, Shukla G, Liem S, Alexander GS, Lombardo J, Palmer JD, Flanders AE, Dicker AP, Sair HI, Jones CK, Venkataraman A, Jiang M, So TY, Chen C, Heng PA, Dou Q, Kozubek M, Lux F, Michálek J, Matula P, Keřkovský M, Kopřivová T, Dostál M, Vybíhal V, Vogelbaum MA, Mitchell JR, Farinhas J, Maldjian JA, Yogananda CGB, Pinho MC, Reddy D, Holcomb J, Wagner BC, Ellingson BM, Cloughesy TF, Raymond C, Oughourlian T, Hagiwara A, Wang C, To MS, Bhardwaj S, Chong C, Agzarian M, Falcão AX, Martins SB, Teixeira BCA, Sprenger F, Menotti D, Lucio DR, LaMontagne P, Marcus D, Wiestler B, Kofler F, Ezhov I, Metz M, Jain R, Lee M, Lui YW, McKinley R, Slotboom J, Radojewski P, Meier R, Wiest R, Murcia D, Fu E, Haas R, Thompson J, Ormond DR, Badve C, Sloan AE, Vadmal V, Waite K, Colen RR, Pei L, Ak M, Srinivasan A, Bapuraj JR, Rao A, Wang N, Yoshiaki O, Moritani T, Turk S, Lee J, Prabhudesai S, Morón F, Mandel J, Kamnitsas K, Glocker B, Dixon LVM, Williams M, Zampakis P, Panagiotopoulos V, Tsiganos P, Alexiou S, Haliassos I, Zacharaki EI, Moustakas K, Kalogeropoulou C, Kardamakis DM, Choi YS, Lee SK, Chang JH, Ahn SS, Luo B, Poisson L, Wen N, Tiwari P, Verma R, Bareja R, Yadav I, Chen J, Kumar N, Smits M, van der Voort SR, Alafandi A, Incekara F, Wijnenga MMJ, Kapsas G, Gahrmann R, Schouten JW, Dubbink HJ, Vincent AJPE, van den Bent MJ, French PJ, Klein S, Yuan Y, Sharma S, Tseng TC, Adabi S, Niclou SP, Keunen O, Hau AC, Vallières M, Fortin D, Lepage M, Landman B, Ramadass K, Xu K, Chotai S, Chambless LB, Mistry A, Thompson RC, Gusev Y, Bhuvaneshwar K, Sayah A, Bencheqroun C, Belouali A, Madhavan S, Booth TC, Chelliah A, Modat M, Shuaib H, Dragos C, Abayazeed A, Kolodziej K, Hill M, Abbassy A, Gamal S, Mekhaimar M, Qayati M, Reyes M, Park JE, Yun J, Kim HS, Mahajan A, Muzi M, Benson S, Beets-Tan RGH, Teuwen J, Herrera-Trujillo A, Trujillo M, Escobar W, Abello A, Bernal J, Gómez J, Choi J, Baek S, Kim Y, Ismael H, Allen B, Buatti JM, Kotrotsou A, Li H, Weiss T, Weller M, Bink A, Pouymayou B, Shaykh HF, Saltz J, Prasanna P, Shrestha S, Mani KM, Payne D, Kurc T, Pelaez E, Franco-Maldonado H, Loayza F, Quevedo S, Guevara P, Torche E, Mendoza C, Vera F, Ríos E, López E, Velastin SA, Ogbole G, Soneye M, Oyekunle D, Odafe-Oyibotha O, Osobu B, Shu'aibu M, Dorcas A, Dako F, Simpson AL, Hamghalam M, Peoples JJ, Hu R, Tran A, Cutler D, Moraes FY, Boss MA, Gimpel J, Veettil DK, Schmidt K, Bialecki B, Marella S, Price C, Cimino L, Apgar C, Shah P, Menze B, Barnholtz-Sloan JS, Martin J, Bakas S. Federated learning enables big data for rare cancer boundary detection. Nat Commun 2022; 13:7346. [PMID: 36470898 PMCID: PMC9722782 DOI: 10.1038/s41467-022-33407-5] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 09/16/2022] [Indexed: 12/12/2022] Open
Abstract
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.
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Affiliation(s)
- Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Satyam Ghodasara
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Felix Sahm
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Maximilian Zenk
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Wolfgang Wick
- Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Evan Calabrese
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jeffrey Rudie
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Javier Villanueva-Meyer
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Soonmee Cha
- Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Manali Jadhav
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Umang Pandey
- Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, Maharashtra, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, Karnataka, India
| | - John Garrett
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Matthew Larson
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Robert Jeraj
- Department of Radiology, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
| | - Stuart Currie
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Russell Frood
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Kavi Fatania
- Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | | | - Josep Puig
- Department of Radiology (IDI), Girona Biomedical Research Institute (IdIBGi), Josep Trueta University Hospital, Girona, Spain
| | - Johannes Trenkler
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Josef Pichler
- Department of Neurooncology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Georg Necker
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Andreas Haunschmidt
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
| | - Stephan Meckel
- Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
- Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Spencer Liem
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Gregory S Alexander
- Department of Radiation Oncology, University of Maryland, Baltimore, MD, USA
| | - Joseph Lombardo
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Joshua D Palmer
- Department of Radiation Oncology, The James Cancer Hospital and Solove Research Institute, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Adam E Flanders
- Department of Radiology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Adam P Dicker
- Department of Radiation Oncology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Craig K Jones
- The Malone Center for Engineering in Healthcare, The Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Meirui Jiang
- The Chinese University of Hong Kong, Hong Kong, China
| | - Tiffany Y So
- The Chinese University of Hong Kong, Hong Kong, China
| | - Cheng Chen
- The Chinese University of Hong Kong, Hong Kong, China
| | | | - Qi Dou
- The Chinese University of Hong Kong, Hong Kong, China
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Filip Lux
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Jan Michálek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Petr Matula
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Tereza Kopřivová
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, Faculty of Medicine, Masaryk University, Brno and University Hospital Brno, Brno, Czech Republic
- Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Václav Vybíhal
- Department of Neurosurgery, Faculty of Medicine, Masaryk University, Brno, and University Hospital and Czech Republic, Brno, Czech Republic
| | - Michael A Vogelbaum
- Department of Neuro Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - J Ross Mitchell
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Joaquim Farinhas
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Marco C Pinho
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Divya Reddy
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - James Holcomb
- University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Benjamin M Ellingson
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Timothy F Cloughesy
- UCLA Neuro-Oncology Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CaA, USA
| | - Catalina Raymond
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Talia Oughourlian
- UCLA Brain Tumor Imaging Laboratory (BTIL), Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Akifumi Hagiwara
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chencai Wang
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Minh-Son To
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
- Division of Surgery and Perioperative Medicine, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Sargam Bhardwaj
- College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia
| | - Chee Chong
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
| | - Marc Agzarian
- South Australia Medical Imaging, Flinders Medical Centre, Bedford Park, SA, Australia
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | | | - Bernardo C A Teixeira
- Instituto de Neurologia de Curitiba, Curitiba, Paraná, Brazil
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Flávia Sprenger
- Department of Radiology, Hospital de Clínicas da Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - David Menotti
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Diego R Lucio
- Department of Informatics, Universidade Federal do Paraná, Curitiba, Paraná, Brazil
| | - Pamela LaMontagne
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
| | - Florian Kofler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- TranslaTUM (Zentralinstitut für translationale Krebsforschung der Technischen Universität München), Klinikum rechts der Isar, Munich, Germany
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
| | - Marie Metz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rajan Jain
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY, USA
| | - Matthew Lee
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Department of Radiology, NYU Grossman School of Medicine, New York, NY, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Piotr Radojewski
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Raphael Meier
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging, University Institute of Diagnostic and Interventional Neuroradiology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Derrick Murcia
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Eric Fu
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Rourke Haas
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - John Thompson
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - David Ryan Ormond
- Department of Neurosurgery, Anschutz Medical Campus, University of Colorado, Aurora, CO, USA
| | - Chaitra Badve
- Department of Radiology, University Hospitals Cleveland, Cleveland, OH, USA
| | - Andrew E Sloan
- Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Cleveland, OH, USA
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Vachan Vadmal
- Department of Neurosurgery, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kristin Waite
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
| | - Rivka R Colen
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linmin Pei
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat Ak
- Department of Radiology, Neuroradiology Division, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashok Srinivasan
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - J Rajiv Bapuraj
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Nicholas Wang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Ota Yoshiaki
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Toshio Moritani
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Sevcan Turk
- Department of Neuroradiology, University of Michigan, Ann Arbor, MI, USA
| | - Joonsang Lee
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Snehal Prabhudesai
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fanny Morón
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Mandel
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - Konstantinos Kamnitsas
- Department of Computing, Imperial College London, London, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Luke V M Dixon
- Department of Radiology, Imperial College NHS Healthcare Trust, London, UK
| | - Matthew Williams
- Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, UK
| | - Peter Zampakis
- Department of NeuroRadiology, University of Patras, Patras, Greece
| | | | - Panagiotis Tsiganos
- Clinical Radiology Laboratory, Department of Medicine, University of Patras, Patras, Greece
| | - Sotiris Alexiou
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Ilias Haliassos
- Department of Neuro-Oncology, University of Patras, Patras, Greece
| | - Evangelia I Zacharaki
- Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | | | | | | | | | | | | | - Sung Soo Ahn
- Yonsei University College of Medicine, Seoul, Korea
| | - Bing Luo
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
| | - Laila Poisson
- Public Health Sciences, Henry Ford Health System, Detroit, MI, USA
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, Shanghai, China
| | | | - Ruchika Verma
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
- Case Western Reserve University, Cleveland, OH, USA
| | - Rohan Bareja
- Case Western Reserve University, Cleveland, OH, USA
| | - Ipsa Yadav
- Case Western Reserve University, Cleveland, OH, USA
| | | | - Neeraj Kumar
- University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Marion Smits
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Sebastian R van der Voort
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Ahmed Alafandi
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Fatih Incekara
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Maarten M J Wijnenga
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Georgios Kapsas
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Renske Gahrmann
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Joost W Schouten
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Hendrikus J Dubbink
- Department of Pathology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Arnaud J P E Vincent
- Department of Neurosurgery, Brain Tumor Center, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Martin J van den Bent
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Pim J French
- Department of Neurology, Brain Tumor Center, Erasmus MC Cancer Institute, Rotterdam, Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Yading Yuan
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sonam Sharma
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tzu-Chi Tseng
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Saba Adabi
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Simone P Niclou
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Olivier Keunen
- Translation Radiomics, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Ann-Christin Hau
- NORLUX Neuro-Oncology Laboratory, Department of Cancer Research, Luxembourg Institute of Health, Luxembourg, Luxembourg
- Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
| | - Martin Vallières
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - David Fortin
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Martin Lepage
- Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Nuclear Medicine and Radiobiology, Sherbrooke Molecular Imaging Centre, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Bennett Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiwen Xu
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Silky Chotai
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lola B Chambless
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Akshitkumar Mistry
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Reid C Thompson
- Department of Neurosurgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuriy Gusev
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Krithika Bhuvaneshwar
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anousheh Sayah
- Division of Neuroradiology & Neurointerventional Radiology, Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Camelia Bencheqroun
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Anas Belouali
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics (ICBI), Georgetown University, Washington, DC, USA
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, Ruskin Wing, King's College Hospital NHS Foundation Trust, London, UK
| | - Alysha Chelliah
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Haris Shuaib
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Carmen Dragos
- Stoke Mandeville Hospital, Mandeville Road, Aylesbury, UK
| | | | | | | | | | - Shady Gamal
- University of Cairo School of Medicine, Giza, Egypt
| | | | | | | | - Ji Eun Park
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Jihye Yun
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology, Asan Medical Center, Seoul, South Korea
| | - Abhishek Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, UK
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Sean Benson
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
- GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
| | - Jonas Teuwen
- Netherlands Cancer Institute, Amsterdam, Netherlands
| | | | | | - William Escobar
- Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
- Universidad del Valle, Cali, Colombia
| | | | - Jose Bernal
- Universidad del Valle, Cali, Colombia
- The University of Edinburgh, Edinburgh, UK
| | | | - Joseph Choi
- Department of Industrial and Systems Engineering, University of Iowa, Iowa, USA
| | - Stephen Baek
- Department of Industrial and Systems Engineering, Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Yusung Kim
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Heba Ismael
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - Bryan Allen
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | - John M Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, IA, USA
| | | | - Hongwei Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Tobias Weiss
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Andrea Bink
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Bertrand Pouymayou
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Sampurna Shrestha
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Kartik M Mani
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Radiation Oncology, Stony Brook University, Stony Brook, NY, USA
| | - David Payne
- Department of Radiology, Stony Brook University, Stony Brook, NY, USA
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Enrique Pelaez
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | - Francis Loayza
- Escuela Superior Politecnica del Litoral, Guayaquil, Guayas, Ecuador
| | | | | | | | | | - Franco Vera
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Elvis Ríos
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Eduardo López
- Universidad de Concepción, Concepción, Biobío, Chile
| | - Sergio A Velastin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
| | - Godwin Ogbole
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mayowa Soneye
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | | | - Babatunde Osobu
- Department of Radiology, University College Hospital Ibadan, Oyo, Nigeria
| | - Mustapha Shu'aibu
- Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
| | - Adeleye Dorcas
- Department of Radiology, Obafemi Awolowo University Ile-Ife, Ile-Ife, Osun, Nigeria
| | - Farouk Dako
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amber L Simpson
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Mohammad Hamghalam
- School of Computing, Queen's University, Kingston, ON, Canada
- Department of Electrical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Jacob J Peoples
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Ricky Hu
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Anh Tran
- School of Computing, Queen's University, Kingston, ON, Canada
| | - Danielle Cutler
- The Faculty of Arts & Sciences, Queen's University, Kingston, ON, Canada
| | - Fabio Y Moraes
- Department of Oncology, Queen's University, Kingston, ON, Canada
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - James Gimpel
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Deepak Kattil Veettil
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Kendall Schmidt
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Brian Bialecki
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Sailaja Marella
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Cynthia Price
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Lisa Cimino
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Charles Apgar
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | | | - Bjoern Menze
- Department of Informatics, Technical University of Munich, Munich, Bavaria, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Jill S Barnholtz-Sloan
- National Cancer Institute, National Institute of Health, Division of Cancer Epidemiology and Genetics, Bethesda, MD, USA
- Center for Biomedical Informatics and Information Technology, National Cancer Institute (NCI), National Institute of Health, Bethesda, MD, USA
| | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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14
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Pflüger I, Wald T, Isensee F, Schell M, Meredig H, Schlamp K, Bernhardt D, Brugnara G, Heußel CP, Debus J, Wick W, Bendszus M, Maier-Hein KH, Vollmuth P. Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks. Neurooncol Adv 2022; 4:vdac138. [PMID: 36105388 PMCID: PMC9466273 DOI: 10.1093/noajnl/vdac138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
Reliable detection and precise volumetric quantification of brain metastases (BM) on MRI are essential for guiding treatment decisions. Here we evaluate the potential of artificial neural networks (ANN) for automated detection and quantification of BM.
Methods
A consecutive series of 308 patients with BM was used for developing an ANN (with a 4:1 split for training/testing) for automated volumetric assessment of contrast-enhancing tumors (CE) and non-enhancing FLAIR signal abnormality including edema (NEE). An independent consecutive series of 30 patients was used for external testing. Performance was assessed case-wise for CE and NEE and lesion-wise for CE using the case-wise/lesion-wise DICE-coefficient (C/L-DICE), positive predictive value (L-PPV) and sensitivity (C/L-Sensitivity).
Results
The performance of detecting CE lesions on the validation dataset was not significantly affected when evaluating different volumetric thresholds (0.001–0.2 cm3; P = .2028). The median L-DICE and median C-DICE for CE lesions were 0.78 (IQR = 0.6–0.91) and 0.90 (IQR = 0.85–0.94) in the institutional as well as 0.79 (IQR = 0.67–0.82) and 0.84 (IQR = 0.76–0.89) in the external test dataset. The corresponding median L-Sensitivity and median L-PPV were 0.81 (IQR = 0.63–0.92) and 0.79 (IQR = 0.63–0.93) in the institutional test dataset, as compared to 0.85 (IQR = 0.76–0.94) and 0.76 (IQR = 0.68–0.88) in the external test dataset. The median C-DICE for NEE was 0.96 (IQR = 0.92–0.97) in the institutional test dataset as compared to 0.85 (IQR = 0.72–0.91) in the external test dataset.
Conclusion
The developed ANN-based algorithm (publicly available at www.github.com/NeuroAI-HD/HD-BM) allows reliable detection and precise volumetric quantification of CE and NEE compartments in patients with BM.
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Affiliation(s)
- Irada Pflüger
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg , Germany
| | - Tassilo Wald
- Medical Image Computing, German Cancer Research Center (DKFZ) , Heidelberg , Germany
| | - Fabian Isensee
- Medical Image Computing, German Cancer Research Center (DKFZ) , Heidelberg , Germany
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg , Germany
| | - Hagen Meredig
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg , Germany
| | - Kai Schlamp
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Clinic for Thoracic Diseases (Thoraxklinik), Heidelberg University Hospital , Heidelberg , Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University Munich , Munich , Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg , Germany
| | - Claus Peter Heußel
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Clinic for Thoracic Diseases (Thoraxklinik), Heidelberg University Hospital , Heidelberg , Germany
- Member of the Cerman Center for Lung Research (DZL), Translational Lung Research Center (TLRC) , Heidelberg , Germany
| | - Juergen Debus
- Department of Radiation Oncology, Heidelberg University Hospital , Heidelberg , Germany
- Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg University Hospital , Heidelberg , Germany
- German Cancer Consotium (DKTK), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ) , Heidelberg , Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ) , Heidelberg , Germany
| | - Wolfgang Wick
- Neurology Clinic, Heidelberg University Hospital , Heidelberg , Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ) , Heidelberg , Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg , Germany
| | - Klaus H Maier-Hein
- Medical Image Computing, German Cancer Research Center (DKFZ) , Heidelberg , Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg , Germany
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15
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Karimian-Jazi K, Neuberger U, Schregel K, Brugnara G, Bendszus M, Breckwoldt MO, Schwarz D, Jäger LB, Wick W. Diagnostic value of gadolinium contrast administration for spinal cord magnetic resonance imaging in multiple sclerosis patients and correlative markers of lesion enhancement. Mult Scler J Exp Transl Clin 2021; 7:20552173211047978. [PMID: 34868625 PMCID: PMC8637714 DOI: 10.1177/20552173211047978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 09/02/2021] [Indexed: 11/17/2022] Open
Abstract
Background Magnetic resonance imaging is essential for monitoring people with multiple
sclerosis, but the diagnostic value of gadolinium contrast administration in
spine magnetic resonance imaging is unclear. Objective To assess the diagnostic value of gadolinium contrast administration in spine
magnetic resonance imaging follow-up examinations and identify imaging
markers correlating with lesion enhancement. Methods A total of 65 multiple sclerosis patients with at least 2 spinal magnetic
resonance imaging follow-up examinations were included. Spine magnetic
resonance imaging was performed at 3 Tesla with a standardized protocol
(sagittal and axial T2-weighted turbo spin echo and T1-weighted
post-contrast sequences). T2 lesion load and enhancing lesions were assessed
by two independent neuroradiologists for lesion size, localization, and T2
signal ratio (T2 signallesion/T2 signalnormal appearing
spinal cord). Results A total of 68 new spinal T2 lesions and 20 new contrast-enhancing lesions
developed during follow-up. All enhancing lesions had a discernable
correlate as a new T2 lesion. Lesion enhancement correlated with a higher T2
signal ratio compared to non-enhancing lesions (T2 signal ratio: 2.0 ± 0.4
vs. 1.4 ± 0.2, ****p < 0.001). Receiver operating
characteristics analysis showed an optimal cutoff value of signal ratio 1.78
to predict lesion enhancement (82% sensitivity and 97% specificity). Conclusion Gadolinium contrast administration is dispensable in follow-up spine magnetic
resonance imaging if no new T2 lesions are present. Probability of
enhancement correlates with the T2 signal ratio.
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Affiliation(s)
- Kianush Karimian-Jazi
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
| | - Ulf Neuberger
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
| | - Katharina Schregel
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
| | - Gianluca Brugnara
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
| | - Michael O Breckwoldt
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium, German Cancer Research Center, Heidelberg, Germany
| | - Daniel Schwarz
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | | | - Wolfgang Wick
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ), Heidelberg, Germany Neurology Clinic and National Center for Tumor Diseases, University Hospital Heidelberg, Heidelberg, Germany
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16
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Pfleiderer K, Turco V, Horvat NK, Hunger J, Karimian-Jazi K, Schregel K, Brugnara G, Nguyen D, Jähne K, Fischer M, Alsasa A, Bunse T, Schlesner M, Muckenthaler M, Weissleder R, Wick W, Heiland S, Vollmuth P, Bendszus M, Rodell CB, Breckwoldt MO, Platten M. NIMG-48. TLR7/8-AGONIST-LOADED NANOPARTICLES REPROGRAM TUMOR-ASSOCIATED MYELOID CELLS FOR EFFECTIVE IMMUNOTHERAPY OF EXPERIMENTAL GLIOMA AND MRI-BASED TREATMENT MONITORING. Neuro Oncol 2021. [DOI: 10.1093/neuonc/noab196.546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Drivers of glioblastoma progression include the immunosuppressive tumor microenvironment (TME), dominated by tumor-associated myeloid cells. Therefore, we investigated a new approach targeting the myeloid compartment to reprogram myeloid cells in the TME using a β-cyclodextrin nanoparticle (CDNP) formulation encapsulating the toll-like receptor 7 and 8 (TLR7/8) agonist R848. Biodistribution confirmed specific targeting of CDNP-R848 to tumor-associated macrophages (TAMs) (labeling efficiency: 34.0% ± 22.2%), whereas tumor microglia (5.4% ± 4.4%) and splenic macrophages (13.2% ± 0.7%) revealed less uptake. Interestingly, intravenous application of CDNP-R848 induced strong tumor regression with an overall response rate of 80% (2.5% complete response, 52.5% partial response and 25% stable disease, n=40 mice) in Gl261 syngeneic experimental gliomas, while CDNP vehicle treated animals showed exponential tumor growth (100% progressive disease, n=12 mice). As advanced imaging is essential to monitor intracranial disease and possibly predict response and resistance, we performed high resolution magnetic resonance imaging using ultrasmall iron oxide nanoparticles (USPIO) for macrophage tracking. Increased levels of USPIO uptake in vehicle treated animals compared to CDNP-R848 treated animals were found as an early marker of responding mice (ΔT2*: -11.7 ± 4.2 vs -4.0 ± 2.8 ms, p=0.01). This correlated with an increased influx of myeloid cells into the TME of vehicle treated animals and showed a strong correlation of macrophage recruitment and USPIO uptake (R2: 0.78, p=0.004). Mechanistically, phenotyping of macrophages (CD45high/CD11b+) indicated a pro-inflammatory shift of TAMs with an increased infiltration of pro-inflammatory F4/80+/MHCII+ macrophages during CDNP-R848 treatment. Surprisingly, the anti-tumor effect of CDNP-R848 was independent of CD8+ T cells, CD4+ T cells or NK cells during selective depletion experiments. In summary, this work demonstrates the ability of myeloid-targeted therapies to re-shape the tumor microenvironment for an effective immunotherapy of glioma.
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Affiliation(s)
- Kira Pfleiderer
- German Cancer Research Center (DKFZ), Baden-Wurttemberg, Germany
| | - Verena Turco
- German Cancer Research Center (DKFZ), Baden-Wurttemberg, Germany
| | - Natalie K Horvat
- Molecular Medicine Partnership Unit (MMPU), European Molecular Biology Laboratory (EMBL), Baden-Wurttemberg, Germany
| | - Jessica Hunger
- German Cancer Research Center (DKFZ), Baden-Wurttemberg, Germany
| | | | - Katharina Schregel
- Neuroradiology Department, University Hospital, Baden-Wurttemberg, Germany
| | - Gianluca Brugnara
- Neuroradiology Department, University Hospital, Baden-Wurttemberg, Germany
| | - Duy Nguyen
- German Cancer Research Center (DKFZ), Baden-Wurttemberg, Germany
| | - Kristine Jähne
- German Cancer Research Center (DKFZ), Baden-Wurttemberg, Germany
| | - Manuel Fischer
- Neuroradiology Department, University Hospital, Baden-Wurttemberg, Germany
| | - Abdulrahman Alsasa
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, USA
| | - Theresa Bunse
- German Cancer Research Center (DKFZ), Baden-Wurttemberg, Germany
| | | | - Martina Muckenthaler
- Molecular Medicine Partnership Unit (MMPU), European Molecular Biology Laboratory (EMBL), Baden-Wurttemberg, Germany
| | - Ralph Weissleder
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Wolfgang Wick
- German Cancer Research Center (DKFZ), Baden-Wurttemberg, Germany
| | - Sabine Heiland
- Neuroradiology Department, University Hospital, Baden-Wurttemberg, Germany
| | - Philipp Vollmuth
- Neuroradiology Department, University Hospital, Baden-Wurttemberg, Germany
| | - Martin Bendszus
- Neuroradiology Department, University Hospital, Baden-Wurttemberg, Germany
| | - Christopher B Rodell
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, USA
| | | | - Michael Platten
- German Cancer Research Center (DKFZ), Baden-Wurttemberg, Germany
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17
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Jayachandran Preetha C, Meredig H, Brugnara G, Mahmutoglu MA, Foltyn M, Isensee F, Kessler T, Pflüger I, Schell M, Neuberger U, Petersen J, Wick A, Heiland S, Debus J, Platten M, Idbaih A, Brandes AA, Winkler F, van den Bent MJ, Nabors B, Stupp R, Maier-Hein KH, Gorlia T, Tonn JC, Weller M, Wick W, Bendszus M, Vollmuth P. Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study. Lancet Digit Health 2021; 3:e784-e794. [PMID: 34688602 DOI: 10.1016/s2589-7500(21)00205-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 07/14/2021] [Accepted: 08/10/2021] [Indexed: 12/30/2022]
Abstract
BACKGROUND Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the management of patients with cancer. However, studies have shown gadolinium deposition in the brain after repeated GBCA administration with yet unknown clinical significance. We aimed to assess the feasibility and diagnostic value of synthetic post-contrast T1-weighted MRI generated from pre-contrast MRI sequences through deep convolutional neural networks (dCNN) for tumour response assessment in neuro-oncology. METHODS In this multicentre, retrospective cohort study, we used MRI examinations to train and validate a dCNN for synthesising post-contrast T1-weighted sequences from pre-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery sequences. We used MRI scans with availability of these sequences from 775 patients with glioblastoma treated at Heidelberg University Hospital, Heidelberg, Germany (775 MRI examinations); 260 patients who participated in the phase 2 CORE trial (1083 MRI examinations, 59 institutions); and 505 patients who participated in the phase 3 CENTRIC trial (3147 MRI examinations, 149 institutions). Separate training runs to rank the importance of individual sequences and (for a subset) diffusion-weighted imaging were conducted. Independent testing was performed on MRI data from the phase 2 and phase 3 EORTC-26101 trial (521 patients, 1924 MRI examinations, 32 institutions). The similarity between synthetic and true contrast enhancement on post-contrast T1-weighted MRI was quantified using the structural similarity index measure (SSIM). Automated tumour segmentation and volumetric tumour response assessment based on synthetic versus true post-contrast T1-weighted sequences was performed in the EORTC-26101 trial and agreement was assessed with Kaplan-Meier plots. FINDINGS The median SSIM score for predicting contrast enhancement on synthetic post-contrast T1-weighted sequences in the EORTC-26101 test set was 0·818 (95% CI 0·817-0·820). Segmentation of the contrast-enhancing tumour from synthetic post-contrast T1-weighted sequences yielded a median tumour volume of 6·31 cm3 (5·60 to 7·14), thereby underestimating the true tumour volume by a median of -0·48 cm3 (-0·37 to -0·76) with the concordance correlation coefficient suggesting a strong linear association between tumour volumes derived from synthetic versus true post-contrast T1-weighted sequences (0·782, 0·751-0·807, p<0·0001). Volumetric tumour response assessment in the EORTC-26101 trial showed a median time to progression of 4·2 months (95% CI 4·1-5·2) with synthetic post-contrast T1-weighted and 4·3 months (4·1-5·5) with true post-contrast T1-weighted sequences (p=0·33). The strength of the association between the time to progression as a surrogate endpoint for predicting the patients' overall survival in the EORTC-26101 cohort was similar when derived from synthetic post-contrast T1-weighted sequences (hazard ratio of 1·749, 95% CI 1·282-2·387, p=0·0004) and model C-index (0·667, 0·622-0·708) versus true post-contrast T1-weighted MRI (1·799, 95% CI 1·314-2·464, p=0·0003) and model C-index (0·673, 95% CI 0·626-0·711). INTERPRETATION Generating synthetic post-contrast T1-weighted MRI from pre-contrast MRI using dCNN is feasible and quantification of the contrast-enhancing tumour burden from synthetic post-contrast T1-weighted MRI allows assessment of the patient's response to treatment with no significant difference by comparison with true post-contrast T1-weighted sequences with administration of GBCAs. This finding could guide the application of dCNN in radiology to potentially reduce the necessity of GBCA administration. FUNDING Deutsche Forschungsgemeinschaft.
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Affiliation(s)
| | - Hagen Meredig
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Mustafa A Mahmutoglu
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martha Foltyn
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Fabian Isensee
- Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Tobias Kessler
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany; Clinical Cooperation Unit Neurooncology, German Cancer Research Center, Heidelberg, Germany
| | - Irada Pflüger
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Ulf Neuberger
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Jens Petersen
- Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Antje Wick
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Institute of Radiation Oncology, Heidelberg, Germany; Heidelberg Ion-Beam Therapy Center, Heidelberg, Germany
| | - Michael Platten
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center, Heidelberg, Germany; Department of Neurology, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Ahmed Idbaih
- Sorbonne Université, Inserm, Institut du Cerveau, Assistance Publique-Hôpitaux de Paris, Hôpitaux Universitaires La Pitié Salpêtrière-Charles Foix, Service de Neurologie 2-Mazarin, Paris, France
| | - Alba A Brandes
- Department of Medical Oncology, Azienda USL of Bologna, Bologna, Italy
| | - Frank Winkler
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany; Clinical Cooperation Unit Neurooncology, German Cancer Research Center, Heidelberg, Germany
| | | | - Burt Nabors
- Department of Neurology and O'Neal Comprehensive Cancer Center, Division of Neuro-Oncology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Roger Stupp
- Malnati Brain Tumor Institute of the Lurie Comprehensive Cancer Center, Department of Neurological Surgery and Department of Neurology, Northwestern Medicine and Northwestern University, Chicago, IL, USA
| | - Klaus H Maier-Hein
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Thierry Gorlia
- European Organisation for Research and Treatment of Cancer, Brussels, Belgium
| | | | - Michael Weller
- Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Wolfgang Wick
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany; Clinical Cooperation Unit Neurooncology, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
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18
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Brugnara G, Herweh C, Neuberger U, Bo Hansen M, Ulfert C, Mahmutoglu MA, Foltyn M, Nagel S, Schönenberger S, Heiland S, Ringleb PA, Bendszus M, Möhlenbruch M, Pfaff JAR, Vollmuth P. Dynamics of cerebral perfusion and oxygenation parameters following endovascular treatment of acute ischemic stroke. J Neurointerv Surg 2021; 14:neurintsurg-2020-017163. [PMID: 33762405 PMCID: PMC8785045 DOI: 10.1136/neurintsurg-2020-017163] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 02/06/2021] [Accepted: 02/10/2021] [Indexed: 11/03/2022]
Abstract
BACKGROUND We studied the effects of endovascular treatment (EVT) and the impact of the extent of recanalization on cerebral perfusion and oxygenation parameters in patients with acute ischemic stroke (AIS) and large vessel occlusion (LVO). METHODS Forty-seven patients with anterior LVO underwent computed tomography perfusion (CTP) before and immediately after EVT. The entire ischemic region (Tmax >6 s) was segmented before intervention, and tissue perfusion (time-to-maximum (Tmax), time-to-peak (TTP), mean transit time (MTT), cerebral blood volume (CBV), cerebral blood flow (CBF)) and oxygenation (coefficient of variation (COV), capillary transit time heterogeneity (CTH), metabolic rate of oxygen (CMRO2), oxygen extraction fraction (OEF)) parameters were quantified from the segmented area at baseline and the corresponding area immediately after intervention, as well as within the ischemic core and penumbra. The impact of the extent of recanalization (modified Treatment in Cerebral Infarction (mTICI)) on CTP parameters was assessed with the Wilcoxon test and Pearson's correlation coefficients. RESULTS The Tmax, MTT, OEF and CTH values immediately after EVT were lower in patients with complete (as compared with incomplete) recanalization, whereas CBF and COV values were higher (P<0.05) and no differences were found in other parameters. The ischemic penumbra immediately after EVT was lower in patients with complete recanalization as compared with those with incomplete recanalization (P=0.002), whereas no difference was found for the ischemic core (P=0.12). Specifically, higher mTICI scores were associated with a greater reduction of ischemic penumbra volumes (R²=-0.48 (95% CI -0.67 to -0.22), P=0.001) but not of ischemic core volumes (P=0.098). CONCLUSIONS Our study demonstrates that the ischemic penumbra is the key target of successful EVT in patients with AIS and largely determines its efficacy on a tissue level. Furthermore, we confirm the validity of the mTICI score as a surrogate parameter of interventional success on a tissue perfusion level.
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Affiliation(s)
- Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Baden-Württemberg, Germany
| | - Christian Herweh
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Baden-Württemberg, Germany
| | - Ulf Neuberger
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Baden-Württemberg, Germany
| | - Mikkel Bo Hansen
- Center of Functionally Integrative Neuroscience and MINDLab, Aarhus Universitet, Aarhus, Midtjylland, Denmark
| | - Christian Ulfert
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Baden-Württemberg, Germany
| | - Mustafa Ahmed Mahmutoglu
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Baden-Württemberg, Germany
| | - Martha Foltyn
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Baden-Württemberg, Germany
| | - Simon Nagel
- Department of Neurology, Heidelberg University Hospital, Heidelberg, Baden-Württemberg, Germany
| | - Silvia Schönenberger
- Department of Neurology, Heidelberg University Hospital, Heidelberg, Baden-Württemberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Baden-Württemberg, Germany
| | - Peter Arthur Ringleb
- Department of Neurology, Heidelberg University Hospital, Heidelberg, Baden-Württemberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Baden-Württemberg, Germany
| | - Markus Möhlenbruch
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Baden-Württemberg, Germany
| | - Johannes Alex Rolf Pfaff
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Baden-Württemberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Baden-Württemberg, Germany
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19
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Brugnara G, Neuberger U, Vollmuth P. Response by Brugnara et al Regarding Article, "The Sense or Futility of Outcome Prediction in Acute Stroke for Endovascular Treatment Decision-Making". Stroke 2021; 52:e85-e86. [PMID: 33493042 DOI: 10.1161/strokeaha.120.033453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Gianluca Brugnara
- Department of Neuroradiology and Computational Neuroimaging Section, Heidelberg University Hospital, Germany
| | - Ulf Neuberger
- Department of Neuroradiology and Computational Neuroimaging Section, Heidelberg University Hospital, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology and Computational Neuroimaging Section, Heidelberg University Hospital, Germany
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20
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Brugnara G, Neuberger U, Mahmutoglu MA, Foltyn M, Herweh C, Nagel S, Schönenberger S, Heiland S, Ulfert C, Ringleb PA, Bendszus M, Möhlenbruch MA, Pfaff JA, Vollmuth P. Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning. Stroke 2020; 51:3541-3551. [DOI: 10.1161/strokeaha.120.030287] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background and Purpose:
This study assessed the predictive performance and relative importance of clinical, multimodal imaging, and angiographic characteristics for predicting the clinical outcome of endovascular treatment for acute ischemic stroke.
Methods:
A consecutive series of 246 patients with acute ischemic stroke and large vessel occlusion in the anterior circulation who underwent endovascular treatment between April 2014 and January 2018 was analyzed. Clinical, conventional imaging (electronic Alberta Stroke Program Early CT Score, acute ischemic volume, site of vessel occlusion, and collateral score), and advanced imaging characteristics (CT-perfusion with quantification of ischemic penumbra and infarct core volumes) before treatment as well as angiographic (interval groin puncture-recanalization, modified Thrombolysis in Cerebral Infarction score) and postinterventional clinical (National Institutes of Health Stroke Scale score after 24 hours) and imaging characteristics (electronic Alberta Stroke Program Early CT Score, final infarction volume after 18–36 hours) were assessed. The modified Rankin Scale (mRS) score at 90 days (mRS-90) was used to measure patient outcome (favorable outcome: mRS-90 ≤2 versus unfavorable outcome: mRS-90 >2). Machine-learning with gradient boosting classifiers was used to assess the performance and relative importance of the extracted characteristics for predicting mRS-90.
Results:
Baseline clinical and conventional imaging characteristics predicted mRS-90 with an area under the receiver operating characteristics curve of 0.740 (95% CI, 0.733–0.747) and an accuracy of 0.711 (95% CI, 0.705–0.717). Advanced imaging with CT-perfusion did not improved the predictive performance (area under the receiver operating characteristics curve, 0.747 [95% CI, 0.740–0.755]; accuracy, 0.720 [95% CI, 0.714–0.727];
P
=0.150). Further inclusion of angiographic and postinterventional characteristics significantly improved the predictive performance (area under the receiver operating characteristics curve, 0.856 [95% CI, 0.850–0.861]; accuracy, 0.804 [95% CI, 0.799–0.810];
P
<0.001). The most important parameters for predicting mRS 90 were National Institutes of Health Stroke Scale score after 24 hours (importance =100%), premorbid mRS score (importance =44%) and final infarction volume on postinterventional CT after 18 to 36 hours (importance =32%).
Conclusions:
Integrative assessment of clinical, multimodal imaging, and angiographic characteristics with machine-learning allowed to accurately predict the clinical outcome following endovascular treatment for acute ischemic stroke. Thereby, premorbid mRS was the most important clinical predictor for mRS-90, and the final infarction volume was the most important imaging predictor, while the extent of hemodynamic impairment on CT-perfusion before treatment had limited importance.
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Affiliation(s)
- Gianluca Brugnara
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Ulf Neuberger
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Mustafa A. Mahmutoglu
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Martha Foltyn
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Christian Herweh
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Simon Nagel
- Neurology Clinic (S.N., S.S., P.A.R.), Heidelberg University Hospital, Germany
| | | | - Sabine Heiland
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Christian Ulfert
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | | | - Martin Bendszus
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Markus A. Möhlenbruch
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Johannes A.R. Pfaff
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology (G.B., U.N., M.A.M., M.F., Ch.H., S.H., C.U., M.B., M.A.M., J.A.R.P., P.V.), Heidelberg University Hospital, Germany
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21
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Schell M, Pflüger I, Brugnara G, Isensee F, Neuberger U, Foltyn M, Kessler T, Sahm F, Wick A, Nowosielski M, Heiland S, Weller M, Platten M, Maier-Hein KH, Von Deimling A, Van Den Bent MJ, Gorlia T, Wick W, Bendszus M, Kickingereder P. Validation of diffusion MRI phenotypes for predicting response to bevacizumab in recurrent glioblastoma: post-hoc analysis of the EORTC-26101 trial. Neuro Oncol 2020; 22:1667-1676. [PMID: 32393964 PMCID: PMC7690360 DOI: 10.1093/neuonc/noaa120] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND This study validated a previously described diffusion MRI phenotype as a potential predictive imaging biomarker in patients with recurrent glioblastoma receiving bevacizumab (BEV). METHODS A total of 396/596 patients (66%) from the prospective randomized phase II/III EORTC-26101 trial (with n = 242 in the BEV and n = 154 in the non-BEV arm) met the inclusion criteria with availability of anatomical and diffusion MRI sequences at baseline prior treatment. Apparent diffusion coefficient (ADC) histograms from the contrast-enhancing tumor volume were fitted to a double Gaussian distribution and the mean of the lower curve (ADClow) was used for further analysis. The predictive ability of ADClow was assessed with biomarker threshold models and multivariable Cox regression for overall survival (OS) and progression-free survival (PFS). RESULTS ADClow was associated with PFS (hazard ratio [HR] = 0.625, P = 0.007) and OS (HR = 0.656, P = 0.031). However, no (predictive) interaction between ADClow and the treatment arm was present (P = 0.865 for PFS, P = 0.722 for OS). Independent (prognostic) significance of ADClow was retained after adjusting for epidemiological, clinical, and molecular characteristics (P ≤ 0.02 for OS, P ≤ 0.01 PFS). The biomarker threshold model revealed an optimal ADClow cutoff of 1241*10-6 mm2/s for OS. Thereby, median OS for BEV-patients with ADClow ≥ 1241 was 10.39 months versus 8.09 months for those with ADClow < 1241 (P = 0.004). Similarly, median OS for non-BEV patients with ADClow ≥ 1241 was 9.80 months versus 7.79 months for those with ADClow < 1241 (P = 0.054). CONCLUSIONS ADClow is an independent prognostic parameter for stratifying OS and PFS in patients with recurrent glioblastoma. Consequently, the previously suggested role of ADClow as predictive imaging biomarker could not be confirmed within this phase II/III trial.
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Affiliation(s)
- Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Irada Pflüger
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Fabian Isensee
- Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Ulf Neuberger
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martha Foltyn
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Tobias Kessler
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center, Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center, Heidelberg, Germany
| | - Antje Wick
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Martha Nowosielski
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center, Heidelberg, Germany
- Department of Neurology, Medical University, Innsbruck, Austria
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Michael Weller
- Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Michael Platten
- Department of Neurology, Mannheim Medical Center, University of Heidelberg, Mannheim, Germany
| | - Klaus H Maier-Hein
- Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
- Department of Neurology, Mannheim Medical Center, University of Heidelberg, Mannheim, Germany
| | - Andreas Von Deimling
- Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center, Heidelberg, Germany
| | | | - Thierry Gorlia
- European Organisation for Research and Treatment of Cancer, Brussels, Belgium
| | - Wolfgang Wick
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Research Center, Heidelberg, Germany
- Clinical Cooperation Unit Neuropathology, German Cancer Research Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Kickingereder
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Section for Computational Neuroimaging, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
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Kickingereder P, Brugnara G, Hansen MB, Nowosielski M, Pflüger I, Schell M, Isensee F, Foltyn M, Neuberger U, Kessler T, Sahm F, Wick A, Heiland S, Weller M, Platten M, von Deimling A, Maier-Hein KH, Østergaard L, van den Bent MJ, Gorlia T, Wick W, Bendszus M. Noninvasive Characterization of Tumor Angiogenesis and Oxygenation in Bevacizumab-treated Recurrent Glioblastoma by Using Dynamic Susceptibility MRI: Secondary Analysis of the European Organization for Research and Treatment of Cancer 26101 Trial. Radiology 2020; 297:164-175. [PMID: 32720870 DOI: 10.1148/radiol.2020200978] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Background Relevance of antiangiogenic treatment with bevacizumab in patients with glioblastoma is controversial because progression-free survival benefit did not translate into an overall survival (OS) benefit in randomized phase III trials. Purpose To perform longitudinal characterization of intratumoral angiogenesis and oxygenation by using dynamic susceptibility contrast agent-enhanced (DSC) MRI and evaluate its potential for predicting outcome from administration of bevacizumab. Materials and Methods In this secondary analysis of the prospective randomized phase II/III European Organization for Research and Treatment of Cancer 26101 trial conducted between October 2011 and December 2015 in 596 patients with first recurrence of glioblastoma, the subset of patients with availability of anatomic MRI and DSC MRI at baseline and first follow-up was analyzed. Patients were allocated into those administered bevacizumab (hereafter, the BEV group; either bevacizumab monotherapy or bevacizumab with lomustine) and those not administered bevacizumab (hereafter, the non-BEV group with lomustine monotherapy). Contrast-enhanced tumor volume, noncontrast-enhanced T2 fluid-attenuated inversion recovery (FLAIR) signal abnormality volume, Gaussian-normalized relative cerebral blood volume (nrCBV), Gaussian-normalized relative blood flow (nrCBF), and tumor metabolic rate of oxygen (nTMRO2) was quantified. The predictive ability of these imaging parameters was assessed with multivariable Cox regression and formal interaction testing. Results A total of 254 of 596 patients were evaluated (mean age, 57 years ± 11; 155 men; 161 in the BEV group and 93 in non-BEV group). Progression-free survival was longer in the BEV group (3.7 months; 95% confidence interval [CI]: 3.0, 4.2) compared with the non-BEV group (2.5 months; 95% CI: 1.5, 2.9; P = .01), whereas OS was not different (P = .15). The nrCBV decreased for the BEV group (-16.3%; interquartile range [IQR], -39.5% to 12.0%; P = .01), but not for the non-BEV group (1.2%; IQR, -17.9% to 23.3%; P = .19) between baseline and first follow-up. An identical pattern was observed for both nrCBF and nTMRO2 values. Contrast-enhanced tumor and noncontrast-enhanced T2 FLAIR signal abnormality volumes decreased for the BEV group (-66% [IQR, -83% to -35%] and -33% [IQR, -71% to -5%], respectively; P < .001 for both), whereas they increased for the non-BEV group (30% [IQR, -17% to 98%], P = .001; and 10% [IQR, -13% to 82%], P = .02, respectively) between baseline and first follow-up. None of the assessed MRI parameters were predictive for OS in the BEV group. Conclusion Bevacizumab treatment decreased tumor volumes, angiogenesis, and oxygenation, thereby reflecting its effectiveness for extending progression-free survival; however, these parameters were not predictive of overall survival (OS), which highlighted the challenges of identifying patients that derive an OS benefit from bevacizumab. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Dillon in this issue.
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Affiliation(s)
- Philipp Kickingereder
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Gianluca Brugnara
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Mikkel Bo Hansen
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Martha Nowosielski
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Irada Pflüger
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Marianne Schell
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Fabian Isensee
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Martha Foltyn
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Ulf Neuberger
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Tobias Kessler
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Felix Sahm
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Antje Wick
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Sabine Heiland
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Michael Weller
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Michael Platten
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Andreas von Deimling
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Klaus H Maier-Hein
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Leif Østergaard
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Martin J van den Bent
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Thierry Gorlia
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Wolfgang Wick
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
| | - Martin Bendszus
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (P.K., G.B., I.P., M.S., M.F., U.N., S.H., M.B.); Center of Functionally Integrative Neuroscience and MINDLab, Aarhus University Hospital, Aarhus, Denmark (M.B.H., L.Ø.); Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany (M.N., T.K., A.W., W.W.); Department of Neurology, Medical University Innsbruck, Innsbruck, Austria (M.N.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.I., K.H.M.H.); Clinical Cooperation Unit Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.K., W.W.); Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany (F.S., A.v.D.); Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg, Germany (F.S., A.v.D.); Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland (M.W.); Department of Neurology, Medical Faculty Mannheim, MCTN, University of Heidelberg, Mannheim, Germany (M.P.); Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.H.M.H.); Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark (L.Ø.); Brain Tumor Center at Erasmus MC Cancer Institute, Rotterdam, the Netherlands (M.J.v.d.B.); and European Organization for Research and Treatment of Cancer (EORTC), Brussels, Belgium (T.G.)
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23
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Foltyn M, Nieto Taborda KN, Neuberger U, Brugnara G, Reinhardt A, Stichel D, Heiland S, Herold-Mende C, Unterberg A, Debus J, von Deimling A, Wick W, Bendszus M, Kickingereder P. T2/FLAIR-mismatch sign for noninvasive detection of IDH-mutant 1p/19q non-codeleted gliomas: validity and pathophysiology. Neurooncol Adv 2020; 2:vdaa004. [PMID: 32642675 PMCID: PMC7212872 DOI: 10.1093/noajnl/vdaa004] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background This study aimed to assess the validity and pathophysiology of the T2/FLAIR-mismatch sign for noninvasive identification of isocitrate dehydrogenase (IDH)-mutant 1p/19q non-codeleted glioma. Methods Magnetic resonance imaging scans from 408 consecutive patients with newly diagnosed glioma (113 lower-grade gliomas and 295 glioblastomas) were evaluated for the presence of T2/FLAIR-mismatch sign by 2 independent reviewers. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the performance of the T2/FLAIR-mismatch sign for identifying IDH-mutant 1p/19q non-codeleted tumors. An exploratory analysis of differences in contrast-enhancing tumor volumes, apparent diffusion coefficient (ADC) values, and relative cerebral blood volume (rCBV) values in IDH-mutant gliomas with versus without the presence of a T2/FLAIR-mismatch sign (as well as analysis of spatial differences within tumors with the presence of a T2/FLAIR-mismatch sign) was performed. Results The T2/FLAIR-mismatch sign was present in 12 cases with lower-grade glioma (10.6%), all of them being IDH-mutant 1p/19q non-codeleted tumors (sensitivity = 10.9%, specificity = 100%, PPV = 100%, NPV = 3.0%, accuracy = 13.3%). There was a substantial interrater agreement to identify the T2/FLAIR-mismatch sign (Cohen's kappa = 0.75 [95% CI, 0.57-0.93]). The T2/FLAIR-mismatch sign was not identified in any other molecular subgroup, including IDH-mutant glioblastoma cases (n = 5). IDH-mutant gliomas with a T2/FLAIR-mismatch sign showed significantly higher ADC (P < .0001) and lower rCBV values (P = .0123) as compared to IDH-mutant gliomas without a T2/FLAIR-mismatch sign. Moreover, in IDH-mutant gliomas with T2/FLAIR-mismatch sign the ADC values were significantly lower in the FLAIR-hyperintense rim as compared to the FLAIR-hypointense core of the tumor (P = .0005). Conclusions This study confirms the high specificity of the T2/FLAIR-mismatch sign for noninvasive identification of IDH-mutant 1p/19q non-codeleted gliomas; however, sensitivity is low and applicability is limited to lower-grade gliomas. Whether the higher ADC and lower rCBV values in IDH-mutant gliomas with a T2/FLAIR-mismatch sign (as compared to those without) translate into a measurable prognostic effect requires investigation in future studies. Moreover, spatial differences in ADC values between the core and rim of tumors with a T2/FLAIR-mismatch sign potentially reflect specific distinctions in tumor cellularity and microenvironment.
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Affiliation(s)
- Martha Foltyn
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | | | - Ulf Neuberger
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Annekathrin Reinhardt
- Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Damian Stichel
- Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Christel Herold-Mende
- Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Andreas Unterberg
- Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg Institute of Radiation Oncology and National Center for Radiation Research in Oncology, Heidelberg, Germany.,Division of Molecular and Translational Radiation Oncology, National Center for Tumor Diseases, Heidelberg University Hospital and DKFZ, Heidelberg, Germany
| | - Andreas von Deimling
- Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany.,Clinical Cooperation Unit Neuropathology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wolfgang Wick
- Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany.,Clinical Cooperation Unit Neuro-oncology, DKTK, DKFZ, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Philipp Kickingereder
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
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24
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Brugnara G, Isensee F, Neuberger U, Bonekamp D, Petersen J, Diem R, Wildemann B, Heiland S, Wick W, Bendszus M, Maier-Hein K, Kickingereder P. Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis. Eur Radiol 2020; 30:2356-2364. [PMID: 31900702 DOI: 10.1007/s00330-019-06593-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 11/09/2019] [Accepted: 11/13/2019] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Patients with multiple sclerosis (MS) regularly undergo MRI for assessment of disease burden. However, interpretation may be time consuming and prone to intra- and interobserver variability. Here, we evaluate the potential of artificial neural networks (ANN) for automated volumetric assessment of MS disease burden and activity on MRI. METHODS A single-institutional dataset with 334 MS patients (334 MRI exams) was used to develop and train an ANN for automated identification and volumetric segmentation of T2/FLAIR-hyperintense and contrast-enhancing (CE) lesions. Independent testing was performed in a single-institutional longitudinal dataset with 82 patients (266 MRI exams). We evaluated lesion detection performance (F1 scores), lesion segmentation agreement (DICE coefficients), and lesion volume agreement (concordance correlation coefficients [CCC]). Independent evaluation was performed on the public ISBI-2015 challenge dataset. RESULTS The F1 score was maximized in the training set at a detection threshold of 7 mm3 for T2/FLAIR lesions and 14 mm3 for CE lesions. In the training set, mean F1 scores were 0.867 for T2/FLAIR lesions and 0.636 for CE lesions, as compared to 0.878 for T2/FLAIR lesions and 0.715 for CE lesions in the test set. Using these thresholds, the ANN yielded mean DICE coefficients of 0.834 and 0.878 for segmentation of T2/FLAIR and CE lesions in the training set (fivefold cross-validation). Corresponding DICE coefficients in the test set were 0.846 for T2/FLAIR lesions and 0.908 for CE lesions, and the CCC was ≥ 0.960 in each dataset. CONCLUSIONS Our results highlight the capability of ANN for quantitative state-of-the-art assessment of volumetric lesion load on MRI and potentially enable a more accurate assessment of disease burden in patients with MS. KEY POINTS • Artificial neural networks (ANN) can accurately detect and segment both T2/FLAIR and contrast-enhancing MS lesions in MRI data. • Performance of the ANN was consistent in a clinically derived dataset, with patients presenting all possible disease stages in MRI scans acquired from standard clinical routine rather than with high-quality research sequences. • Computer-aided evaluation of MS with ANN could streamline both clinical and research procedures in the volumetric assessment of MS disease burden as well as in lesion detection.
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Affiliation(s)
- Gianluca Brugnara
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Fabian Isensee
- Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ulf Neuberger
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - David Bonekamp
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jens Petersen
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
- Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ricarda Diem
- Department of Neurology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Brigitte Wildemann
- Department of Neurology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Wolfgang Wick
- Department of Neurology, University of Heidelberg Medical Center, Heidelberg, Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DKTK), DKFZ, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Klaus Maier-Hein
- Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Philipp Kickingereder
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany.
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25
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Gagliardi F, Narayanan A, Gallotti AL, Pieri V, Mazzoleni S, Cominelli M, Rezzola S, Corsini M, Brugnara G, Altabella L, Politi LS, Bacigaluppi M, Falini A, Castellano A, Ronca R, Poliani PL, Mortini P, Galli R. Enhanced SPARCL1 expression in cancer stem cells improves preclinical modeling of glioblastoma by promoting both tumor infiltration and angiogenesis. Neurobiol Dis 2019; 134:104705. [PMID: 31830525 DOI: 10.1016/j.nbd.2019.104705] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 11/14/2019] [Accepted: 12/08/2019] [Indexed: 12/16/2022] Open
Abstract
Glioblastoma (GBM) is the most malignant brain tumor of adults and is characterized by extensive cell dissemination within the brain parenchyma and enhanced angiogenesis. Effective preclinical modeling of these key features suffers from several shortcomings. Aim of this study was to determine whether modulating the expression of extracellular matrix (ECM) modifiers in proneural (PN) and mesenchymal (MES) cancer stem cells (CSCs) and in conventional glioma cell lines (GCLs) might improve tumor invasion and vascularization. To this end, we selected secreted, acidic and rich in cysteine-like 1 (SPARCL1) as a potential mediator of ECM remodeling in GBM. SPARCL1 transcript and protein expression was assessed in PN and MES CSCs as well as GCLs, in their xenografts and in patient-derived specimens by qPCR, WB and IHC. SPARCL1 expression was then enforced in both CSCs and GCLs by lentiviral-based transduction. The effect of SPARCL1 gain-of-function on microvascular proliferation, microglia activation and advanced imaging features was tested in intracranial xenografts by IHC and MRI and validated by chorioallantoic membrane (CAM) assays. SPARCL1 expression significantly enhanced the infiltrative and neoangiogenic features of PN and MES CSC/GCL-induced tumors, with the concomitant activation of inflammatory responses associated with the tumor microenvironment, thus resulting in experimental GBMs that reproduced both the parenchymal infiltration and the increased microvascular density, typical of GBM. Overall, these results indicate that SPARCL1 overexpression might be instrumental for the generation of CSC-derived preclinical models of GBM in which the main pathognomonic hallmarks of GBMs are retrievable, making them suitable for effective preclinical testing of therapeutics.
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Affiliation(s)
- Filippo Gagliardi
- Neural Stem Cell Biology Unit, Division of Neuroscience, San Raffaele Scientific Institute, Via Olgettina 58, Milan 20132, Italy; Department Neurosurgery and Gamma Knife Radiosurgery, San Raffaele Scientific Institute, Via Olgettina 60, Milan 20132, Italy
| | - Ashwin Narayanan
- Neural Stem Cell Biology Unit, Division of Neuroscience, San Raffaele Scientific Institute, Via Olgettina 58, Milan 20132, Italy
| | - Alberto Luigi Gallotti
- Neural Stem Cell Biology Unit, Division of Neuroscience, San Raffaele Scientific Institute, Via Olgettina 58, Milan 20132, Italy; Department Neurosurgery and Gamma Knife Radiosurgery, San Raffaele Scientific Institute, Via Olgettina 60, Milan 20132, Italy
| | - Valentina Pieri
- Neural Stem Cell Biology Unit, Division of Neuroscience, San Raffaele Scientific Institute, Via Olgettina 58, Milan 20132, Italy; Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University and San Raffaele Scientific Institute, Milan 20132, Italy
| | - Stefania Mazzoleni
- Neural Stem Cell Biology Unit, Division of Neuroscience, San Raffaele Scientific Institute, Via Olgettina 58, Milan 20132, Italy
| | - Manuela Cominelli
- Department Molecular and Translational Medicine, Pathology Unit, University of Brescia, Brescia 25124, Italy
| | - Sara Rezzola
- Department Molecular and Translational Medicine, Experimental Oncology and Immunology, University of Brescia, Brescia 25124, Italy
| | - Michela Corsini
- Department Molecular and Translational Medicine, Experimental Oncology and Immunology, University of Brescia, Brescia 25124, Italy
| | - Gianluca Brugnara
- Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University and San Raffaele Scientific Institute, Milan 20132, Italy
| | - Luisa Altabella
- Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University and San Raffaele Scientific Institute, Milan 20132, Italy
| | - Letterio Salvatore Politi
- Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University and San Raffaele Scientific Institute, Milan 20132, Italy
| | - Marco Bacigaluppi
- Neuroimmunology Unit, Institute of Experimental Neurology (INSPE), Division of Neuroscience, San Raffaele Scientific Institute, Via Olgettina 58, Milan 20132, Italy
| | - Andrea Falini
- Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University and San Raffaele Scientific Institute, Milan 20132, Italy
| | - Antonella Castellano
- Neuroradiology Unit and CERMAC, Vita-Salute San Raffaele University and San Raffaele Scientific Institute, Milan 20132, Italy
| | - Roberto Ronca
- Department Molecular and Translational Medicine, Experimental Oncology and Immunology, University of Brescia, Brescia 25124, Italy
| | - Pietro Luigi Poliani
- Department Molecular and Translational Medicine, Pathology Unit, University of Brescia, Brescia 25124, Italy
| | - Pietro Mortini
- Department Neurosurgery and Gamma Knife Radiosurgery, San Raffaele Scientific Institute, Via Olgettina 60, Milan 20132, Italy
| | - Rossella Galli
- Neural Stem Cell Biology Unit, Division of Neuroscience, San Raffaele Scientific Institute, Via Olgettina 58, Milan 20132, Italy.
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26
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Isensee F, Schell M, Pflueger I, Brugnara G, Bonekamp D, Neuberger U, Wick A, Schlemmer H, Heiland S, Wick W, Bendszus M, Maier‐Hein KH, Kickingereder P. Automated brain extraction of multisequence MRI using artificial neural networks. Hum Brain Mapp 2019; 40:4952-4964. [PMID: 31403237 PMCID: PMC6865732 DOI: 10.1002/hbm.24750] [Citation(s) in RCA: 213] [Impact Index Per Article: 42.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 07/19/2019] [Accepted: 07/23/2019] [Indexed: 01/18/2023] Open
Abstract
Brain extraction is a critical preprocessing step in the analysis of neuroimaging studies conducted with magnetic resonance imaging (MRI) and influences the accuracy of downstream analyses. The majority of brain extraction algorithms are, however, optimized for processing healthy brains and thus frequently fail in the presence of pathologically altered brain or when applied to heterogeneous MRI datasets. Here we introduce a new, rigorously validated algorithm (termed HD-BET) relying on artificial neural networks that aim to overcome these limitations. We demonstrate that HD-BET outperforms six popular, publicly available brain extraction algorithms in several large-scale neuroimaging datasets, including one from a prospective multicentric trial in neuro-oncology, yielding state-of-the-art performance with median improvements of +1.16 to +2.50 points for the Dice coefficient and -0.66 to -2.51 mm for the Hausdorff distance. Importantly, the HD-BET algorithm, which shows robust performance in the presence of pathology or treatment-induced tissue alterations, is applicable to a broad range of MRI sequence types and is not influenced by variations in MRI hardware and acquisition parameters encountered in both research and clinical practice. For broader accessibility, the HD-BET prediction algorithm is made freely available (www.neuroAI-HD.org) and may become an essential component for robust, automated, high-throughput processing of MRI neuroimaging data.
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Affiliation(s)
- Fabian Isensee
- Medical Image ComputingGerman Cancer Research Center (DKFZ)HeidelbergGermany
- Faculty of BiosciencesUniversity of HeidelbergHeidelbergGermany
| | - Marianne Schell
- Department of NeuroradiologyHeidelberg University HospitalHeidelbergGermany
| | | | - Gianluca Brugnara
- Department of NeuroradiologyHeidelberg University HospitalHeidelbergGermany
| | | | - Ulf Neuberger
- Department of NeuroradiologyHeidelberg University HospitalHeidelbergGermany
| | - Antje Wick
- Neurology ClinicHeidelberg University HospitalHeidelbergGermany
| | | | - Sabine Heiland
- Department of NeuroradiologyHeidelberg University HospitalHeidelbergGermany
| | - Wolfgang Wick
- Neurology ClinicHeidelberg University HospitalHeidelbergGermany
- German Cancer Consortium (DKTK)German Cancer Research Center (DKFZ)HeidelbergGermany
| | - Martin Bendszus
- Department of NeuroradiologyHeidelberg University HospitalHeidelbergGermany
| | - Klaus H. Maier‐Hein
- Medical Image ComputingGerman Cancer Research Center (DKFZ)HeidelbergGermany
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27
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Foltyn M, Natalia Nieto Taborda K, Neuberger U, Reinhardt A, Brugnara G, Unterberg A, Debus J, Herold-Mende C, von Deimling A, Wick W, Bendszus M, Kickingereder P. NIMG-02. NON-INVASIVE DETECTION OF IDH MUTANT 1p19q NON-CODELETED GLIOMAS USING THE T2-FLAIR MISMATCH SIGN. Neuro Oncol 2019. [DOI: 10.1093/neuonc/noz175.674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
OBJECTIVE
To assess the validity and pathophysiology of the T2/FLAIR mismatch sign for non-invasive identification of IDH-mutant 1p/19q non-codeleted glioma.
METHODS
MRI scans from 408 consecutive patients with newly diagnosed glioma (113 lower-grade glioma and 295 glioblastoma) were evaluated for the presence of a T2/FLAIR-mismatch sign (defined as complete/near-complete hyperintense signal on T2w, simultaneous hypointense signal on FLAIR except for a hyperintense peripheral rim) by two independent reviewers. Sensitivity, specificity, accuracy, positive and negative predictive value (PPV, NPV) were calculated to assess the performance of the T2/FLAIR-mismatch sign for identifying IDH-mutant 1p/19q non-codeleted tumors. An exploratory analysis of spatial differences in ADC and rCBV values comparing the FLAIR-hypointense core vs. hyperintense rim in cases with presence of a T2/FLAIR-mismatch sign was performed.
RESULTS
There was substantial interrater agreement to identify the T2/FLAIR-mismatch sign (Cohen’s Kappa = 0.75 [95% CI 0.57–0.93]). The T2/FLAIR-mismatch sign was present in 12 cases with lower-grade glioma (10.6%), all of them were IDH-mutant, 1p/19q non-codeleted tumors (sensitivity=10.9%, specificity=100%, PPV=100%, NPV=3.0%, accuracy=13.3%). The T2/FLAIR-mismatch sign was not identified in any other molecular subgroup, especially not in any of the IDH-mutant glioblastoma cases (n=5). In tumors with T2/FLAIR-mismatch sign the ADC values were significantly lower in the rim as compared to the core (p=0.0005) whereas there was no difference in rCBV values (p=0.4258).
CONCLUSION
This study confirms the high specificity of the T2/FLAIR-mismatch sign for non-invasive identification of IDH-mutant 1p/19q non-codeleted gliomas, although sensitivity is low and applicability is limited to lower-grade gliomas. The identified spatial differences in ADC values between the core and rim of tumors with a T2/FLAIR-mismatch sign potentially reflects differences in tumor cellularity and microenvironment.
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Affiliation(s)
- Martha Foltyn
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | | | - Ulf Neuberger
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Annekathrin Reinhardt
- Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Andreas Unterberg
- Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Christel Herold-Mende
- Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Andreas von Deimling
- Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Wolfgang Wick
- Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
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28
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Tejada Neyra MA, Neuberger U, Reinhardt A, Brugnara G, Bonekamp D, Sill M, Wick A, Jones DTW, Radbruch A, Unterberg A, Debus J, Heiland S, Schlemmer HP, Herold-Mende C, Pfister S, von Deimling A, Wick W, Capper D, Bendszus M, Kickingereder P. Voxel-wise radiogenomic mapping of tumor location with key molecular alterations in patients with glioma. Neuro Oncol 2019; 20:1517-1524. [PMID: 30107597 DOI: 10.1093/neuonc/noy134] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background This study aims to evaluate the impact of tumor location on key molecular alterations on a single voxel level in patients with newly diagnosed glioma. Methods A consecutive series of n = 237 patients with newly diagnosed glioblastoma and n = 131 patients with lower-grade glioma was analyzed. Volumetric tumor segmentation was performed on preoperative MRI with a semi-automated approach and images were registered to the standard Montreal Neurological Institute 152 space. Using a voxel-based lesion symptom mapping (VLSM) analysis, we identified specific brain regions that were associated with tumor-specific molecular alterations. We assessed a predefined set of n = 17 molecular characteristics in the glioblastoma cohort and n = 2 molecular characteristics in the lower-grade glioma cohort. Permutation adjustment (n = 1000 iterations) was used to correct for multiple testing, and voxel t-values that were greater than the t-value in >95% of the permutations were retained in the VLSM results (α = 0.05, power > 0.8). Results Tumor location predilection for isocitrate dehydrogenase (IDH) mutant tumors was found in both glioblastoma and lower-grade glioma cohorts, each showing a concordant predominance in the frontal lobe adjacent to the rostral extension of the lateral ventricles (permutation-adjusted P = 0.021 for the glioblastoma and 0.013 for the lower-grade glioma cohort). Apart from that, the VLSM analysis did not reveal a significant association of the tumor location with any other key molecular alteration in both cohorts (permutation-adjusted P > 0.05 each). Conclusion Our study highlights the unique properties of IDH mutations and underpins the hypothesis that the rostral extension of the lateral ventricles is a potential location for the cell of origin in IDH-mutant gliomas.
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Affiliation(s)
| | - Ulf Neuberger
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Annekathrin Reinhardt
- Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - David Bonekamp
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin Sill
- Hopp-Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany.,Division of Pediatric Neurooncology, DKFZ, Heidelberg, Germany.,German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany
| | - Antje Wick
- Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany
| | - David T W Jones
- Hopp-Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany.,Division of Pediatric Neurooncology, DKFZ, Heidelberg, Germany.,German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany
| | - Alexander Radbruch
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Andreas Unterberg
- Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, University of Heidelberg Medical Center, Heidelberg Institute of Radiation Oncology (HIRO) and National Center for Radiation Research in Oncology (NCOR), Heidelberg, Germany.,Molecular and Translational Radiation Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital and DKFZ, Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | | | - Christel Herold-Mende
- Department of Neurosurgery, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Stefan Pfister
- Hopp-Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany.,Division of Pediatric Neurooncology, DKFZ, Heidelberg, Germany.,German Cancer Consortium (DKTK) Core Center Heidelberg, Heidelberg, Germany.,Department of Pediatric Oncology, Hematology and Immunology, Heidelberg University Hospital, Heidelberg, Germany
| | - Andreas von Deimling
- Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany.,DKTK, Clinical Cooperation Unit Neuropathology, DKFZ, Heidelberg, Germany
| | - Wolfgang Wick
- Neurology Clinic, University of Heidelberg Medical Center, Heidelberg, Germany.,Clinical Cooperation Unit Neurooncology, DKTK, DKFZ, Heidelberg, Germany
| | - David Capper
- Department of Neuropathology, University of Heidelberg Medical Center, Heidelberg, Germany.,Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute for Neuropathology, Berlin, Germany.,DKTK, Partner Site Berlin, DKFZ, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Philipp Kickingereder
- Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany
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29
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Kickingereder P, Isensee F, Tursunova I, Petersen J, Neuberger U, Bonekamp D, Brugnara G, Schell M, Kessler T, Foltyn M, Harting I, Sahm F, Prager M, Nowosielski M, Wick A, Nolden M, Radbruch A, Debus J, Schlemmer HP, Heiland S, Platten M, von Deimling A, van den Bent MJ, Gorlia T, Wick W, Bendszus M, Maier-Hein KH. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol 2019; 20:728-740. [PMID: 30952559 DOI: 10.1016/s1470-2045(19)30098-1] [Citation(s) in RCA: 228] [Impact Index Per Article: 45.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 01/10/2019] [Accepted: 01/15/2019] [Indexed: 12/20/2022]
Abstract
BACKGROUND The Response Assessment in Neuro-Oncology (RANO) criteria and requirements for a uniform protocol have been introduced to standardise assessment of MRI scans in both clinical trials and clinical practice. However, these criteria mainly rely on manual two-dimensional measurements of contrast-enhancing (CE) target lesions and thus restrict both reliability and accurate assessment of tumour burden and treatment response. We aimed to develop a framework relying on artificial neural networks (ANNs) for fully automated quantitative analysis of MRI in neuro-oncology to overcome the inherent limitations of manual assessment of tumour burden. METHODS In this retrospective study, we compiled a single-institution dataset of MRI data from patients with brain tumours being treated at Heidelberg University Hospital (Heidelberg, Germany; Heidelberg training dataset) to develop and train an ANN for automated identification and volumetric segmentation of CE tumours and non-enhancing T2-signal abnormalities (NEs) on MRI. Independent testing and large-scale application of the ANN for tumour segmentation was done in a single-institution longitudinal testing dataset from the Heidelberg University Hospital and in a multi-institutional longitudinal testing dataset from the prospective randomised phase 2 and 3 European Organisation for Research and Treatment of Cancer (EORTC)-26101 trial (NCT01290939), acquired at 38 institutions across Europe. In both longitudinal datasets, spatial and temporal tumour volume dynamics were automatically quantified to calculate time to progression, which was compared with time to progression determined by RANO, both in terms of reliability and as a surrogate endpoint for predicting overall survival. We integrated this approach for fully automated quantitative analysis of MRI in neuro-oncology within an application-ready software infrastructure and applied it in a simulated clinical environment of patients with brain tumours from the Heidelberg University Hospital (Heidelberg simulation dataset). FINDINGS For training of the ANN, MRI data were collected from 455 patients with brain tumours (one MRI per patient) being treated at Heidelberg hospital between July 29, 2009, and March 17, 2017 (Heidelberg training dataset). For independent testing of the ANN, an independent longitudinal dataset of 40 patients, with data from 239 MRI scans, was collected at Heidelberg University Hospital in parallel with the training dataset (Heidelberg test dataset), and 2034 MRI scans from 532 patients at 34 institutions collected between Oct 26, 2011, and Dec 3, 2015, in the EORTC-26101 study were of sufficient quality to be included in the EORTC-26101 test dataset. The ANN yielded excellent performance for accurate detection and segmentation of CE tumours and NE volumes in both longitudinal test datasets (median DICE coefficient for CE tumours 0·89 [95% CI 0·86-0·90], and for NEs 0·93 [0·92-0·94] in the Heidelberg test dataset; CE tumours 0·91 [0·90-0·92], NEs 0·93 [0·93-0·94] in the EORTC-26101 test dataset). Time to progression from quantitative ANN-based assessment of tumour response was a significantly better surrogate endpoint than central RANO assessment for predicting overall survival in the EORTC-26101 test dataset (hazard ratios ANN 2·59 [95% CI 1·86-3·60] vs central RANO 2·07 [1·46-2·92]; p<0·0001) and also yielded a 36% margin over RANO (p<0·0001) when comparing reliability values (ie, agreement in the quantitative volumetrically defined time to progression [based on radiologist ground truth vs automated assessment with ANN] of 87% [266 of 306 with sufficient data] compared with 51% [155 of 306] with local vs independent central RANO assessment). In the Heidelberg simulation dataset, which comprised 466 patients with brain tumours, with 595 MRI scans obtained between April 27, and Sept 17, 2018, automated on-demand processing of MRI scans and quantitative tumour response assessment within the simulated clinical environment required 10 min of computation time (average per scan). INTERPRETATION Overall, we found that ANN enabled objective and automated assessment of tumour response in neuro-oncology at high throughput and could ultimately serve as a blueprint for the application of ANN in radiology to improve clinical decision making. Future research should focus on prospective validation within clinical trials and application for automated high-throughput imaging biomarker discovery and extension to other diseases. FUNDING Medical Faculty Heidelberg Postdoc-Program, Else Kröner-Fresenius Foundation.
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Affiliation(s)
| | - Fabian Isensee
- Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Irada Tursunova
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Jens Petersen
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ulf Neuberger
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - David Bonekamp
- Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Tobias Kessler
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martha Foltyn
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Inga Harting
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Felix Sahm
- Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Marcel Prager
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martha Nowosielski
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany; Department of Neurology, Medical University Innsbruck, Innsbruck, Austria
| | - Antje Wick
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany
| | - Marco Nolden
- Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alexander Radbruch
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany; Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Institute of Radiation Oncology, Heidelberg, Germany; Heidelberg Ion-Beam Therapy Center, Heidelberg, Germany
| | | | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Michael Platten
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Neurology, Mannheim Medical Center, University of Heidelberg, Mannheim, Germany
| | - Andreas von Deimling
- Department of Neuropathology, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Thierry Gorlia
- European Organisation for Research and Treatment of Cancer, Brussels, Belgium
| | - Wolfgang Wick
- Neurology Clinic, Heidelberg University Hospital, Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus H Maier-Hein
- Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
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30
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Politi LS, Bianchi Marzoli S, Godi C, Panzeri M, Ciasca P, Brugnara G, Castaldo A, Di Bella D, Taroni F, Nanetti L, Mariotti C. MRI Evidence of Cerebellar and Extraocular Muscle Atrophy Differently Contributing to Eye Movement Abnormalities in SCA2 and SCA28 Diseases. Invest Ophthalmol Vis Sci 2017; 57:2714-20. [PMID: 27196319 DOI: 10.1167/iovs.15-18732] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
PURPOSE Spinocerebellar ataxias type 2 and 28 (SCA2, SCA28) are autosomal dominant disorders characterized by progressive cerebellar and oculomotor abnormalities. We aimed to investigate cerebellar, brainstem, and extraocular muscle involvement in the mitochondrial SCA28 disease compared with SCA2. METHODS We obtained orbital and brain 1.5 T-magnetic resonance images (MRI) in eight SCA28 subjects, nine SCA2, and nine age-matched healthy subjects. Automated segmentation of cerebellum and frontal lobe was performed using Freesurfer software. Manual segmentations for midbrain, pons, and extraocular muscles were performed using OsiriX. RESULTS Eye movement abnormalities in SCA2 subjects were characterized by slow horizontal saccades. Subjects with SCA28 variably presented hypometric saccades, saccadic horizontal pursuit, impaired horizontal gaze holding, and superior eyelid ptosis. Quantitative brain MRI demonstrated that cerebellar and pons volumes were significantly reduced in both SCA2 and SCA28 subjects compared with controls (P < 0.03), and in SCA2 subjects compared with SCA28 (P < 0.01). Midbrain and frontal lobe volumes were also significantly reduced in SCA2 compared to controls (P < 0.03), whereas these volumes did not differ between SCA2 and SCA28 and between SCA28 and control subjects. The extraocular muscle areas were 37% to 48% smaller in SCA28 subjects compared with controls (P < 0.002), and 14% to 36% smaller compared with SCA2 subjects (P < 0.03). Extraocular muscle areas did not differ between SCA2 and controls. CONCLUSIONS Our MRI findings support the hypothesis of different cerebellar and extraocular myopathic contributions in the eye movement abnormalities in SCA2 and SCA28 diseases. In SCA28, a myopathic defect selectively involving the extraocular muscles supports a specific impairment of mitochondrial energy metabolism.
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Affiliation(s)
| | - Stefania Bianchi Marzoli
- Neuro-Ophthalmology Service, Department of Ophthalmology, Fondazione IRCCS Istituto Auxologico Italiano, Milan
| | - Claudia Godi
- Neuroradiology Department and Neuroradiology Research Unit San Raffaele Scientific Institute, Milan
| | - Marta Panzeri
- Unit of Genetics of Neurodegenerative and Metabolic Diseases, Fondazione IRCCS-Istituto Neurologico Carlo Besta, Milan, Italy
| | - Paola Ciasca
- Neuro-Ophthalmology Service, Department of Ophthalmology, Fondazione IRCCS Istituto Auxologico Italiano, Milan
| | - Gianluca Brugnara
- Neuroradiology Department and Neuroradiology Research Unit San Raffaele Scientific Institute, Milan
| | - Anna Castaldo
- Unit of Genetics of Neurodegenerative and Metabolic Diseases, Fondazione IRCCS-Istituto Neurologico Carlo Besta, Milan, Italy
| | - Daniela Di Bella
- Unit of Genetics of Neurodegenerative and Metabolic Diseases, Fondazione IRCCS-Istituto Neurologico Carlo Besta, Milan, Italy
| | - Franco Taroni
- Unit of Genetics of Neurodegenerative and Metabolic Diseases, Fondazione IRCCS-Istituto Neurologico Carlo Besta, Milan, Italy
| | - Lorenzo Nanetti
- Unit of Genetics of Neurodegenerative and Metabolic Diseases, Fondazione IRCCS-Istituto Neurologico Carlo Besta, Milan, Italy
| | - Caterina Mariotti
- Unit of Genetics of Neurodegenerative and Metabolic Diseases, Fondazione IRCCS-Istituto Neurologico Carlo Besta, Milan, Italy
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31
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Politi LS, Brugnara G, Castellano A, Cadioli M, Altabella L, Peviani M, Mazzoleni S, Falini A, Galli R. T1-Weighted Dynamic Contrast-Enhanced MRI Is a Noninvasive Marker of Epidermal Growth Factor Receptor vIII Status in Cancer Stem Cell-Derived Experimental Glioblastomas. AJNR Am J Neuroradiol 2016; 37:E49-51. [PMID: 26988813 DOI: 10.3174/ajnr.a4774] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- L S Politi
- Neuroimaging Research, Hematology/Oncology Division Boston Children's Hospital/Dana Farber Cancer Institute Boston, Massachusetts Radiology Department University of Massachusetts Medical School Worcester, Massachusetts Neuroradiology Unit and CERMAC Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute Milan, Italy
| | - G Brugnara
- Neuroradiology Unit and CERMAC Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute Milan, Italy
| | - A Castellano
- Neuroradiology Unit and CERMAC Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute Milan, Italy
| | - M Cadioli
- Neuroradiology Unit and CERMAC Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute Milan, Italy
| | - L Altabella
- Neuroradiology Unit and CERMAC Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute Milan, Italy
| | - M Peviani
- Neuroimaging Research, Hematology/Oncology Division Boston Children's Hospital/Dana Farber Cancer Institute Boston, Massachusetts Neuroradiology Unit and CERMAC Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute Milan, Italy
| | - S Mazzoleni
- Neural Stem Cell Biology Unit, Division of Regenerative Medicine, Stem Cells and Gene Therapy IRCCS San Raffaele Scientific Institute Milan, Italy
| | - A Falini
- Neuroradiology Unit and CERMAC Vita-Salute San Raffaele University and IRCCS San Raffaele Scientific Institute Milan, Italy
| | - R Galli
- Neural Stem Cell Biology Unit, Division of Regenerative Medicine, Stem Cells and Gene Therapy IRCCS San Raffaele Scientific Institute, Milan, Italy
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32
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Castellano A, Papinutto N, Cadioli M, Brugnara G, Iadanza A, Scigliuolo G, Pareyson D, Uziel G, Köhler W, Aubourg P, Falini A, Henry RG, Politi LS, Salsano E. Quantitative MRI of the spinal cord and brain in adrenomyeloneuropathy:in vivoassessment of structural changes. Brain 2016; 139:1735-46. [DOI: 10.1093/brain/aww068] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Accepted: 02/15/2016] [Indexed: 11/13/2022] Open
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