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1. Deep Learning–based Recurrence Prediction in Patients with Non–muscle-invasive Bladder Cancer

Author: Ilaria Jansen, Daniel M. de Bruin, Marit Lucas, Henk A. Marquering, Ton G. van Leeuwen, Jorg R. Oddens, Biomedical Engineering and Physics, Graduate School, ACS - Atherosclerosis & ischemic syndromes, CCA - Imaging and biomarkers, APH - Quality of Care, ANS - Cellular & Molecular Mechanisms, ANS - Compulsivity, Impulsivity & Attention, APH - Personalized Medicine, Urology, Radiology and Nuclear Medicine, ANS - Neurovascular Disorders, and ANS - Brain Imaging
Subjects: medicine.medical_specialty, Urology, Population, 030232 urology & nephrology, Logistic regression, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, In patient, Recurrence prediction, education, education.field_of_study, Bladder cancer, business.industry, Deep learning, medicine.disease, Disease recurrence, Urinary Bladder Neoplasms, 030220 oncology & carcinogenesis, Histopathology, Radiology, Artificial intelligence, Prediction, business, Non muscle invasive
Abstract: Background: Non–muscle-invasive bladder cancer (NMIBC) is characterized by frequent recurrence of the disease, which is difficult to predict. Objective: To combine digital histopathology slides with clinical data to predict 1- and 5-yr recurrence-free survival of NMIBC patients using deep learning. Design, setting, and participants: Data of patients undergoing a transurethral resection of a bladder tumor between 2000 and 2018 at a Dutch academic medical center were selected. Corresponding histological slides were digitized. A three-step approach was used to predict 1- and 5-yr recurrence-free survival. First, a segmentation network was used to detect the urothelium on the digital histopathology slides. Second, a selection network was trained for the selection of patches associated with recurrence. Third, a classification network, combining the information of the selection network with clinical data, was trained to give the probability of 1- and 5-yr recurrence-free survival. Outcome measurements and statistical analysis: The accuracy of the deep learning–based model was compared with a multivariable logistic regression model using clinical data only. Results and limitations: In the 1- and 5-yr follow-up cohorts, 359 and 281 patients were included with recurrence rates of 27% and 63%, respectively. The areas under the curve (AUCs) of the model combining digital histopathology slide data with clinical data were 0.62 and 0.76 for 1- and 5-yr recurrence predictions, respectively, which were higher than those of the model using digital histopathology slide data only (AUCs of 0.56 and 0.72, respectively) and the multivariable logistic regression (AUCs of 0.58 and 0.57, respectively). Conclusions: In our population, the deep learning–based model combining digital histopathology slides and clinical data enhances the prediction of recurrence (within 5 yr) compared with models using clinical data or image data only. Patient summary: By combining histopathology images and patient record data using deep learning, the prediction of recurrence in bladder cancer patients is enhanced. The proposed deep learning method showed promising results for the 5-yr prediction of recurrence when combining clinical data and digital histopathology images. Although not yet validated for clinical practice, this deep learning approach has the potential to improve recurrence prediction.
Published: 2022
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2. Toward Automated In Vivo Bladder Tumor Stratification Using Confocal Laser Endomicroscopy

Author: C. Dilara Savci-Heijink, Marit Lucas, Ton G. van Leeuwen, Henk A. Marquering, Esmee I. M. L. Liem, Jan Erik Freund, Daniel M. de Bruin, Biomedical Engineering and Physics, Graduate School, APH - Personalized Medicine, APH - Quality of Care, CCA - Imaging and biomarkers, Urology, Pathology, Radiology and Nuclear Medicine, and ACS - Atherosclerosis & ischemic syndromes
Subjects: Confocal laser endomicroscopy, medicine.medical_specialty, medicine.diagnostic_test, business.industry, Urology, Urinary system, 030232 urology & nephrology, Cystoscopy, Extractor, 03 medical and health sciences, 0302 clinical medicine, In vivo, 030220 oncology & carcinogenesis, Bladder tumor, Medicine, Radiology, Urothelium, business, Grading (tumors)
Abstract: Purpose: Urothelial carcinoma of the bladder (UCB) is the most common urinary cancer. White-light cystoscopy (WLC) forms the corner stone for the diagnosis of UCB. However, histopathological assessment is required for adjuvant treatment selection. Probe-based confocal laser endomicroscopy (pCLE) enables visualization of the microarchitecture of bladder lesions during WLC, which allows for real-time tissue differentiation and grading of UCB. To improve the diagnostic process of UCB, computer-aided classification of pCLE videos of in vivo bladder lesions were evaluated in this study. Materials and Methods: We implemented preprocessing methods to optimize contrast and to reduce striping artifacts in each individual pCLE frame. Subsequently, a semiautomatic frame selection was performed. The selected frames were used to train a feature extractor based on pretrained ImageNet networks. A recurrent neural network, in specific long short-term memory (LSTM), was used to predict the grade of bladder lesions. Differentiation of lesions was performed at two levels, namely (i) healthy and benign vs malignant tissue and (ii) low-grade vs high-grade papillary UCB. A total of 53 patients with 72 lesions were included in this study, resulting in ∼140,000 pCLE frames. Results: The semiautomated frame selection reduced the number of frames to ∼66,500 informative frames. The accuracy for differentiation of (i) healthy and benign vs malignant urothelium was 79% and (ii) high-grade and low-grade papillary UCB was 82%. Conclusions: A feature extractor in combination with LSTM results in proper stratification of pCLE videos of in vivo bladder lesions.
Published: 2019
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3. The First In Vivo Needle‐Based Optical Coherence Tomography in Human Prostate: A Safety and Feasibility Study

Author: Berrend G. Muller, Christophe K. Mannaerts, Marit Lucas, Theo M. de Reijke, Ton G. van Leeuwen, Daniel Martijn de Bruin, Rob A. A. van Kollenburg, Abel Swaan, C. Dilara Savci-Heijink, Graduate School, ACS - Atherosclerosis & ischemic syndromes, CCA - Imaging and biomarkers, APH - Quality of Care, APH - Personalized Medicine, Biomedical Engineering and Physics, Pathology, and Urology
Subjects: medicine.medical_specialty, Percutaneous, genetic structures, diagnosis, Dermatology, 01 natural sciences, 010309 optics, 030207 dermatology & venereal diseases, 03 medical and health sciences, Prostate cancer, 0302 clinical medicine, Optical coherence tomography, In vivo, Prostate, 0103 physical sciences, Biopsy, medicine, optical coherence tomography (OCT), biopsy, Gleason, medicine.diagnostic_test, business.industry, prostate cancer, medicine.disease, eye diseases, medicine.anatomical_structure, Adenocarcinoma, Surgery, Histopathology, sense organs, Optical Coherence Tomography, business, Nuclear medicine
Abstract: Objective: To demonstrate the safety and feasibility of clinical in vivo needle-based optical coherence tomography (OCT) imaging of the prostate. Materials and Methods: Two patients with prostate cancer underwent each two percutaneous in vivo needle-based OCT measurements before transperineal template mapping biopsy. The OCT probe was introduced via a needle and positioned under ultrasound guidance. To test the safety, adverse events were recorded during and after the procedure. To test the feasibility, OCT and US images were studied during and after the procedure. Corresponding regions for OCT and biopsy were determined. A uropathologist evaluated and annotated the histopathology. Three experts assessed all the corresponding OCT images. The OCT and biopsy conclusions for the corresponding regions were compared. Results: No adverse events during and following the, in total four, in vivo needle-based OCT measurements were reported. The OCT measurements showed images of prostatic tissue with a penetration depth of ~1.5 mm. The histological-proven tissue types, which were also found in the overlapping OCT images, were benign glands, stroma, glandular atrophy, and adenocarcinoma (Gleason pattern 3). Conclusions: Clinical in vivo needle-based OCT of the prostate is feasible with no adverse events during measurements. OCT images displayed detailed prostatic tissue with a imaging depth up to ~1.5 mm. We could co-register four histological-proven tissue types with OCT images. The feasibility of in vivo OCT in the prostate opens the pathway to the next phase of needle-based OCT studies in the prostate. Lasers Surg. Med. 51:390–398, 2019. © 2019 The Authors. Lasers in Surgery and Medicine Published by Wiley Periodicals, Inc.
Published: 2019
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4. MP73-19 DEEP LEARNING FOR HISTOPATHOLOGY AND CLINICAL CHARACTERISTICS BASED RECURRENCE PREDICTION IN NON-MUSCLE INVASIVE BLADDER CANCER PATIENTS

Author: Ton G. van Leeuwen, C. Dilara Savci-Heijink, Ilaria Jansen, Daniel M. de Bruin, Marit Lucas, Henk A. Marquering, and Jorg R. Oddens
Subjects: medicine.medical_specialty, Bladder cancer, business.industry, Urology, Concordance, medicine, Histopathology, Recurrence prediction, Radiology, medicine.disease, Non muscle invasive, business, humanities
Abstract: INTRODUCTION AND OBJECTIVE:The EORTC and CUETO are used to assess the risk for recurrence in non-muscle invasive bladder cancer (NMIBC). Low concordance rates with recurrence are reported for those...
Published: 2020
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5. MP73-18 RECURRENCE PREDICTION IN BLADDER CANCER PATIENTS USING SURVIVAL ANALYSIS

Author: Jorg R. Oddens, Henk A. Marquering, Ton G. van Leeuwen, Ilaria Jansen, Daniel M. de Bruin, and Marit Lucas
Subjects: Oncology, medicine.medical_specialty, Bladder cancer, business.industry, Urology, medicine.disease, humanities, eye diseases, Internal medicine, Medicine, Recurrence prediction, business, Risk assessment, Survival analysis
Abstract: INTRODUCTION AND OBJECTIVE:The EORTC, CUETO and EAU are the three most commonly used risk assessment methods for the prediction of recurrence in non-muscle invasive bladder cancer (NMIBC). However,...
Published: 2020
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6. MP72-16 DETECTION AND GRADING OF BLADDER CANCER ON HISTOLOGICAL SLIDES USING DEEP LEARING

Author: C. D. Savci-Heijink, Jakko A. Nieuwenhuijzen, Henk A. Marquering, Marit Lucas, Ton G. van Leeuwen, Judith Bosschieter, Ilaria Jansen, Sybren L. Meijer, and Daniel M. de Bruin
Subjects: medicine.medical_specialty, Bladder cancer, business.industry, Urology, medicine, Radiology, business, medicine.disease, Grading (tumors)
Abstract: INTRODUCTION AND OBJECTIVE:Histological grade is an important predictor for recurrence and progression in non-muscle invasive bladder cancer (NMIBC). However, it is subjected to low interobserver a...
Published: 2020
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7. Recurrence in non-muscle invasive bladder cancer patients: External validation of the EORTC, CUETO and EAU risk tables and towards a non-linear survival model

Author: Daniel M. de Bruin, Jorg R. Oddens, Ton G. van Leeuwen, Ilaria Jansen, Henk A. Marquering, Marit Lucas, Biomedical Engineering and Physics, Graduate School, APH - Personalized Medicine, APH - Quality of Care, Urology, Radiology and Nuclear Medicine, CCA - Cancer Treatment and Quality of Life, and ACS - Atherosclerosis & ischemic syndromes
Subjects: Oncology, medicine.medical_specialty, Cox Proportional Hazards, Urology, Concordance, Population, 030232 urology & nephrology, 03 medical and health sciences, 0302 clinical medicine, Recurrence, Internal medicine, medicine, Cumulative incidence, education, Survival analysis, Risk stratification, education.field_of_study, Bladder cancer, business.industry, Proportional hazards model, Random survival forest, External validation, medicine.disease, 030220 oncology & carcinogenesis, business, Non muscle invasive, Non-muscle invasive bladder cancer
Abstract: BACKGROUND: EORTC, CUETO and EAU are the most commonly used risk stratification models for recurrence and progression in non-muscle invasive bladder cancer (NMIBC). OBJECTIVE: We assessed the predictive value of the EORTC, CUETO and EAU risk group stratification methods for our population and explore options to improve the predictive value using Cox Proportional Hazards (CPH), Boosted Cox regression and a non-linear Random Survival Forest (RSF) model. MATERIALS: Our retrospective database included of 452 NMIBC patients who underwent a transurethral resection of bladder tumor (TURBT) between 2000 and 2018 in our hospital. The cumulative incidence of recurrence was calculated at one- and five-years for all risk stratification methods. A customized CPH, Boosted Cox and RSF models were trained in order to predict recurrence, and the performances were compared. RESULTS: Risk stratification using the EORTC, CUETO and EAU showed small differences in recurrence probabilities between the risk groups as determined by the risk stratification. The concordance indices (C-index) were low and ranged between 0.51 and 0.57. The predictive accuracies of CPH, Boosted Cox and RSF models were also moderate, with C-indices ranging from 0.61 to 0.64. CONCLUSIONS: Prediction of recurrence in patients with NMIBC based on patient characteristics is difficult. Alternative (non-linear) approaches have the potential to improve the predictive value. Nonetheless, the currently used characteristics are unable to properly stratify between the recurrence risks of patients.
Published: 2020
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8. Histopathology: ditch the slides, because digital and 3D are on show

Author: Patricia J. Zondervan, Marit Lucas, C. Dilara Savci-Heijink, Henk A. Marquering, Sybren L. Meijer, Daniel M. de Bruin, Ilaria Jansen, APH - Quality of Care, APH - Personalized Medicine, Graduate School, CCA - Cancer biology and immunology, Biomedical Engineering and Physics, Pathology, ACS - Amsterdam Cardiovascular Sciences, ANS - Amsterdam Neuroscience, Radiology and Nuclear Medicine, Urology, AGEM - Amsterdam Gastroenterology Endocrinology Metabolism, and ACS - Atherosclerosis & ischemic syndromes
Subjects: Urologic Diseases, 0301 basic medicine, medicine.medical_specialty, Urology, 03 medical and health sciences, Imaging, Three-Dimensional, 0302 clinical medicine, medicine, Humans, Digital pathology, In patient, Medical physics, Diagnosis, Computer-Assisted, Urinary tract pathology, Computers, business.industry, Diagnostic Techniques, Urological, Workload, Computer-aided diagnosis, Topic Paper, 030104 developmental biology, 030220 oncology & carcinogenesis, Interobserver Variation, Histopathology, Three-dimensional, business
Abstract: Due to the growing field of digital pathology, more and more digital histology slides are becoming available. This improves the accessibility, allows teleconsultations from specialized pathologists, improves education, and might give urologist the possibility to review the slides in patient management systems. Moreover, by stacking multiple two-dimensional (2D) digital slides, three-dimensional volumes can be created, allowing improved insight in the growth pattern of a tumor. With the addition of computer-aided diagnosis systems, pathologist can be guided to regions of interest, potentially reducing the workload and interobserver variation. Digital (3D) pathology has the potential to improve dialog between the pathologist and urologist, and, therefore, results in a better treatment selection for urologic patients. Electronic supplementary material The online version of this article (10.1007/s00345-018-2202-1) contains supplementary material, which is available to authorized users.
Published: 2018
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9. Machine learning for grading and prognosis of esophageal dysplasia using mass spectrometry and histological imaging

Author: Marta Martin-Lorenzo, Janita E. van Timmeren, Benjamin Balluff, Onno J. de Boer, Henry C. Woodruff, Ron M. A. Heeren, Sybren L. Meijer, Philippe Lambin, Manon Beuque, Daniel M. de Bruin, Marit Lucas, RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy, Precision Medicine, Imaging Mass Spectrometry (IMS), RS: M4I - Imaging Mass Spectrometry (IMS), Beeldvorming, Biomedical Engineering and Physics, ACS - Atherosclerosis & ischemic syndromes, CCA - Imaging and biomarkers, Urology, and Pathology
Subjects: Esophageal Neoplasms, H&E stain, Health Informatics, DIAGNOSIS, Machine learning, computer.software_genre, Mass Spectrometry, Mass spectrometry imaging, H&E staining, Barrett Esophagus, Barrett's esophagus, medicine, Humans, Esophagus, Grading (tumors), business.industry, Cancer, Deep learning, H&E stainfing, ADENOCARCINOMA, medicine.disease, digestive system diseases, Computer Science Applications, medicine.anatomical_structure, Dysplasia, Disease Progression, Adenocarcinoma, Artificial intelligence, business, Precancerous Conditions, computer
Abstract: Background: Barrett's esophagus (BE) is a precursor lesion of esophageal adenocarcinoma and may progress from non-dysplastic through low-grade dysplasia (LGD) to high-grade dysplasia (HGD) and cancer. Grading BE is of crucial prognostic value and is currently based on the subjective evaluation of biopsies. This study aims to investigate the potential of machine learning (ML) using spatially resolved molecular data from mass spectrometry imaging (MSI) and histological data from microscopic hematoxylin and eosin (H&E)-stained imaging for computer-aided diagnosis and prognosis of BE. Methods: Biopsies from 57 patients were considered, divided into non-dysplastic (n = 15), LGD non-progressive (n = 14), LGD progressive (n = 14), and HGD (n = 14). MSI experiments were conducted at 50 x 50 mu m spatial resolution per pixel corresponding to a tile size of 96x96 pixels in the co-registered H&E images, making a total of 144,823 tiles for the whole dataset. Results: ML models were trained to distinguish epithelial tissue from stroma with area-under-the-curve (AUC) values of 0.89 (MSI) and 0.95 (H&E)) and dysplastic grade (AUC of 0.97 (MSI) and 0.85 (H&E)) on a tile level, and low-grade progressors from non-progressors on a patient level (accuracies of 0.72 (MSI) and 0.48 (H&E)). Conclusions: In summary, while the H&E-based classifier was best at distinguishing tissue types, the MSI-based model was more accurate at distinguishing dysplastic grades and patients at progression risk, which demonstrates the complementarity of both approaches. Data are available via ProteomeXchange with identifier PXD028949.
Published: 2021
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10. Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies

Author: Ton G. van Leeuwen, Marit Lucas, Sybren L. Meijer, Onno J. de Boer, Daniel M. de Bruin, Henk A. Marquering, C. Dilara Savci-Heijink, Ilaria Jansen, Biomedical Engineering and Physics, Graduate School, ACS - Atherosclerosis & ischemic syndromes, APH - Personalized Medicine, APH - Quality of Care, CCA - Imaging and biomarkers, Pathology, ACS - Heart failure & arrhythmias, Urology, and Radiology and Nuclear Medicine
Subjects: 0301 basic medicine, Male, medicine.medical_specialty, Gleason patterns, Concordance, Biopsy, Convolutional neural network, Pathology and Forensic Medicine, Pattern Recognition, Automated, 03 medical and health sciences, Prostate cancer, 0302 clinical medicine, Deep Learning, Prostate, Predictive Value of Tests, Image Interpretation, Computer-Assisted, medicine, Humans, Molecular Biology, Grading (tumors), Automation, Laboratory, Observer Variation, medicine.diagnostic_test, business.industry, Deep learning, Prostatic Neoplasms, Reproducibility of Results, Cell Biology, General Medicine, medicine.disease, Gleason pattern, 030104 developmental biology, medicine.anatomical_structure, 030220 oncology & carcinogenesis, Original Article, Artificial intelligence, Radiology, Neoplasm Grading, business, Grade groups
Abstract: Histopathologic grading of prostate cancer using Gleason patterns (GPs) is subject to a large inter-observer variability, which may result in suboptimal treatment of patients. With the introduction of digitization and whole-slide images of prostate biopsies, computer-aided grading becomes feasible. Computer-aided grading has the potential to improve histopathological grading and treatment selection for prostate cancer. Automated detection of GPs and determination of the grade groups (GG) using a convolutional neural network. In total, 96 prostate biopsies from 38 patients are annotated on pixel-level. Automated detection of GP 3 and GP ≥ 4 in digitized prostate biopsies is performed by re-training the Inception-v3 convolutional neural network (CNN). The outcome of the CNN is subsequently converted into probability maps of GP ≥ 3 and GP ≥ 4, and the GG of the whole biopsy is obtained according to these probability maps. Differentiation between non-atypical and malignant (GP ≥ 3) areas resulted in an accuracy of 92% with a sensitivity and specificity of 90 and 93%, respectively. The differentiation between GP ≥ 4 and GP ≤ 3 was accurate for 90%, with a sensitivity and specificity of 77 and 94%, respectively. Concordance of our automated GG determination method with a genitourinary pathologist was obtained in 65% (κ = 0.70), indicating substantial agreement. A CNN allows for accurate differentiation between non-atypical and malignant areas as defined by GPs, leading to a substantial agreement with the pathologist in defining the GG. Electronic supplementary material The online version of this article (10.1007/s00428-019-02577-x) contains supplementary material, which is available to authorized users.
Published: 2019

11. Three-dimensional histopathological reconstruction of bladder tumours

Author: Esmee I. M. L. Liem, Ton G. van Leeuwen, Sybren L. Meijer, Henk A. Marquering, Marit Lucas, C. Dilara Savci-Heijink, Ilaria Jansen, Onno J. de Boer, Daniel M. de Bruin, Graduate School, ACS - Atherosclerosis & ischemic syndromes, APH - Personalized Medicine, APH - Quality of Care, CCA - Imaging and biomarkers, Biomedical Engineering and Physics, Pathology, Urology, ACS - Heart failure & arrhythmias, and Radiology and Nuclear Medicine
Subjects: 0301 basic medicine, Pathology, medicine.medical_specialty, Histology, En-bloc, Urinary Bladder, H&E stain, Tumour reconstruction, Magnification, Pathology and Forensic Medicine, 03 medical and health sciences, Digital image, 0302 clinical medicine, Imaging, Three-Dimensional, lcsh:Pathology, Medicine, Humans, Bladder cancer, digital pathology, business.industry, Research, 3D reconstruction, Digital pathology, General Medicine, medicine.disease, 030104 developmental biology, Urinary Bladder Neoplasms, 030220 oncology & carcinogenesis, business, Spatial relationship, Nuclear medicine, Software, lcsh:RB1-214, 3D
Abstract: Background Histopathological analysis is the cornerstone in bladder cancer (BCa) diagnosis. These analysis suffer from a moderate observer agreement in the staging of bladder cancer. Three-dimensional reconstructions have the potential to support the pathologists in visualizing spatial arrangements of structures, which may improve the interpretation of specimen. The aim of this study is to present three-dimensional (3D) reconstructions of histology images. Methods En-bloc specimens of transurethral bladder tumour resections were formalin fixed and paraffin embedded. Specimens were cut into sections of 4 μm and stained with Hematoxylin and Eosin (H&E). With a Phillips IntelliSite UltraFast scanner, glass slides were digitized at 20x magnification. The digital images were aligned by performing rigid and affine image alignment. The tumour and the muscularis propria (MP) were manually delineated to create 3D segmentations. In conjunction with a 3D display, the results were visualized with the Vesalius3D interactive visualization application for a 3D workstation. Results En-bloc resection was performed in 21 BCa patients. Per case, 26–30 sections were included for the reconstruction into a 3D volume. Five cases were excluded due to export problems, size of the dataset or condition of the tissue block. Qualitative evaluation suggested an accurate registration for 13 out of 16 cases. The segmentations allowed full 3D visualization and evaluation of the spatial relationship of the BCa tumour and the MP. Conclusion Digital scanning of en-bloc resected specimens allows a full-fledged 3D reconstruction and analysis and has a potential role to support pathologists in the staging of BCa.
Published: 2019

12. Penumbral imaging and functional outcome in patients with anterior circulation ischaemic stroke treated with endovascular thrombectomy versus medical therapy: a meta-analysis of individual patient-level data

Author: Bruce C V Campbell, Charles B L M Majoie, Gregory W Albers, Bijoy K Menon, Nawaf Yassi, Gagan Sharma, Wim H van Zwam, Robert J van Oostenbrugge, Andrew M Demchuk, Francis Guillemin, Philip White, Antoni Dávalos, Aad van der Lugt, Kenneth S Butcher, Aboubaker Cherifi, Henk A Marquering, Geoffrey Cloud, Juan M Macho Fernández, Jeremy Madigan, Catherine Oppenheim, Geoffrey A Donnan, Yvo B W E M Roos, Jai Shankar, Hester Lingsma, Alain Bonafé, Hélène Raoult, María Hernández-Pérez, Aditya Bharatha, Reza Jahan, Olav Jansen, Sébastien Richard, Elad I Levy, Olvert A Berkhemer, Marc Soudant, Lucia Aja, Stephen M Davis, Timo Krings, Marie Tisserand, Luis San Román, Alejandro Tomasello, Debbie Beumer, Scott Brown, David S Liebeskind, Serge Bracard, Keith W Muir, Diederik W J Dippel, Mayank Goyal, Jeffrey L Saver, Tudor G Jovin, Michael D Hill, Peter J Mitchell, Puck SS Fransen, Lucie A van den Berg, Hester F Lingsma, Albert J Yoo, Wouter J Schonewille, Jan Albert Vos, Paul J Nederkoorn, Marieke JH Wermer, Marianne AA van Walderveen, Julie Staals, Jeannette Hofmeijer, Jacques A. van Oostayen, Geert J. Lycklama à Nijeholt, Jelis Boiten, Patrick A. Brouwer, Bart J. Emmer, Sebastiaan F. de Bruijn, Lukas C. van Dijk, Jaap Kappelle, Rob H Lo, Ewoud J. van Dijk, Joost de Vries, Paul L.M. de Kort, Willem Jan J. van Rooij, Jan S.P. van den Berg, Boudewijn A.A.M. van Hasselt, Leo A.M. Aerden, René J. Dallinga, Marieke C. Visser, Joseph C.J. Bot, Patrick C. Vroomen, Omid Eshghi, Tobien H.C.M.L. Schreuder, Roel J.J. Heijboer, Koos Keizer, Alexander V. Tielbeek, Heleen M. den Hertog, Dick G. Gerrits, Renske M. van den Berg-Vos, Giorgos B. Karas, Ewout W. Steyerberg, Zwenneke Flach, Henk A. Marquering, Marieke E.S. Sprengers, Sjoerd F.M. Jenniskens, Ludo F.M. Beenen, René van den Berg, Peter J. Koudstaal, Wim H. van Zwam, Yvo B.W.E.M. Roos, Robert J. van Oostenbrugge, Charles B.L.M. Majoie, Diederik W.J. Dippel, Martin M. Brown, Thomas Liebig, Theo Stijnen, Tommy Andersson, Heinrich Mattle, Nils Wahlgren, Esther van der Heijden, Naziha Ghannouti, Nadine Fleitour, Imke Hooijenga, Corina Puppels, Wilma Pellikaan, Annet Geerling, Annemieke Lindl-Velema, Gina van Vemde, Ans de Ridder, Paut Greebe, José de Bont-Stikkelbroeck, Joke de Meris, Kirsten Janssen, Willy Struijk, Silvan Licher, Nikki Boodt, Adriaan Ros, Esmee Venema, Ilse Slokkers, Raymie-Jayce Ganpat, Maxim Mulder, Nawid Saiedie, Alis Heshmatollah, Stefanie Schipperen, Stefan Vinken, Tiemen van Boxtel, Jeroen Koets, Merel Boers, Emilie Santos, Jordi Borst, Ivo Jansen, Manon Kappelhof, Marit Lucas, Ralph Geuskens, Renan Sales Barros, Roeland Dobbe, Marloes Csizmadia, MD Hill, M Goyal, AM Demchuk, BK Menon, M Eesa, KJ Ryckborst, MR Wright, NR Kamal, L Andersen, PA Randhawa, T Stewart, S Patil, P Minhas, M Almekhlafi, S Mishra, F Clement, T Sajobi, A Shuaib, WJ Montanera, D Roy, FL Silver, TG Jovin, DF Frei, B Sapkota, JL Rempel, J Thornton, D Williams, D Tampieri, AY Poppe, D Dowlatshahi, JH Wong, AP Mitha, S Subramaniam, G Hull, MW Lowerison, M Salluzzi, M Maxwell, S Lacusta, E Drupals, K Armitage, PA Barber, EE Smith, WF Morrish, SB Coutts, C Derdeyn, B Demaerschalk, D Yavagal, R Martin, R Brant, Y Yu, RA Willinsky, A Weill, C Kenney, H Aram, PK Stys, TW Watson, G Klein, D Pearson, P Couillard, A Trivedi, D Singh, E Klourfeld, O Imoukhuede, D Nikneshan, S Blayney, R Reddy, P Choi, M Horton, T Musuka, V Dubuc, TS Field, J Desai, S Adatia, A Alseraya, V Nambiar, R van Dijk, NJ Newcommon, B Schwindt, KS Butcher, T Jeerakathil, B Buck, K Khan, SS Naik, DJ Emery, RJ Owen, TB Kotylak, RA Ashforth, TA Yeo, D McNally, M Siddiqui, M Saqqur, D Hussain, H Kalashyan, A Manosalva, M Kate, L Gioia, S Hasan, A Mohammad, M Muratoglu, A Cullen, P Brennan, A O'Hare, S Looby, D Hyland, S Duff, M McCusker, B Hallinan, S Lee, J McCormack, A Moore, M O'Connor, C Donegan, L Brewer, A Martin, S Murphy, K O'Rourke, S Smyth, P Kelly, T Lynch, T Daly, P O'Brien, A O'Driscoll, M Martin, R Collins, T Coughlan, D McCabe, D O'Neill, M Mulroy, O Lynch, T Walsh, M O'Donnell, T Galvin, J Harbison, P McElwaine, K Mulpeter, C McLoughlin, M Reardon, E Harkin, E Dolan, M Watts, N Cunningham, C Fallon, S Gallagher, P Cotter, M Crowe, R Doyle, I Noone, M Lapierre, VA Coté, S Lanthier, C Odier, A Durocher, J Raymond, N Daneault, Y Deschaintre, B Jankowitz, L Baxendell, L Massaro, C Jackson-Graves, S Decesare, P Porter, K Armbruster, A Adams, J Billigan, J Oakley, A Ducruet, A Jadhav, D-V Giurgiutiu, A Aghaebrahim, V Reddy, M Hammer, M Starr, V Totoraitis, L Wechsler, S Streib, S Rangaraju, D Campbell, M Rocha, D Gulati, T Krings, L Kalman, A Cayley, J Williams, R Wiegner, LK Casaubon, C Jaigobin, JM del Campo, E Elamin, JD Schaafsma, R Agid, R Farb, K ter Brugge, BL Sapkoda, BW Baxter, K Barton, A Knox, A Porter, A Sirelkhatim, T Devlin, C Dellinger, N Pitiyanuvath, J Patterson, J Nichols, S Quarfordt, J Calvert, H Hawk, C Fanale, A Bitner, A Novak, D Huddle, R Bellon, D Loy, J Wagner, I Chang, E Lampe, B Spencer, R Pratt, R Bartt, S Shine, G Dooley, T Nguyen, M Whaley, K McCarthy, J Teitelbaum, W Poon, N Campbell, M Cortes, C Lum, R Shamloul, S Robert, G Stotts, M Shamy, N Steffenhagen, D Blacquiere, M Hogan, M AlHazzaa, G Basir, H Lesiuk, D Iancu, M Santos, H Choe, DC Weisman, K Jonczak, A Blue-Schaller, Q Shah, L MacKenzie, B Klein, K Kulandaivel, O Kozak, DJ Gzesh, LJ Harris, JS Khoury, J Mandzia, D Pelz, S Crann, L Fleming, K Hesser, B Beauchamp, B Amato-Marzialli, M Boulton, P Lopez- Ojeda, M Sharma, S Lownie, R Chan, R Swartz, P Howard, D Golob, D Gladstone, K Boyle, M Boulos, J Hopyan, V Yang, L Da Costa, CA Holmstedt, AS Turk, R Navarro, E Jauch, S Ozark, R Turner, S Phillips, J Shankar, J Jarrett, G Gubitz, W Maloney, R Vandorpe, M Schmidt, J Heidenreich, G Hunter, M Kelly, R Whelan, L Peeling, PA Burns, A Hunter, I Wiggam, E Kerr, M Watt, A Fulton, P Gordon, I Rennie, P Flynn, G Smyth, S O'Leary, N Gentile, G Linares, P McNelis, K Erkmen, P Katz, A Azizi, M Weaver, C Jungreis, S Faro, P Shah, H Reimer, V Kalugdan, G Saposnik, A Bharatha, Y Li, P Kostyrko, T Marotta, W Montanera, D Sarma, D Selchen, J Spears, JH Heo, K Jeong, DJ Kim, BM Kim, YD Kim, D Song, K-J Lee, J Yoo, OY Bang, S Rho, J Lee, P Jeon, KH Kim, J Cha, SJ Kim, S Ryoo, MJ Lee, S-I Sohn, C-H Kim, H-G Ryu, J-H Hong, H-W Chang, C-Y Lee, J Rha, Bruce CV Campbell, Leonid Churilov, Bernard Yan, Richard Dowling, Thomas J Oxley, Teddy Y Wu, Gabriel Silver, Amy McDonald, Rachael McCoy, Timothy J Kleinig, Rebecca Scroop, Helen M Dewey, Marion Simpson, Mark Brooks, Bronwyn Coulton, Martin Krause, Timothy J Harrington, Brendan Steinfort, Kenneth Faulder, Miriam Priglinger, Susan Day, Thanh Phan, Winston Chong, Michael Holt, Ronil V Chandra, Henry Ma, Dennis Young, Kitty Wong, Tissa Wijeratne, Hans Tu, Elizabeth Mackay, Sherisse Celestino, Christopher F Bladin, Poh Sien Loh, Amanda Gilligan, Zofia Ross, Skye Coote, Tanya Frost, Mark W Parsons, Ferdinand Miteff, Christopher R Levi, Timothy Ang, Neil Spratt, Lara Kaauwai, Monica Badve, Henry Rice, Laetitia de Villiers, P. Alan Barber, Ben McGuinness, Ayton Hope, Maurice Moriarty, Patricia Bennett, Andrew Wong, Alan Coulthard, Andrew Lee, Jim Jannes, Deborah Field, Simon Salinas, Elise Cowley, Barry Snow, John Kolbe, Richard Stark, John King, Richard Macdonnell, John Attia, Cate D'Este, Hans-Christoph Diener, Elad I. Levy, Vitor Mendes Pereira, Gregory W. Albers, Christophe Cognard, David J. Cohen, Werner Hacke, Tudor G. Jovin, Heinrich P. Mattle, Raul G. Nogueira, Adnan H. Siddiqui, Dileep R. Yavagal, Rüdiger von Kummer, Wade Smith, Francis Turjman, Scott Hamilton, Richard Chiacchierini, Arun Amar, Nerses Sanossian, Yince Loh, B Baxter, VK Reddy, A Horev, M Star, A Siddiqui, LN Hopkins, K Snyder, R Sawyer, S Hall, V Costalat, C Riquelme, P Machi, E Omer, C Arquizan, I Mourand, M Charif, X Ayrignac, N Menjot de Champfleur, N Leboucq, G Gascou, M Moynier, R du Mesnil de Rochemont, O Singer, J Berkefeld, C Foerch, M Lorenz, W Pfeilschifer, E Hattingen, M Wagner, SJ You, S Lescher, H Braun, S Dehkharghani, SR Belagaje, A Anderson, A Lima, M Obideen, D Haussen, R Dharia, M Frankel, V Patel, K Owada, A Saad, L Amerson, C Horn, S Doppelheuer, K Schindler, DK Lopes, M Chen, R Moftakhar, C Anton, M Smreczak, JS Carpenter, S Boo, A Rai, T Roberts, A Tarabishy, L Gutmann, C Brooks, J Brick, J Domico, G Reimann, K Hinrichs, M Becker, E Heiss, C Selle, A Witteler, S Al-Boutros, M-J Danch, A Ranft, S Rohde, K Burg, C Weimar, V Zegarac, C Hartmann, M Schlamann, S Göricke, A Ringlestein, I Wanke, C Mönninghoff, M Dietzold, R Budzik, T Davis, G Eubank, WJ Hicks, P Pema, N Vora, J Mejilla, M Taylor, W Clark, A Rontal, J Fields, B Peterson, G Nesbit, H Lutsep, H Bozorgchami, R Priest, O Ologuntoye, S Barnwell, A Dogan, K Herrick, C Takahasi, N Beadell, B Brown, S Jamieson, MS Hussain, A Russman, F Hui, D Wisco, K Uchino, Z Khawaja, I Katzan, G Toth, E Cheng-Ching, M Bain, S Man, A Farrag, P George, S John, L Shankar, A Drofa, R Dahlgren, A Bauer, A Itreat, A Taqui, R Cerejo, A Richmond, P Ringleb, M Bendszus, M Möhlenbruch, T Reiff, H Amiri, J Purrucker, C Herweh, M Pham, O Menn, I Ludwig, I Acosta, C Villar, W Morgan, C Sombutmai, F Hellinger, E Allen, M Bellew, R Gandhi, E Bonwit, J Aly, RD Ecker, D Seder, J Morris, M Skaletsky, J Belden, C Baker, LS Connolly, P Papanagiotou, C Roth, A Kastrup, M Politi, F Brunner, M Alexandrou, H Merdivan, C Ramsey, C Given II, S Renfrow, V Deshmukh, K Sasadeusz, F Vincent, JT Thiesing, J Putnam, A Bhatt, A Kansara, D Caceves, T Lowenkopf, L Yanase, J Zurasky, S Dancer, B Freeman, T Scheibe-Mirek, J Robison, J Roll, D Clark, M Rodriguez, B-FM Fitzsimmons, O Zaidat, JR Lynch, M Lazzaro, T Larson, L Padmore, E Das, A Farrow-Schmidt, A Hassan, W Tekle, C Cate, O Jansen, C Cnyrim, F Wodarg, C Wiese, A Binder, C Riedel, A Rohr, N Lang, H Laufs, S Krieter, L Remonda, M Diepers, J Añon, K Nedeltchev, T Kahles, S Biethahn, M Lindner, V Chang, C Gächter, C Esperon, M Guglielmetti, JF Arenillas Lara, M Martínez Galdámez, AI Calleja Sanz, E Cortijo Garcia, P Garcia Bermejo, S Perez, P Mulero Carrillo, E Crespo Vallejo, M Ruiz Piñero, L Lopez Mesonero, FJ Reyes Muñoz, C Brekenfeld, J-H Buhk, A Krützelmann, G Thomalla, B Cheng, C Beck, J Hoppe, E Goebell, B Holst, U Grzyska, G Wortmann, S Starkman, G Duckwiler, R Jahan, N Rao, S Sheth, K Ng, A Noorian, V Szeder, M Nour, M McManus, J Huang, J Tarpley, S Tateshima, N Gonzalez, L Ali, D Liebeskind, J Hinman, M Calderon-Arnulphi, C Liang, J Guzy, S Koch, K DeSousa, G Gordon-Perue, M Elhammady, E Peterson, V Pandey, S Dharmadhikari, P Khandelwal, A Malik, R Pafford, P Gonzalez, K Ramdas, G Andersen, D Damgaard, P Von Weitzel-Mudersbach, C Simonsen, N Ruiz de Morales Ayudarte, M Poulsen, L Sørensen, S Karabegovich, M Hjørringgaard, N Hjort, T Harbo, K Sørensen, E Deshaies, D Padalino, A Swarnkar, JG Latorre, E Elnour, Z El-Zammar, M Villwock, H Farid, A Balgude, L Cross, K Hansen, M Holtmannspötter, D Kondziella, J Hoejgaard, S Taudorf, H Soendergaard, A Wagner, M Cronquist, T Stavngaard, M Cortsen, LH Krarup, T Hyldal, H-P Haring, S Guggenberger, M Hamberger, J Trenkler, M Sonnberger, K Nussbaumer, C Dominger, E Bach, BD Jagadeesan, R Taylor, J Kim, K Shea, R Tummala, H Zacharatos, D Sandhu, M Ezzeddine, A Grande, D Hildebrandt, K Miller, J Scherber, A Hendrickson, M Jumaa, S Zaidi, T Hendrickson, V Snyder, M Killer-Oberpfalzer, J Mutzenbach, F Weymayr, E Broussalis, K Stadler, A Jedlitschka, A Malek, N Mueller-Kronast, P Beck, C Martin, D Summers, J Day, I Bettinger, W Holloway, K Olds, S Arkin, N Akhtar, C Boutwell, S Crandall, M Schwartzman, C Weinstein, B Brion, S Prothmann, J Kleine, K Kreiser, T Boeckh-Behrens, H Poppert, S Wunderlich, ML Koch, V Biberacher, A Huberle, G Gora-Stahlberg, B Knier, T Meindl, D Utpadel-Fischler, M Zech, M Kowarik, C Seifert, B Schwaiger, A Puri, S Hou, A Wakhloo, M Moonis, N Henninger, R Goddeau, F Massari, A Minaeian, JD Lozano, M Ramzan, C Stout, A Patel, A Tunguturi, S Onteddu, R Carandang, M Howk, M Ribó, E Sanjuan, M Rubiera, J Pagola, A Flores, M Muchada, P Meler, E Huerga, S Gelabert, P Coscojuela, A Tomasello, D Rodriguez, E Santamarina, O Maisterra, S Boned, L Seró, A Rovira, CA Molina, M Millán, L Muñoz, N Pérez de la Ossa, M Gomis, L Dorado, E López-Cancio, E Palomeras, J Munuera, P García Bermejo, S Remollo, C Castaño, R García-Sort, P Cuadras, P Puyalto, M Hernández-Pérez, M Jiménez, A Martínez-Piñeiro, G Lucente, A Dávalos, A Chamorro, X Urra, V Obach, A Cervera, S Amaro, L Llull, J Codas, M Balasa, J Navarro, H Ariño, A Aceituno, S Rudilosso, A Renu, JM Macho, L San Roman, J Blasco, A López, N Macías, P Cardona, H Quesada, F Rubio, L Cano, B Lara, MA de Miquel, L Aja, J Serena, E Cobo, Kennedy R Lees, J Arenillas, R Roberts, F Al-Ajlan, L Zimmel, S Patel, J Martí-Fàbregas, M Salvat-Plana, S Bracard, Xavier Ducrocq, René Anxionnat, Pierre-Alexandre Baillot, Charlotte Barbier, Anne-Laure Derelle, Jean-Christophe Lacour, Yves Samson, Nader Sourour, Flore Baronnet-Chauvet, Frédéric Clarencon, Sophie Crozier, Sandrine Deltour, Federico Di Maria, Raphael Le Bouc, Anne Leger, Gurkan Mutlu, Charlotte Rosso, Zoltan Szatmary, Marion Yger, Chiara Zavanone, Serge Bakchine, Laurent Pierot, Nathalie Caucheteux, Laurent Estrade, Krzysztof Kadziolka, Alexandre Leautaud, Céline Renkes, Isabelle Serre, Hubert Desal, Benoît Guillon, Claire Boutoleau-Bretonniere, Benjamin Daumas-Duport, Solène De Gaalon, Pascal Derkinderen, Sarah Evain, Fanny Herisson, David-Axel Laplaud, Thibaud Lebouvier, Alina Lintia-Gaultier, Hélène Pouclet-Courtemanche, Tiphaine Rouaud, Violaine Rouaud Jaffrenou, Aurélia Schunck, Mathieu Sevin-Allouet, Frederique Toulgoat, Sandrine Wiertlewski, Jean-Yves Gauvrit, Thomas Ronziere, Vincent Cahagne, Jean-Christophe Ferre, Jean-François Pinel, Jean-Louis Mas, Jean-François Meder, Amen-Adam Al Najjar-Carpentier, Julia Birchenall, Eric Bodiguel, David Calvet, Valérie Domigo, Sylvie Godon-Hardy, Vincent Guiraud, Catherine Lamy, Loubna Majhadi, Ludovic Morin, Olivier Naggara, Denis Trystram, Guillaume Turc, Jérôme Berge, Igor Sibon, Patrice Menegon, Xavier Barreau, François Rouanet, Sabrina Debruxelles, Annabelle Kazadi, Pauline Renou, Olivier Fleury, Anne Pasco-Papon, Frédéric Dubas, Jildaz Caroff, Sophie Godard Ducceschi, Marie-Aurélie Hamon, Alderic Lecluse, Guillaume Marc, Maurice Giroud, Frédéric Ricolfi, Yannick Bejot, Adrien Chavent, Arnaud Gentil, Apolline Kazemi, Guy-Victor Osseby, Charlotte Voguet, Marie-Hélène Mahagne, Jacques Sedat, Yves Chau, Laurent Suissa, Sylvain Lachaud, Emmanuel Houdart, Christian Stapf, Frédérique Buffon Porcher, Hugues Chabriat, Pierre Guedin, Dominique Herve, Eric Jouvent, Jérôme Mawet, Jean-Pierre Saint-Maurice, Hans-Martin Schneble, Norbert Nighoghossian, Nadia-Nawel Berhoune, Françoise Bouhour, Tae-Hee Cho, Laurent Derex, Sandra Felix, Hélène Gervais-Bernard, Benjamin Gory, Luis Manera, Laura Mechtouff, Thomas Ritzenthaler, Roberto Riva, Fabrizio Salaris Silvio, Caroline Tilikete, Raphael Blanc, Michaël Obadia, Mario Bruno Bartolini, Antoine Gueguen, Michel Piotin, Silvia Pistocchi, Hocine Redjem, Jacques Drouineau, Jean-Philippe Neau, Gaelle Godeneche, Matthias Lamy, Emilia Marsac, Stephane Velasco, Pierre Clavelou, Emmanuel Chabert, Nathalie Bourgois, Catherine Cornut-Chauvinc, Anna Ferrier, Jean Gabrillargues, Betty Jean, Anna-Raquel Marques, Nicolas Vitello, Olivier Detante, Marianne Barbieux, Kamel Boubagra, Isabelle Favre Wiki, Katia Garambois, Florence Tahon, Vasdev Ashok, Oguzhan Coskun, Georges Rodesch, Bertrand Lapergue, Frédéric Bourdain, Serge Evrard, Philippe Graveleau, Jean Pierre Decroix, Adrien Wang, François Sellal, Guido Ahle, Gabriela Carelli, Marie-Hélène Dugay, Claude Gaultier, Ariel Pablo Lebedinsky, Lavinia Lita, Raul Mariano Musacchio, Catherine Renglewicz-Destuynder, Alain Tournade, Françis Vuillemet, Francisco Macian Montoro, Charbel Mounayer, Frederic Faugeras, Laetitia Gimenez, Catherine Labach, Géraldine Lautrette, Christian Denier, Guillaume Saliou, Olivier Chassin, Claire Dussaule, Elsa Melki, Augustin Ozanne, Francesco Puccinelli, Marina Sachet, Mariana Sarov, Jean-François Bonneville, Thierry Moulin, Alessandra Biondi, Elisabeth De Bustos Medeiros, Fabrice Vuillier, Patrick Courtheoux, Fausto Viader, Marion Apoil-Brissard, Mathieu Bataille, Anne-Laure Bonnet, Julien Cogez, Emmanuel Touze, Xavier Leclerc, Didier Leys, Mohamed Aggour, Pierre Aguettaz, Marie Bodenant, Charlotte Cordonnier, Dominique Deplanque, Marie Girot, Hilde Henon, Erwah Kalsoum, Christian Lucas, Jean-Pierre Pruvo, Paolo Zuniga, Caroline Arquizan, Vincent Costalat, Paolo Machi, Isabelle Mourand, Carlos Riquelme, Pierre Bounolleau, Charles Arteaga, Anthony Faivre, Marc Bintner, Patrice Tournebize, Cyril Charlin, Françoise Darcel, Pascale Gauthier-Lasalarie, Marcia Jeremenko, Servane Mouton, Jean-Baptiste Zerlauth, Chantal Lamy, Deramond Hervé, Hosseini Hassan, André Gaston, Francis-Guy Barral, Pierre Garnier, Rémy Beaujeux, Valérie Wolff, Denis Herbreteau, Séverine Debiais, Alicia Murray, Gary Ford, Andy Clifton, Janet Freeman, Ian Ford, Hugh Markus, Joanna Wardlaw, Andy Molyneux, Thompson Robinson, Steff Lewis, John Norrie, Fergus Robertson, Richard Perry, Anand Dixit, Andrew Clifton, Christine Roffe, Sanjeev Nayak, Kyriakos Lobotesis, Craig Smith, Amit Herwadkar, Naga Kandasamy, Tony Goddard, John Bamford, Ganesh Subramanian, Rob Lenthall, Edward Littleton, Sal Lamin, Kelley Storey, Rita Ghatala, Azra Banaras, John Aeron-Thomas, Bath Hazel, Holly Maguire, Emelda Veraque, Louise Harrison, Rekha Keshvara, James Cunningham, Clinical Neurophysiology, Weimar, Christian (Beitragende*r), Radiology and nuclear medicine, Rheumatology, ACS - Atherosclerosis & ischemic syndromes, RS: Carim - B05 Cerebral small vessel disease, RS: CARIM - R3.03 - Cerebral small vessel disease, RS: Carim - B06 Imaging, Beeldvorming, MUMC+: DA BV Medisch Specialisten Radiologie (9), RS: CARIM - R3.11 - Imaging, MUMC+: MA Neurologie (3), Klinische Neurowetenschappen, MUMC+: MA AIOS Neurologie (9), The Royal Melbourne Hospital, Academic Medical Center - Academisch Medisch Centrum [Amsterdam] (AMC), University of Amsterdam [Amsterdam] (UvA), Stanford University, University of Calgary, The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University [Maastricht], Centre d'investigation clinique - Epidémiologie clinique [Nancy] (CIC-EC), Centre d'investigation clinique [Nancy] (CIC), Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institute of Neuroscience [Newcastle] (ION), Newcastle University [Newcastle], Universitat Autònoma de Barcelona (UAB), Erasmus University Medical Center [Rotterdam] (Erasmus MC), University of Alberta, Centre d'Investigation Clinique - Innovation Technologique [Nancy] (CIC-IT), Monash University [Melbourne], St George’s University Hospitals, Institut de psychiatrie et neurosciences (U894 / UMS 1266), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), Dalhousie University [Halifax], Neuroradiologie [Hôpital Gui de Chauliac], Hôpital Gui de Chauliac [Montpellier], Service de Neuroradiologie [Rennes], CHU Pontchaillou [Rennes], St. Michael's Hospital, University of California [Los Angeles] (UCLA), University of California, University Medical Center of Schleswig–Holstein = Universitätsklinikum Schleswig-Holstein (UKSH), Kiel University, Centre d'investigation clinique plurithématique Pierre Drouin [Nancy] (CIC-P), Défaillance Cardiovasculaire Aiguë et Chronique (DCAC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Service de neurologie [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), University at Buffalo [SUNY] (SUNY Buffalo), State University of New York (SUNY), Toronto Western Hospital, Hôpital Foch [Suresnes], Vall d'Hebron University Hospital [Barcelona], Département de neuroradiologie diagnostique et thérapeutique [CHRU Nancy], Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), University of Glasgow, Queen Elizabeth University Hospital (Glasgow), David Geffen School of Medicine [Los Angeles], University of California-University of California, Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Radiology & Nuclear Medicine, Public Health, Neurology, Radiology and Nuclear Medicine, ACS - Microcirculation, ANS - Neurovascular Disorders, ACS - Amsterdam Cardiovascular Sciences, Graduate School, ACS - Pulmonary hypertension & thrombosis, AGEM - Digestive immunity, AGEM - Re-generation and cancer of the digestive system, ARD - Amsterdam Reproduction and Development, Biomedical Engineering and Physics, APH - Personalized Medicine, APH - Quality of Care, and AMS - Restoration & Development
Subjects: SELECTION, COMPUTED TOMOGRAPHIC PERFUSION, Medizin, Perfusion scanning, 030204 cardiovascular system & hematology, Brain Ischemia, 0302 clinical medicine, Modified Rankin Scale, REPERFUSION, Stroke, ComputingMilieux_MISCELLANEOUS, Thrombectomy, Aged, 80 and over, medicine.diagnostic_test, Penumbra, Endovascular Procedures, Brain, Middle Aged, Magnetic Resonance Imaging, 3. Good health, Treatment Outcome, Cerebral blood flow, Tissue Plasminogen Activator, INFARCT, Cardiology, Female, TRIAL, CT, medicine.medical_specialty, Perfusion Imaging, Neuroimaging, [SDV.IB.MN]Life Sciences [q-bio]/Bioengineering/Nuclear medicine, 03 medical and health sciences, Fibrinolytic Agents, ALTEPLASE, Internal medicine, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Humans, Aged, business.industry, MECHANICAL THROMBECTOMY, Other Research Radboud Institute for Health Sciences [Radboudumc 0], Magnetic resonance imaging, Odds ratio, medicine.disease, Neurology (clinical), business, Tomography, X-Ray Computed, 030217 neurology & neurosurgery, Fibrinolytic agent, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract: Background CT perfusion (CTP) and diffusion or perfusion MRI might assist patient selection for endovascular thrombectomy. We aimed to establish whether imaging assessments of irreversibly injured ischaemic core and potentially salvageable penumbra volumes were associated with functional outcome and whether they interacted with the treatment effect of endovascular thrombectomy on functional outcome.Methods In this systematic review and meta-analysis, the HERMES collaboration pooled patient-level data from all randomised controlled trials that compared endovascular thrombectomy (predominantly using stent retrievers) with standard medical therapy in patients with anterior circulation ischaemic stroke, published in PubMed from Jan 1,2010, to May 31, 2017. The primary endpoint was functional outcome, assessed by the modified Rankin Scale (mRS) at 90 days after stroke. Ischaemic core was estimated, before treatment with either endovascular thrombectomy or standard medical therapy, by CTP as relative cerebral blood flow less than 30% of normal brain blood flow or by MRI as an apparent diffusion coefficient less than 620 mu m(2)/s. Critically hypoperfused tissue was estimated as the volume of tissue with a CTP time to maximum longer than 6 s. Mismatch volume (ie, the estimated penumbral volume) was calculated as critically hypoperfused tissue volume minus ischaemic core volume. The association of ischaemic core and penumbral volumes with 90-day mRS score was analysed with multivariable logistic regression (functional independence, defined as mRS score 0-2) and ordinal logistic regression (functional improvement by at least one mRS category) in all patients and in a subset of those with more than 50% endovascular reperfusion, adjusted for baseline prognostic variables. The meta-analysis was prospectively designed by the HERMES executive committee, but not registered.Findings We identified seven studies with 1764 patients, all of which were included in the meta-analysis. CTP was available and assessable for 591 (34%) patients and diffusion MRI for 309 (18%) patients. Functional independence was worse in patients who had CTP versus those who had diffusion MRI, after adjustment for ischaemic core volume (odds ratio [OR] 0.47 [95% CI 0.30-0.72], p=0.0007), so the imaging modalities were not pooled. Increasing ischaemic core volume was associated with reduced likelihood of functional independence (CTP OR 0.77 [0.69-0.86] per 10 mL, p(interaction)=0.29; diffusion MRI OR 0.87 [0.81-0.94] per 10 mL, p(interaction)=0.94). Mismatch volume, examined only in the CTP group because of the small numbers of patients who had perfusion MRI, was not associated with either functional independence or functional improvement. In patients with CTP with more than 50% endovascular reperfusion (n=186), age, ischaemic core volume, and imaging-to-reperfusion time were independently associated with functional improvement. Risk of bias between studies was generally low.Interpretation Estimated ischaemic core volume was independently associated with functional independence and functional improvement but did not modify the treatment benefit of endovascular thrombectomy over standard medical therapy for improved functional outcome. Combining ischaemic core volume with age and expected imagingto-reperfusion time will improve assessment of prognosis and might inform endovascular thrombectomy treatment decisions. Copyright (C) 2018 Elsevier Ltd. All rights reserved.
Published: 2019
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13. Strategies for managing multi-patient 3D mass spectrometry imaging data

Author: Ron M. A. Heeren, C. D. Savci-Heijink, Sybren L. Meijer, Daniel M. de Bruin, Marit Lucas, Martin R. L. Paine, O. J. de Boer, Henk A. Marquering, Ilaria Jansen, Benjamin Balluff, D. R. N. Vos, Shane R. Ellis, Imaging Mass Spectrometry (IMS), RS: M4I - Imaging Mass Spectrometry (IMS), Pathology, ACS - Heart failure & arrhythmias, Graduate School, CCA - Imaging and biomarkers, ACS - Atherosclerosis & ischemic syndromes, Radiology and Nuclear Medicine, ANS - Brain Imaging, Urology, Biomedical Engineering and Physics, and APH - Quality of Care
Subjects: 0301 basic medicine, Male, medicine.medical_specialty, Sample (material), Biophysics, Biochemistry, Mass spectrometry imaging, Cohort Studies, 03 medical and health sciences, Imaging, Three-Dimensional, medicine, PARAFFIN-EMBEDDED TISSUE, Humans, HETEROGENEITY, Biomarker discovery, BRAIN, Formalin-fixed paraffin-embedded tissue, Grading (tumors), Sampling bias, Reproducibility, Bladder cancer, 030102 biochemistry & molecular biology, IDENTIFICATION, business.industry, 3-dimensional, Cancer, RELEVANT SIGNALS, MS, medicine.disease, CANCER, Neoplasm Proteins, 030104 developmental biology, Urinary Bladder Neoplasms, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization, Female, Radiology, PROTEOMIC ANALYSIS, business, LIPIDS
Abstract: Mass spectrometry imaging (MSI) has emerged as a powerful tool in biomedical research to reveal the localization of a broad scale of compounds ranging from metabolites to proteins in diseased tissues, such as malignant tumors. MSI is most commonly used for the two-dimensional imaging of tissues from multiple patients or for the three-dimensional (3D) imaging of tissue from a single patient. These applications are potentially introducing a sampling bias on a sample or patient level, respectively. The aim of this study is therefore to investigate the consequences of sampling bias on sample representativeness and on the precision of biomarker discovery for histological grading of human bladder cancers by MSI. We therefore submitted formalin-fixed paraffin-embedded tissues from 14 bladder cancer patients with varying histological grades to 3D analysis by matrix-assisted laser desorption/ionization (MALDI) MSI. We found that, after removing 20% of the data based on novel outlier detection routines for 3D-MSI data based on the evaluation of digestion efficacy and z-directed regression, on average 33% of a sample has to be measured in order to obtain sufficient coverage of the existing biological variance within a tissue sample.Significance: In this study, 3D MALDI-MSI is applied for the first time on a cohort of bladder cancer patients using formalin-fixed paraffin-embedded (FFPE) tissue of bladder cancer resections. This work portrays the reproducibility that can be achieved when employing an optimized sample preparation and subsequent data evaluation approach. Our data shows the influence of sampling bias on the variability of the results, especially for a small patient cohort. Furthermore, the presented data analysis workflow can be used by others as a 3D FFPE data-analysis pipeline working on multi-patient 3D-MSI studies.
Published: 2018

14. Effect of general anaesthesia on functional outcome in patients with anterior circulation ischaemic stroke having endovascular thrombectomy versus standard care: a meta-analysis of individual patient data

Author: Bruce C V Campbell, Wim H van Zwam, Mayank Goyal, Bijoy K Menon, Diederik W J Dippel, Andrew M Demchuk, Serge Bracard, Philip White, Antoni Dávalos, Charles B L M Majoie, Aad van der Lugt, Gary A Ford, Natalia Pérez de la Ossa, Michael Kelly, Romain Bourcier, Geoffrey A Donnan, Yvo B W E M Roos, Oh Young Bang, Raul G Nogueira, Thomas G Devlin, Lucie A van den Berg, Frédéric Clarençon, Paul Burns, Jeffrey Carpenter, Olvert A Berkhemer, Dileep R Yavagal, Vitor Mendes Pereira, Xavier Ducrocq, Anand Dixit, Helena Quesada, Jonathan Epstein, Stephen M Davis, Olav Jansen, Marta Rubiera, Xabier Urra, Emilien Micard, Hester F Lingsma, Olivier Naggara, Scott Brown, Francis Guillemin, Keith W Muir, Robert J van Oostenbrugge, Jeffrey L Saver, Tudor G Jovin, Michael D Hill, Peter J Mitchell, Puck SS Fransen, Debbie Beumer, Albert J Yoo, Wouter J Schonewille, Jan Albert Vos, Paul J Nederkoorn, Marieke JH Wermer, Marianne AA van Walderveen, Julie Staals, Jeannette Hofmeijer, Jacques A. van Oostayen, Geert J. Lycklama à Nijeholt, Jelis Boiten, Patrick A. Brouwer, Bart J. Emmer, Sebastiaan F. de Bruijn, Lukas C. van Dijk, Jaap Kappelle, Rob H Lo, Ewoud J. van Dijk, Joost de Vries, Paul L.M. de Kort, Willem Jan J. van Rooij, Jan S.P. van den Berg, Boudewijn A.A.M. van Hasselt, Leo A.M. Aerden, René J. Dallinga, Marieke C. Visser, Joseph C.J. Bot, Patrick C. Vroomen, Omid Eshghi, Tobien H.C.M.L. Schreuder, Roel J.J. Heijboer, Koos Keizer, Alexander V. Tielbeek, Heleen M. den Hertog, Dick G. Gerrits, Renske M. van den Berg-Vos, Giorgos B. Karas, Ewout W. Steyerberg, Zwenneke Flach, Henk A. Marquering, Marieke E.S. Sprengers, Sjoerd F.M. Jenniskens, Ludo F.M. Beenen, René van den Berg, Peter J. Koudstaal, Wim H. van Zwam, Yvo B.W.E.M. Roos, Robert J. van Oostenbrugge, Charles B.L.M. Majoie, Diederik W.J. Dippel, Martin M. Brown, Thomas Liebig, Theo Stijnen, Tommy Andersson, Heinrich Mattle, Nils Wahlgren, Esther van der Heijden, Naziha Ghannouti, Nadine Fleitour, Imke Hooijenga, Corina Puppels, Wilma Pellikaan, Annet Geerling, Annemieke Lindl-Velema, Gina van Vemde, Ans de Ridder, Paut Greebe, José de Bont-Stikkelbroeck, Joke de Meris, Kirsten Janssen, Willy Struijk, Silvan Licher, Nikki Boodt, Adriaan Ros, Esmee Venema, Ilse Slokkers, Raymie-Jayce Ganpat, Maxim Mulder, Nawid Saiedie, Alis Heshmatollah, Stefanie Schipperen, Stefan Vinken, Tiemen van Boxtel, Jeroen Koets, Merel Boers, Emilie Santos, Jordi Borst, Ivo Jansen, Manon Kappelhof, Marit Lucas, Ralph Geuskens, Renan Sales Barros, Roeland Dobbe, Marloes Csizmadia, MD Hill, M Goyal, AM Demchuk, BK Menon, M Eesa, KJ Ryckborst, MR Wright, NR Kamal, L Andersen, PA Randhawa, T Stewart, S Patil, P Minhas, M Almekhlafi, S Mishra, F Clement, T Sajobi, A Shuaib, WJ Montanera, D Roy, FL Silver, TG Jovin, DF Frei, B Sapkota, JL Rempel, J Thornton, D Williams, D Tampieri, AY Poppe, D Dowlatshahi, JH Wong, AP Mitha, S Subramaniam, G Hull, MW Lowerison, M Salluzzi, M Maxwell, S Lacusta, E Drupals, K Armitage, PA Barber, EE Smith, WF Morrish, SB Coutts, C Derdeyn, B Demaerschalk, D Yavagal, R Martin, R Brant, Y Yu, RA Willinsky, A Weill, C Kenney, H Aram, PK Stys, TW Watson, G Klein, D Pearson, P Couillard, A Trivedi, D Singh, E Klourfeld, O Imoukhuede, D Nikneshan, S Blayney, R Reddy, P Choi, M Horton, T Musuka, V Dubuc, TS Field, J Desai, S Adatia, A Alseraya, V Nambiar, R van Dijk, NJ Newcommon, B Schwindt, KS Butcher, T Jeerakathil, B Buck, K Khan, SS Naik, DJ Emery, RJ Owen, TB Kotylak, RA Ashforth, TA Yeo, D McNally, M Siddiqui, M Saqqur, D Hussain, H Kalashyan, A Manosalva, M Kate, L Gioia, S Hasan, A Mohammad, M Muratoglu, A Cullen, P Brennan, A O'Hare, S Looby, D Hyland, S Duff, M McCusker, B Hallinan, S Lee, J McCormack, A Moore, M O'Connor, C Donegan, L Brewer, A Martin, S Murphy, K O'Rourke, S Smyth, P Kelly, T Lynch, T Daly, P O'Brien, A O'Driscoll, M Martin, R Collins, T Coughlan, D McCabe, D O'Neill, M Mulroy, O Lynch, T Walsh, M O'Donnell, T Galvin, J Harbison, P McElwaine, K Mulpeter, C McLoughlin, M Reardon, E Harkin, E Dolan, M Watts, N Cunningham, C Fallon, S Gallagher, P Cotter, M Crowe, R Doyle, I Noone, M Lapierre, VA Coté, S Lanthier, C Odier, A Durocher, J Raymond, N Daneault, Y Deschaintre, B Jankowitz, L Baxendell, L Massaro, C Jackson-Graves, S Decesare, P Porter, K Armbruster, A Adams, J Billigan, J Oakley, A Ducruet, A Jadhav, D-V Giurgiutiu, A Aghaebrahim, V Reddy, M Hammer, M Starr, V Totoraitis, L Wechsler, S Streib, S Rangaraju, D Campbell, M Rocha, D Gulati, T Krings, L Kalman, A Cayley, J Williams, R Wiegner, LK Casaubon, C Jaigobin, JM del Campo, E Elamin, JD Schaafsma, R Agid, R Farb, K ter Brugge, BL Sapkoda, BW Baxter, K Barton, A Knox, A Porter, A Sirelkhatim, T Devlin, C Dellinger, N Pitiyanuvath, J Patterson, J Nichols, S Quarfordt, J Calvert, H Hawk, C Fanale, A Bitner, A Novak, D Huddle, R Bellon, D Loy, J Wagner, I Chang, E Lampe, B Spencer, R Pratt, R Bartt, S Shine, G Dooley, T Nguyen, M Whaley, K McCarthy, J Teitelbaum, W Poon, N Campbell, M Cortes, C Lum, R Shamloul, S Robert, G Stotts, M Shamy, N Steffenhagen, D Blacquiere, M Hogan, M AlHazzaa, G Basir, H Lesiuk, D Iancu, M Santos, H Choe, DC Weisman, K Jonczak, A Blue-Schaller, Q Shah, L MacKenzie, B Klein, K Kulandaivel, O Kozak, DJ Gzesh, LJ Harris, JS Khoury, J Mandzia, D Pelz, S Crann, L Fleming, K Hesser, B Beauchamp, B Amato-Marzialli, M Boulton, P Lopez- Ojeda, M Sharma, S Lownie, R Chan, R Swartz, P Howard, D Golob, D Gladstone, K Boyle, M Boulos, J Hopyan, V Yang, L Da Costa, CA Holmstedt, AS Turk, R Navarro, E Jauch, S Ozark, R Turner, S Phillips, J Shankar, J Jarrett, G Gubitz, W Maloney, R Vandorpe, M Schmidt, J Heidenreich, G Hunter, M Kelly, R Whelan, L Peeling, PA Burns, A Hunter, I Wiggam, E Kerr, M Watt, A Fulton, P Gordon, I Rennie, P Flynn, G Smyth, S O'Leary, N Gentile, G Linares, P McNelis, K Erkmen, P Katz, A Azizi, M Weaver, C Jungreis, S Faro, P Shah, H Reimer, V Kalugdan, G Saposnik, A Bharatha, Y Li, P Kostyrko, T Marotta, W Montanera, D Sarma, D Selchen, J Spears, JH Heo, K Jeong, DJ Kim, BM Kim, YD Kim, D Song, K-J Lee, J Yoo, OY Bang, S Rho, J Lee, P Jeon, KH Kim, J Cha, SJ Kim, S Ryoo, MJ Lee, S-I Sohn, C-H Kim, H-G Ryu, J-H Hong, H-W Chang, C-Y Lee, J Rha, Bruce CV Campbell, Leonid Churilov, Bernard Yan, Richard Dowling, Nawaf Yassi, Thomas J Oxley, Teddy Y Wu, Gabriel Silver, Amy McDonald, Rachael McCoy, Timothy J Kleinig, Rebecca Scroop, Helen M Dewey, Marion Simpson, Mark Brooks, Bronwyn Coulton, Martin Krause, Timothy J Harrington, Brendan Steinfort, Kenneth Faulder, Miriam Priglinger, Susan Day, Thanh Phan, Winston Chong, Michael Holt, Ronil V Chandra, Henry Ma, Dennis Young, Kitty Wong, Tissa Wijeratne, Hans Tu, Elizabeth Mackay, Sherisse Celestino, Christopher F Bladin, Poh Sien Loh, Amanda Gilligan, Zofia Ross, Skye Coote, Tanya Frost, Mark W Parsons, Ferdinand Miteff, Christopher R Levi, Timothy Ang, Neil Spratt, Lara Kaauwai, Monica Badve, Henry Rice, Laetitia de Villiers, P. Alan Barber, Ben McGuinness, Ayton Hope, Maurice Moriarty, Patricia Bennett, Andrew Wong, Alan Coulthard, Andrew Lee, Jim Jannes, Deborah Field, Gagan Sharma, Simon Salinas, Elise Cowley, Barry Snow, John Kolbe, Richard Stark, John King, Richard Macdonnell, John Attia, Cate D'Este, Hans-Christoph Diener, Elad I. Levy, Alain Bonafé, Reza Jahan, Gregory W. Albers, Christophe Cognard, David J. Cohen, Werner Hacke, Tudor G. Jovin, Heinrich P. Mattle, Raul G. Nogueira, Adnan H. Siddiqui, Dileep R. Yavagal, Rüdiger von Kummer, Wade Smith, Francis Turjman, Scott Hamilton, Richard Chiacchierini, Arun Amar, Nerses Sanossian, Yince Loh, B Baxter, VK Reddy, A Horev, M Star, A Siddiqui, LN Hopkins, K Snyder, R Sawyer, S Hall, V Costalat, C Riquelme, P Machi, E Omer, C Arquizan, I Mourand, M Charif, X Ayrignac, N Menjot de Champfleur, N Leboucq, G Gascou, M Moynier, R du Mesnil de Rochemont, O Singer, J Berkefeld, C Foerch, M Lorenz, W Pfeilschifer, E Hattingen, M Wagner, SJ You, S Lescher, H Braun, S Dehkharghani, SR Belagaje, A Anderson, A Lima, M Obideen, D Haussen, R Dharia, M Frankel, V Patel, K Owada, A Saad, L Amerson, C Horn, S Doppelheuer, K Schindler, DK Lopes, M Chen, R Moftakhar, C Anton, M Smreczak, JS Carpenter, S Boo, A Rai, T Roberts, A Tarabishy, L Gutmann, C Brooks, J Brick, J Domico, G Reimann, K Hinrichs, M Becker, E Heiss, C Selle, A Witteler, S Al-Boutros, M-J Danch, A Ranft, S Rohde, K Burg, C Weimar, V Zegarac, C Hartmann, M Schlamann, S Göricke, A Ringlestein, I Wanke, C Mönninghoff, M Dietzold, R Budzik, T Davis, G Eubank, WJ Hicks, P Pema, N Vora, J Mejilla, M Taylor, W Clark, A Rontal, J Fields, B Peterson, G Nesbit, H Lutsep, H Bozorgchami, R Priest, O Ologuntoye, S Barnwell, A Dogan, K Herrick, C Takahasi, N Beadell, B Brown, S Jamieson, MS Hussain, A Russman, F Hui, D Wisco, K Uchino, Z Khawaja, I Katzan, G Toth, E Cheng-Ching, M Bain, S Man, A Farrag, P George, S John, L Shankar, A Drofa, R Dahlgren, A Bauer, A Itreat, A Taqui, R Cerejo, A Richmond, P Ringleb, M Bendszus, M Möhlenbruch, T Reiff, H Amiri, J Purrucker, C Herweh, M Pham, O Menn, I Ludwig, I Acosta, C Villar, W Morgan, C Sombutmai, F Hellinger, E Allen, M Bellew, R Gandhi, E Bonwit, J Aly, RD Ecker, D Seder, J Morris, M Skaletsky, J Belden, C Baker, LS Connolly, P Papanagiotou, C Roth, A Kastrup, M Politi, F Brunner, M Alexandrou, H Merdivan, C Ramsey, C Given II, S Renfrow, V Deshmukh, K Sasadeusz, F Vincent, JT Thiesing, J Putnam, A Bhatt, A Kansara, D Caceves, T Lowenkopf, L Yanase, J Zurasky, S Dancer, B Freeman, T Scheibe-Mirek, J Robison, J Roll, D Clark, M Rodriguez, B-FM Fitzsimmons, O Zaidat, JR Lynch, M Lazzaro, T Larson, L Padmore, E Das, A Farrow-Schmidt, A Hassan, W Tekle, C Cate, O Jansen, C Cnyrim, F Wodarg, C Wiese, A Binder, C Riedel, A Rohr, N Lang, H Laufs, S Krieter, L Remonda, M Diepers, J Añon, K Nedeltchev, T Kahles, S Biethahn, M Lindner, V Chang, C Gächter, C Esperon, M Guglielmetti, JF Arenillas Lara, M Martínez Galdámez, AI Calleja Sanz, E Cortijo Garcia, P Garcia Bermejo, S Perez, P Mulero Carrillo, E Crespo Vallejo, M Ruiz Piñero, L Lopez Mesonero, FJ Reyes Muñoz, C Brekenfeld, J-H Buhk, A Krützelmann, G Thomalla, B Cheng, C Beck, J Hoppe, E Goebell, B Holst, U Grzyska, G Wortmann, S Starkman, G Duckwiler, R Jahan, N Rao, S Sheth, K Ng, A Noorian, V Szeder, M Nour, M McManus, J Huang, J Tarpley, S Tateshima, N Gonzalez, L Ali, D Liebeskind, J Hinman, M Calderon-Arnulphi, C Liang, J Guzy, S Koch, K DeSousa, G Gordon-Perue, M Elhammady, E Peterson, V Pandey, S Dharmadhikari, P Khandelwal, A Malik, R Pafford, P Gonzalez, K Ramdas, G Andersen, D Damgaard, P Von Weitzel-Mudersbach, C Simonsen, N Ruiz de Morales Ayudarte, M Poulsen, L Sørensen, S Karabegovich, M Hjørringgaard, N Hjort, T Harbo, K Sørensen, E Deshaies, D Padalino, A Swarnkar, JG Latorre, E Elnour, Z El-Zammar, M Villwock, H Farid, A Balgude, L Cross, K Hansen, M Holtmannspötter, D Kondziella, J Hoejgaard, S Taudorf, H Soendergaard, A Wagner, M Cronquist, T Stavngaard, M Cortsen, LH Krarup, T Hyldal, H-P Haring, S Guggenberger, M Hamberger, J Trenkler, M Sonnberger, K Nussbaumer, C Dominger, E Bach, BD Jagadeesan, R Taylor, J Kim, K Shea, R Tummala, H Zacharatos, D Sandhu, M Ezzeddine, A Grande, D Hildebrandt, K Miller, J Scherber, A Hendrickson, M Jumaa, S Zaidi, T Hendrickson, V Snyder, M Killer-Oberpfalzer, J Mutzenbach, F Weymayr, E Broussalis, K Stadler, A Jedlitschka, A Malek, N Mueller-Kronast, P Beck, C Martin, D Summers, J Day, I Bettinger, W Holloway, K Olds, S Arkin, N Akhtar, C Boutwell, S Crandall, M Schwartzman, C Weinstein, B Brion, S Prothmann, J Kleine, K Kreiser, T Boeckh-Behrens, H Poppert, S Wunderlich, ML Koch, V Biberacher, A Huberle, G Gora-Stahlberg, B Knier, T Meindl, D Utpadel-Fischler, M Zech, M Kowarik, C Seifert, B Schwaiger, A Puri, S Hou, A Wakhloo, M Moonis, N Henninger, R Goddeau, F Massari, A Minaeian, JD Lozano, M Ramzan, C Stout, A Patel, A Tunguturi, S Onteddu, R Carandang, M Howk, M Ribó, E Sanjuan, M Rubiera, J Pagola, A Flores, M Muchada, P Meler, E Huerga, S Gelabert, P Coscojuela, A Tomasello, D Rodriguez, E Santamarina, O Maisterra, S Boned, L Seró, A Rovira, CA Molina, M Millán, L Muñoz, N Pérez de la Ossa, M Gomis, L Dorado, E López-Cancio, E Palomeras, J Munuera, P García Bermejo, S Remollo, C Castaño, R García-Sort, P Cuadras, P Puyalto, M Hernández-Pérez, M Jiménez, A Martínez-Piñeiro, G Lucente, A Dávalos, A Chamorro, X Urra, V Obach, A Cervera, S Amaro, L Llull, J Codas, M Balasa, J Navarro, H Ariño, A Aceituno, S Rudilosso, A Renu, JM Macho, L San Roman, J Blasco, A López, N Macías, P Cardona, H Quesada, F Rubio, L Cano, B Lara, MA de Miquel, L Aja, J Serena, E Cobo, Gregory W Albers, Kennedy R Lees, J Arenillas, R Roberts, F Al-Ajlan, L Zimmel, S Patel, J Martí-Fàbregas, M Salvat-Plana, S Bracard, René Anxionnat, Pierre-Alexandre Baillot, Charlotte Barbier, Anne-Laure Derelle, Jean-Christophe Lacour, Sébastien Richard, Yves Samson, Nader Sourour, Flore Baronnet-Chauvet, Frédéric Clarencon, Sophie Crozier, Sandrine Deltour, Federico Di Maria, Raphael Le Bouc, Anne Leger, Gurkan Mutlu, Charlotte Rosso, Zoltan Szatmary, Marion Yger, Chiara Zavanone, Serge Bakchine, Laurent Pierot, Nathalie Caucheteux, Laurent Estrade, Krzysztof Kadziolka, Alexandre Leautaud, Céline Renkes, Isabelle Serre, Hubert Desal, Benoît Guillon, Claire Boutoleau-Bretonniere, Benjamin Daumas-Duport, Solène De Gaalon, Pascal Derkinderen, Sarah Evain, Fanny Herisson, David-Axel Laplaud, Thibaud Lebouvier, Alina Lintia-Gaultier, Hélène Pouclet-Courtemanche, Tiphaine Rouaud, Violaine Rouaud Jaffrenou, Aurélia Schunck, Mathieu Sevin-Allouet, Frederique Toulgoat, Sandrine Wiertlewski, Jean-Yves Gauvrit, Thomas Ronziere, Vincent Cahagne, Jean-Christophe Ferre, Jean-François Pinel, Hélène Raoult, Jean-Louis Mas, Jean-François Meder, Amen-Adam Al Najjar-Carpentier, Julia Birchenall, Eric Bodiguel, David Calvet, Valérie Domigo, Sylvie Godon-Hardy, Vincent Guiraud, Catherine Lamy, Loubna Majhadi, Ludovic Morin, Denis Trystram, Guillaume Turc, Jérôme Berge, Igor Sibon, Patrice Menegon, Xavier Barreau, François Rouanet, Sabrina Debruxelles, Annabelle Kazadi, Pauline Renou, Olivier Fleury, Anne Pasco-Papon, Frédéric Dubas, Jildaz Caroff, Sophie Godard Ducceschi, Marie-Aurélie Hamon, Alderic Lecluse, Guillaume Marc, Maurice Giroud, Frédéric Ricolfi, Yannick Bejot, Adrien Chavent, Arnaud Gentil, Apolline Kazemi, Guy-Victor Osseby, Charlotte Voguet, Marie-Hélène Mahagne, Jacques Sedat, Yves Chau, Laurent Suissa, Sylvain Lachaud, Emmanuel Houdart, Christian Stapf, Frédérique Buffon Porcher, Hugues Chabriat, Pierre Guedin, Dominique Herve, Eric Jouvent, Jérôme Mawet, Jean-Pierre Saint-Maurice, Hans-Martin Schneble, Norbert Nighoghossian, Nadia-Nawel Berhoune, Françoise Bouhour, Tae-Hee Cho, Laurent Derex, Sandra Felix, Hélène Gervais-Bernard, Benjamin Gory, Luis Manera, Laura Mechtouff, Thomas Ritzenthaler, Roberto Riva, Fabrizio Salaris Silvio, Caroline Tilikete, Raphael Blanc, Michaël Obadia, Mario Bruno Bartolini, Antoine Gueguen, Michel Piotin, Silvia Pistocchi, Hocine Redjem, Jacques Drouineau, Jean-Philippe Neau, Gaelle Godeneche, Matthias Lamy, Emilia Marsac, Stephane Velasco, Pierre Clavelou, Emmanuel Chabert, Nathalie Bourgois, Catherine Cornut-Chauvinc, Anna Ferrier, Jean Gabrillargues, Betty Jean, Anna-Raquel Marques, Nicolas Vitello, Olivier Detante, Marianne Barbieux, Kamel Boubagra, Isabelle Favre Wiki, Katia Garambois, Florence Tahon, Vasdev Ashok, Oguzhan Coskun, Georges Rodesch, Bertrand Lapergue, Frédéric Bourdain, Serge Evrard, Philippe Graveleau, Jean Pierre Decroix, Adrien Wang, François Sellal, Guido Ahle, Gabriela Carelli, Marie-Hélène Dugay, Claude Gaultier, Ariel Pablo Lebedinsky, Lavinia Lita, Raul Mariano Musacchio, Catherine Renglewicz-Destuynder, Alain Tournade, Françis Vuillemet, Francisco Macian Montoro, Charbel Mounayer, Frederic Faugeras, Laetitia Gimenez, Catherine Labach, Géraldine Lautrette, Christian Denier, Guillaume Saliou, Olivier Chassin, Claire Dussaule, Elsa Melki, Augustin Ozanne, Francesco Puccinelli, Marina Sachet, Mariana Sarov, Jean-François Bonneville, Thierry Moulin, Alessandra Biondi, Elisabeth De Bustos Medeiros, Fabrice Vuillier, Patrick Courtheoux, Fausto Viader, Marion Apoil-Brissard, Mathieu Bataille, Anne-Laure Bonnet, Julien Cogez, Emmanuel Touze, Xavier Leclerc, Didier Leys, Mohamed Aggour, Pierre Aguettaz, Marie Bodenant, Charlotte Cordonnier, Dominique Deplanque, Marie Girot, Hilde Henon, Erwah Kalsoum, Christian Lucas, Jean-Pierre Pruvo, Paolo Zuniga, Caroline Arquizan, Vincent Costalat, Paolo Machi, Isabelle Mourand, Carlos Riquelme, Pierre Bounolleau, Charles Arteaga, Anthony Faivre, Marc Bintner, Patrice Tournebize, Cyril Charlin, Françoise Darcel, Pascale Gauthier-Lasalarie, Marcia Jeremenko, Servane Mouton, Jean-Baptiste Zerlauth, Chantal Lamy, Deramond Hervé, Hosseini Hassan, André Gaston, Francis-Guy Barral, Pierre Garnier, Rémy Beaujeux, Valérie Wolff, Denis Herbreteau, Séverine Debiais, Alicia Murray, Gary Ford, Martin M Brown, Andy Clifton, Janet Freeman, Ian Ford, Hugh Markus, Joanna Wardlaw, Andy Molyneux, Thompson Robinson, Steff Lewis, John Norrie, Fergus Robertson, Richard Perry, Geoffrey Cloud, Andrew Clifton, Jeremy Madigan, Christine Roffe, Sanjeev Nayak, Kyriakos Lobotesis, Craig Smith, Amit Herwadkar, Naga Kandasamy, Tony Goddard, John Bamford, Ganesh Subramanian, Rob Lenthall, Edward Littleton, Sal Lamin, Kelley Storey, Rita Ghatala, Azra Banaras, John Aeron-Thomas, Bath Hazel, Holly Maguire, Emelda Veraque, Louise Harrison, Rekha Keshvara, James Cunningham, University of Melbourne, University of Calgary, Erasmus University Medical Center [Rotterdam] (Erasmus MC), Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Centre d'investigation clinique [Nancy] (CIC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), University of Amsterdam [Amsterdam] (UvA), Universitat Autònoma de Barcelona (UAB), Département de neuroradiologie diagnostique et thérapeutique [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), ANS - Neurovascular Disorders, Radiology and Nuclear Medicine, ACS - Amsterdam Cardiovascular Sciences, Neurology, Graduate School, Other Research, APH - Personalized Medicine, APH - Quality of Care, Biomedical Engineering and Physics, ARD - Amsterdam Reproduction and Development, AGEM - Amsterdam Gastroenterology Endocrinology Metabolism, ACS - Microcirculation, ACS - Atherosclerosis & ischemic syndromes, ACS - Pulmonary hypertension & thrombosis, Radiology & Nuclear Medicine, Public Health, Weimar, Christian (Beitragende*r), Molecular cell biology and Immunology, Pathology, Radiology and nuclear medicine, Amsterdam Neuroscience - Neurovascular Disorders, Rheumatology, Beeldvorming, RS: CARIM - R3.03 - Cerebral small vessel disease, RS: CARIM - R3.11 - Imaging, MUMC+: DA BV Medisch Specialisten Radiologie (9), Klinische Neurowetenschappen, and MUMC+: MA Neurologie (3)
Subjects: Male, medicine.medical_specialty, Sedation, Medizin, [SDV.IB.MN]Life Sciences [q-bio]/Bioengineering/Nuclear medicine, 030204 cardiovascular system & hematology, Anesthesia, General, CONTROLLED-TRIAL, THERAPY, law.invention, Brain Ischemia, 03 medical and health sciences, 0302 clinical medicine, All institutes and research themes of the Radboud University Medical Center, Randomized controlled trial, law, Modified Rankin Scale, Journal Article, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Medicine, Humans, General anaesthesia, Stroke, Aged, Randomized Controlled Trials as Topic, Thrombectomy, Aged, 80 and over, business.industry, Other Research Radboud Institute for Health Sciences [Radboudumc 0], Odds ratio, MR, Middle Aged, Outcome and Process Assessment (Health Care), medicine.disease, Disorders of movement Donders Center for Medical Neuroscience [Radboudumc 3], 3. Good health, Surgery, Outcome and Process Assessment, Health Care, Meta-analysis, Observational study, Female, Neurology (clinical), medicine.symptom, business, 030217 neurology & neurosurgery, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, Meta-Analysis
Abstract: BACKGROUND: General anaesthesia (GA) during endovascular thrombectomy has been associated with worse patient outcomes in observational studies compared with patients treated without GA. We assessed functional outcome in ischaemic stroke patients with large vessel anterior circulation occlusion undergoing endovascular thrombectomy under GA, versus thrombectomy not under GA (with or without sedation) versus standard care (ie, no thrombectomy), stratified by the use of GA versus standard care.METHODS: For this meta-analysis, patient-level data were pooled from all patients included in randomised trials in PuMed published between Jan 1, 2010, and May 31, 2017, that compared endovascular thrombectomy predominantly done with stent retrievers with standard care in anterior circulation ischaemic stroke patients (HERMES Collaboration). The primary outcome was functional outcome assessed by ordinal analysis of the modified Rankin scale (mRS) at 90 days in the GA and non-GA subgroups of patients treated with endovascular therapy versus those patients treated with standard care, adjusted for baseline prognostic variables. To account for between-trial variance we used mixed-effects modelling with a random effect for trials incorporated in all models. Bias was assessed using the Cochrane method. The meta-analysis was prospectively designed, but not registered.FINDINGS: Seven trials were identified by our search; of 1764 patients included in these trials, 871 were allocated to endovascular thrombectomy and 893 were assigned standard care. After exclusion of 74 patients (72 did not undergo the procedure and two had missing data on anaesthetic strategy), 236 (30%) of 797 patients who had endovascular procedures were treated under GA. At baseline, patients receiving GA were younger and had a shorter delay between stroke onset and randomisation but they had similar pre-treatment clinical severity compared with patients who did not have GA. Endovascular thrombectomy improved functional outcome at 3 months both in patients who had GA (adjusted common odds ratio (cOR) 1·52, 95% CI 1·09-2·11, p=0·014) and in those who did not have GA (adjusted cOR 2·33, 95% CI 1·75-3·10, pINTERPRETATION: Worse outcomes after endovascular thrombectomy were associated with GA, after adjustment for baseline prognostic variables. These data support avoidance of GA whenever possible. The procedure did, however, remain effective versus standard care in patients treated under GA, indicating that treatment should not be withheld in those who require anaesthesia for medical reasons.FUNDING: Medtronic.
Published: 2018
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15. Thrombus Permeability Is Associated With Improved Functional Outcome and Recanalization in Patients With Ischemic Stroke

Author: Emilie M.M. Santos, Henk A. Marquering, Mark D. den Blanken, Olvert A. Berkhemer, Anna M.M. Boers, Albert J. Yoo, Ludo F. Beenen, Kilian M. Treurniet, Carrie Wismans, Kim van Noort, Hester F. Lingsma, Diederik W.J. Dippel, Aad van der Lugt, Wim H. van Zwam, Yvo B.W.E.M. Roos, Robert J. van Oostenbrugge, Wiro J. Niessen, Charles B. Majoie, Puck S.S. Fransen, Debbie Beumer, Lucie A. van den Berg, Wouter J. Schonewille, Jan Albert Vos, Paul J. Nederkoorn, Marieke J.H. Wermer, Marianne A.A. van Walderveen, Julie Staals, Jeannette Hofmeijer, Jacques A. van Oostayen, Geert J. Lycklama à Nijeholt, Jelis Boiten, Patrick A. Brouwer, Bart J. Emmer, Sebastiaan F. de Bruijn, Lukas C. van Dijk, L. Jaap Kappelle, Rob H. Lo, Ewoud J. van Dijk, Joost de Vries, Paul L.M. de Kort, Jan S.P. van den Berg, Boudewijn A.A.M. van Hasselt, Leo A.M. Aerden, René J. Dallinga, Marieke C. Visser, Joseph C.J. Bot, Patrick C. Vroomen, Omid Eshghi, Tobien H.C.M.L. Schreuder, Roel J.J. Heijboer, Koos Keizer, Alexander V. Tielbeek, Heleen M. den Hertog, Dick G. Gerrits, Renske M. van den Berg-Vos, Giorgos B. Karas, Marieke E.S. Sprengers, Sjoerd F.M. Jenniskens, René van den Berg, Peter J. Koudstaal, H. Zwenneke Flach, Ewout W. Steyerberg, Martin M. Brown, Thomas Liebig, Theo Stijnen, Tommy Andersson, Heinrich P. Mattle, Nils Wahlgren, Esther van der Heijden, Naziha Ghannouti, Nadine Fleitour, Imke Hooijenga, Annemieke Lindl-Velema, Corina Puppels, Wilma Pellikaan, Kirsten Janssen, Nicole Aaldering, Marjan Elfrink, Joke de Meris, Annet Geerlings, Gina van Vemde, Ans de Ridder, Paut Greebe, José de Bont-Stikkelbroeck, Willy Struijk, Tiny Simons, Gert Messchendorp, Friedus van der Minne, Hester Bongenaar, Karin Bodde, Silvan Licher, Nikki Boodt, Adriaan Ros, Esmee Venema, Ilse Slokkers, Raymie-Jayce Ganpat, Maxim Mulder, Nawid Saiedie, Alis Heshmatollah, Stefanie Schipperen, Stefan Vinken, Tiemen van Boxtel, Jeroen Koets, Merel Boers, Jordi Borst, Ivo Jansen, Manon Kappelhof, Marit Lucas, Ralph Geuskens, Renan Sales Barros, Roeland Dobbe, Marloes Csizmadia, Other departments, Radiology and Nuclear Medicine, ANS - Neurovascular Disorders, ACS - Amsterdam Cardiovascular Sciences, Biomedical Engineering and Physics, Graduate School, Other Research, Neurology, Beeldvorming, MUMC+: DA BV Medisch Specialisten Radiologie (9), RS: CARIM - R3.11 - Imaging, RS: CARIM - R3.03 - Cerebral small vessel disease, Klinische Neurowetenschappen, MUMC+: MA Med Staf Spec Neurologie (9), Medical Informatics, Radiology & Nuclear Medicine, and Public Health
Subjects: Male, Brain Ischemia, 030218 nuclear medicine & medical imaging, Brain ischemia, arteries, 0302 clinical medicine, Stroke, Netherlands, Endovascular Procedures, Middle Aged, Disorders of movement Donders Center for Medical Neuroscience [Radboudumc 3], Thrombosis, stroke, reperfusion, Treatment Outcome, medicine.anatomical_structure, cardiovascular system, Female, TRIAL, Radiology, INTRAVENOUS THROMBOLYSIS, Cardiology and Cardiovascular Medicine, BURDEN, Rare cancers Radboud Institute for Health Sciences [Radboudumc 9], Artery, medicine.medical_specialty, Capillary Permeability, 03 medical and health sciences, medicine, Humans, cardiovascular diseases, Thrombus, VESSEL OCCLUSIONS, thrombosis, Aged, Advanced and Specialized Nursing, business.industry, Other Research Radboud Institute for Health Sciences [Radboudumc 0], Blood flow, medicine.disease, CT ANGIOGRAPHY, Confidence interval, Radiography, Intracranial Thrombosis, SIZE, Neurology (clinical), permeability, business, 030217 neurology & neurosurgery
Abstract: Background and Purpose— Preclinical studies showed that thrombi can be permeable and may, therefore, allow for residual blood flow in occluded arteries of patients having acute ischemic stroke. This perviousness may increase tissue oxygenation, improve thrombus dissolution, and augment intra-arterial treatment success. We hypothesize that the combination of computed tomographic angiography and noncontrast computed tomography imaging allows measurement of contrast agent penetrating a permeable thrombus, and it is associated with improved outcome. Methods— Thrombus and contralateral artery attenuations in noncontrast computed tomography and computed tomographic angiography images were measured in 184 Multicenter Randomized Clinical trial of Endovascular treatment of acute ischemic stroke in the Netherlands (MR CLEAN) patients with thin-slice images. Two quantitative estimators of the thrombus permeability were introduced: computed tomographic angiography attenuation increase (Δ) and thrombus void fraction (ε). Patients were dichotomized as having a pervious or impervious thrombus and associated with outcome, recanalization, and final infarct volume. Results— Patients with Δ≥10.9 HU (n=81 [44%]) and ε≥6.5% (n=77 [42%]) were classified as having a pervious thrombus. These patients were 3.2 (95% confidence interval, 1.7–6.4) times more likely to have a favorable outcome, and 2.5 (95% confidence interval, 1.3–4.8) times more likely to recanalyze, for Δ based classification, and similarly for ε. These odds ratios were independent from intravenous or intra-arterial treatment. Final infarct volume was negatively correlated with both perviousness estimates (correlation coefficient, −0.39 for Δ and −0.40 for ε). Conclusions— This study shows that simultaneous measurement of thrombus attenuation in noncontrast computed tomography and computed tomographic angiography allows for quantification of thrombus perviousness. Thrombus perviousness is strongly associated with improved functional outcome, smaller final infarct volume, and higher recanalization rate.
Published: 2016

16. Characteristics of Misclassified CT Perfusion Ischemic Core in Patients with Acute Ischemic Stroke

Author: Sjoerd F. M. Jenniskens, Clean trial investigators, Jordi Borst, Henk A. Marquering, Ralph R. E. G. Geuskens, Wim H. van Zwam, Yvo B.W.E.M. Roos, Marit Lucas, A. M. Merel Boers, Diederik W.J. Dippel, Marianne A. A. van Walderveen, Olvert A. Berkhemer, Charles B. L. M. Majoie, Biomedical Engineering and Physics, Other departments, ACS - Amsterdam Cardiovascular Sciences, ANS - Amsterdam Neuroscience, Radiology and Nuclear Medicine, Neurology, RS: CARIM - R3 - Vascular biology, and MUMC+: DA BV Medisch Specialisten Radiologie (9)
Subjects: SELECTION, medicine.medical_specialty, PENUMBRA, Science, viruses, Blood volume, Perfusion scanning, Brain Ischemia, Brain ischemia, Text mining, THEORETIC BASIS, MAPS, Image Processing, Computer-Assisted, Humans, Medicine, PART 1, heterocyclic compounds, Stroke, Blood Volume, Multidisciplinary, business.industry, Penumbra, Other Research Radboud Institute for Health Sciences [Radboudumc 0], COMPUTED-TOMOGRAPHY PERFUSION, medicine.disease, Perfusion, enzymes and coenzymes (carbohydrates), Cerebral blood flow, CEREBRAL-BLOOD-FLOW, TECHNICAL IMPLEMENTATIONS, INFARCT VOLUME, Radiology, Tomography, X-Ray Computed, business, Nuclear medicine, BRAIN PERFUSION, Research Article
Abstract: Contains fulltext : 152013.PDF (Publisher’s version ) (Open Access) BACKGROUND: CT perfusion (CTP) is used to estimate the extent of ischemic core and penumbra in patients with acute ischemic stroke. CTP reliability, however, is limited. This study aims to identify regions misclassified as ischemic core on CTP, using infarct on follow-up noncontrast CT. We aim to assess differences in volumetric and perfusion characteristics in these regions compared to areas that ended up as infarct on follow-up. MATERIALS AND METHODS: This study included 35 patients with >100 mm brain coverage CTP. CTP processing was performed using Philips software (IntelliSpace 7.0). Final infarct was automatically segmented on follow-up noncontrast CT and used as reference. CTP and follow-up noncontrast CT image data were registered. This allowed classification of ischemic lesion agreement (core on CTP: rMTT>/=145%, aCBV
Published: 2015

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16 results on '"Marit Lucas"'

1. Deep Learning–based Recurrence Prediction in Patients with Non–muscle-invasive Bladder Cancer

2. Toward Automated In Vivo Bladder Tumor Stratification Using Confocal Laser Endomicroscopy

3. The First In Vivo Needle‐Based Optical Coherence Tomography in Human Prostate: A Safety and Feasibility Study

4. MP73-19 DEEP LEARNING FOR HISTOPATHOLOGY AND CLINICAL CHARACTERISTICS BASED RECURRENCE PREDICTION IN NON-MUSCLE INVASIVE BLADDER CANCER PATIENTS

5. MP73-18 RECURRENCE PREDICTION IN BLADDER CANCER PATIENTS USING SURVIVAL ANALYSIS

6. MP72-16 DETECTION AND GRADING OF BLADDER CANCER ON HISTOLOGICAL SLIDES USING DEEP LEARING

7. Recurrence in non-muscle invasive bladder cancer patients: External validation of the EORTC, CUETO and EAU risk tables and towards a non-linear survival model

8. Histopathology: ditch the slides, because digital and 3D are on show

9. Machine learning for grading and prognosis of esophageal dysplasia using mass spectrometry and histological imaging

10. Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies

11. Three-dimensional histopathological reconstruction of bladder tumours

12. Penumbral imaging and functional outcome in patients with anterior circulation ischaemic stroke treated with endovascular thrombectomy versus medical therapy: a meta-analysis of individual patient-level data

13. Strategies for managing multi-patient 3D mass spectrometry imaging data

14. Effect of general anaesthesia on functional outcome in patients with anterior circulation ischaemic stroke having endovascular thrombectomy versus standard care: a meta-analysis of individual patient data

15. Thrombus Permeability Is Associated With Improved Functional Outcome and Recanalization in Patients With Ischemic Stroke

16. Characteristics of Misclassified CT Perfusion Ischemic Core in Patients with Acute Ischemic Stroke

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