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Vande Vyvere T, Pisică D, Wilms G, Claes L, Van Dyck P, Snoeckx A, van den Hauwe L, Pullens P, Verheyden J, Wintermark M, Dekeyzer S, Mac Donald CL, Maas AIR, Parizel PM. Imaging Findings in Acute Traumatic Brain Injury: a National Institute of Neurological Disorders and Stroke Common Data Element-Based Pictorial Review and Analysis of Over 4000 Admission Brain Computed Tomography Scans from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Study. J Neurotrauma 2024. [PMID: 38482818 DOI: 10.1089/neu.2023.0553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2024] Open
Abstract
In 2010, the National Institute of Neurological Disorders and Stroke (NINDS) created a set of common data elements (CDEs) to help standardize the assessment and reporting of imaging findings in traumatic brain injury (TBI). However, as opposed to other standardized radiology reporting systems, a visual overview and data to support the proposed standardized lexicon are lacking. We used over 4000 admission computed tomography (CT) scans of patients with TBI from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study to develop an extensive pictorial overview of the NINDS TBI CDEs, with visual examples and background information on individual pathoanatomical lesion types, up to the level of supplemental and emerging information (e.g., location and estimated volumes). We documented the frequency of lesion occurrence, aiming to quantify the relative importance of different CDEs for characterizing TBI, and performed a critical appraisal of our experience with the intent to inform updating of the CDEs. In addition, we investigated the co-occurrence and clustering of lesion types and the distribution of six CT classification systems. The median age of the 4087 patients in our dataset was 50 years (interquartile range, 29-66; range, 0-96), including 238 patients under 18 years old (5.8%). Traumatic subarachnoid hemorrhage (45.3%), skull fractures (37.4%), contusions (31.3%), and acute subdural hematoma (28.9%) were the most frequently occurring CT findings in acute TBI. The ranking of these lesions was the same in patients with mild TBI (baseline Glasgow Coma Scale [GCS] score 13-15) compared with those with moderate-severe TBI (baseline GCS score 3-12), but the frequency of occurrence was up to three times higher in moderate-severe TBI. In most TBI patients with CT abnormalities, there was co-occurrence and clustering of different lesion types, with significant differences between mild and moderate-severe TBI patients. More specifically, lesion patterns were more complex in moderate-severe TBI patients, with more co-existing lesions and more frequent signs of mass effect. These patients also had higher and more heterogeneous CT score distributions, associated with worse predicted outcomes. The critical appraisal of the NINDS CDEs was highly positive, but revealed that full assessment can be time consuming, that some CDEs had very low frequencies, and identified a few redundancies and ambiguity in some definitions. Whilst primarily developed for research, implementation of CDE templates for use in clinical practice is advocated, but this will require development of an abbreviated version. In conclusion, with this study, we provide an educational resource for clinicians and researchers to help assess, characterize, and report the vast and complex spectrum of imaging findings in patients with TBI. Our data provides a comprehensive overview of the contemporary landscape of TBI imaging pathology in Europe, and the findings can serve as empirical evidence for updating the current NINDS radiologic CDEs to version 3.0.
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Affiliation(s)
- Thijs Vande Vyvere
- Department of Radiology, Antwerp University Hospital, Antwerp, Belgium
- Department of Molecular Imaging and Radiology (MIRA), Faculty of Medicine and Health Science, University of Antwerp, Antwerp, Belgium
| | - Dana Pisică
- Department of Neurosurgery, Erasmus MC - University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Public Health, Erasmus MC - University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Guido Wilms
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | - Lene Claes
- icometrix, Research and Development, Leuven, Belgium
| | - Pieter Van Dyck
- Department of Radiology, Antwerp University Hospital, Antwerp, Belgium
- Department of Molecular Imaging and Radiology (MIRA), Faculty of Medicine and Health Science, University of Antwerp, Antwerp, Belgium
| | - Annemiek Snoeckx
- Department of Radiology, Antwerp University Hospital, Antwerp, Belgium
- Department of Molecular Imaging and Radiology (MIRA), Faculty of Medicine and Health Science, University of Antwerp, Antwerp, Belgium
| | - Luc van den Hauwe
- Department of Radiology, Antwerp University Hospital, Antwerp, Belgium
| | - Pim Pullens
- Department of Imaging, University Hospital Ghent; IBITech/MEDISIP, Engineering and Architecture, Ghent University; Ghent Institute for Functional and Metabolic Imaging, Ghent University, Belgium
| | - Jan Verheyden
- icometrix, Research and Development, Leuven, Belgium
| | - Max Wintermark
- Department of Neuroradiology, University of Texas MD Anderson Center, Houston, Texas, USA
| | - Sven Dekeyzer
- Department of Radiology, Antwerp University Hospital, Antwerp, Belgium
- Department of Radiology, University Hospital Ghent, Belgium
| | - Christine L Mac Donald
- Department of Neurological Surgery, School of Medicine, Harborview Medical Center, Seattle, Washington, USA
- Department of Neurological Surgery, School of Medicine, University of Washington, Seattle, Washington, USA
| | - Andrew I R Maas
- Department of Neurosurgery, Antwerp University Hospital, Antwerp, Belgium
- Department of Translational Neuroscience, Faculty of Medicine and Health Science, University of Antwerp, Antwerp, Belgium
| | - Paul M Parizel
- Department of Radiology, Royal Perth Hospital (RPH) and University of Western Australia (UWA), Perth, Australia; Western Australia National Imaging Facility (WA NIF) node, Australia
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Jiang B, Ozkara BB, Creeden S, Zhu G, Ding VY, Chen H, Lanzman B, Wolman D, Shams S, Trinh A, Li Y, Khalaf A, Parker JJ, Halpern CH, Wintermark M. Validation of a deep learning model for traumatic brain injury detection and NIRIS grading on non-contrast CT: a multi-reader study with promising results and opportunities for improvement. Neuroradiology 2023; 65:1605-1617. [PMID: 37269414 DOI: 10.1007/s00234-023-03170-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 05/21/2023] [Indexed: 06/05/2023]
Abstract
PURPOSE This study aimed to assess and externally validate the performance of a deep learning (DL) model for the interpretation of non-contrast computed tomography (NCCT) scans of patients with suspicion of traumatic brain injury (TBI). METHODS This retrospective and multi-reader study included patients with TBI suspicion who were transported to the emergency department and underwent NCCT scans. Eight reviewers, with varying levels of training and experience (two neuroradiology attendings, two neuroradiology fellows, two neuroradiology residents, one neurosurgery attending, and one neurosurgery resident), independently evaluated NCCT head scans. The same scans were evaluated using the version 5.0 of the DL model icobrain tbi. The establishment of the ground truth involved a thorough assessment of all accessible clinical and laboratory data, as well as follow-up imaging studies, including NCCT and magnetic resonance imaging, as a consensus amongst the study reviewers. The outcomes of interest included neuroimaging radiological interpretation system (NIRIS) scores, the presence of midline shift, mass effect, hemorrhagic lesions, hydrocephalus, and severe hydrocephalus, as well as measurements of midline shift and volumes of hemorrhagic lesions. Comparisons using weighted Cohen's kappa coefficient were made. The McNemar test was used to compare the diagnostic performance. Bland-Altman plots were used to compare measurements. RESULTS One hundred patients were included, with the DL model successfully categorizing 77 scans. The median age for the total group was 48, with the omitted group having a median age of 44.5 and the included group having a median age of 48. The DL model demonstrated moderate agreement with the ground truth, trainees, and attendings. With the DL model's assistance, trainees' agreement with the ground truth improved. The DL model showed high specificity (0.88) and positive predictive value (0.96) in classifying NIRIS scores as 0-2 or 3-4. Trainees and attendings had the highest accuracy (0.95). The DL model's performance in classifying various TBI CT imaging common data elements was comparable to that of trainees and attendings. The average difference for the DL model in quantifying the volume of hemorrhagic lesions was 6.0 mL with a wide 95% confidence interval (CI) of - 68.32 to 80.22, and for midline shift, the average difference was 1.4 mm with a 95% CI of - 3.4 to 6.2. CONCLUSION While the DL model outperformed trainees in some aspects, attendings' assessments remained superior in most instances. Using the DL model as an assistive tool benefited trainees, improving their NIRIS score agreement with the ground truth. Although the DL model showed high potential in classifying some TBI CT imaging common data elements, further refinement and optimization are necessary to enhance its clinical utility.
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Affiliation(s)
- Bin Jiang
- Department of Radiology, Neuroradiology Division, Stanford University, Stanford, CA, USA
| | | | - Sean Creeden
- Deparment of Neuroradiology, University of Illinois College of Medicine Peoria, Peoria, IL, USA
| | - Guangming Zhu
- Department of Neurology, The University of Arizona, Tucson, AZ, USA
| | - Victoria Y Ding
- Department of Medicine, Stanford University, Stanford, CA, USA
| | - Hui Chen
- Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX, USA
| | - Bryan Lanzman
- Department of Radiology, Neuroradiology Division, Stanford University, Stanford, CA, USA
| | - Dylan Wolman
- Department of Neuroimaging and Neurointervention, Stanford University, Stanford, CA, USA
| | - Sara Shams
- Department of Radiology, Neuroradiology Division, Stanford University, Stanford, CA, USA
- Department of Radiology, Karolinska University Hospital, Stockholm, Sweden
- Institution for Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Austin Trinh
- Department of Neuroimaging and Neurointervention, Stanford University, Stanford, CA, USA
| | - Ying Li
- Department of Radiology, Neuroradiology Division, Stanford University, Stanford, CA, USA
| | - Alexander Khalaf
- Department of Neuroimaging and Neurointervention, Stanford University, Stanford, CA, USA
| | - Jonathon J Parker
- Device-Based Neuroelectronics Laboratory, Mayo Clinic, Phoenix, AZ, USA
- Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, USA
| | - Casey H Halpern
- Department of Neurosurgery, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
- Department of Surgery, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - Max Wintermark
- Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX, USA.
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Khormali M, Soleimanipour S, Baigi V, Ehteram H, Talari H, Naghdi K, Ghaemi O, Sharif-Alhoseini M. Comparing Predictive Utility of Head Computed Tomography Scan-Based Scoring Systems for Traumatic Brain Injury: A Retrospective Study. Brain Sci 2023; 13:1145. [PMID: 37626500 PMCID: PMC10452909 DOI: 10.3390/brainsci13081145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 07/22/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023] Open
Abstract
This study compared the predictive utility of Marshall, Rotterdam, Stockholm, Helsinki, and NeuroImaging Radiological Interpretation System (NIRIS) scorings based on early non-contrast brain computed tomography (CT) scans in patients with traumatic brain injury (TBI). The area under a receiver operating characteristic curve (AUROC) was used to determine the predictive utility of scoring systems. Subgroup analyses were performed among patients with head AIS scores > 1. A total of 996 patients were included, of whom 786 (78.9%) were males. In-hospital mortality, ICU admission, neurosurgical intervention, and prolonged total hospital length of stay (THLOS) were recorded for 27 (2.7%), 207 (20.8%), 82 (8.2%), and 205 (20.6%) patients, respectively. For predicting in-hospital mortality, all scoring systems had AUROC point estimates above 0.9 and 0.75 among all included patients and patients with head AIS > 1, respectively, without any significant differences. The Marshall and NIRIS scoring systems had higher AUROCs for predicting ICU admission and neurosurgery than the other scoring systems. For predicting THLOS ≥ seven days, although the NIRIS and Marshall scoring systems seemed to have higher AUROC point estimates when all patients were analyzed, five scoring systems performed roughly the same in the head AIS > 1 subgroup.
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Affiliation(s)
- Moein Khormali
- Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran 14166-34793, Iran; (M.K.); (V.B.); (K.N.)
| | - Saeed Soleimanipour
- Department of Radiology, Sina Hospital, Tehran University of Medical Sciences, Tehran 14166-34793, Iran;
| | - Vali Baigi
- Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran 14166-34793, Iran; (M.K.); (V.B.); (K.N.)
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran 14166-34793, Iran
| | - Hassan Ehteram
- Department of Pathology, School of Medicine, Kashan University of Medical Sciences, Kashan 87159-88141, Iran;
| | - Hamidreza Talari
- Trauma Research Center, Kashan University of Medical Sciences, Kashan 87159-88141, Iran;
- Department of Radiology, Kashan University of Medical Sciences, Kashan 87159-88141, Iran
| | - Khatereh Naghdi
- Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran 14166-34793, Iran; (M.K.); (V.B.); (K.N.)
| | - Omid Ghaemi
- Department of Radiology, Imam Khomeini Hospital, Tehran University of Medical Science, Tehran 14166-34793, Iran;
- Department of Radiology, Shariati Hospital, Tehran University of Medical Science, Tehran 14166-34793, Iran
| | - Mahdi Sharif-Alhoseini
- Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran 14166-34793, Iran; (M.K.); (V.B.); (K.N.)
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Sadighi N, Talari H, Zafarmandi S, Ahmadianfard S, Baigi V, Fakharian E, Moussavi N, Sharif-Alhoseini M. Prediction of In-Hospital Outcomes in Patients with Traumatic Brain Injury Using Computed Tomographic Scoring Systems: A Comparison Between Marshall, Rotterdam, and Neuroimaging Radiological Interpretation Systems. World Neurosurg 2023; 175:e271-e277. [PMID: 36958718 DOI: 10.1016/j.wneu.2023.03.067] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 03/15/2023] [Accepted: 03/16/2023] [Indexed: 03/25/2023]
Abstract
OBJECTIVE This study aimed to compare the prognostic value of Marshall, Rotterdam, and Neuroimaging Radiological Interpretation Systems (NIRIS) in predicting the in-hospital outcomes of patients with traumatic brain injury. METHODS We identified 250 patients with traumatic brain injury in a retrospective single-center cohort from 2019 to 2020. Computed tomography (CT) scans were reviewed by two radiologists and scored according to three CT scoring systems. One-month outcomes were evaluated, including hospitalization, intensive care unit admission, neurosurgical procedure, and mortality. Logistic regression analysis was performed to identify scoring systems and outcome relationships. The best cutoff value was calculated using the receiver operating characteristic curve model. RESULTS Eighteen patients (7.2%) died in the 1-month follow-up. The mean age and Glasgow Coma Scale of survivors differed significantly from nonsurvivors. Subarachnoid hemorrhage and compressed/absent cisterns were dead patients' most frequent CT findings. All three scoring systems had good discrimination power in mortality prediction (area under the receiver operating characteristic curve of the Marshall, Rotterdam, and NIRIS was 0.78, 0.86, and 0.84, respectively). Regarding outcome, three systems directly correlated with unfavorable outcome prediction. CONCLUSIONS The Marshall, Rotterdam, and NIRIS are good predictive models for mortality and outcome prediction, with slight superiority of the Rotterdam in mortality prediction and the Marshall in intensive care unit admission and neurosurgical procedures.
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Affiliation(s)
- Nahid Sadighi
- Radiology Department, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamidreza Talari
- Radiology Department, Kashan University of Medical Sciences, Kashan, Iran; Trauma Research Center, Kashan University of Medical Sciences, Kashan, Iran
| | - Sahar Zafarmandi
- Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Vali Baigi
- Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Esmaeil Fakharian
- Trauma Research Center, Kashan University of Medical Sciences, Kashan, Iran; Neurosurgery Department, Kashan University of Medical Sciences, Kashan, Iran
| | - Nushin Moussavi
- Trauma Research Center, Kashan University of Medical Sciences, Kashan, Iran; Surgery Department, Kashan University of Medical Sciences, Kashan, Iran
| | - Mahdi Sharif-Alhoseini
- Sina Trauma and Surgery Research Center, Tehran University of Medical Sciences, Tehran, Iran.
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Patterson KN, Nordin A, Beyene TJ, Onwuka A, Bergus K, Horvath KZ, Sribnick EA, Thakkar RK. Implementation of a Level 1 Neuro Trauma Activation at a Tertiary Pediatric Trauma Center. J Surg Res 2022; 275:308-317. [DOI: 10.1016/j.jss.2022.02.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 12/08/2021] [Accepted: 02/10/2022] [Indexed: 11/16/2022]
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Chinese Admission Warning Strategy for Predicting the Hospital Discharge Outcome in Patients with Traumatic Brain Injury. J Clin Med 2022; 11:jcm11040974. [PMID: 35207247 PMCID: PMC8880692 DOI: 10.3390/jcm11040974] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 02/05/2022] [Accepted: 02/09/2022] [Indexed: 02/05/2023] Open
Abstract
Objective: To develop and validate an admission warning strategy that incorporates the general emergency department indicators for predicting the hospital discharge outcome of patients with traumatic brain injury (TBI) in China. Methods: This admission warning strategy was developed in a primary cohort that consisted of 605 patients with TBI who were admitted within 6 h of injury. The least absolute shrinkage and selection operator and multivariable logistic regression analysis were used to develop the early warning strategy of selected indicators. Two sub-cohorts consisting of 180 and 107 patients with TBI were used for the external validation. Results: Indicators of the strategy included three categories: baseline characteristics, imaging and laboratory indicators. This strategy displayed good calibration and good discrimination. A high C-index was reached in the internal validation. The multicenter external validation cohort still showed good discrimination C-indices. Decision curve analysis (DCA) showed the actual needs of this strategy when the possibility threshold was 0.01 for the primary cohort, and at thresholds of 0.02–0.83 and 0.01–0.88 for the two sub-cohorts, respectively. In addition, this strategy exhibited a significant prognostic capacity compared to the traditional single predictors, and this optimization was also observed in two external validation cohorts. Conclusions: We developed and validated an admission warning strategy that can be quickly deployed in the emergency department. This strategy can be used as an ideal tool for predicting hospital discharge outcomes and providing objective evidence for early informed consent of the hospital discharge outcome to the family members of TBI patients.
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Mishra R, Ucros HEV, Florez-Perdomo WA, Suarez JR, Moscote-Salazar LR, Rahman MM, Agrawal A. Predictive Value of Rotterdam Score and Marshall Score in Traumatic Brain Injury: A Contemporary Review. INDIAN JOURNAL OF NEUROTRAUMA 2021. [DOI: 10.1055/s-0041-1727404] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
AbstractThis article conducts a contemporary comparative review of the medical literature to update and establish evidence as to which framework among Rotterdam and Marshall computed tomography (CT)-based scoring systems predicts traumatic brain injury (TBI) outcomes better. The scheme followed was following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines for literature search. The search started on August 15, 2020 and ended on December 31, 2020. The combination terms used were Medical Subject Headings terms, combination keywords, and specific words used for describing various pathologies of TBI to identify the most relevant article in each database. PICO question to guide the search strategy was: “what is the use of Marshall (I) versus Rotterdam score (C) in TBI patients (P) for mortality risk stratification (O).” The review is based on 46 references which included a full review of 14 articles for adult TBI patients and 6 articles for pediatric TBI articles comparing Rotterdam and Marshall CT scores. The review includes 8,243 patients, of which 2,365 were pediatric and 5,878 were adult TBI patients. Marshall CT classification is not ordinal, is more descriptive, has better inter-rater reliability, and poor performance in a specific group of TBI patients requiring decompressive craniectomy. Rotterdam CT classification is ordinal, has better discriminatory power, and a better description of the dynamics of intracranial changes. The two scoring systems are complimentary. A combination of clinical parameters, severity, ischemic and hemodynamic parameters, and CT scoring system could predict the prognosis of TBI patients with significant accuracy. None of the classifications has good evidence for use in pediatric patients.
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Affiliation(s)
- Rakesh Mishra
- Department of Neurosurgery, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
| | - Harold Enrique Vasquez Ucros
- Department of Medicina General, Universidad del Sinú - Elias Bechara Zainúm de Cartagena, Cartagena, Colombia
- Jefe de Investigacion ENCEPHALOS en Consejo LatinoAmericano de Neurointensivismo-CLaNi, Cartagena, Colombia
| | - William Andres Florez-Perdomo
- Department of Medicina General, Universidad Surcolombiana, Medico Investigador Consejo Latinoamericano de Neurointensivismo - CLaNi, Clinica Sahagún IPS SA, Cordoba, Columbia
| | - José Rojas Suarez
- Department of Medicina Intensiva, Epidemiologia Clinica, Intensive Care Research (GRICIO), Universidad de Cartagena, Corporacion Universitaria Rafael Nuñez, Cartagena, Colombia
| | | | - Md. Moshiur Rahman
- Department of Neurosurgery, Holy Family Red Crescent Medical College, Dhaka, Bangladesh
| | - Amit Agrawal
- Department of Neurosurgery, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India
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