Additional co-authors: Endre Czeiter, Marek Czosnyka, Ramon Diaz-Arrastia, Jens P Dreier, Ann-Christine Duhaime, Ari Ercole, Thomas A van Essen, Valery L Feigin, Guoyi Gao, Joseph Giacino, Laura E Gonzalez-Lara, Russell L Gruen, Deepak Gupta, Jed A Hartings, Sean Hill, Ji-yao Jiang, Naomi Ketharanathan, Erwin J O Kompanje, Linda Lanyon, Steven Laureys, Fiona Lecky, Harvey Levin, Hester F Lingsma, Marc Maegele, Marek Majdan, Geoffrey Manley, Jill Marsteller, Luciana Mascia, Charles McFadyen, Stefania Mondello, Virginia Newcombe, Aarno Palotie, Paul M Parizel, Wilco Peul, James Piercy, Suzanne Polinder, Louis Puybasset, Todd E Rasmussen, Rolf Rossaint, Peter Smielewski, Jeannette Soderberg, Simon J Stanworth, Murray B Stein, Nicole von Steinbuchel, William Stewart, Ewout W Steyerberg, Nino Stocchetti, Anneliese Synnot, Braden Te Ao, Olli Tenovuo, Alice Theadom, Dick Tibboel, Walter Videtta, Kevin K W Wang, W Huw Williams, Kristine Yaffe for the InTBIR Participants and Investigators

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Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research

A World Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. Cancer grading is an important aspect for targeted therapy. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. In this paper, we tried to summarize the pathophysiology of brain cancer, imaging modalities of brain cancer and automatic computer assisted methods for brain cancer characterization in a machine and deep learning paradigm. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer’s, Parkinson’s, and Wilson’s disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm.

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A Review on a Deep Learning Perspective in Brain Cancer Classification

Summary Severe traumatic brain injury (TBI) is currently managed in the intensive care unit with a combined medical–surgical approach. Treatment aims to prevent additional brain damage and to optimise conditions for brain recovery. TBI is typically considered and treated as one pathological entity, although in fact it is a syndrome comprising a range of lesions that can require different therapies and physiological goals. Owing to advances in monitoring and imaging, there is now the potential to identify specific mechanisms of brain damage and to better target treatment to individuals or subsets of patients. Targeted treatment is especially relevant for elderly people—who now represent an increasing proportion of patients with TBI—as preinjury comorbidities and their therapies demand tailored management strategies. Progress in monitoring and in understanding pathophysiological mechanisms of TBI could change current management in the intensive care unit, enabling targeted interventions that could ultimately improve outcomes.

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Severe traumatic brain injury: targeted management in the intensive care unit

Objectives To determine whether a panel of blood-based biomarkers can discriminate between patients with suspected mild traumatic brain injury (mTBI) with and without neuroimaging findings (CT and MRI). Methods Study participants presented to the emergency department with suspected mTBI (n = 277) with a CT and MRI scan and healthy controls (n = 49). Plasma concentrations of tau, glial fibrillary acidic protein (GFAP), ubiquitin carboxyl-terminal hydrolase L1, and neurofilament light chain (NFL) were measured using the single-molecule array technology. Results Concentrations of GFAP, tau, and NFL were higher in patients with mTBI, compared with those of controls ( p 9s Conclusion Our study confirms GFAP as a promising marker of brain injury in patients with acute mTBI. A combination of various biomarkers linked to different pathophysiologic mechanisms increases diagnostic subgroup accuracy. This multimarker strategy may guide medical decision making, facilitate the use of MRI scanning, and prove valuable in the stratification of patients with brain injuries in future clinical trials. Classification of evidence Class I evidence that blood concentrations of GFAP, tau, and NFL discriminate patients with mTBI with and without neuroimaging findings.

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Glial fibrillary acidic protein elevations relate to neuroimaging abnormalities after mild TBI.

https://link.springer.com/content/pdf/10.1007%2Fs13311-016-0429-3.pdf

Sleep, Sleep Disorders, and Mild Traumatic Brain Injury. What We Know and What We Need to Know: Findings from a National Working Group

The incidence of traumatic brain injury (TBI) in the United States was 3.5 million cases in 2009, according to the Centers for Disease Control and Prevention. It is a contributing factor in 30.5% of injury-related deaths among civilians. Additionally, since 2000, more than 260,000 service members were diagnosed with TBI, with the vast majority classified as mild or concussive (76%). The objective assessment of TBI via imaging is a critical research gap, both in the military and civilian communities. In 2011, the Department of Defense (DoD) prepared a congressional report summarizing the effectiveness of seven neuroimaging modalities (computed tomography [CT], magnetic resonance imaging [MRI], transcranial Doppler [TCD], positron emission tomography, single photon emission computed tomography, electrophysiologic techniques [magnetoencephalography and electroencephalography], and functional near-infrared spectroscopy) to assess the spectrum of TBI from concussion to coma. For this report, neuroimag...

Shih R

Papers

A Review of the Effectiveness of Neuroimaging Modalities for the Detection of Traumatic Brain Injury