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Semin Neurol 2023; 43(05): 699-711
DOI: 10.1055/s-0043-1775790
Review Article

Prognostic Neuroimaging Biomarkers in Acute Vascular Brain Injury and Traumatic Brain Injury

Lindsey J. Krawchuk
1   Department of Neurology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
,
Matthew F. Sharrock
1   Department of Neurology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
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Abstract

Prognostic imaging biomarkers after acute brain injury inform treatment decisions, track the progression of intracranial injury, and can be used in shared decision-making processes with families. Herein, key established biomarkers and prognostic scoring systems are surveyed in the literature, and their applications in clinical practice and clinical trials are discussed. Biomarkers in acute ischemic stroke include computed tomography (CT) hypodensity scoring, diffusion-weighted lesion volume, and core infarct size on perfusion imaging. Intracerebral hemorrhage biomarkers include hemorrhage volume, expansion, and location. Aneurysmal subarachnoid biomarkers include hemorrhage grading, presence of diffusion-restricting lesions, and acute hydrocephalus. Traumatic brain injury CT scoring systems, contusion expansion, and diffuse axonal injury grading are reviewed. Emerging biomarkers including white matter disease scoring, diffusion tensor imaging, and the automated calculation of scoring systems and volumetrics are discussed.

Keywords

imaging - brain injury - outcome - prognostication - neuroimaging - computed tomography - magnetic resonance imaging


Publication History

Article published online:
06 October 2023

© 2023. Thieme. All rights reserved.

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  • References

  • 1 Becker KJ, Baxter AB, Cohen WA. et al. Withdrawal of support in intracerebral hemorrhage may lead to self-fulfilling prophecies. Neurology 2001; 56 (06) 766-772
    CrossrefPubMedSearch in Google Scholar
  • 2 Hostettler IC, Seiffge DJ, Werring DJ. Intracerebral hemorrhage: an update on diagnosis and treatment. Expert Rev Neurother 2019; 19 (07) 679-694
    CrossrefPubMedSearch in Google Scholar
  • 3 Maher M, Schweizer TA, Macdonald RL. Treatment of spontaneous subarachnoid hemorrhage: guidelines and gaps. Stroke 2020; 51 (04) 1326-1332
    CrossrefPubMedSearch in Google Scholar
  • 4 Volovici V, Steyerberg EW, Cnossen MC. et al. Evolution of evidence and guideline recommendations for the medical management of severe traumatic brain injury. J Neurotrauma 2019; 36 (22) 3183-3189
    CrossrefPubMedSearch in Google Scholar
  • 5 Thomalla G, Gerloff C. Acute imaging for evidence-based treatment of ischemic stroke. Curr Opin Neurol 2019; 32 (04) 521-529
    CrossrefPubMedSearch in Google Scholar
  • 6 Vespa PM. Imaging and decision-making in neurocritical care. Neurol Clin 2014; 32 (01) 211-224
    CrossrefPubMedSearch in Google Scholar
  • 7 Tsao CW, Aday AW, Almarzooq ZI. et al. Heart disease and stroke statistics-2022 update: a report from the American Heart Association. Circulation 2022; 145 (08) e153-e639
    PubMedSearch in Google Scholar
  • 8 Leira EC, Muir KW. EXTEND trial. Stroke 2019; 50 (09) 2637-2639
    CrossrefPubMedSearch in Google Scholar
  • 9 Albers GW, Marks MP, Kemp S. et al. DEFUSE 3 Investigators. Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging. N Engl J Med 2018; 378 (08) 708-718
    CrossrefPubMedSearch in Google Scholar
  • 10 Matsumoto K, Nohara Y, Soejima H, Yonehara T, Nakashima N, Kamouchi M. Stroke prognostic scores and data-driven prediction of clinical outcomes after acute ischemic stroke. Stroke 2020; 51 (05) 1477-1483
    CrossrefPubMedSearch in Google Scholar
  • 11 Vora NA, Shook SJ, Schumacher HC. et al. A 5-item scale to predict stroke outcome after cortical middle cerebral artery territory infarction: validation from results of the Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution (DEFUSE) Study. Stroke 2011; 42 (03) 645-649
    CrossrefPubMedSearch in Google Scholar
  • 12 Michel P, Odier C, Rutgers M. et al. The Acute STroke Registry and Analysis of Lausanne (ASTRAL): design and baseline analysis of an ischemic stroke registry including acute multimodal imaging. Stroke 2010; 41 (11) 2491-2498
    CrossrefPubMedSearch in Google Scholar
  • 13 Ntaios G, Papavasileiou V, Faouzi M, Vanacker P, Wintermark M, Michel P. Acute imaging does not improve ASTRAL score's accuracy despite having a prognostic value. Int J Stroke 2014; 9 (07) 926-931
    CrossrefPubMedSearch in Google Scholar
  • 14 Liu F, Tsang RC, Zhou J. et al. Relationship of Barthel Index and its Short Form with the modified Rankin Scale in acute stroke patients. J Stroke Cerebrovasc Dis 2020; 29 (09) 105033
    CrossrefPubMedSearch in Google Scholar
  • 15 Saver JL, Chaisinanunkul N, Campbell BCV. et al. XIth Stroke Treatment Academic Industry Roundtable. Standardized nomenclature for modified Rankin scale global disability outcomes: consensus recommendations from Stroke Therapy Academic Industry Roundtable XI. Stroke 2021; 52 (09) 3054-3062
    CrossrefPubMedSearch in Google Scholar
  • 16 Campbell BCV, Majoie CBLM, Albers GW. et al. HERMES collaborators. 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. Lancet Neurol 2019; 18 (01) 46-55
    CrossrefPubMedSearch in Google Scholar
  • 17 Chalos V, van der Ende NAM, Lingsma HF. et al. MR CLEAN Investigators. National Institutes of Health Stroke Scale: an alternative primary outcome measure for trials of acute treatment for ischemic stroke. Stroke 2020; 51 (01) 282-290
    CrossrefPubMedSearch in Google Scholar
  • 18 Adams Jr HP, Davis PH, Leira EC. et al. Baseline NIH Stroke Scale score strongly predicts outcome after stroke: a report of the Trial of Org 10172 in Acute Stroke Treatment (TOAST). Neurology 1999; 53 (01) 126-131
    CrossrefPubMedSearch in Google Scholar
  • 19 Seo WK, Liebeskind DS, Yoo B. et al. UCLA Penumbra Imaging Investigators. Predictors and functional outcomes of fast, intermediate, and slow progression among patients with acute ischemic stroke. Stroke 2020; 51 (08) 2553-2557
    CrossrefPubMedSearch in Google Scholar
  • 20 Czap AL, Sheth SA. Overview of imaging modalities in stroke. Neurology 2021; 97 (20, suppl 2): S42-S51
    CrossrefPubMedSearch in Google Scholar
  • 21 Wardlaw JM, Mielke O. Early signs of brain infarction at CT: observer reliability and outcome after thrombolytic treatment–systematic review. Radiology 2005; 235 (02) 444-453
    CrossrefPubMedSearch in Google Scholar
  • 22 Todo K, Sakai N, Kono T. et al. Alberta Stroke Program Early CT Score-Time score predicts outcome after endovascular therapy in patients with acute ischemic stroke: a retrospective single-center study. J Stroke Cerebrovasc Dis 2018; 27 (04) 1041-1046
    CrossrefPubMedSearch in Google Scholar
  • 23 Potter CA, Vagal AS, Goyal M, Nunez DB, Leslie-Mazwi TM, Lev MH. Ct for treatment selection in acute ischemic stroke: a code stroke primer. Radiographics 2019; 39 (06) 1717-1738
    CrossrefPubMedSearch in Google Scholar
  • 24 Barber PA, Demchuk AM, Zhang J, Buchan AM. Validity and reliability of a quantitative computed tomography score in predicting outcome of hyperacute stroke before thrombolytic therapy. ASPECTS Study Group. Alberta Stroke Programme Early CT Score. Lancet 2000; 355 (9216): 1670-1674
    CrossrefPubMedSearch in Google Scholar
  • 25 Kong WY, Tan BYQ, Ngiam NJH. et al. Validation of Serial Alberta Stroke Program Early CT Score as an outcome predictor in thrombolyzed stroke patients. J Stroke Cerebrovasc Dis 2017; 26 (10) 2264-2271
    CrossrefPubMedSearch in Google Scholar
  • 26 González RG, Lev MH, Goldmacher GV. et al. Improved outcome prediction using CT angiography in addition to standard ischemic stroke assessment: results from the STOPStroke study. PLoS One 2012; 7 (01) e30352
    CrossrefPubMedSearch in Google Scholar
  • 27 Hill MD, Demchuk AM, Goyal M. et al. IMS3 Investigators. Alberta Stroke Program early computed tomography score to select patients for endovascular treatment: Interventional Management of Stroke (IMS)-III Trial. Stroke 2014; 45 (02) 444-449
    CrossrefPubMedSearch in Google Scholar
  • 28 El-Koussy M, Schroth G, Brekenfeld C, Arnold M. Imaging of acute ischemic stroke. Eur Neurol 2014; 72 (5–6): 309-316
    CrossrefPubMedSearch in Google Scholar
  • 29 Kim JT, Cho BH, Choi KH. et al. Association between time to treatment and functional outcomes according to the Diffusion-Weighted Imaging Alberta Stroke Program Early Computed Tomography Score in endovascular stroke therapy. Eur J Neurol 2020; 27 (02) 343-351
    CrossrefPubMedSearch in Google Scholar
  • 30 Brinjikji W, Abbasi M, Arnold C. et al. e-ASPECTS software improves interobserver agreement and accuracy of interpretation of aspects score. Interv Neuroradiol 2021; 27 (06) 781-787
    CrossrefPubMedSearch in Google Scholar
  • 31 Thomalla G, Simonsen CZ, Boutitie F. et al. WAKE-UP Investigators. MRI-guided thrombolysis for stroke with unknown time of onset. N Engl J Med 2018; 379 (07) 611-622
    CrossrefPubMedSearch in Google Scholar
  • 32 Nogueira RG, Jadhav AP, Haussen DC. et al. DAWN Trial Investigators. Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct. N Engl J Med 2018; 378 (01) 11-21
    CrossrefPubMedSearch in Google Scholar
  • 33 Powers WJ, Rabinstein AA, Ackerson T. et al. American Heart Association Stroke Council. 2018 Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2018; 49 (03) e46-e110
    CrossrefPubMedSearch in Google Scholar
  • 34 Padroni M, Bernardoni A, Tamborino C. et al. Cerebral blood volume ASPECTS is the best predictor of clinical outcome in acute ischemic stroke: a retrospective, combined semi-quantitative and quantitative assessment. PLoS One 2016; 11 (01) e0147910
    CrossrefPubMedSearch in Google Scholar
  • 35 Katyal A, Bhaskar SMM. Value of pre-intervention CT perfusion imaging in acute ischemic stroke prognosis. Diagn Interv Radiol 2021; 27 (06) 774-785
    CrossrefPubMedSearch in Google Scholar
  • 36 Schaefer PW, Grant PE, Gonzalez RG. Diffusion-weighted MR imaging of the brain. Radiology 2000; 217 (02) 331-345
    CrossrefPubMedSearch in Google Scholar
  • 37 Barrett KM, Ding YH, Wagner DP, Kallmes DF, Johnston KC. ASAP Investigators. Change in diffusion-weighted imaging infarct volume predicts neurologic outcome at 90 days: results of the Acute Stroke Accurate Prediction (ASAP) trial serial imaging substudy. Stroke 2009; 40 (07) 2422-2427
    CrossrefPubMedSearch in Google Scholar
  • 38 Mundiyanapurath S, Diatschuk S, Loebel S. et al. Outcome of patients with proximal vessel occlusion of the anterior circulation and DWI-PWI mismatch is time-dependent. Eur J Radiol 2017; 91: 82-87
    CrossrefPubMedSearch in Google Scholar
  • 39 Sanák D, Nosál' V, Horák D. et al. Impact of diffusion-weighted MRI-measured initial cerebral infarction volume on clinical outcome in acute stroke patients with middle cerebral artery occlusion treated by thrombolysis. Neuroradiology 2006; 48 (09) 632-639
    CrossrefPubMedSearch in Google Scholar
  • 40 Saver JL, Goyal M, Bonafe A. et al. SWIFT PRIME Investigators. Stent-retriever thrombectomy after intravenous t-PA vs. t-PA alone in stroke. N Engl J Med 2015; 372 (24) 2285-2295
    CrossrefPubMedSearch in Google Scholar
  • 41 Lansberg MG, Straka M, Kemp S. et al. DEFUSE 2 study investigators. MRI profile and response to endovascular reperfusion after stroke (DEFUSE 2): a prospective cohort study. Lancet Neurol 2012; 11 (10) 860-867
    CrossrefPubMedSearch in Google Scholar
  • 42 McTaggart RA, Jovin TG, Lansberg MG. et al. DEFUSE 2 Investigators. Alberta stroke program early computed tomographic scoring performance in a series of patients undergoing computed tomography and MRI: reader agreement, modality agreement, and outcome prediction. Stroke 2015; 46 (02) 407-412
    CrossrefPubMedSearch in Google Scholar
  • 43 Bang OY, Saver JL, Buck BH. et al. UCLA Collateral Investigators. Impact of collateral flow on tissue fate in acute ischaemic stroke. J Neurol Neurosurg Psychiatry 2008; 79 (06) 625-629
    CrossrefPubMedSearch in Google Scholar
  • 44 Liebeskind DS, Tomsick TA, Foster LD. et al. IMS III Investigators. Collaterals at angiography and outcomes in the Interventional Management of Stroke (IMS) III trial. Stroke 2014; 45 (03) 759-764
    CrossrefPubMedSearch in Google Scholar
  • 45 Tan BYQ, Wan-Yee K, Paliwal P. et al. Good intracranial collaterals trump poor ASPECTS (Alberta Stroke Program Early CT Score) for intravenous thrombolysis in anterior circulation acute ischemic stroke. Stroke 2016; 47 (09) 2292-2298
    CrossrefPubMedSearch in Google Scholar
  • 46 Bal S, Bhatia R, Menon BK. et al. Time dependence of reliability of noncontrast computed tomography in comparison to computed tomography angiography source image in acute ischemic stroke. Int J Stroke 2015; 10 (01) 55-60
    CrossrefPubMedSearch in Google Scholar
  • 47 Seker F, Pereira-Zimmermann B, Pfaff J. et al. Collateral scores in acute ischemic stroke : a retrospective study assessing the suitability of collateral scores as standalone predictors of clinical outcome. Clin Neuroradiol 2020; 30 (04) 789-793
    CrossrefPubMedSearch in Google Scholar
  • 48 Cho TH, Nighoghossian N, Tahon F. et al. Brain stem diffusion-weighted imaging lesion score: a potential marker of outcome in acute basilar artery occlusion. AJNR Am J Neuroradiol 2009; 30 (01) 194-198
    CrossrefPubMedSearch in Google Scholar
  • 49 Caruso P, Ridolfi M, Lugnan C. et al. Multimodal CT pc-ASPECTS in infratentorial stroke: diagnostic and prognostic value. Neurol Sci 2021; 42 (10) 4231-4240
    CrossrefPubMedSearch in Google Scholar
  • 50 Lu WZ, Lin HA, Bai CH, Lin SF. Posterior circulation acute stroke prognosis early CT scores in predicting functional outcomes: a meta-analysis. PLoS One 2021; 16 (02) e0246906
    CrossrefPubMedSearch in Google Scholar
  • 51 Hanley DF, Thompson RE, Rosenblum M. et al. MISTIE III Investigators. Efficacy and safety of minimally invasive surgery with thrombolysis in intracerebral haemorrhage evacuation (MISTIE III): a randomised, controlled, open-label, blinded endpoint phase 3 trial. Lancet 2019; 393 (10175): 1021-1032
    CrossrefPubMedSearch in Google Scholar
  • 52 Broderick JP, Brott TG, Duldner JE, Tomsick T, Huster G. Volume of intracerebral hemorrhage. A powerful and easy-to-use predictor of 30-day mortality. Stroke 1993; 24 (07) 987-993
    CrossrefPubMedSearch in Google Scholar
  • 53 Gregório T, Pipa S, Cavaleiro P. et al. Prognostic models for intracerebral hemorrhage: systematic review and meta-analysis. BMC Med Res Methodol 2018; 18 (01) 145
    CrossrefPubMedSearch in Google Scholar
  • 54 Hemphill III JC, Bonovich DC, Besmertis L, Manley GT, Johnston SC. The ICH score: a simple, reliable grading scale for intracerebral hemorrhage. Stroke 2001; 32 (04) 891-897
    CrossrefPubMedSearch in Google Scholar
  • 55 Rost NS, Smith EE, Chang Y. et al. Prediction of functional outcome in patients with primary intracerebral hemorrhage: the FUNC score. Stroke 2008; 39 (08) 2304-2309
    CrossrefPubMedSearch in Google Scholar
  • 56 Shah VA, Thompson RE, Yenokyan G. et al. One-year outcome trajectories and factors associated with functional recovery among survivors of intracerebral and intraventricular hemorrhage with initial severe disability. JAMA Neurol 2022; 79 (09) 856-868
    CrossrefPubMedSearch in Google Scholar
  • 57 Hansen BM, Ullman N, Muschelli J. et al. MISTIE and CLEAR Investigators. Relationship of white matter lesions with intracerebral hemorrhage expansion and functional outcome: MISTIE II and CLEAR III. Neurocrit Care 2020; 33 (02) 516-524
    CrossrefPubMedSearch in Google Scholar
  • 58 LoPresti MA, Bruce SS, Camacho E. et al. Hematoma volume as the major determinant of outcomes after intracerebral hemorrhage. J Neurol Sci 2014; 345 (1–2): 3-7
    CrossrefPubMedSearch in Google Scholar
  • 59 Falcone GJ, Biffi A, Brouwers HB. et al. Predictors of hematoma volume in deep and lobar supratentorial intracerebral hemorrhage. JAMA Neurol 2013; 70 (08) 988-994
    CrossrefPubMedSearch in Google Scholar
  • 60 Jaffe J, AlKhawam L, Du H. et al. Outcome predictors and spectrum of treatment eligibility with prospective protocolized management of intracerebral hemorrhage. Neurosurgery 2009; 64 (03) 436-445 , discussion 445–446
    CrossrefPubMedSearch in Google Scholar
  • 61 Webb AJS, Ullman NL, Morgan TC. et al. MISTIE and CLEAR Investigators. Accuracy of the ABC/2 score for intracerebral hemorrhage: systematic review and analysis of MISTIE, CLEAR-IVH, and CLEAR III. Stroke 2015; 46 (09) 2470-2476
    CrossrefPubMedSearch in Google Scholar
  • 62 Brouwers HB, Chang Y, Falcone GJ. et al. Predicting hematoma expansion after primary intracerebral hemorrhage. JAMA Neurol 2014; 71 (02) 158-164
    CrossrefPubMedSearch in Google Scholar
  • 63 Sharrock MF, Mould WA, Ali H. et al. 3D deep neural network segmentation of intracerebral hemorrhage: development and validation for clinical trials. Neuroinformatics 2021; 19 (03) 403-415
    CrossrefPubMedSearch in Google Scholar
  • 64 Morotti A, Boulouis G, Charidimou A. et al. Hematoma expansion in intracerebral hemorrhage with unclear onset. Neurology 2021; 96 (19) e2363-e2371
    CrossrefPubMedSearch in Google Scholar
  • 65 Yogendrakumar V, Ramsay T, Menon BK, Qureshi AI, Saver JL, Dowlatshahi D. Hematoma expansion shift analysis to assess acute intracerebral hemorrhage treatments. Neurology 2021; 97 (08) e755-e764
    CrossrefPubMedSearch in Google Scholar
  • 66 Brouwers HB, Greenberg SM. Hematoma expansion following acute intracerebral hemorrhage. Cerebrovasc Dis 2013; 35 (03) 195-201
    CrossrefPubMedSearch in Google Scholar
  • 67 Mayer SA, Brun NC, Begtrup K. et al. FAST Trial Investigators. Efficacy and safety of recombinant activated factor VII for acute intracerebral hemorrhage. N Engl J Med 2008; 358 (20) 2127-2137
    CrossrefPubMedSearch in Google Scholar
  • 68 Sprigg N, Flaherty K, Appleton JP. et al. TICH-2 Investigators. Tranexamic acid for hyperacute primary IntraCerebral Haemorrhage (TICH-2): an international randomised, placebo-controlled, phase 3 superiority trial. Lancet 2018; 391 (10135): 2107-2115
    CrossrefPubMedSearch in Google Scholar
  • 69 Moullaali TJ, Wang X, Martin RH. et al. Statistical analysis plan for pooled individual patient data from two landmark randomized trials (INTERACT2 and ATACH-II) of intensive blood pressure lowering treatment in acute intracerebral hemorrhage. Int J Stroke 2019; 14 (03) 321-328
    CrossrefPubMedSearch in Google Scholar
  • 70 You S, Zheng D, Delcourt C. et al. Determinants of early versus delayed neurological deterioration in intracerebral hemorrhage. Stroke 2019; 50 (06) 1409-1414
    CrossrefPubMedSearch in Google Scholar
  • 71 de Havenon A, Majersik JJ, Stoddard G. et al. Increased blood pressure variability contributes to worse outcome after intracerebral hemorrhage an analysis of ATACH-2. Stroke 2018; 49 (08) 1981-1984
    CrossrefPubMedSearch in Google Scholar
  • 72 Ng D, Churilov L, Mitchell P, Dowling R, Yan B. The CT swirl sign is associated with hematoma expansion in intracerebral hemorrhage. AJNR Am J Neuroradiol 2018; 39 (02) 232-237
    CrossrefPubMedSearch in Google Scholar
  • 73 Selariu E, Zia E, Brizzi M, Abul-Kasim K. Swirl sign in intracerebral haemorrhage: definition, prevalence, reliability and prognostic value. BMC Neurol 2012; 12: 109
    CrossrefPubMedSearch in Google Scholar
  • 74 Wada R, Aviv RI, Fox AJ. et al. CT angiography “spot sign” predicts hematoma expansion in acute intracerebral hemorrhage. Stroke 2007; 38 (04) 1257-1262
    CrossrefPubMedSearch in Google Scholar
  • 75 Tanaka K, Toyoda K. Clinical strategies against early hematoma expansion following intracerebral hemorrhage. Front Neurosci 2021; 15: 677744
    CrossrefPubMedSearch in Google Scholar
  • 76 Li Q, Dong F, Wang Q, Xu F, Zhang M. A model comprising the blend sign and black hole sign shows good performance for predicting early intracerebral haemorrhage expansion: a comprehensive evaluation of CT features. Eur Radiol 2021; 31 (12) 9131-9138
    CrossrefPubMedSearch in Google Scholar
  • 77 Chen K, Deng L, Li Q, Luo L. Are computed-tomography-based hematoma radiomics features reproducible and predictive of intracerebral hemorrhage expansion? an in vitro experiment and clinical study. Br J Radiol 2021; 94 (1121): 20200724
    CrossrefPubMedSearch in Google Scholar
  • 78 Muschelli J, Ullman NL, Sweeney EM. et al. Quantitative Intracerebral Hemorrhage Localization. Stroke 2015; 46 (11) 3270-3273
    CrossrefPubMedSearch in Google Scholar
  • 79 Hussein HM, Tariq NA, Palesch YY, Qureshi AI. ATACH Investigators. Reliability of hematoma volume measurement at local sites in a multicenter acute intracerebral hemorrhage clinical trial. Stroke 2013; 44 (01) 237-239
    CrossrefPubMedSearch in Google Scholar
  • 80 Hanley DF. Intraventricular hemorrhage: severity factor and treatment target in spontaneous intracerebral hemorrhage. Stroke 2009; 40 (04) 1533-1538
    CrossrefPubMedSearch in Google Scholar
  • 81 Morgan TCM, Dawson J, Spengler D. et al. CLEAR and VISTA Investigators. The Modified Graeb Score: an enhanced tool for intraventricular hemorrhage measurement and prediction of functional outcome. Stroke 2013; 44 (03) 635-641
    CrossrefPubMedSearch in Google Scholar
  • 82 Venkatasubramanian C, Mlynash M, Finley-Caulfield A. et al. Natural history of perihematomal edema after intracerebral hemorrhage measured by serial magnetic resonance imaging. Stroke 2011; 42 (01) 73-80
    CrossrefPubMedSearch in Google Scholar
  • 83 Murthy SB, Moradiya Y, Dawson J, Lees KR, Hanley DF, Ziai WC. VISTA-ICH Collaborators. Perihematomal edema and functional outcomes in intracerebral hemorrhage: influence of hematoma volume and location. Stroke 2015; 46 (11) 3088-3092
    CrossrefPubMedSearch in Google Scholar
  • 84 Urday S, Beslow LA, Dai F. et al. Rate of perihematomal edema expansion predicts outcome after intracerebral hemorrhage. Crit Care Med 2016; 44 (04) 790-797
    CrossrefPubMedSearch in Google Scholar
  • 85 Hemorrhagic Stroke Academia Industry (HEADS) Roundtable Participants, Second HEADS Roundtable Participants. Recommendations for clinical trials in ICH: the Second Hemorrhagic Stroke Academia Industry Roundtable. Stroke 2020; 51 (04) 1333-1338
    CrossrefPubMedSearch in Google Scholar
  • 86 Mould WA, Carhuapoma JR, Muschelli J. et al. MISTIE Investigators. Minimally invasive surgery plus recombinant tissue-type plasminogen activator for intracerebral hemorrhage evacuation decreases perihematomal edema. Stroke 2013; 44 (03) 627-634
    CrossrefPubMedSearch in Google Scholar
  • 87 Mould WA, Muschelli J, Avadhani R. et al. Reduction in perihematomal edema leads to improved clinical outcomes: results from the MISTIE III trial. Stroke 2019; 50 50(suppl 1): A15-A15
    CrossrefPubMedSearch in Google Scholar
  • 88 Marchina S, Trevino-Calderon JA, Hassani S. et al. Perihematomal edema and clinical outcome after intracerebral hemorrhage: a systematic review and meta-analysis. Neurocrit Care 2022; 37 (01) 351-362
    CrossrefPubMedSearch in Google Scholar
  • 89 Chan V, Lindsay P, McQuiggan J, Zagorski B, Hill MD, O'Kelly C. Declining admission and mortality rates for subarachnoid hemorrhage in Canada between 2004 and 2015. Stroke 2019; 50 (01) 181-184
    CrossrefPubMedSearch in Google Scholar
  • 90 Lovelock CE, Rinkel GJE, Rothwell PM. Time trends in outcome of subarachnoid hemorrhage: population-based study and systematic review. Neurology 2010; 74 (19) 1494-1501
    CrossrefPubMedSearch in Google Scholar
  • 91 Rinkel GJE, Algra A. Long-term outcomes of patients with aneurysmal subarachnoid haemorrhage. Lancet Neurol 2011; 10 (04) 349-356
    CrossrefPubMedSearch in Google Scholar
  • 92 Chalard K, Szabo V, Pavillard F. et al. Long-term outcome in patients with aneurysmal subarachnoid hemorrhage requiring mechanical ventilation. PLoS One 2021; 16 (03) e0247942
    CrossrefPubMedSearch in Google Scholar
  • 93 Fragata I, Canhão P. Imaging predictors of outcome in acute spontaneous subarachnoid hemorrhage: a review of the literature. Acta Radiol 2019; 60 (02) 247-259
    CrossrefPubMedSearch in Google Scholar
  • 94 Rosen DS, Macdonald RL. Subarachnoid hemorrhage grading scales: a systematic review. Neurocrit Care 2005; 2 (02) 110-118
    CrossrefPubMedSearch in Google Scholar
  • 95 Eagles ME, Tso MK, Macdonald RL. Cognitive impairment, functional outcome, and delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage. World Neurosurg 2019; 124: e558-e562
    CrossrefPubMedSearch in Google Scholar
  • 96 van Donkelaar CE, Bakker NA, Birks J. et al. Prediction of outcome after aneurysmal subarachnoid hemorrhage. Stroke 2019; 50 (04) 837-844
    CrossrefPubMedSearch in Google Scholar
  • 97 Fang Y, Lu J, Zheng J. et al. Comparison of aneurysmal subarachnoid hemorrhage grading scores in patients with aneurysm clipping and coiling. Sci Rep 2020; 10 (01) 9199
    CrossrefPubMedSearch in Google Scholar
  • 98 Fisher CM, Kistler JP, Davis JM. Relation of cerebral vasospasm to subarachnoid hemorrhage visualized by computerized tomographic scanning. Neurosurgery 1980; 6 (01) 1-9
    CrossrefPubMedSearch in Google Scholar
  • 99 Frontera JA, Claassen J, Schmidt JM. et al. Prediction of symptomatic vasospasm after subarachnoid hemorrhage: the modified fisher scale. Neurosurgery 2006; 59 (01) 21-27 , discussion 21–27
    CrossrefPubMedSearch in Google Scholar
  • 100 Bretz JS, Von Dincklage F, Woitzik J. et al. The Hijdra scale has significant prognostic value for the functional outcome of Fisher grade 3 patients with subarachnoid hemorrhage. Clin Neuroradiol 2017; 27 (03) 361-369
    CrossrefPubMedSearch in Google Scholar
  • 101 van der Steen WE, Leemans EL, van den Berg R. et al. Radiological scales predicting delayed cerebral ischemia in subarachnoid hemorrhage: systematic review and meta-analysis. Neuroradiology 2019; 61 (03) 247-256
    CrossrefPubMedSearch in Google Scholar
  • 102 Thanellas A, Peura H, Lavinto M. et al. Development and external validation of a deep learning algorithm to identify and localize subarachnoid hemorrhage on CT scans. Neurology 2023; 100 (12) e1257-e1266
    CrossrefPubMedSearch in Google Scholar
  • 103 Vergouwen MDI, Vermeulen M, van Gijn J. et al. Definition of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage as an outcome event in clinical trials and observational studies: proposal of a multidisciplinary research group. Stroke 2010; 41 (10) 2391-2395
    CrossrefPubMedSearch in Google Scholar
  • 104 Frontera JA, Ahmed W, Zach V. et al. Acute ischaemia after subarachnoid haemorrhage, relationship with early brain injury and impact on outcome: a prospective quantitative MRI study. J Neurol Neurosurg Psychiatry 2015; 86 (01) 71-78
    CrossrefPubMedSearch in Google Scholar
  • 105 Sato K, Shimizu H, Fujimura M, Inoue T, Matsumoto Y, Tominaga T. Acute-stage diffusion-weighted magnetic resonance imaging for predicting outcome of poor-grade aneurysmal subarachnoid hemorrhage. J Cereb Blood Flow Metab 2010; 30 (06) 1110-1120
    CrossrefPubMedSearch in Google Scholar
  • 106 Ayling OGS, Ibrahim GM, Alotaibi NM, Gooderham PA, Macdonald RL. Dissociation of early and delayed cerebral infarction after aneurysmal subarachnoid hemorrhage. Stroke 2016; 47 (12) 2945-2951
    CrossrefPubMedSearch in Google Scholar
  • 107 Vergouwen MDI, Ilodigwe D, Macdonald RL. Cerebral infarction after subarachnoid hemorrhage contributes to poor outcome by vasospasm-dependent and -independent effects. Stroke 2011; 42 (04) 924-929
    CrossrefPubMedSearch in Google Scholar
  • 108 Wong GKC, Nung RCH, Sitt JCM. et al. Location, infarct load, and 3-month outcomes of delayed cerebral infarction after aneurysmal subarachnoid hemorrhage. Stroke 2015; 46 (11) 3099-3104
    CrossrefPubMedSearch in Google Scholar
  • 109 Paisan GM, Ding D, Starke RM, Crowley RW, Liu KC. Shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage: predictors and long-term functional outcomes. Neurosurgery 2018; 83 (03) 393-402
    CrossrefPubMedSearch in Google Scholar
  • 110 Kuo LT, Huang APH. The pathogenesis of hydrocephalus following aneurysmal subarachnoid hemorrhage. Int J Mol Sci 2021; 22 (09) 5050
    CrossrefPubMedSearch in Google Scholar
  • 111 Lenski M, Biczok A, Huge V. et al. Role of cerebrospinal fluid markers for predicting shunt-dependent hydrocephalus in patients with subarachnoid hemorrhage and external ventricular drain placement. World Neurosurg 2019; 121: e535-e542
    CrossrefPubMedSearch in Google Scholar
  • 112 Knol DS, van Gijn J, Kruitwagen CLJJ, Rinkel GJE. Size of third and fourth ventricle in obstructive and communicating acute hydrocephalus after aneurysmal subarachnoid hemorrhage. J Neurol 2011; 258 (01) 44-49
    CrossrefPubMedSearch in Google Scholar
  • 113 Won YD, Na MK, Kim CH. et al. The frontal skull Hounsfield unit value can predict ventricular enlargement in patients with subarachnoid haemorrhage. Sci Rep 2018; 8 (01) 10178
    CrossrefPubMedSearch in Google Scholar
  • 114 Schweitzer AD, Niogi SN, Whitlow CT, Tsiouris AJ. Traumatic brain injury: Imaging patterns and complications. Radiographics 2019; 39 (06) 1571-1595
    CrossrefPubMedSearch in Google Scholar
  • 115 Wintermark M, Sanelli PC, Anzai Y, Tsiouris AJ, Whitlow CT. ACR Head Injury Institute, ACR Head Injury Institute. Imaging evidence and recommendations for traumatic brain injury: conventional neuroimaging techniques. J Am Coll Radiol 2015; 12 (02) e1-e14
    CrossrefPubMedSearch in Google Scholar
  • 116 Zuckerman DA, Giacino JT, Bodien YG. Traumatic brain injury: what is a favorable outcome?. J Neurotrauma 2022; 39 (13–14): 1010-1012
    CrossrefPubMedSearch in Google Scholar
  • 117 Olsen A, Babikian T, Bigler ED. et al. Toward a global and reproducible science for brain imaging in neurotrauma: the ENIGMA adult moderate/severe traumatic brain injury working group. Brain Imaging Behav 2021; 15 (02) 526-554
    CrossrefPubMedSearch in Google Scholar
  • 118 Roberts I, Yates D, Sandercock P. et al. CRASH trial collaborators. Effect of intravenous corticosteroids on death within 14 days in 10008 adults with clinically significant head injury (MRC CRASH trial): randomised placebo-controlled trial. Lancet 2004; 364 (9442): 1321-1328
    CrossrefPubMedSearch in Google Scholar
  • 119 Perel P, Arango M, Clayton T. et al. MRC CRASH Trial Collaborators. Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients. BMJ 2008; 336 (7641): 425-429
    CrossrefPubMedSearch in Google Scholar
  • 120 Haghbayan H, Boutin A, Laflamme M. et al. The prognostic value of MRI in moderate and severe traumatic brain injury: a systematic review and meta-analysis. Crit Care Med 2017; 45 (12) e1280-e1288
    CrossrefPubMedSearch in Google Scholar
  • 121 Thelin EP, Nelson DW, Vehviläinen J. et al. Evaluation of novel computerized tomography scoring systems in human traumatic brain injury: an observational, multicenter study. PLoS Med 2017; 14 (08) e1002368
    CrossrefPubMedSearch in Google Scholar
  • 122 Elkbuli A, Shaikh S, McKenney K, Shanahan H, McKenney M, McKenney K. Utility of the Marshall & Rotterdam Classification scores in predicting outcomes in trauma patients. J Surg Res 2021; 264: 194-198
    CrossrefPubMedSearch in Google Scholar
  • 123 Asim M, El-Menyar A, Parchani A. et al. Rotterdam and Marshall scores for prediction of in-hospital mortality in patients with traumatic brain injury: an observational study. Brain Inj 2021; 35 (07) 803-811
    CrossrefPubMedSearch in Google Scholar
  • 124 Pargaonkar R, Kumar V, Menon G, Hegde A. Comparative study of computed tomographic scoring systems and predictors of early mortality in severe traumatic brain injury. J Clin Neurosci 2019; 66: 100-106
    CrossrefPubMedSearch in Google Scholar
  • 125 Brown AW, Pretz CR, Bell KR. et al. Predictive utility of an adapted Marshall head CT classification scheme after traumatic brain injury. Brain Inj 2019; 33 (05) 610-617
    CrossrefPubMedSearch in Google Scholar
  • 126 Huang YH, Deng YH, Lee TC, Chen WF. Rotterdam computed tomography score as a prognosticator in head-injured patients undergoing decompressive craniectomy. Neurosurgery 2012; 71 (01) 80-85
    CrossrefPubMedSearch in Google Scholar
  • 127 Rafay M, Gulzar F, Iqbal N, Sharif S. Prognostic computed tomography scores in traumatic brain injury. Clin Neurol Neurosurg 2020; 195: 105957
    CrossrefPubMedSearch in Google Scholar
  • 128 Raj R, Siironen J, Skrifvars MB, Hernesniemi J, Kivisaari R. Predicting outcome in traumatic brain injury: development of a novel computerized tomography classification system (Helsinki computerized tomography score). Neurosurgery 2014; 75 (06) 632-646 , discussion 646–647
    CrossrefPubMedSearch in Google Scholar
  • 129 Williams JR, Nieblas-Bedolla E, Feroze A. et al. and The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Investigators. Prognostic value of hemorrhagic brainstem injury on early computed tomography: a TRACK-TBI study. Neurocrit Care 2021; 35 (02) 335-346
    CrossrefPubMedSearch in Google Scholar
  • 130 Adatia K, Newcombe VFJ, Menon DK. Contusion progression following traumatic brain injury: a review of clinical and radiological predictors, and influence on outcome. Neurocrit Care 2021; 34 (01) 312-324
    CrossrefPubMedSearch in Google Scholar
  • 131 Simon DW, McGeachy MJ, Bayır H, Clark RSB, Loane DJ, Kochanek PM. The far-reaching scope of neuroinflammation after traumatic brain injury. Nat Rev Neurol 2017; 13 (03) 171-191
    CrossrefPubMedSearch in Google Scholar
  • 132 Pergakis M, Badjatia N, Chaturvedi S. et al. BIIB093 (IV glibenclamide): an investigational compound for the prevention and treatment of severe cerebral edema. Expert Opin Investig Drugs 2019; 28 (12) 1031-1040
    CrossrefPubMedSearch in Google Scholar
  • 133 Jha RM, Bell J, Citerio G. et al. Role of sulfonylurea receptor 1 and glibenclamide in traumatic brain injury: a review of the evidence. Int J Mol Sci 2020; 21 (02) 409
    CrossrefPubMedSearch in Google Scholar
  • 134 Chang EF, Meeker M, Holland MC. Acute traumatic intraparenchymal hemorrhage: risk factors for progression in the early post-injury period. Neurosurgery 2006; 58 (04) 647-656 , discussion 647–656
    CrossrefPubMedSearch in Google Scholar
  • 135 Kurland D, Hong C, Aarabi B, Gerzanich V, Simard JM. Hemorrhagic progression of a contusion after traumatic brain injury: a review. J Neurotrauma 2012; 29 (01) 19-31
    CrossrefPubMedSearch in Google Scholar
  • 136 Carnevale JA, Segar DJ, Powers AY. et al. Blossoming contusions: identifying factors contributing to the expansion of traumatic intracerebral hemorrhage. J Neurosurg 2018; 129 (05) 1305-1316
    CrossrefPubMedSearch in Google Scholar
  • 137 Iaccarino C, Schiavi P, Picetti E. et al. Patients with brain contusions: predictors of outcome and relationship between radiological and clinical evolution. J Neurosurg 2014; 120 (04) 908-918
    CrossrefPubMedSearch in Google Scholar
  • 138 Juratli TA, Zang B, Litz RJ. et al. Early hemorrhagic progression of traumatic brain contusions: frequency, correlation with coagulation disorders, and patient outcome: a prospective study. J Neurotrauma 2014; 31 (17) 1521-1527
    CrossrefPubMedSearch in Google Scholar
  • 139 Skandsen T, Kvistad KA, Solheim O, Lydersen S, Strand IH, Vik A. Prognostic value of magnetic resonance imaging in moderate and severe head injury: a prospective study of early MRI findings and one-year outcome. J Neurotrauma 2011; 28 (05) 691-699
    CrossrefPubMedSearch in Google Scholar
  • 140 Jolly AE, Bălăeţ M, Azor A. et al. Detecting axonal injury in individual patients after traumatic brain injury. Brain 2021; 144 (01) 92-113
    CrossrefPubMedSearch in Google Scholar
  • 141 Gentry LR. Imaging of closed head injury. Radiology 1994; 191 (01) 1-17
    CrossrefPubMedSearch in Google Scholar
  • 142 van Eijck MM, Schoonman GG, van der Naalt J, de Vries J, Roks G. Diffuse axonal injury after traumatic brain injury is a prognostic factor for functional outcome: a systematic review and meta-analysis. Brain Inj 2018; 32 (04) 395-402
    CrossrefPubMedSearch in Google Scholar
  • 143 Moen KG, Brezova V, Skandsen T, Håberg AK, Folvik M, Vik A. Traumatic axonal injury: the prognostic value of lesion load in corpus callosum, brain stem, and thalamus in different magnetic resonance imaging sequences. J Neurotrauma 2014; 31 (17) 1486-1496
    CrossrefPubMedSearch in Google Scholar
  • 144 Ezaki Y, Tsutsumi K, Morikawa M, Nagata I. Role of diffusion-weighted magnetic resonance imaging in diffuse axonal injury. Acta Radiol 2006; 47 (07) 733-740
    CrossrefPubMedSearch in Google Scholar
  • 145 Hergan K, Schaefer PW, Sorensen AG, Gonzalez RG, Huisman TAGM. Diffusion-weighted MRI in diffuse axonal injury of the brain. Eur Radiol 2002; 12 (10) 2536-2541
    CrossrefPubMedSearch in Google Scholar
  • 146 Douglas DB, Iv M, Douglas PK. et al. Diffusion tensor imaging of TBI: potentials and challenges. Top Magn Reson Imaging 2015; 24 (05) 241-251
    CrossrefPubMedSearch in Google Scholar
  • 147 Hulkower MB, Poliak DB, Rosenbaum SB, Zimmerman ME, Lipton ML. A decade of DTI in traumatic brain injury: 10 years and 100 articles later. AJNR Am J Neuroradiol 2013; 34 (11) 2064-2074
    CrossrefPubMedSearch in Google Scholar