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Advances in computed tomography-based prognostic methods for intracerebral hemorrhage

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Abstract

Spontaneous intracerebral hemorrhage (ICH) has high morbidity and mortality. Computed tomography (CT) plays an important role in the diagnosis, treatment, and research of cerebrovascular diseases. Non-contrast CT is widely used in the clinical diagnosis of ICH because of its high imaging speed and high sensitivity and specificity in the detection of stroke. Many markers-based CT imaging, quantitative parameters, and artificial intelligence (AI) methods based on CT are increasingly used for the prediction of hematoma expansion (HE), prognosis of ICH, and the evaluation of perihematomal edema (PHE). Therefore, we performed a comprehensive review of studies, focusing on current research evidence related to CT use for the prediction of HE and prognostic. This review discusses recent insights into, outlines current limitations, and puts forward suggestions for the challenges and directions of future research. Although at present the prognosis for ICH is not optimistic, the treatment methods remain controversial. However, identifying imaging markers that can evaluate and predict existing possible existing therapeutic targets could help to provide individualized advice for patients and achieve patient risk stratification, which is a key step in improving treatment outcomes.

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Availability of data and material

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

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Abbreviations

ICH:

Intracerebral hemorrhage

CT:

Computed tomography

AI:

Artificial intelligence

HE:

Hematoma expansion

PHE:

Perihematomal edema

NCCT:

Non-contrast computed tomography

ROI:

Region of interest

IC:

Iodine concentration

GSI:

Gemstone spectral imaging

HVD:

Hematoma distance

PHEVE:

Perihematomal edema volume expansion

EED:

Edema extension distance

PHEAV:

Perihematomal edema absolute volume

rPHE:

Relative perihematomal edema

PHEER:

Perihematomal edema expansion rate

IVH:

Intraventricular hemorrhage

ML:

Machine learning

DL:

Deep learning

SVM:

Support vector machine

CNN:

Convolutional neural networks

SBP:

Systolic blood pressure

References

  1. Acosta JN, Leasure AC, Kuohn LR, Both CP, Petersen NH, Sansing LH et al (2021) Admission hemoglobin levels are associated with functional outcome in spontaneous intracerebral hemorrhage. Crit Care Med 49(5):828–837. https://doi.org/10.1097/ccm.0000000000004891

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Al-Nakshabandi NA (2001) The swirl sign. Radiology 218(2):433. https://doi.org/10.1148/radiology.218.2.r01fe09433

    Article  CAS  PubMed  Google Scholar 

  3. Appelboom G, Bruce SS, Hickman ZL, Zacharia BE, Carpenter AM, Vaughan KA et al (2013) Volume-dependent effect of perihaematomal oedema on outcome for spontaneous intracerebral haemorrhages. J Neurol Neurosurg Psychiatry 84(5):488–493. https://doi.org/10.1136/jnnp-2012-303160

    Article  PubMed  Google Scholar 

  4. Babi M-A, James ML (2017) Peri-hemorrhagic edema and secondary hematoma expansion after intracerebral hemorrhage: from benchwork to practical aspects. Front Neurol 8:4. https://doi.org/10.3389/fneur.2017.00004

    Article  PubMed  PubMed Central  Google Scholar 

  5. Barras CD, Tress BM, Christensen S, MacGregor L, Collins M, Desmond PM et al (2009) Density and shape as CT predictors of intracerebral hemorrhage growth. Stroke 40(4):1325–1331. https://doi.org/10.1161/strokeaha.108.536888

    Article  PubMed  Google Scholar 

  6. Blacquiere D, Demchuk AM, Al-Hazzaa M, Deshpande A, Petrcich W, Aviv RI et al (2015) Intracerebral hematoma morphologic appearance on noncontrast computed tomography predicts significant hematoma expansion. Stroke 46(11):3111–3116. https://doi.org/10.1161/strokeaha.115.010566

    Article  CAS  PubMed  Google Scholar 

  7. Bonatti M, Lombardo F, Zamboni GA, Pernter P, Pozzi Mucelli R, Bonatti G (2017) Dual-energy CT of the brain: comparison between DECT angiography-derived virtual unenhanced images and true unenhanced images in the detection of intracranial haemorrhage. Eur Radiol 27(7):2690–2697. https://doi.org/10.1007/s00330-016-4658-y

    Article  PubMed  Google Scholar 

  8. Boulouis G, Morotti A, Brouwers HB, Charidimou A, Jessel MJ, Auriel E et al (2016) Association between hypodensities detected by computed tomography and hematoma expansion in patients with intracerebral hemorrhage. JAMA Neurol 73(8):961–968. https://doi.org/10.1001/jamaneurol.2016.1218

    Article  PubMed  PubMed Central  Google Scholar 

  9. Delcourt C, Sato S, Zhang S, Sandset EC, Zheng D, Chen X et al (2017) Intracerebral hemorrhage location and outcome among INTERACT2 participants. Neurology 88(15):1408–1414. https://doi.org/10.1212/wnl.0000000000003771

    Article  PubMed  PubMed Central  Google Scholar 

  10. Delcourt C, Zhang S, Arima H, Sato S, Al-Shahi Salman R, Wang X et al (2016) Significance of hematoma shape and density in intracerebral hemorrhage: the intensive blood pressure reduction in acute intracerebral hemorrhage trial study. Stroke 47(5):1227–1232. https://doi.org/10.1161/strokeaha.116.012921

    Article  CAS  PubMed  Google Scholar 

  11. Demchuk AM, Dowlatshahi D, Rodriguez-Luna D, Molina CA, Blas YS, Dzialowski I et al (2012) Prediction of haematoma growth and outcome in patients with intracerebral haemorrhage using the CT-angiography spot sign (PREDICT): a prospective observational study. Lancet Neurol 11(4):307–314. https://doi.org/10.1016/s1474-4422(12)70038-8

    Article  PubMed  Google Scholar 

  12. Deng L, Zhang Y-D, Ji J-W, Yang W-S, Wei X, Shen Y-Q et al (2020) Hematoma ventricle distance on computed tomography predicts poor outcome in intracerebral hemorrhage. Front Neurosci 14:589050. https://doi.org/10.3389/fnins.2020.589050

    Article  PubMed  PubMed Central  Google Scholar 

  13. Dhar R, Falcone GJ, Chen Y, Hamzehloo A, Kirsch EP, Noche RB et al (2020 Feb) Deep learning for automated measurement of hemorrhage and perihematomal edema in supratentorial intracerebral hemorrhage. Stroke 51(2):648–651. https://doi.org/10.1161/strokeaha.119.027657

    Article  PubMed  Google Scholar 

  14. Dowlatshahi D, Wasserman JK, Momoli F, Petrcich W, Stotts G, Hogan M et al (2014) Evolution of computed tomography angiography spot sign is consistent with a site of active hemorrhage in acute intracerebral hemorrhage. Stroke 45(1):277–280. https://doi.org/10.1161/strokeaha.113.003387

    Article  PubMed  Google Scholar 

  15. Durocher M, Knepp B, Yee A, Jickling G, Rodriguez F, Ng K et al (2020) Molecular correlates of hemorrhage and edema volumes following human intracerebral hemorrhage implicate inflammation, autophagy, mRNA splicing, and T cell receptor signaling. Transl Stroke Res. https://doi.org/10.1007/s12975-020-00869-y

    Article  PubMed  PubMed Central  Google Scholar 

  16. Eslami V, Tahsili-Fahadan P, Rivera-Lara L, Gandhi D, Ali H, Parry-Jones A et al (2019) Influence of intracerebral hemorrhage location on outcomes in patients with severe intraventricular hemorrhage. Stroke 50(7):1688–1695. https://doi.org/10.1161/strokeaha.118.024187

    Article  PubMed  PubMed Central  Google Scholar 

  17. Feigin VL, Lawes CMM, Bennett DA, Barker-Collo SL, Parag V (2009) Worldwide stroke incidence and early case fatality reported in 56 population-based studies: a systematic review. Lancet Neurol 8(4):355–369. https://doi.org/10.1016/s1474-4422(09)70025-0

    Article  PubMed  Google Scholar 

  18. Fu F, Sun S, Liu L, Li J, Su Y, Li Y (2018) Iodine concentration: a new, important characteristic of the spot sign that predicts haematoma expansion. Eur Radiol 28(10):4343–4349. https://doi.org/10.1007/s00330-018-5415-1

    Article  PubMed  Google Scholar 

  19. Fujii Y, Tanaka R, Takeuchi S, Koike T, Minakawa T, Sasaki O (1994) Hematoma enlargement in spontaneous intracerebral hemorrhage. J Neurosurg 80(1):51–57. https://doi.org/10.3171/jns.1994.80.1.0051

    Article  CAS  PubMed  Google Scholar 

  20. Gebel JM, Jauch EC, Brott TG, Khoury J, Sauerbeck L, Salisbury S et al (2002) Relative edema volume is a predictor of outcome in patients with hyperacute spontaneous intracerebral hemorrhage. Stroke 33(11):2636–2641. https://doi.org/10.1161/01.str.0000035283.34109.ea

    Article  PubMed  Google Scholar 

  21. He G-N, Guo H-Z, Han X, Wang E-F, Zhang Y-Q (2018) Comparison of CT black hole sign and other CT features in predicting hematoma expansion in patients with ICH. J Neurol 265(8):1883–1890. https://doi.org/10.1007/s00415-018-8932-6

    Article  PubMed  Google Scholar 

  22. Huang Y, Liang C, He L, Tian J, Liang C, Chen X et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. JCO 34(18):2157–2164. https://doi.org/10.1200/JCO.2015.65.9128

    Article  Google Scholar 

  23. Ironside N, Chen C-J, Ding D, Mayer SA, Connolly ES (2019) Perihematomal edema after spontaneous intracerebral hemorrhage. Stroke 50(6):1626–1633. https://doi.org/10.1161/strokeaha.119.024965

    Article  PubMed  Google Scholar 

  24. Ironside N, Chen C-J, Dreyer V, Christophe B, Buell TJ, Connolly ES (2020) Location-specific differences in hematoma volume predict outcomes in patients with spontaneous intracerebral hemorrhage. Int J Stroke 15(1):90–102. https://doi.org/10.1177/1747493019830589

    Article  PubMed  Google Scholar 

  25. Ironside N, Chen C-J, Mutasa S, Sim JL, Ding D, Marfatiah S et al (2020) Fully automated segmentation algorithm for perihematomal edema volumetry after spontaneous intracerebral hemorrhage. Stroke 51(3):815–823. https://doi.org/10.1161/strokeaha.119.026764

    Article  PubMed  Google Scholar 

  26. Islam M, Sanghani P, See AAQ, James ML, King NKK, Ren H. ICHNet: intracerebral hemorrhage (ICH) segmentation using deep learning. In: Crimi A, Bakas S, Kuijf H, Keyvan F, Reyes M, van Walsum T, editors. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Cham: Springer International Publishing; 2019. p. 456–63. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-030-11723-8_46

  27. J Z, J S, T H, Z Z, Y C, G Z, et al. Radiomic features of magnetic resonance images as novel preoperative predictive factors of bone invasion in meningiomas. Eur J Radiol. 2020; 11 : 132:109287. https://doi.org/10.1016/j.ejrad.2020.109287

  28. Jingnan Pu, Shi W (2017) Expert consensus on management for cerebral edema caused by intracerebral hemorrhage. Practical Journal of Cardiac Cerebral Pneumal and Vascular Disease 25(08):1–6. https://doi.org/10.3969/j.issn.1008-5971.2017.08.001

    Article  Google Scholar 

  29. Kahn CE (2017) From images to actions: opportunities for artificial intelligence in radiology. Radiology 285(3):719–720. https://doi.org/10.1148/radiol.2017171734

    Article  PubMed  Google Scholar 

  30. Latchaw RE, Alberts MJ, Lev MH, Connors JJ, Harbaugh RE, Higashida RT et al (2009) Recommendations for imaging of acute ischemic stroke: a scientific statement from the American Heart Association. Stroke 40(11):3646–3678. https://doi.org/10.1161/strokeaha.108.192616

    Article  PubMed  Google Scholar 

  31. Leasure AC, Kuohn LR, Vanent KN, Bevers MB, Kimberly WT, Steiner T, et al. Association of serum IL-6 (interleukin 6) with functional outcome after intracerebral hemorrhage. Stroke. 2021;STROKEAHA120032888. https://doi.org/10.1161/strokeaha.120.032888

  32. Leasure AC, Qureshi AI, Murthy SB, Kamel H, Goldstein JN, Walsh KB et al (2019) Intensive blood pressure reduction and perihematomal edema expansion in deep intracerebral hemorrhage. Stroke 50(8):2016–2022. https://doi.org/10.1161/strokeaha.119.024838

    Article  PubMed  PubMed Central  Google Scholar 

  33. Li L, Wei M, Liu B, Atchaneeyasakul K, Zhou F, Pan Z et al (2021) Deep learning for hemorrhagic lesion detection and segmentation on brain CT images. IEEE J Biomed Health Inform 25(5):1646–1659. https://doi.org/10.1109/jbhi.2020.3028243

    Article  PubMed  Google Scholar 

  34. Li Q, Liu Q-J, Yang W-S, Wang X-C, Zhao L-B, Xiong X et al (2017) Island sign: an imaging predictor for early hematoma expansion and poor outcome in patients with intracerebral hemorrhage. Stroke 48(11):3019–3025. https://doi.org/10.1161/strokeaha.117.017985

    Article  PubMed  Google Scholar 

  35. Li Q, Shen Y-Q, Xie X-F, Xue M-Z, Cao D, Yang W-S et al (2019) Expansion-prone hematoma: defining a population at high risk of hematoma growth and poor outcome. Neurocrit Care 30(3):601–608. https://doi.org/10.1007/s12028-018-0644-3

    Article  CAS  PubMed  Google Scholar 

  36. Li Q, Yang W-S, Chen S-L, Lv F-R, Lv F-J, Hu X et al (2018) Black hole sign predicts poor outcome in patients with intracerebral hemorrhage. Cerebrovasc Dis 45(1–2):48–53. https://doi.org/10.1159/000486163

    Article  PubMed  Google Scholar 

  37. Li Q, Zhang G, Huang Y-J, Dong M-X, Lv F-J, Wei X et al (2015) Blend sign on computed tomography: novel and reliable predictor for early hematoma growth in patients with intracerebral hemorrhage. Stroke 46(8):2119–2123. https://doi.org/10.1161/strokeaha.115.009185

    Article  PubMed  Google Scholar 

  38. Li Q, Zhang G, Xiong X, Wang X-C, Yang W-S, Li K-W et al (2016) Black hole sign: novel imaging marker that predicts hematoma growth in patients with intracerebral hemorrhage. Stroke 47(7):1777–1781. https://doi.org/10.1161/strokeaha.116.013186

    Article  PubMed  Google Scholar 

  39. Li Z, Li M, Shi SX, Yao N, Cheng X, Guo A, et al. Brain transforms natural killer cells that exacerbate brain edema after intracerebral hemorrhage. J Exp Med. 2020;217(12). https://doi.org/10.1084/jem.20200213

  40. Lim-Hing K, Rincon F (2017) Secondary hematoma expansion and perihemorrhagic edema after intracerebral hemorrhage: from bench work to practical aspects. Front Neurol 8:74. https://doi.org/10.3389/fneur.2017.00074

    Article  PubMed  PubMed Central  Google Scholar 

  41. Lin L, Dou Q, Jin Y-M, Zhou G-Q, Tang Y-Q, Chen W-L et al (2019) Deep learning for automated contouring of primary tumor volumes by MRI for nasopharyngeal carcinoma. Radiology 291(3):677–686. https://doi.org/10.1148/radiol.2019182012

    Article  PubMed  Google Scholar 

  42. Ma C, Zhang Y, Niyazi T, Wei J, Guocai G, Liu J et al (2019) Radiomics for predicting hematoma expansion in patients with hypertensive intraparenchymal hematomas. Eur J Radiol 115:10–15. https://doi.org/10.1016/j.ejrad.2019.04.001

    Article  PubMed  Google Scholar 

  43. Morotti A, Arba F, Boulouis G, Charidimou A (2020) Noncontrast CT markers of intracerebral hemorrhage expansion and poor outcome: a meta-analysis. Neurology 95(14):632–643. https://doi.org/10.1212/wnl.0000000000010660

    Article  PubMed  Google Scholar 

  44. Morotti A, Dowlatshahi D, Boulouis G, Al-Ajlan F, Demchuk AM, Aviv RI et al (2018) Predicting intracerebral hemorrhage expansion with noncontrast computed tomography: the BAT score. Stroke 49(5):1163–1169. https://doi.org/10.1161/strokeaha.117.020138

    Article  PubMed  PubMed Central  Google Scholar 

  45. Moullaali TJ, Wang X, Martin RH, Shipes VB, Robinson TG, Chalmers J et al (2019) Blood pressure control and clinical outcomes in acute intracerebral haemorrhage: a preplanned pooled analysis of individual participant data. Lancet Neurol 18(9):857–864. https://doi.org/10.1016/s1474-4422(19)30196-6

    Article  PubMed  Google Scholar 

  46. Mouridsen K, Thurner P, Zaharchuk G (2020) Artificial intelligence applications in stroke. Stroke 51(8):2573–2579. https://doi.org/10.1161/strokeaha.119.027479

    Article  PubMed  Google Scholar 

  47. Murthy SB, Moradiya Y, Dawson J, Lees KR, Hanley DF, Ziai WC et al (2015) Perihematomal edema and functional outcomes in intracerebral hemorrhage: influence of hematoma volume and location. Stroke 46(11):3088–3092. https://doi.org/10.1161/strokeaha.115.010054

    Article  PubMed  Google Scholar 

  48. Nawabi J, Kniep H, Elsayed S, Friedrich C, Sporns P, Rusche T et al (2021) Imaging-based outcome prediction of acute intracerebral hemorrhage. Transl Stroke Res. https://doi.org/10.1007/s12975-021-00891-8

    Article  PubMed  PubMed Central  Google Scholar 

  49. Ng D, Churilov L, Mitchell P, Dowling R, Yan B (2018) The CT swirl sign is associated with hematoma expansion in intracerebral hemorrhage. AJNR Am J Neuroradiol 39(2):232–237. https://doi.org/10.3174/ajnr.a5465

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Orito K, Hirohata M, Nakamura Y, Takeshige N, Aoki T, Hattori G et al (2016) Leakage sign for primary intracerebral hemorrhage: a novel predictor of hematoma growth. Stroke 47(4):958–963. https://doi.org/10.1161/strokeaha.115.011578

    Article  PubMed  PubMed Central  Google Scholar 

  51. Parry-Jones AR, Wang X, Sato S, Mould WA, Vail A, Anderson CS et al (2015) Edema extension distance: outcome measure for phase II clinical trials targeting edema after intracerebral hemorrhage. Stroke 46(6):e137-140. https://doi.org/10.1161/strokeaha.115.008818

    Article  PubMed  Google Scholar 

  52. Peeters MTJ, de Kort KJD, Houben R, Henneman WJP, van Oostenbrugge RJ, Staals J et al (2021) Dual-energy CT angiography improves accuracy of spot sign for predicting hematoma expansion in intracerebral hemorrhage. J Stroke 23(1):82–90. https://doi.org/10.5853/jos.2020.03531

    Article  PubMed  PubMed Central  Google Scholar 

  53. Pszczolkowski S, Manzano-Patrón JP, Law ZK, Krishnan K, Ali A, Bath PM et al (2021) Quantitative CT radiomics-based models for prediction of haematoma expansion and poor functional outcome in primary intracerebral haemorrhage. Eur Radiol 31(10):7945–7959. https://doi.org/10.1007/s00330-021-07826-9

    Article  PubMed  PubMed Central  Google Scholar 

  54. Q L, G Z, Yj H, Mx D, Fj L, X W, et al. Blend sign on computed tomography: novel and reliable predictor for early hematoma growth in patients with intracerebral hemorrhage. Stroke. 2015; 46(8):2119–23. https://doi.org/10.1161/strokeaha.115.009185

  55. Qureshi AI, Foster LD, Lobanova I, Huang W, Suarez JI (2020) Intensive blood pressure lowering in patients with moderate to severe grade acute cerebral hemorrhage: post hoc analysis of antihypertensive treatment of acute cerebral hemorrhage (ATACH)-2 trial. Cerebrovasc Dis 49(3):244–252. https://doi.org/10.1159/000506358

    Article  CAS  PubMed  Google Scholar 

  56. Qureshi AI, Palesch YY, Barsan WG, Hanley DF, Hsu CY, Martin RL et al (2016) Intensive blood-pressure lowering in patients with acute cerebral hemorrhage. N Engl J Med 375(11):1033–1043. https://doi.org/10.1056/nejmoa1603460

    Article  PubMed  PubMed Central  Google Scholar 

  57. Roh D, Boehme A, Young C, Roth W, Gutierrez J, Flaherty M et al (2020) Hematoma expansion is more frequent in deep than lobar intracerebral hemorrhage. Neurology 95(24):e3386–e3393. https://doi.org/10.1212/wnl.0000000000010990

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Romero JM, Brouwers HB, Lu J, Delgado Almandoz JE, Kelly H, Heit J et al (2013) Prospective validation of the computed tomographic angiography spot sign score for intracerebral hemorrhage. Stroke 44(11):3097–3102. https://doi.org/10.1161/strokeaha.113.002752

    Article  PubMed  PubMed Central  Google Scholar 

  59. Schneider H, Huynh TJ, Demchuk AM, Dowlatshahi D, Rodriguez-Luna D, Silva Y et al (2018) Combining spot sign and intracerebral hemorrhage score to estimate functional outcome: analysis from the PREDICT cohort. Stroke 49(6):1511–1514. https://doi.org/10.1161/strokeaha.118.020679

    Article  PubMed  Google Scholar 

  60. Selariu E, Zia E, Brizzi M, Abul-Kasim K (2012) Swirl sign in intracerebral haemorrhage: definition, prevalence, reliability and prognostic value. BMC Neurol 12:109. https://doi.org/10.1186/1471-2377-12-109

    Article  PubMed  PubMed Central  Google Scholar 

  61. Sharrock MF, Mould WA, Ali H, Hildreth M, Awad IA, Hanley DF et al (2021) 3D deep neural network segmentation of intracerebral hemorrhage: development and validation for clinical trials. Neuroinformatics 19(3):403–415. https://doi.org/10.1007/s12021-020-09493-5

    Article  PubMed  Google Scholar 

  62. Shimoda Y, Ohtomo S, Arai H, Okada K, Tominaga T (2017) Satellite sign: a poor outcome predictor in intracerebral hemorrhage. Cerebrovasc Dis 44(3–4):105–112. https://doi.org/10.1159/000477179

    Article  PubMed  Google Scholar 

  63. Song Z, Tang Z, Liu H, Guo D, Cai J, Zhou Z (2021) A clinical-radiomics nomogram may provide a personalized 90-day functional outcome assessment for spontaneous intracerebral hemorrhage. Eur Radiol 31(7):4949–4959. https://doi.org/10.1007/s00330-021-07828-7

    Article  PubMed  Google Scholar 

  64. Sporns PB, Schwake M, Kemmling A, Minnerup J, Schwindt W, Niederstadt T et al (2017) Comparison of spot sign, blend sign and black hole sign for outcome prediction in patients with intracerebral hemorrhage. J Stroke 19(3):333–339. https://doi.org/10.5853/jos.2016.02061

    Article  PubMed  PubMed Central  Google Scholar 

  65. Sporns PB, Schwake M, Schmidt R, Kemmling A, Minnerup J, Schwindt W et al (2017) Computed tomographic blend sign is associated with computed tomographic angiography spot sign and predicts secondary neurological deterioration after intracerebral hemorrhage. Stroke 48(1):131–135. https://doi.org/10.1161/strokeaha.116.014068

    Article  PubMed  Google Scholar 

  66. Su X, Chen N, Sun H, Liu Y, Yang X, Wang W et al (2020) Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain. Neuro Oncol 22(3):393–401. https://doi.org/10.1093/neuonc/noz184

    Article  CAS  PubMed  Google Scholar 

  67. Takeda R, Ogura T, Ooigawa H, Fushihara G, Yoshikawa S, Okada D et al (2013) A practical prediction model for early hematoma expansion in spontaneous deep ganglionic intracerebral hemorrhage. Clin Neurol Neurosurg 115(7):1028–1031. https://doi.org/10.1016/j.clineuro.2012.10.016

    Article  PubMed  Google Scholar 

  68. Tan CO, Lam S, Kuppens D, Bergmans RHJ, Parameswaran BK, Forghani R et al (2019) Spot and diffuse signs: quantitative markers of intracranial hematoma expansion at dual-energy CT. Radiology 290(1):179–186. https://doi.org/10.1148/radiol.2018180322

    Article  PubMed  Google Scholar 

  69. Toyoda K, Koga M, Yamamoto H, Foster L, Palesch YY, Wang Y et al (2019) Clinical outcomes depending on acute blood pressure after cerebral hemorrhage. Ann Neurol 85(1):105–113. https://doi.org/10.1002/ana.25379

    Article  PubMed  Google Scholar 

  70. van Asch CJ, Luitse MJ, Rinkel GJ, van der Tweel I, Algra A, Klijn CJ (2010) Incidence, case fatality, and functional outcome of intracerebral haemorrhage over time, according to age, sex, and ethnic origin: a systematic review and meta-analysis. Lancet Neurol 9(2):167–176. https://doi.org/10.1016/s1474-4422(09)70340-0

    Article  PubMed  Google Scholar 

  71. Volbers B, Giede-Jeppe A, Gerner ST, Sembill JA, Kuramatsu JB, Lang S et al (2018) Peak perihemorrhagic edema correlates with functional outcome in intracerebral hemorrhage. Neurology 90(12):e1005–e1012. https://doi.org/10.1007/s00330-019-06378-3

    Article  PubMed  Google Scholar 

  72. Wada R, Aviv RI, Fox AJ, Sahlas DJ, Gladstone DJ, Tomlinson G et al (2007) CT angiography “spot sign” predicts hematoma expansion in acute intracerebral hemorrhage. Stroke 38(4):1257–1262. https://doi.org/10.1161/01.str.0000259633.59404.f3

    Article  PubMed  Google Scholar 

  73. Wu TY, Sharma G, Strbian D, Putaala J, Desmond PM, Tatlisumak T et al (2017) Natural history of perihematomal edema and impact on outcome after intracerebral hemorrhage. Stroke 48(4):873–879. https://doi.org/10.1161/strokeaha.116.014416

    Article  PubMed  Google Scholar 

  74. Xie H, Ma S, Wang X, Zhang X. Noncontrast computer tomography–based radiomics model for predicting intracerebral hemorrhage expansion: preliminary findings and comparison with conventional radiological model. Eur Radiol [Internet]. 2020 Jan [cited 2020 Sep 15];30(1):87–98. https://doi.org/10.1007/s00330-019-06378-3

  75. Xu H, Li R, Duan Y, Wang J, Liu S, Zhang Y et al (2017) Quantitative assessment on blood-brain barrier permeability of acute spontaneous intracerebral hemorrhage in basal ganglia: a CT perfusion study. Neuroradiology 59(7):677–684. https://doi.org/10.1007/s00234-017-1852-9

    Article  PubMed  Google Scholar 

  76. Xu J, Zhang R, Zhou Z, Wu C, Gong Q, Zhang H et al (2020) Deep network for the automatic segmentation and quantification of intracranial hemorrhage on CT. Front Neurosci 14:541817. https://doi.org/10.3389/fnins.2020.541817

    Article  PubMed  Google Scholar 

  77. Yang Guangwei; Xiao Hua; Liu Yuzhou; Hu Shan; Liu Yi. Perihematomal edema in basal ganglia intracerebral hemorrhage by using radiomics approach of CT images. Chin J Neuromed. 2019;18:1248–1254. https://doi.org/10.3760/cma.j.issn.1671-8925.2019.12.010

  78. Yang J, Arima H, Wu G, Heeley E, Delcourt C, Zhou J et al (2015) Prognostic significance of perihematomal edema in acute intracerebral hemorrhage: pooled analysis from the intensive blood pressure reduction in acute cerebral hemorrhage trial studies. Stroke 46(4):1009–1013. https://doi.org/10.1161/strokeaha.114.007154

    Article  PubMed  Google Scholar 

  79. Jun Y, Ziming H, Hao W, Dongyuan L, Huibin K, Zhe H et al (2019) Role of radiomics model in prediction of hematoma enlargement in early stage of hypertensive intracerebral hemorrhage. Chin J Neuromed, January 25:49–54. https://doi.org/10.3760/cma.j.issn.1671-8925.2019.01.009

    Article  Google Scholar 

  80. Yu N, Yu H, Li H, Ma N, Hu C, Wang J. A Robust deep learning segmentation method for hematoma volumetric detection in intracerebral hemorrhage. Stroke. 2021;STROKEAHA120032243. https://doi.org/10.1161/strokeaha.120.032243

  81. Yu Z, Zheng J, He M, Guo R, Ma L, You C et al (2019) Accuracy of swirl sign for predicting hematoma enlargement in intracerebral hemorrhage: a meta-analysis. J Neurol Sci 399:155–160. https://doi.org/10.1016/j.jns.2019.02.032

    Article  PubMed  Google Scholar 

  82. Yun XU, Ming Liu, Liying Cui. Chinese guidelines for the imaging application in cerebrovascular diseases. Chin J Neurol 2020;4250–268. https://doi.org/10.3760/cma.j.cn113694-20191007-00615

  83. Zhang M, Chen J, Zhan C, Liu J, Chen Q, Xia T et al (2020) Blend sign is a strong predictor of the extent of early hematoma expansion in spontaneous intracerebral hemorrhage. Front Neurol 11:334. https://doi.org/10.3389/fneur.2020.00334

    Article  PubMed  PubMed Central  Google Scholar 

  84. Zhang S, Sun H, Su X, Yang X, Wang W, Wan X et al (2021) Automated machine learning to predict the co-occurrence of isocitrate dehydrogenase mutations and O6-methylguanine-DNA methyltransferase promoter methylation in patients with gliomas. J Magn Reson Imaging 54(1):197–205. https://doi.org/10.1002/jmri.27498

    Article  PubMed  Google Scholar 

  85. Zhao X, Chen K, Wu G, Zhang G, Zhou X, Lv C et al (2021) Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema. Eur Radiol. https://doi.org/10.1007/s00330-020-07558-2

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This study was supported by grants of National Natural Science Foundation of China (No. 82071872), Lanzhou University Second Hospital Second Hospital “Cuiying Technology Innovation Plan” Applied Basic Research Project (No. CY2018-QN09), and Science and Technology Program of Gansu Province (21YF5FA123).

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Author notes

  1. Xiaoyu Huang and Dan Wang contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou, 730030, China

    Xiaoyu Huang, Dan Wang, Shenglin Li, Qing Zhou & Junlin Zhou

  2. Second Clinical School, Lanzhou University, Lanzhou, China

    Xiaoyu Huang, Shenglin Li, Qing Zhou & Junlin Zhou

  3. Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China

    Xiaoyu Huang, Dan Wang, Shenglin Li, Qing Zhou & Junlin Zhou

  4. Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China

    Xiaoyu Huang, Dan Wang, Shenglin Li, Qing Zhou & Junlin Zhou

Authors
  1. Xiaoyu Huang
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  2. Dan Wang
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  3. Shenglin Li
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  4. Qing Zhou
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  5. Junlin Zhou
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Contributions

Conceptualization: XH, DW

Data curation: XH, DW

Funding acquisition: JZ

Investigation: SL, QZ

Supervision: SL, JZ

Validation: JZ

Visualization: XH, DW

Writing—original draft: XH, DW

Writing—review and editing: JZ

Corresponding author

Correspondence to Junlin Zhou.

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All authors have read and approved submission of the revised manuscript. The material in the abstract has not been published and is not being considered for publication elsewhere in whole or in part in any language.

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The authors declare no competing interests.

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Huang, X., Wang, D., Li, S. et al. Advances in computed tomography-based prognostic methods for intracerebral hemorrhage. Neurosurg Rev 45, 2041–2050 (2022). https://doi.org/10.1007/s10143-022-01760-0

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  • DOI: https://doi.org/10.1007/s10143-022-01760-0

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