SpringerLink
Log in

Exploration of Multiparameter Hematoma 3D Image Analysis for Predicting Outcome After Intracerebral Hemorrhage

  • Original Work
  • Published:
Neurocritical Care Aims and scope Submit manuscript

Abstract

Background

Rapid diagnosis and proper management of intracerebral hemorrhage (ICH) play a crucial role in the outcome. Prediction of the outcome with a high degree of accuracy based on admission data including imaging information can potentially influence clinical decision-making practice.

Methods

We conducted a retrospective multicenter study of consecutive ICH patients admitted between 2012–2017. Medical history, admission data, and initial head computed tomography (CT) scan were collected. CT scans were semiautomatically segmented for hematoma volume, hematoma density histograms, and sphericity index (SI). Discharge unfavorable outcomes were defined as death or severe disability (modified Rankin Scores 4–6). We compared (1) hematoma volume alone; (2) multiparameter imaging data including hematoma volume, location, density heterogeneity, SI, and midline shift; and (3) multiparameter imaging data with clinical information available on admission for ICH outcome prediction. Multivariate analysis and predictive modeling were used to determine the significance of hematoma characteristics on the outcome.

Results

We included 430 subjects in this analysis. Models using automated hematoma segmentation showed incremental predictive accuracies for in-hospital mortality using hematoma volume only: area under the curve (AUC): 0.85 [0.76–0.93], multiparameter imaging data (hematoma volume, location, CT density, SI, and midline shift): AUC: 0.91 [0.86–0.97], and multiparameter imaging data plus clinical information on admission (Glasgow Coma Scale (GCS) score and age): AUC: 0.94 [0.89–0.99]. Similarly, severe disability predictive accuracy varied from AUC: 0.84 [0.76–0.93] for volume-only model to AUC: 0.88 [0.80–0.95] for imaging data models and AUC: 0.92 [0.86–0.98] for imaging plus clinical predictors.

Conclusions

Multiparameter models combining imaging and admission clinical data show high accuracy for predicting discharge unfavorable outcome after ICH.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Hemphill JC 3rd, Greenberg SM, Anderson CS, Becker K, Bendok BR, Cushman M, et al. Guidelines for the management of spontaneous intracerebral hemorrhage: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2015;46:2032–60.

    Article  PubMed  Google Scholar 

  2. Feigin VL. Stroke in develo** countries: can the epidemic be stopped and outcomes improved? Lancet Neurol. 2007;6:94–7.

    Article  PubMed  Google Scholar 

  3. Feigin VL, Lawes CM, Bennett DA, Anderson CS. Stroke epidemiology: a review of population-based studies of incidence, prevalence, and case-fatality in the late 20th century. Lancet Neurol. 2003;2:43–53.

    Article  PubMed  Google Scholar 

  4. Moon JS, Janjua N, Ahmed S, Kirmani JF, Harris-Lane P, Jacob M, et al. Prehospital neurologic deterioration in patients with intracerebral hemorrhage. Crit Care Med. 2008;36:172–5.

    Article  PubMed  Google Scholar 

  5. Yang W-S, Li Q, Li R, Liu Q-J, Wang X-C, Zhao L-B, et al. Defining the optimal midline shift threshold to predict poor outcome in patients with supratentorial spontaneous intracerebral hemorrhage. Neurocrit Care. 2018;28:314–21.

    Article  PubMed  Google Scholar 

  6. Kiphuth IC, Huttner HB, Breuer L, Schwab S, Kohrmann M. Sonographic monitoring of midline shift predicts outcome after intracerebral hemorrhage. Cerebrovasc Dis. 2012;34:297–304.

    Article  PubMed  Google Scholar 

  7. Fogelholm R, Murros K, Rissanen A, Avikainen S. Admission blood glucose and short term survival in primary intracerebral haemorrhage: a population based study. J Neurol Neurosurg Psychiatry. 2005;76:349–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 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;24:987–93.

    Article  CAS  PubMed  Google Scholar 

  9. Al-Shahi Salman R, Frantzias J, Lee RJ, Lyden PD, Battey TWK, Ayres AM, et al. Absolute risk and predictors of the growth of acute spontaneous intracerebral haemorrhage: a systematic review and meta-analysis of individual patient data. Lancet Neurol. 2018;17:885–94.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Chalela JA, Kidwell CS, Nentwich LM, Luby M, Butman JA, Demchuk AM, et al. Magnetic resonance imaging and computed tomography in emergency assessment of patients with suspected acute stroke: a prospective comparison. Lancet. 2007;369:293–8.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Rodriguez-Luna D, Boyko M, Subramaniam S, Klourfeld E, Jo P, Diederichs BJ, et al. Magnitude of hematoma volume measurement error in intracerebral hemorrhage. Stroke. 2016;47:1124–6.

    Article  PubMed  Google Scholar 

  12. Divani AA, Majidi S, Luo X, Souslian FG, Zhang J, Abosch A, et al. The ABCs of accurate volumetric measurement of cerebral hematoma. Stroke. 2011;42:1569–74.

    Article  PubMed  Google Scholar 

  13. Webb AJ, Ullman NL, Morgan TC, Muschelli J, Kornbluth J, Awad IA, et al. Accuracy of the ABC/2 Score for Intracerebral Hemorrhage: systematic Review and Analysis of MISTIE, CLEAR-IVH, and CLEAR III. Stroke. 2015;46:2470–6.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Hussein HM, Tariq NA, Palesch YY, Qureshi AI. Reliability of hematoma volume measurement at local sites in a multicenter acute intracerebral hemorrhage clinical trial. Stroke. 2013;44:237–9.

    Article  PubMed  Google Scholar 

  15. Wang CW, Juan CJ, Liu YJ, Hsu HH, Liu HS, Chen CY, et al. Volume-dependent overestimation of spontaneous intracerebral hematoma volume by the ABC/2 formula. Acta Radiol. 2009;50:306–11.

    Article  PubMed  Google Scholar 

  16. Boulouis G, Morotti A, Brouwers H, et al. Association between hypodensities detected by computed tomography and hematoma expansion in patients with intracerebral hemorrhage. JAMA Neurol. 2016;73:961–8.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Barras CD, Tress BM, Christensen S, Collins M, Desmond PM, Skolnick BE, et al. Quantitative CT densitometry for predicting intracerebral hemorrhage growth. AJNR Am J Neuroradiol. 2013;34:1139–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Boulouis G, Morotti A, Brouwers HB, Charidimou A, Jessel MJ, Auriel E, et al. Noncontrast computed tomography hypodensities predict poor outcome in intracerebral hemorrhage patients. Stroke. 2016;47:2511–6.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Delcourt C, Zhang S, Arima H, Sato S, Al-Shahi Salman R, Wang X, et al. Significance of hematoma shape and density in intracerebral hemorrhage: the intensive blood pressure reduction in acute intracerebral hemorrhage trial study. Stroke. 2016;47:1227–32.

    Article  CAS  PubMed  Google Scholar 

  20. Zhang D, Chen J, Xue Q. Heterogeneity signs on noncontrast computed tomography predict hematoma expansion after intracerebral hemorrhage: a meta-analysis. Biomed Res. 2018;2018:6038193.

    Google Scholar 

  21. Hevesi M, Bershad EM, Jafari M, Mayer SA, Selim M, Suarez JI, et al. Untreated hypertension as predictor of in-hospital mortality in intracerebral hemorrhage: a multi-center study. J Crit Care. 2018;43:235–9.

    Article  PubMed  Google Scholar 

  22. Divani AA, Hevesi M, Pulivarthi S, Luo X, Souslian F, Suarez JI, et al. Predictors of nosocomial pneumonia in intracerebral hemorrhage patients: a multi-center observational study. Neurocrit Care. 2015;22:234–42.

    Article  CAS  PubMed  Google Scholar 

  23. Hemphill JC 3rd, Bonovich DC, Besmertis L, Manley GT, Johnston SC. The ICH score: a simple, reliable grading scale for intracerebral hemorrhage. Stroke. 2001;32:891–7.

    Article  PubMed  Google Scholar 

  24. Poplawski NJ, Shirinifard A, Agero U, Gens JS, Swat M, Glazier JA. Front instabilities and invasiveness of simulated 3D avascular tumors. PLoS ONE. 2010;5:e10641.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lee JH, Park CM, Park SJ, Bae JS, Lee SM, Goo JM. Value of computerized 3D shape analysis in differentiating encapsulated from invasive thymomas. PLoS ONE. 2015;10:e0126175.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Liao CC, Chen YF, **ao F. Brain midline shift measurement and its automation: a review of techniques and algorithms. Int J Biomed Imaging. 2018;2018:4303161.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Hummel M, Edelmann D, Kopp-Schneider A. Clustering of samples and variables with mixed-type data. PLoS ONE. 2017;12:e0188274.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Harrell FE. Regression modeling strategies with applications to linear models, logistic regression and survival analysis. Stat Med 2003;22:2531.

    Article  Google Scholar 

  29. Kuhn M, Johnson K. Applied predictive modeling. New York: Springer; 2013.

    Book  Google Scholar 

  30. Tian TS. Functional data analysis in brain imaging studies. Front Psychol. 2010;1:35.

    PubMed  PubMed Central  Google Scholar 

  31. Petersen A, Zhao J, Carmichael O, Muller HG. Quantifying individual brain connectivity with functional principal component analysis for networks. Brain Connect. 2016;6:540–7.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Ramsay JO, Silverman BW. Functional data analysis (springer series in statistics). 2nd ed. New York: Springer; 2005.

    Book  Google Scholar 

  33. Petersen A, Muller H-G. Functional data analysis for density functions by transformation to a Hilbert space. Ann Stat. 2016;44:183–218.

    Article  Google Scholar 

  34. Al-Mufti F, Thabet AM, Singh T, El-Ghanem M, Amuluru K, Gandhi CD. Clinical and radiographic predictors of intracerebral hemorrhage outcome. Interv Neurol. 2018;7:118–36.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Safatli DA, Gunther A, Schlattmann P, Schwarz F, Kalff R, Ewald C. Predictors of 30-day mortality in patients with spontaneous primary intracerebral hemorrhage. Surg Neurol Int. 2016;7:2152–7806.

    Google Scholar 

  36. Flemming KD, Wijdicks EF, Li H. Can we predict poor outcome at presentation in patients with lobar hemorrhage? Cerebrovasc Dis. 2001;11:183–9.

    Article  CAS  PubMed  Google Scholar 

  37. Nilsson OG, Lindgren A, Brandt L, Saveland H. Prediction of death in patients with primary intracerebral hemorrhage: a prospective study of a defined population. J Neurosurg. 2002;97:531–6.

    Article  PubMed  Google Scholar 

  38. Wang CW, Liu YJ, Lee YH, Hueng DY, Fan HC, Yang FC, et al. Hematoma shape, hematoma size, Glasgow coma scale score and ICH score: which predicts the 30-day mortality better for intracerebral hematoma? PLoS ONE. 2014;9:e102326.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Barras CD, Tress BM, Christensen S, MacGregor L, Collins M, Desmond PM, et al. Density and shape as CT predictors of intracerebral hemorrhage growth. Stroke. 2009;40:1325–31.

    Article  PubMed  Google Scholar 

  40. Nowinski WL, Gomolka RS, Qian G, Gupta V, Ullman NL, Hanley DF. Characterization of intraventricular and intracerebral hematomas in non-contrast CT. Neuroradiol J. 2014;27:299–315.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Hanley DF. Intraventricular hemorrhage and ICH outcomes: severity factor and treatment target. Stroke J Cereb Circ. 2009;40:1533–8.

    Article  Google Scholar 

  42. Gregorio T, Pipa S, Cavaleiro P, Atanasio G, Albuquerque I, Chaves PC, et al. Prognostic models for intracerebral hemorrhage: systematic review and meta-analysis. BMC Med Res Methodol. 2018;18:145.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Bharath K, Kurtek S, Rao A, Baladandayuthapani V. Radiologic image-based statistical shape analysis of brain tumours. J R Stat Soc Ser C Appl Stat. 2018;67:1357–78.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Goldberg-Zimring D, Talos IF, Bhagwat JG, Haker SJ, Black PM, Zou KH. Statistical validation of brain tumor shape approximation via spherical harmonics for image-guided neurosurgery. Acad Radiol. 2005;12:459–66.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Crawford L, Monod A, Chen A, Mukherjee S, Rabadan R. Functional data analysis using a topological summary statistic: the smooth Euler characteristic transform. 2016. ar**v:161106818.

Download references

Author information

Authors and Affiliations

  1. Vital Images, Minnetonka, MN, USA

    Pascal Salazar

  2. Department of Neurology, San Camillo de’ Lellis District General Hospital, Rieti, Italy

    Mario Di Napoli

  3. Department of Neurology, University of Minnesota, MMC 295, 420 Delaware Street S.E., Minneapolis, MN, 55455, USA

    Mostafa Jafari, Alibay Jafarli & Afshin A. Divani

  4. Department of Neurology, Neurosurgery, and Anesthesia/Critical Care Medicine, Johns Hopkins, Baltimore, USA

    Wendy Ziai

  5. Department of Statistics and Applied Probability, University of California, Santa Barbara, CA, USA

    Alexander Petersen

  6. Department of Neurology, Henry Ford Health System, Detroit, MI, USA

    Stephan A. Mayer

  7. Department of Neurology, Baylor College of Medicine, Houston, TX, USA

    Eric M. Bershad & Rahul Damani

  8. Department of Neurosurgery, University of Minnesota, Minneapolis, MN, USA

    Afshin A. Divani

Authors
  1. Pascal Salazar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mario Di Napoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mostafa Jafari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alibay Jafarli
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wendy Ziai
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alexander Petersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Stephan A. Mayer
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Eric M. Bershad
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Rahul Damani
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Afshin A. Divani
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

PS designed and performed statistical analysis and contributed to manuscript preparation and subsequent revisions. MDN contributed to the study design, manuscript preparation, and subsequent revisions. MJ contributed to data collection and manuscript preparation. AJ contributed to data collection and manuscript preparation. WZ contributed to manuscript preparation. SAM contributed to manuscript preparation. AP contributed to statistical analysis design and manuscript preparation. EMB contributed to manuscript preparation. RD contributed to manuscript preparation. AAD designed the study, interpreted the results, prepared the manuscript the subsequent revisions, and supervised the project.

Corresponding author

Correspondence to Afshin A. Divani.

Ethics declarations

Source of Support

No funding.

Conflict of interest

Dr. Salazar is an employee of Vital Images (Minnetonka, MN, USA).

Ethical Approval/Informed Consent

The Institutional Review Boards at Hennepin Healthcare System and Fairview Health Services in Minneapolis, Minnesota, approved the study. The data are reported based on the recommendations from STROBE (Strengthening The Reporting of OBservational Studies in Epidemiology). Informed consent is not required for this retrospective study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 456 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Salazar, P., Di Napoli, M., Jafari, M. et al. Exploration of Multiparameter Hematoma 3D Image Analysis for Predicting Outcome After Intracerebral Hemorrhage. Neurocrit Care 32, 539–549 (2020). https://doi.org/10.1007/s12028-019-00783-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12028-019-00783-8

Keywords

Search

Navigation