European Radiology

, Volume 28, Issue 10, pp 4324–4333 | Cite as

Remote brain microhaemorrhages may predict haematoma in glioma patients treated with radiation therapy

  • Augustin LeclerEmail author
  • Frédérique Charbonneau
  • Dimitri Psimaras
  • Marie-Astrid Metten
  • Antoine Gueguen
  • Khe Hoang Xuan
  • Loic Feuvret
  • Julien Savatovsky



To evaluate the prevalence of cerebral remote microhaemorrhages (RMH) and remote haematomas (RH) using magnetic resonance susceptibility-weighted imaging (SWI) among patients treated for gliomas during follow-up.


We conducted a retrospective single centre longitudinal study on 58 consecutive patients treated for gliomas from January 2009 through December 2010. Our institutional review board approved this study. We evaluated the presence and number of RMH and RH found outside the brain tumour on follow-up MR imaging. We performed univariate and bivariate analyses to identify predictors for RMH and RH and Kaplan–Meier survival analysis techniques.


Twenty-five (43%) and four patients (7%) developed at least one RMH or RH, respectively, during follow-up. The risk was significantly higher for patients who received radiation therapy (49% and 8% versus 0%) (p = 0.02). The risk of developing RH was significantly higher in patients with at least one RMH and a high burden of RMH. The mean age of those presenting with at least one RMH or RH was significantly lower.


RMH were common in adult survivors of gliomas who received radiation therapy and may predict the onset of RH during follow-up, mainly in younger patients.

Key Points

• Brain RMH and RH are significantly more likely to occur after RT.

• RMH occur in almost half of the patients treated with RT.

• RMH and RH are significantly more frequent in younger patients.

• RH occur only in patients with RMH.


Magnetic resonance imaging Diagnostic imaging Radiation effects Radiotherapy Intracranial haemorrhages 





Magnetic resonance


Remote haematoma


Remote microhaemorrhage


Radiation therapy


Susceptibility-weighted imaging



Laura McMaster provided professional English-language medical editing of this article.


The authors state that this work has not received any funding.

Compliance with ethical standards


The scientific guarantor of this publication is Augustin Lecler.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Our institutional review board approved this study and waived the informed consent requirement due to its retrospective nature.


• retrospective

• observational study

• performed at one institution

Supplementary material

330_2018_5356_MOESM1_ESM.docx (742 kb)
ESM 1 (DOCX 742 kb)


  1. 1.
    Shinohara C, Muragaki Y, Maruyama T et al (2008) Long-term prognostic assessment of 185 newly diagnosed gliomas: grade III glioma showed prognosis comparable to that of Grade II glioma. Jpn J Clin Oncol 38:730–733CrossRefGoogle Scholar
  2. 2.
    Stupp R, Mason WP, van den Bent MJ et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352:987–996CrossRefGoogle Scholar
  3. 3.
    Douw L, Klein M, Fagel SS et al (2009) Cognitive and radiological effects of radiotherapy in patients with low-grade glioma: long-term follow-up. Lancet Neurol 8:810–8182CrossRefGoogle Scholar
  4. 4.
    Peters S, Pahl R, Claviez A, Jansen O (2013) Detection of irreversible changes in susceptibility-weighted images after whole-brain irradiation of children. Neuroradiology 55:853–859CrossRefGoogle Scholar
  5. 5.
    Lupo JM, Chuang CF, Chang SM et al (2012) 7-Tesla susceptibility-weighted imaging to assess the effects of radiotherapy on normal-appearing brain in patients with glioma. Int J Radiat Oncol Biol Phys 82:e493–e500CrossRefGoogle Scholar
  6. 6.
    Jain R, Robertson PL, Gandhi D et al (2005) Radiation-induced cavernomas of the brain. AJNR Am J Neuroradiol 26:1158–1162PubMedGoogle Scholar
  7. 7.
    Bian W, Hess CP, Chang SM et al (2014) Susceptibility-weighted MR imaging of radiation therapy-induced cerebral microbleeds in patients with glioma: a comparison between 3T and 7T. Neuroradiology 56:91–96CrossRefGoogle Scholar
  8. 8.
    Zeng Q-S, Kang X-S, Li C-F, Zhou G-Y (2011) Detection of hemorrhagic hypointense foci in radiation injury region using susceptibility-weighted imaging. Acta Radiol 52:115–119CrossRefGoogle Scholar
  9. 9.
    Lupo JM, Molinaro AM, Essock-Burns E et al (2016) The effects of anti-angiogenic therapy on the formation of radiation-induced microbleeds in normal brain tissue of patients with glioma. Neuro Oncol 18:87–95CrossRefGoogle Scholar
  10. 10.
    Walker MD, Alexander E, Hunt WE et al (1978) Evaluation of BCNU and/or radiotherapy in the treatment of anaplastic gliomas. A cooperative clinical trial. J Neurosurg 49:333–343PubMedGoogle Scholar
  11. 11.
    Valk PE, Dillon WP (1991) Radiation injury of the brain. AJNR Am J Neuroradiol 12:45–62PubMedGoogle Scholar
  12. 12.
    Roddy E, Sear K, Felton E et al (2016) Presence of cerebral microbleeds is associated with worse executive function in pediatric brain tumor survivors. Neuro-Oncol 18:1548–1558CrossRefGoogle Scholar
  13. 13.
    Charidimou A, Werring DJ (2012) Cerebral microbleeds and cognition in cerebrovascular disease: an update. J Neurol Sci 322:50–55CrossRefGoogle Scholar
  14. 14.
    Valenti R, Del Bene A, Poggesi A et al (2016) Cerebral microbleeds in patients with mild cognitive impairment and small vessel disease: The Vascular Mild Cognitive Impairment (VMCI)-Tuscany study. J Neurol Sci 368:195–202CrossRefGoogle Scholar
  15. 15.
    Ayaz M, Boikov AS, Haacke EM et al (2010) Imaging cerebral microbleeds using susceptibility weighted imaging: one step toward detecting vascular dementia. J Magn Reson Imaging JMRI 31:142–148CrossRefGoogle Scholar
  16. 16.
    Werring DJ, Gregoire SM, Cipolotti L (2010) Cerebral microbleeds and vascular cognitive impairment. J Neurol Sci 299:131–135CrossRefGoogle Scholar
  17. 17.
    Landis JR, Koch GG (1977) An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics 33:363–374CrossRefGoogle Scholar
  18. 18.
    Core Team R (2014) R: A language and environment for statistical computing. In: R Foundation for Statistical Computing, Vienna Accessed date: 2014
  19. 19.
    Reinhold HS, Hopewell JW (1980) Late changes in the architecture of blood vessels of the rat brain after irradiation. Br J Radiol 53:693–696CrossRefGoogle Scholar
  20. 20.
    Cutsforth-Gregory JK, Lanzino G, Link MJ et al (2015) Characterization of radiation-induced cavernous malformations and comparison with a nonradiation cavernous malformation cohort. J Neurosurg 122:1214–1222CrossRefGoogle Scholar
  21. 21.
    Smart D (2017) Radiation toxicity in the central nervous system: mechanisms and strategies for injury reduction. Semin Radiat Oncol 27:332–339CrossRefGoogle Scholar
  22. 22.
    Vinchon M, Leblond P, Caron S et al (2011) Radiation-induced tumors in children irradiated for brain tumor: a longitudinal study. Childs Nerv Syst 27:445–453CrossRefGoogle Scholar
  23. 23.
    Mirimanoff R-O, Gorlia T, Mason W et al (2006) Radiotherapy and temozolomide for newly diagnosed glioblastoma: recursive partitioning analysis of the EORTC 26981/22981-NCIC CE3 phase III randomized trial. J Clin Oncol 24:2563–2569CrossRefGoogle Scholar
  24. 24.
    Cairncross G, Wang M, Shaw E et al (2013) Phase III trial of chemoradiotherapy for anaplastic oligodendroglioma: long-term results of RTOG 9402. J Clin Oncol 31:337–343CrossRefGoogle Scholar
  25. 25.
    Ducray F, Idbaih A (2013) Neuro-oncology: anaplastic oligodendrogliomas-value of early chemotherapy. Nat Rev Neurol 9:7–8CrossRefGoogle Scholar
  26. 26.
    Jastaniyah N, Murtha A, Pervez N et al (2013) Phase I study of hypofractionated intensity modulated radiation therapy with concurrent and adjuvant temozolomide in patients with glioblastoma multiforme. Radiat Oncol Lond Engl 8:38CrossRefGoogle Scholar
  27. 27.
    Combs SE (2017) Does proton therapy have a future in CNS tumors? Curr Treat Options Neurol 19:12CrossRefGoogle Scholar
  28. 28.
    Floyd NS, Woo SY, Teh BS et al (2004) Hypofractionated intensity-modulated radiotherapy for primary glioblastoma multiforme. Int J Radiat Oncol Biol Phys 58:721–726CrossRefGoogle Scholar
  29. 29.
    Chen Y-D, Feng J, Fang T et al (2013) Effect of intensity-modulated radiotherapy versus three-dimensional conformal radiotherapy on clinical outcomes in patients with glioblastoma multiforme. Chin Med J (Engl) 126:2320–2324Google Scholar
  30. 30.
    Zach L, Stall B, Ning H et al (2009) A dosimetric comparison of four treatment planning methods for high grade glioma. Radiat Oncol Lond Engl 4:45CrossRefGoogle Scholar
  31. 31.
    Letarte N, Bressler LR, Villano JL (2013) Bevacizumab and central nervous system (CNS) hemorrhage. Cancer Chemother Pharmacol 71:1561–1565CrossRefGoogle Scholar
  32. 32.
    Zuo P-Y, Chen X-L, Liu Y-W et al (2014) Increased risk of cerebrovascular events in patients with cancer treated with bevacizumab: a meta-analysis. PLoS One 9:e102484CrossRefGoogle Scholar
  33. 33.
    Hapani S, Sher A, Chu D, Wu S (2010) Increased risk of serious hemorrhage with bevacizumab in cancer patients: a meta-analysis. Oncology 79:27–38CrossRefGoogle Scholar
  34. 34.
    Kamba T, McDonald DM (2007) Mechanisms of adverse effects of anti-VEGF therapy for cancer. Br J Cancer 96:1788–1795CrossRefGoogle Scholar
  35. 35.
    de Souza JM, Domingues RC, Cruz LCH et al (2008) Susceptibility-weighted imaging for the evaluation of patients with familial cerebral cavernous malformations: a comparison with t2-weighted fast spin-echo and gradient-echo sequences. AJNR Am J Neuroradiol 29:154–158CrossRefGoogle Scholar
  36. 36.
    de Champfleur NM, Langlois C, Ankenbrandt WJ et al (2011) Magnetic resonance imaging evaluation of cerebral cavernous malformations with susceptibility-weighted imaging. Neurosurgery 68:641–647 discussion 647-648CrossRefGoogle Scholar
  37. 37.
    Bian W, Banerjee S, Kelly DAC et al (2015) Simultaneous imaging of radiation-induced cerebral microbleeds, arteries and veins, using a multiple gradient echo sequence at 7 Tesla. J Magn Reson Imaging JMRI 42:269–279CrossRefGoogle Scholar
  38. 38.
    Jeerakathil T, Wolf PA, Beiser A et al (2004) Cerebral microbleeds: prevalence and associations with cardiovascular risk factors in the Framingham Study. Stroke 35:1831–1835CrossRefGoogle Scholar
  39. 39.
    Tsushima Y, Tanizaki Y, Aoki J, Endo K (2002) MR detection of microhemorrhages in neurologically healthy adults. Neuroradiology 44:31–36CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of RadiologyFondation Ophtalmologique Adolphe de RothschildParisFrance
  2. 2.Department of NeurologyGroupe Hospitalier Pitié-Salpêtrière, AP-HPParisFrance
  3. 3.Clinical Research UnitFondation Ophtalmologique Adolphe de RothschildParisFrance
  4. 4.Department of NeurologyFondation Ophtalmologique Adolphe de RothschildParisFrance
  5. 5.Department of NeurooncologyGroupe Hospitalier Pitié-Salpêtrière, AP-HPParisFrance
  6. 6.Department of RadiotherapyGroupe Hospitalier Pitié-Salpêtrière, AP-HPParisFrance
  7. 7.Imagerie Medicale Paris 13ParisFrance

Personalised recommendations