Reproducibility of volume analysis of dynamic susceptibility contrast perfusion-weighted imaging in untreated glioblastomas.
Cerebral blood volume
Dynamic susceptibility contrast
Glioblastoma
Neuro-oncology
Observer variation
Journal
Neuroradiology
ISSN: 1432-1920
Titre abrégé: Neuroradiology
Pays: Germany
ID NLM: 1302751
Informations de publication
Date de publication:
Sep 2022
Sep 2022
Historique:
received:
19
11
2021
accepted:
25
03
2022
pubmed:
3
4
2022
medline:
13
8
2022
entrez:
2
4
2022
Statut:
ppublish
Résumé
Despite a high variability, the hotspot method is widely used to calculate the cerebral blood volume (CBV) of glioblastomas on DSC-MRI. Our aim was to investigate inter- and intra-observer reproducibility of parameters calculated with the hotspot or a volume method and that of an original parameter assessing the fraction of pixels in the tumour volume displaying rCBV > 2: %rCBV > 2. Twenty-seven consecutive patients with untreated glioblastoma (age: 63, women: 11) were retrospectively included. Three observers calculated the maximum tumour CBV value (rCBVmax) normalized with a reference ROI in the contralateral white matter (CBVWM) with (i) the hotspot method and (ii) with a volume method following tumour segmentation on 3D contrast-enhanced T1-WI. From this volume method, %rCBV > 2 was also assessed. After 8-12 weeks, one observer repeated all delineations. Intraclass (ICC) and Lin's (LCC) correlation coefficients were used to determine reproducibility. Inter-observer reproducibility of rCBVmax was fair with the hotspot and good with the volume method (ICC = 0.46 vs 0.65, p > 0.05). For CBVWM, it was fair with the hotspot and excellent with the volume method (0.53 vs 0.84, p < 0.05). Reproducibility of one pairwise combination of observers was significantly better for both rCBVmax and CBVWM (LCC = 0.33 vs 0.75; 0.52 vs 0.89, p < 0.05). %rCBV > 2 showed excellent inter- and intra-observer reproducibility (ICC = 0.94 and 0.91). Calculated in glioblastomas with a volume method, rCBVmax and CBVWM yielded good to excellent reproducibility but only fair with the hotspot method. Overall, the volume analysis offers a highly reproducible parameter, %rCBV > 2, that could be promising during the follow-up of such heterogeneous tumours.
Identifiants
pubmed: 35364709
doi: 10.1007/s00234-022-02937-6
pii: 10.1007/s00234-022-02937-6
doi:
Substances chimiques
Contrast Media
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1763-1771Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Thust SC, Heiland S, Falini A, Jäger HR, Waldman AD, Sundgren PC et al (2018) Glioma imaging in Europe: a survey of 220 centres and recommendations for best clinical practice. Eur Radiol 28(8):3306–3317
doi: 10.1007/s00330-018-5314-5
Law M, Yang S, Wang H, Babb JS, Johnson G, Cha S (2003) Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. AJNR Am J Neuroradiol 24(10):1989–1998
pubmed: 14625221
pmcid: 8148904
Boxerman JL, Ellingson BM, Jeyapalan S, Elinzano H, Harris RJ, Rogg JM et al (2017) Longitudinal DSC-MRI for distinguishing tumor recurrence from pseudoprogression in patients with a high-grade glioma. Am J Clin Oncol 40(3):228–34
doi: 10.1097/COC.0000000000000156
Gonçalves FG, Chawla S, Mohan S (2020) Emerging MRI techniques to redefine treatment response in patients with glioblastoma: MRI treatment response in glioblastoma. Journal of Magnetic Resonance Imaging [Internet]. 19 mars 2020 [cité 6 avr 2020]; Disponible sur: https://doi.org/10.1002/jmri.27105
Paulson ES, Schmainda KM (2008) Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors. Radiology 249(2):601–613
doi: 10.1148/radiol.2492071659
Sadeghi N, D’Haene N, Decaestecker C, Levivier M, Metens T, Maris C et al (2008) Apparent diffusion coefficient and cerebral blood volume in brain gliomas: relation to tumor cell density and tumor microvessel density based on stereotactic biopsies. Am J Neuroradiol 29(3):476–82
doi: 10.3174/ajnr.A0851
Wetzel SG, Cha S, Johnson G, Lee P, Law M, Kasow DL et al (2002) Relative cerebral blood volume measurements in intracranial mass lesions: interobserver and intraobserver reproducibility study. Radiology 224(3):797–803
doi: 10.1148/radiol.2243011014
Lupo JM, Cha S, Chang SM, Nelson SJ (2005) Dynamic susceptibility-weighted perfusion imaging of high-grade gliomas: characterization of spatial heterogeneity. AJNR Am J Neuroradiol 26(6):1446–1454
pubmed: 15956514
pmcid: 8149056
Dijkstra H, Sijens PE, van der Hoorn A, van Laar PJ (2020) Inter-observer reproducibility of quantitative dynamic susceptibility contrast and diffusion MRI parameters in histogram analysis of gliomas. Acta Radiologica 61(1):76–84
doi: 10.1177/0284185119852729
Oei MTH, Meijer FJA, Mordang J-J, Smit EJ, Idema AJS, Goraj BM et al (2018) Observer variability of reference tissue selection for relativecerebral blood volume measurements in glioma patients. Eur Radiol 28(9):3902–3911
doi: 10.1007/s00330-018-5353-y
Latysheva A, Emblem KE, Brandal P, Vik-Mo EO, Pahnke J, Røysland K et al (2019) Dynamic susceptibility contrast and diffusion MR imaging identify oligodendroglioma as defined by the 2016 WHO classification for brain tumors: histogram analysis approach. Neuroradiology 61(5):545–555
doi: 10.1007/s00234-019-02173-5
Iv M, Liu X, Lavezo J, Gentles AJ, Ghanem R, Lummus S et al (2019) Perfusion MRI-Based fractional tumor burden differentiates between tumor and treatment effect in recurrent glioblastomas and informs clinical decision-making. AJNR Am J Neuroradiol 40(10):1649–1657
pubmed: 31515215
pmcid: 7028562
PubMed Central Full Text PDF [Internet]. [cité 27 avr 2020]. Disponible sur: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4588759/pdf/nov095.pdf
Boxerman JL, Schmainda KM, Weisskoff RM (2006) Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am J Neuroradiol 27(4):859–867
pubmed: 16611779
pmcid: 8134002
Gasparetto EL, Pawlak MA, Patel SH, Huse J, Woo JH, Krejza J et al (2009) Posttreatment recurrence of malignant brain neoplasm: accuracy of relative cerebral blood volume fraction in discriminating low from high malignant histologic volume fraction. Radiology 250(3):887–96
doi: 10.1148/radiol.2502071444
Roques M, Catalaa I, Attal J, Ferrier M, Patsoura S, Gramada R et al (2018) Intérêt d’une analyse volumique de la perfusion en IRM dans le diagnostic de pseudoprogression lors du suivi des glioblastomes traités par radio-chimiothérapie concomitantes. J Neuroradiol 45(2):78
doi: 10.1016/j.neurad.2018.01.039
Cicchetti DV (1994) Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess 6(4):284–290
doi: 10.1037/1040-3590.6.4.284
Altman DG, Bland JM (2002) Commentary on quantifying agreement between two methods of measurement. Clin Chem 48(5):801–2
doi: 10.1093/clinchem/48.5.801
Walter SD, Eliasziw M, Donner A (1998) Sample size and optimal designs for reliability studies. Stat Med 17:101–110
doi: 10.1002/(SICI)1097-0258(19980115)17:1<101::AID-SIM727>3.0.CO;2-E
Smits M, Bendszus M, Collette S, Postma LA, Dhermain F, Hagenbeek RE et al (2019) Repeatability and reproducibility of relative cerebral blood volume measurement of recurrent glioma in a multicentre trial setting. Eur J Cancer 114:89–96
doi: 10.1016/j.ejca.2019.03.007
Wang B, Zhao B, Zhang Y, Ge M, Zhao P, na Sun et al (2018) Absolute CBV for the differentiation of recurrence and radionecrosis of brain metastases after gamma knife radiotherapy: a comparison with relative CBV. Clin Radiol 73(8):758.e1–758.e7
Iv M, Liu X, Lavezo J, Gentles AJ, Ghanem R, Lummus S et al (2019) Perfusion MRI-based fractional tumor burden differentiates between tumor and treatment effect in recurrent glioblastomas and informs clinical decision-making. American Journal of Neuroradiology [Internet]. 12 sept 2019 [cité 30 mars 2020]; Disponible sur: https://doi.org/10.3174/ajnr.A6211
Thust SC, van den Bent MJ, Smits M (2018) Pseudoprogression of brain tumors. J Magn Reson Imaging. https://doi.org/10.1002/jmri.26171
doi: 10.1002/jmri.26171
pubmed: 29734497
pmcid: 6175399
Melguizo-Gavilanes I, Bruner JM, Guha-Thakurta N, Hess KR, Puduvalli VK (2015) Characterization of pseudoprogression in patients with glioblastoma: is histology the gold standard? J Neurooncol 123(1):141–150
doi: 10.1007/s11060-015-1774-5
Barboriak DP, Zhang Z, Desai P, Snyder BS, Safriel Y, McKinstry RC, et al. Interreader variability of dynamic contrast-enhanced MRI of recurrent glioblastoma: the multicenter ACRIN 6677/RTOG 0625 study. Radiology [Internet]. 27 nov 2018 [cité 14 mars 2019]; Disponible sur: https://doi.org/10.1148/radiol.2019181296
Welker K, Boxerman J, Kalnin A, Kaufmann T, Shiroishi M, Wintermark M et al (2015) ASFNR recommendations for clinical performance of MR dynamic susceptibility contrast perfusion imaging of the brain. Am J Neuroradiol 36(6):E41-51
doi: 10.3174/ajnr.A4341
Jung SC, Choi SH, Yeom JA, Kim J-H, Ryoo I, Kim SC et al (2013) Cerebral blood volume analysis in glioblastomas using dynamic susceptibility contrast-enhanced perfusion MRI: a comparison of manual and semiautomatic segmentation methods. PLOS ONE 8(8):e69323
doi: 10.1371/journal.pone.0069323