Standardizované a pokročilé techniky MR v diagnostice dětských nádorů mozku

Česká verze

Authors: P. Hanzlíková ^1,2,3; R. Martínek ⁴; D. Vilímek ^1,4; H. Medřická ^5,6; E. Štěpánová ^5,6
Authors place of work: Ústav radiodiagnostický, FN Ostrava ¹; Ústav zobrazovacích metod, LF OU, Ostrava ²; Radiologická klinika LF UP a FN Olomouc ³; Katedra kybernetiky a biomedicínského inženýrství VŠB – TU Ostrava ⁴; Oddělení dětské neurologie FN Ostrava ⁵; Katedra neurověd LF OU Ostrava ⁶
Published in the journal: Cesk Slov Neurol N 2023; 86(2): 107-113
Category: Přehledný referát
doi: https://doi.org/10.48095/cccsnn2023107

Summary

The overview report is devoted to the standardized protocol for intracranial imaging of the childhood brain tumors according to the European Society for Pediatric Oncology (SIOPE) recommendations to increase the effectiveness of the initial examination of such sick children, as well as for the possibility of comparing subsequent MRI. The protocol takes into account the differences in the hardware and software equipment of workplaces. It is divided into basic (mandatory) parts and extension sequences, which are already carried out in specialized centres. The communication presents the essential use of sequences according to the power of the MRI device and emphasizes the possibilities of using new techniques. The advanced part of the protocol offers the basic principle and implementation options.

Keywords:

multimodal magnetic resonance imaging – pediatric brain tumour – standard imaging protocol – advanced imaging protocol

Zdroje

1. Avula S, Peet A, Morana G et al. European Society for Paediatric Oncology (SIOPE) MRI guidelines for imaging patients with central nervous system tumours. Childs Nerv Syst 2021; 37 (8): 2497–2508. doi: 10.1007/s00 381-021-05199-4.

2. Ellingson BM, Bendszus M, Boxerman J et al. Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials. Neuro Oncol 2015; 17 (9): 1188–1198. doi: 10.1093/neuonc/nov095.

3. D‘Arco F, Mertiri L, de Graaf P et al. Guidelines for magnetic resonance imaging in pediatric head and neck pathologies: a multicentre international consensus paper. Neuroradiology 2022; 64 (6): 1081–1100. doi: 10.1007/s00234-022-02950-9.

4. Widmann G, Henninger B, Kremser C et al. MRI sequences in head & neck radiology – state of the art. Rofo 2017; 189 (5): 413–422. doi: 10.1055/s-0043-103280.

5. Leao DJ, Craig PG, Godoy LF et al. Response assessment in neuro-oncology criteria for gliomas: practical approach using conventional and advanced techniques. AJNR Am J Neuroradiol 2020; 41 (1): 10–20. doi: 10.3174/ajnr.A6358.

6. Cooney TM, Cohen KJ, Guimaraes CV et al. Response assessment in diffuse intrinsic pontine glioma: recommendations from the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group. Lancet Oncol 2020; 21 (6): e330–e336. doi: 10.1016/S1470-2045 (20) 30166-2.

7. Erker C, Tamrazi B, Poussaint TY et al. Response assessment in paediatric high-grade glioma: recommendations from the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group. Lancet Oncol 2020; 21 (6): e317–e329. doi: 10.1016/S1470-2045 (20) 301 73-X.

8. Komada T, Naganawa S, Ogawa H et al. Contrast-enhanced MR imaging of metastatic brain tumor at 3 tesla: utility of T (1) -weighted SPACE compared with 2D spin echo and 3D gradient echo sequence. Magn Reson Med Sci 2008; 7 (1): 13–21. doi: 10.2463/mrms.7.13.

9. Bapst B, Amegnizin JL, Vignaud A et al. Post-contrast 3D T1-weighted TSE MR sequences (SPACE, CUBE, VISTA/BRAINVIEW, isoFSE, 3D MVOX): technical aspects and clinical applications. J Neuroradiol 2020; 47 (5): 358–368. doi: 10.1016/j.neurad.2020.01.085.

10. Notohamiprodjo M, Staehler M, Steiner N et al. Combined diffusion-weighted, blood oxygen level-dependent, and dynamic contrast-enhanced MRI for characterization and differentiation of renal cell carcinoma. Acad Radiol 2013; 20 (6): 685–693. doi: 10.1016/ j.acra.2013.01.015.

11. Kanda T, Osawa M, Oba H et al. High signal intensity in dentate nucleus on unenhanced T1-weighted MR images: association with linear versus macrocyclic gadolinium chelate administration. Radiology 2015; 275 (3): 803–809. doi: 10.1148/radiol.14140364.

12. Algin O, Ucar M, Ozmen E et al. Assessment of third ventriculostomy patency with the 3D-SPACE technique: a preliminary multicenter research study. J Neurosurg 2015; 122 (6): 1347–1355. doi: 10.3171/2014.10.JNS14 298.

13. Fukuoka H, Hirai T, Okuda T et al. Comparison of the added value of contrast-enhanced 3D fluid-attenuated inversion recovery and magnetization-prepared rapid acquisition of gradient echo sequences in relation to conventional postcontrast T1-weighted images for the evaluation of leptomeningeal diseases at 3T. AJNR Am J Neuroradiol 2010; 31 (5): 868–873. doi: 10.3174/ajnr.A1937.

14. Besta R, Shankar YU, Kumar A et al. MRI 3D CISS- a novel imaging modality in diagnosing trigeminal neuralgia – a review. J Clin Diagn Res 2016; 10 (3): ZE01–03. doi: 10.7860/JCDR/2016/14011.7348.

15. Kooreman ES, van Houdt PJ, Keesman R et al. ADC measurements on the Unity MR-linac – a recommendation on behalf of the Elekta Unity MR-linac consortium. Radiother Oncol 2020; 153: 106–113. doi: 10.1016/j.radonc.2020.09.046.

16. Novak J, Zarinabad N, Rose H et al. Classification of paediatric brain tumours by diffusion weighted imaging and machine learning. Sci Rep 2021; 11 (1): 2987. doi: 10.1038/s41598-021-82214-3.

17. Porter DA, Heidemann RM. High resolution diffusion-weighted imaging using readout-segmented echo-planar imaging, parallel imaging and a two-dimensional navigator-based reacquisition. Magn Reson Med 2009; 62 (2): 468–475. doi: 10.1002/mrm.22024.

18. Haller S, Barkhof F. Neuroimaging in dementia: a clinical approach. In: Barkhof F, Jager R, Thurnher M et al (eds.). Clinical neuroradiology: the ESNR textbook. Berlín: Springer 2018.

19. Landers MJF, Meesters SPL, van Zandvoort M et al. The frontal aslant tract and its role in executive functions: a quantitative tractography study in glioma patients. Brain Imaging Behav 2022; 16 (3): 1026–1039. doi: 10.1007/s11682-021-00581-x.

20. Ferda J, Kastner J, Mukensnabl P et al. Diffusion tensor magnetic resonance imaging of glial brain tumors. Eur J Radiol 2010; 74 (3): 428–436. doi: 10.1016/ j.ejrad.2009.03.030.

21. Augelli R, Ciceri E, Ghimenton C et al. Magnetic resonance diffusion-tensor imaging metrics in High Grade Gliomas: correlation with IDH1 gene status in WHO 2016 era. Eur J Radiol 2019; 116: 174–179. doi: 10.1016/ j.ejrad.2019.04.020.

22. Wilson M, Andronesi O, Barker PB et al. Methodological consensus on clinical proton MRS of the brain: review and recommendations. Magn Reson Med 2019; 82 (2): 527–550. doi: 10.1002/mrm.27742.

23. Manias KA, Gill SK, MacPherson L et al. Diagnostic accuracy and added value of qualitative radiological review of. Neurooncol Pract 2019; 6 (6): 428–437. doi: 10.1093/nop/npz010.

24. Hedderich D, Kluge A, Pyka T et al. Consistency of normalized cerebral blood volume values in glioblastoma using different leakage correction algorithms on dynamic susceptibility contrast magnetic resonance imaging data without and with preload. J Neuroradiol 2019; 46 (1): 44–51. doi: 10.1016/j.neurad.2018.04. 006.

25. Seiberlich N, Gulani V, Campbell A et al. Quantitative magnetic resonance imaging. Amsterdam: Academic Press 2020.

26. Golay X, Ho ML. Multidelay ASL of the pediatric brain. Br J Radiol 2022; 95 (1134): 20220034. doi: 10.1259/ bjr.20220034.

27. Sharma A, Low JT, Kumthekar P. Advances in the diagnosis and treatment of leptomeningeal disease. Curr Neurol Neurosci Rep 2022; 22 (7): 413–425. doi: 10.1007/s11910-022-01198-3.

28. Nguyen TK, Nguyen EK, Soliman H. An overview of leptomeningeal disease. Ann Palliat Med 2021; 10 (1): 909–922. doi: 10.21037/apm-20-973.

29. D‘Arco F, Khan F, Mankad K et al. Differential diagnosis of posterior fossa tumours in children: new insights. Pediatr Radiol 2018; 48 (13): 1955–1963. doi: 10.1007/s00247-018-4224-7.

30. Dumba M, Fry A, Shelton J et al. Imaging in patients with glioblastoma: a national cohort study. Neurooncol Pract 2022; 9 (6): 487–495. doi: 10.1093/nop/npac048.

31. Latif G, Al Anezi FY, Iskandar DNFA et al. Recent advances in classification of brain tumor from MR images – state of the art review from 2017 to 2021. Curr Med Imaging 2022; 18 (9): 903–918. doi: 10.2174/1573405618666220117151726.

32. Piccardo A, Tortora D, Mascelli S et al. Advanced MR imaging and. Eur J Nucl Med Mol Imaging 2019; 46 (8): 1685–1694. doi: 10.1007/s00259-019-04333-4.

33. Orphanidou-Vlachou E, Kohe SE, Brundler MA et al. Metabolite levels in paediatric brain tumours correlate with histological features. Pathobiology 2018; 85 (3): 157–168. doi: 10.1159/000458423.

34. Branzoli F, Pontoizeau C, Tchara L et al. Cystathionine as a marker for 1p/19q codeleted gliomas by in vivo magnetic resonance spectroscopy. Neuro Oncol 2019; 21 (6): 765–774. doi: 10.1093/neuonc/noz031.

35. Landheer K, Gajdošík M, Juchem C. A semi-LASER, single-voxel spectroscopic sequence with a minimal echo time of 20.1 ms in the human brain at 3 T. NMR Biomed 2020; 33 (9): e4324. doi: 10.1002/nbm.4324.