Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-26T05:58:12.707Z Has data issue: false hasContentIssue false
  • English
  • Français

Magnetic resonance imaging in radiotherapy treatment target volumes definition for brain tumours: a systematic review and meta-analysis

Published online by Cambridge University Press:  11 December 2017

Auwal Abubakar ,
Adamu D. Bojude ,
Aminu U. Usman ,
Idris Garba ,
Abasiama D. Obotiba ,
Mustapha Barde ,
Mutiat N. Miftaudeen  and
Umar Abubakar
Auwal Abubakar*
Affiliation:
Department of Medical Radiography, University of Maiduguri, Maiduguri, Nigeria
Adamu D. Bojude
Affiliation:
Department of Radiology, Gombe Sate University, Gombe, Nigeria
Aminu U. Usman
Affiliation:
Department of Radiology, Gombe Sate University, Gombe, Nigeria
Idris Garba
Affiliation:
Department of Medical Radiography, Bayero University Kano, Kano, Nigeria
Abasiama D. Obotiba
Affiliation:
Department of Medical Radiography, University of Maiduguri, Maiduguri, Nigeria
Mustapha Barde
Affiliation:
Department of Medical Radiography, Bayero University Kano, Kano, Nigeria
Mutiat N. Miftaudeen
Affiliation:
Department of Radiotherapy and Oncology, Usmanu Danfodiyo University Teaching Hospital, Sokkoto, Nigeria
Umar Abubakar
Affiliation:
Department of Radiography, Usmanu Danfodiyo University, Sokoto, Nigeria
*
Correspondence to: Auwal Abubakar, Department of Medical Radiography, College of Medical Sciences, University of Maiduguri, PMB 1069, Maiduguri, Borno State, Nigeria. Tel: +234 706 389 8690. E-mail: a.abubakar@unimaid.edu.ng
Get access Rights & Permissions [Opens in a new window]

Abstract

Purpose

The aim of this study is to establish clinical evidence regarding the use of magnetic resonance imaging (MRI) in target volume definition for radiotherapy treatment planning of brain tumours.

Methods

Primary studies were systematically retrieved from six electronic databases and other sources. Studies included were only those that quantitatively compared computed tomography (CT) and MRI in target volume definition for radiotherapy of brain tumours. Study characteristics and quality were assessed and the data were extracted from eligible studies. Effect estimates for each study was computed as mean percentage difference based on individual patient data where available. The included studies were then combined in meta-analysis using Review Manager (RevMan) software version 5.0.

Result

Five studies with a total number of 72 patients were included in this review. The quality of the studies was rated strong. The percentages mean differences of the studies were 7·47, 11·36, 30·70, 41·69 and −24·6% using CT as the baseline. The result of statistical analysis showed small-to-moderate heterogeneity; τ2=36·8; χ2=6·23; df=4 (p=0·18); I2=36%. The overall effect estimate was −1·85 [95% confidence interval (CI); −7·24, 10·94], Z=0·40 (p=0·069>0·5).

Conclusion

Brain tumour volumes measured using MRI-based method for radiotherapy treatment planning were larger compared with CT defined volumes but the difference lacks statistical significance.

Keywords

brain tumourscomputed tomographymagnetic resonance imagingmeta-analysisradiotherapy treatment planning
Type
Literature Review
Information
Journal of Radiotherapy in Practice , Volume 17 , Issue 3 , September 2018 , pp. 337 - 346
Copyright
© Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Sultanem, K, Patrocinio, H, Lambert, C et al. The use of hypofractionated intensity-modulated irradiation in the treatment of glioblastoma multiforme: preliminary results of a prospective trial. Int J Radiat Oncol Biol Phys 2004; 58 (1): 247–252.Google Scholar
2. Ferlay, J, Shin, H-R, Bray, F, Forman, D, Mathers, C, Parkin, D M. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer 2010; 127 (12): 2893–2917.Google Scholar
3. Burnet, N G, Thomas, S J, Burton, K E, Jefferies, S J. Defining the tumour and target volumes for radiotherapy. Cancer Imaging 2004; 4 (2): 153–161.Google Scholar
4. Mazzara, G P, Velthuizen, R P, Pearlman, J L, Greenberg, H M, Wagner, H. Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation. Int J Radiat Oncol Biol Phys 2004; 59 (1): 300–312.Google Scholar
5. Rajasekar, D, Datta, N R, Gupta, R K, Pradhan, P K, Ayyagari, S. Multimodality image fusion in dose escalation studies of brain tumors. J Appl Clin Med Phys 2003; 4 (1): 8–16.Google Scholar
6. Prabhakar, R, Julka, P K, Ganesh, T, Munshi, A, Joshi, R C, Rath, G K. Feasibility of using MRI alone for 3D radiation treatment planning in brain tumors. Jpn J Clin Oncol 2007; 37 (6): 405–411.Google Scholar
7. Bénard, F, Romsa, J, Hustinx, R. Imaging gliomas with positron emission tomography and single-photon emission computed tomography. Semi Nucl Med 2003; 33 (2): 148–162.Google Scholar
8. Jonsson, J H, Karlsson, M G, Karlsson, M, Nyholm, T. Treatment planning using MRI data: an analysis of the dose calculation accuracy for different treatment regions. Radiat Oncol 2010; 5: 62–62.Google Scholar
9. Devic, S. MRI simulation for radiotherapy treatment planning. Med Phys 2012; 39 (11): 11.Google Scholar
10. Khoo, V S, Adams, E J, Saran, F et al. A Comparison of clinical target volumes determined by CT and MRI for the radiotherapy planning of base of skull meningiomas. Int J Radiat Oncol Biol Phys 2000; 46 (5): 1309–1317.Google Scholar
11. Prabhakar, R, Haresh, K P, Ganesh, T, Joshi, R C, Julka, P K, Rath, G K. Comparison of computed tomography and magnetic resonance based target volume in brain tumors. J Cancer Res Ther 2007; 3 (2): 121–123.Google Scholar
12. Datta, N R, David, R, Gupta, R K, Lal, P. Implications of contrast-enhanced CT-based and MRI-based target volume delineations in radiotherapy treatment planning for brain tumors. J Cancer Res Ther 2008; 4 (1): 9–13.Google Scholar
13. Grosu, AL, Weber, WA, Franz, M et al. Reirradiation of recurrent high-grade gliomas using amino acid PET (SPECT)/CT/MRI image fusion to determine gross tumor volume for stereotactic fractionated radiotherapy. Int J Radiat Oncol Biol Phys 2005; 63 (2): 511–519.Google Scholar
14. Krengli, M, Loi, G, Sacchetti, G et al. Delineation of target volume for radiotherapy of high-grade gliomas by 99m Tc-MIBI SPECT and MRI fusion. Strahlenther Onkol 2007; 183 (12): 689–694.Google Scholar
15. Gehler, B, Paulsen, F, Oksüz, M O et al. [68Ga]-DOTATOC-PET/CT for meningioma IMRT treatment planning. Radiat Oncol 2009; 4: 56–56.Google Scholar
16. Nyuyki, F, Plotkin, M, Graf, R et al. Potential impact of (68)Ga-DOTATOC PET/CT on stereotactic radiotherapy planning of meningiomas. Eur J Nucl Med Mol Imaging 2010; 37 (2): 310–318.Google Scholar
17. Thorwarth, D, Müller, A-C, Pfannenberg, C, Beyer, T. Combined PET/MR imaging using (68)Ga-DOTATOC for radiotherapy treatment planning in meningioma patients. Recent Results Cancer Res 2013; 194: 425–439.Google Scholar
18. Thorwarth, D, Henke, G, Müller, AC et al. Simultaneous 68Ga-DOTATOC-PET/MRI for IMRT treatment planning for meningioma: first experience. Int J Radiat Oncol Biol Phys 2011; 81 (1): 277–283.Google Scholar
19. Thorwarth, D, Geets, X, Paiusco, M. Physical radiotherapy treatment planning based on functional PET/CT data. Radiother Oncol 2010; 96 (3): 317–324.Google Scholar
20. Thorwarth, D, Schaefer, A. Functional target volume delineation for radiation therapy on the basis of positron emission tomography and the correlation with histopathology. Q J Nucl Med Mol Imaging 2010; 54 (5): 490–499.Google Scholar
21. Leibfarth, S, Mönnich, D, Welz, S et al. A strategy for multimodal deformable image registration to integrate PET/MR into radiotherapy treatment planning. Acta Oncol 2013; 52 (7): 1353–1359.Google Scholar
22. Graf, R, Nyuyki, F, Steffen, I G et al. Contribution of 68Ga-DOTATOC PET/CT to target volume delineation of skull base meningiomas treated with stereotactic radiation therapy. Int J Radiat Oncol Biol Phys 2013; 85 (1): 68–73.Google Scholar
23. Graf, R, Plotkin, M, Steffen, I G et al. Magnetic resonance imaging, computed tomography, and 68Ga-DOTATOC positron emission tomography for imaging skull base meningiomas with infracranial extension treated with stereotactic radiotherapy – a case series. Head Face Med 2012; 8: 1.Google Scholar
24. Rosenman, J. Incorporating functional imaging information into radiation treatment. Semin Radiat Oncol 2001; 11 (1): 83–92.Google Scholar
25. Rosenman, J G, Miller, E P, Tracton, G, Cullip, T J. Image registration: an essential part of radiation therapy treatment planning. Int J Radiat Oncol Biol Phys 1998; 40 (1): 197–205.Google Scholar
26. Soler, C, Beauchesne, P, Maatougui, K et al. Technetium-99m sestamibi brain single-photon emission tomography for detection of recurrent gliomas after radiation therapy. Eur J Nucl Med 1998; 25 (12): 1649–1657.Google Scholar
27. Ferrari de Oliveira, L, Azevedo Marques, PM. Coregistration of brain single-positron emission computed tomography and magnetic resonance images using anatomical features. J Digit Imaging 2000; 13 (2 suppl 1): 196–199.Google Scholar
28. Stanescu, T, Jans, H S, Pervez, N, Stavrev, P, Fallone, B G. A study on the magnetic resonance imaging (MRI)-based radiation treatment planning of intracranial lesions. Phys Med Biol 2008; 53 (13): 3579–3593.Google Scholar
29. Kristensen, B H, Laursen, F J, Løgager, V, Geertsen, P F, Krarup-Hansen, A. Dosimetric and geometric evaluation of an open low-field magnetic resonance simulator for radiotherapy treatment planning of brain tumours. Radiother Oncol 2008; 87 (1): 100–109.Google Scholar
30. Ten Haken, R K, Thornton, A F Jr, Sandler, H M et al. A quantitative assessment of the addition of MRI to CT-based, 3-D treatment planning of brain tumors. Radiother Oncol 1992; 25 (2): 121–133.Google Scholar
31. Khoo, V S, Dearnaley, D P, Finnigan, D J, Padhani, A, Tanner, S F, Leach, M O. Magnetic resonance imaging (MRI): considerations and applications in radiotherapy treatment planning. Radiother Oncol 1997; 42 (1): 1–15.Google Scholar
32. Thornton, A F Jr, Sandler, H M, Ten, H et al. The clinical utility of magnetic resonance imaging in 3-dimensional treatment planning of brain neoplasms. Int J Radiat Oncol Biol Phys 1992; 24 (4): 767–775.Google Scholar
33. Lattanzi, J P, Fein, D A, McNeeley, S W, Shaer, A H, Movsas, B, Hanks, G E. Computed tomography-magnetic resonance image fusion: a clinical evaluation of an innovative approach for improved tumor localization in primary central nervous system lesions. Radiat Oncol Investig 1997; 5 (4): 195–205.Google Scholar
34. Yanke, B R, Ten Haken, R K, Aisen, A, Fraass, B A, Thornton, A F Jr. Design of MRI scan protocols for use in 3-D, CT-based treatment planning. Med Dosim 1991; 16 (4): 205–211.Google Scholar
35. Beavis, A W, Gibbs, P, Dealey, R A, Whitton, V J. Radiotherapy treatment planning of brain tumours using MRI alone. Br J Radiol 1998; 71 (845): 544–548.Google Scholar
36. Just, M, Rösler, H P, Higer, H P, Kutzner, J, Thelen, M. MRI-assisted radiation therapy planning of brain tumors – clinical experiences in 17 patients. Magn Reson Imaging 1991; 9 (2): 173–177.Google Scholar
37. Moerland, M A, Beersma, R, Bhagwandien, R, Wijrdeman, H K, Bakker, C J. Analysis and correction of geometric distortions in 1.5 T magnetic resonance images for use in radiotherapy treatment planning. Phys Med Biol 1995; 40 (10): 1651–1654.Google Scholar
38. Weber, D C, Wang, H, Albrecht, S et al. Open low-field magnetic resonance imaging for target definition, dose calculations and set-up verification during three-dimensional CRT for glioblastoma multiforme. Clin Oncol 2008; 20 (2): 157–167.Google Scholar
39. Liberati, A, Altman, D G, Tetzlaff, J et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ 2009; 339: b2700.Google Scholar
40. Higgins, J P T, Green, S. Cochrane Handbook for Systematic Reviews of Interventions. Oxford: Wiley-Blackwell, 2008.Google Scholar
41. Centre for Reviews and Dissemination, University of York. Systematic Reviews: CRD’s Guidance for Undertaking Reviews in Health Care. York, England: University of York Centre for Reviews and Dissemination, 2009.Google Scholar
42.National Collaborating Center for Methods and tools. Qualitative Assessment Tool for Quantitative Studies. http://www.ephpp.ca/tools.html. Accessed on 7th June 2016.Google Scholar
43. Cohen, J. Statistical Power Analysis for Behavioural Sciences, 2nd edition; 1988. USA: Lawrence Erlibaum Associate.Google Scholar
44. Collaboration, C. Cochrane collaboration open learning material. In: Torey D, Lasserson T (eds). Diversity and Heterogeneity. Chichester, England: John Wiley & sons Ltd, 2012: 52–64.Google Scholar
45. Huedo-Medina, T B, Sánchez-Meca, J, Marín-Martínez, F, Botella, J. Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychol Methods 2006; 11 (2): 193–206.Google Scholar
46. Krempien, R C, Schubert, K, Zierhut, D et al. Open low-field magnetic resonance imaging in radiation therapy treatment planning. Int J Radiat Oncol Biol Phys 2002; 53 (5): 1350–1360.Google Scholar
47. Weltens, C, Menten, J, Feron, M et al. Interobserver variations in gross tumor volume delineation of brain tumors on computed tomography and impact of magnetic resonance imaging. Radiother Oncol 2001; 60 (1): 49–59.Google Scholar
48. Fiorentino, A, Caivano, R, Pedicini, P, Fusco, V. Clinical target volume definition for glioblastoma radiotherapy planning: magnetic resonance imaging and computed tomography. Clin Transl Oncol 2013; 15 (9): 754–758.Google Scholar
49. Milker-Zabel, S, Zabel-du Bois, A, Henze, M et al. Improved target volume definition for fractionated stereotactic radiotherapy in patients with intracranial meningiomas by correlation of CT, MRI, and [68Ga]-DOTATOC-PET. Int J Radiat Oncol Biol Phys 2006; 65 (1): 222–227.Google Scholar
50. Huck, S W. Reading Statistics and Research, New York: Pearson 2012.Google Scholar
51. Stall, B L, Zach, H, Ning, J et al. Comparison of T2 and FLAIR imaging for target delineation in high grade gliomas. Radiat Oncol 2010; 5: 5.Google Scholar