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On PTV definition for glioblastoma based on fiber tracking of diffusion tensor imaging data


Autoři: Barbara Witulla aff001;  Nicole Goerig aff001;  Florian Putz aff001;  Benjamin Frey aff001;  Tobias Engelhorn aff002;  Arnd Dörfler aff002;  Michael Uder aff003;  Rainer Fietkau aff001;  Christoph Bert aff001;  Frederik Bernd Laun aff003
Působiště autorů: Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany aff001;  Department of Neuroradiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany aff002;  Institute of Radiology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany aff003
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0227146

Souhrn

Radiotherapy (RT) is commonly applied for the treatment of glioblastoma multiforme (GBM). Following the planning target volume (PTV) definition procedure standardized in guidelines, a 20% risk of missing non-local recurrences is present. Purpose of this study was to evaluate whether diffusion tensor imaging (DTI)-based fiber tracking may be beneficial for PTV definition taking into account the prediction of distant recurrences. 56 GBM patients were examined with magnetic resonance imaging (MRI) including DTI performed before RT after resection of the primary tumor. Follow-up MRIs were acquired in three month intervals. For the seven patients with a distant recurrence, fiber tracking was performed with three algorithms and it was evaluated whether connections existed from the primary tumor region to the distant recurrence. It depended strongly on the used tracking algorithm and the used tracking parameters whether a connection was observed. Most of the connections were weak and thus not usable for PTV definition. Only in one of the seven patients with a recurring tumor, a clear connection was present. It seems unlikely that DTI-based fiber tracking can be beneficial for predicting distant recurrences in the planning of PTVs for glioblastoma multiforme.

Klíčová slova:

Cancer treatment – Diffusion tensor imaging – Glioblastoma multiforme – Lesions – Magnetic resonance imaging – Radiation therapy – Surgical oncology – Tumor resection


Zdroje

1. Behin A, Hoang-Xuan K, Carpentier AF, Delattre J-Y. Primary brain tumours in adults. Lancet. 2003;361(9354):323–31. https://doi.org/10.1016/S0140-6736(03)12328-8 12559880

2. Krebs in Deutschland 2013/2014. 11 ed. Berlin: Robert Koch-Institut und die Gesellschaft der epidemiologischen Krebsregister in Deutschland e.V.; 2017.

3. Notohamiprodjo M, Chandarana H, Mikheev A, Rusinek H, Grinstead J, Feiweier T, et al. Combined intravoxel incoherent motion and diffusion tensor imaging of renal diffusion and flow anisotropy. Magnetic resonance in medicine. 2015;73(4):1526–32. doi: 10.1002/mrm.25245 24752998.

4. Sulman EP, Ismaila N, Armstrong TS, Tsien C, Batchelor TT, Cloughesy T, et al. Radiation Therapy for Glioblastoma: American Society of Clinical Oncology Clinical Practice Guideline Endorsement of the American Society for Radiation Oncology Guideline. J Clin Oncol. 2017;35(3):361–9. doi: 10.1200/JCO.2016.70.7562 27893327.

5. Smoll NR, Schaller K, Gautschi OP. Long-term survival of patients with glioblastoma multiforme (GBM). J Clin Neurosci. 2013;20(5):670–5. doi: 10.1016/j.jocn.2012.05.040 23352352.

6. Azizi AA, Paur S, Kaider A, Dieckmann K, Peyrl A, Chocholous M, et al. Does the interval from tumour surgery to radiotherapy influence survival in paediatric high grade glioma? Strahlenther Onkol. 2018;194(6):552–9. doi: 10.1007/s00066-018-1260-z 29349602

7. Straube C, Elpula G, Gempt J, Gerhardt J, Bette S, Zimmer C, et al. Re-irradiation after gross total resection of recurrent glioblastoma. Strahlenther Onkol. 2017;193(11):897–909. doi: 10.1007/s00066-017-1161-6 28616821

8. Adeberg S, Konig L, Bostel T, Harrabi S, Welzel T, Debus J, et al. Glioblastoma recurrence patterns after radiation therapy with regard to the subventricular zone. Int J Radiat Oncol Biol Phys. 2014;90(4):886–93. doi: 10.1016/j.ijrobp.2014.07.027 25220720.

9. Giese A, Westphal M. Glioma invasion in the central nervous system. Neurosurgery. 1996;39(2):235–50; discussion 50–2. doi: 10.1097/00006123-199608000-00001 8832660.

10. Laun FB, Fritzsche KH, Kuder TA, Stieltjes B. [Introduction to the basic principles and techniques of diffusion-weighted imaging]. Radiologe. 2011;51(3):170–9. doi: 10.1007/s00117-010-2057-y 21424762.

11. Mori S, Kaufmann WE, Pearlson GD, Crain BJ, Stieltjes B, Solaiyappan M, et al. In vivo visualization of human neural pathways by magnetic resonance imaging. Ann Neurol. 2000;47(3):412–4. doi: 10.1002/1531-8249(200003)47:3<412::AID-ANA28>3.0.CO;2-H 10716271

12. Krishnan AP, Asher IM, Davis D, Okunieff P, O’Dell WG. Evidence that MR diffusion tensor imaging (tractography) predicts the natural history of regional progression in patients irradiated conformally for primary brain tumors. Int J Radiat Oncol Biol Phys. 2008;71(5):1553–62. doi: 10.1016/j.ijrobp.2008.04.017 18538491.

13. Brainlab. iPlan Fiber Tracking—clinical white paper.

14. Reisert M, Mader I, Anastasopoulos C, Weigel M, Schnell S, Kiselev V. Global fiber reconstruction becomes practical. Neuroimage. 2011;54(2):955–62. doi: 10.1016/j.neuroimage.2010.09.016 20854913.

15. Neher PF, Côté M-A, Houde J-C, Descoteaux M, Maier-Hein KH. Fiber tractography using machine learning. Neuroimage. 2017;158:417–29. https://doi.org/10.1016/j.neuroimage.2017.07.028 28716716

16. Weinstein D, Kindlmann G, Lundberg E, editors. Tensorlines: advection-diffusion based propagation through diffusion tensor fields. Visualization '99 Proceedings; 1999.

17. Lazar M, Weinstein DM, Tsuruda JS, Hasan KM, Arfanakis K, Meyerand ME, et al. White matter tractography using diffusion tensor deflection. Hum Brain Mapp. 2003;18(4):306–21. doi: 10.1002/hbm.10102 12632468.

18. Chamberland M, Whittingstall K, Fortin D, Mathieu D, Descoteaux M. Real-time multi-peak tractography for instantaneous connectivity display. Front Neuroinform. 2014;8(59). doi: 10.3389/fninf.2014.00059 24910610

19. Tournier J-D, Calamante F, Connelly A. MRtrix: Diffusion tractography in crossing fiber regions. Int J Imaging Syst Technol. 2012;22(1):53–66. doi: 10.1002/ima.22005

20. Mori S, van Zijl PCM. Fiber tracking: principles and strategies—a technical review. NMR Biomed. 2002;15(7–8):468–80. doi: 10.1002/nbm.781 12489096.

21. Neher PF, Laun FB, Stieltjes B, Maier-Hein KH. Fiberfox: facilitating the creation of realistic white matter software phantoms. Magn Reson Med. 2014;72(5):1460–70. doi: 10.1002/mrm.25045 24323973.

22. Bach M, Fritzsche KH, Stieltjes B, Laun FB. Investigation of resolution effects using a specialized diffusion tensor phantom. Magn Reson Med. 2014;71(3):1108–16. doi: 10.1002/mrm.24774 23657980.

23. Takemura H, Caiafa CF, Wandell BA, Pestilli F. Ensemble Tractography. PLoS Comput Biol. 2016;12(2):e1004692. doi: 10.1371/journal.pcbi.1004692 26845558

24. Berberat J, McNamara J, Remonda L, Bodis S, Rogers S. Diffusion tensor imaging for target volume definition in glioblastoma multiforme. Strahlenther Onkol. 2014;190(10):939–43. doi: 10.1007/s00066-014-0676-3 24823897.

25. Jensen MB, Guldberg TL, Harboll A, Lukacova S, Kallehauge JF. Diffusion tensor magnetic resonance imaging driven growth modeling for radiotherapy target definition in glioblastoma. Acta Oncol. 2017;56(11):1639–43. doi: 10.1080/0284186X.2017.1374559 28893125.

26. Unkelbach J, Menze BH, Konukoglu E, Dittmann F, Le M, Ayache N, et al. Radiotherapy planning for glioblastoma based on a tumor growth model: improving target volume delineation. Phys Med Biol. 2014;59(3):747–70. doi: 10.1088/0031-9155/59/3/747 24440875.

27. Angeli S, Emblem KE, Due-Tonnessen P, Stylianopoulos T. Towards patient-specific modeling of brain tumor growth and formation of secondary nodes guided by DTI-MRI. Neuroimage Clin. 2018;20:664–73. doi: 10.1016/j.nicl.2018.08.032 30211003.

28. Bondiau PY, Konukoglu E, Clatz O, Delingette H, Frenay M, Paquis P. Biocomputing: numerical simulation of glioblastoma growth and comparison with conventional irradiation margins. Phys Med. 2011;27(2):103–8. doi: 10.1016/j.ejmp.2010.05.002 21071253.

29. Hathout L, Patel V, Wen P. A 3-dimensional DTI MRI-based model of GBM growth and response to radiation therapy. Int J Oncol. 2016;49(3):1081–7. doi: 10.3892/ijo.2016.3595 27572745.

30. Jbabdi S, Mandonnet E, Duffau H, Capelle L, Swanson KR, Pelegrini-Issac M, et al. Simulation of anisotropic growth of low-grade gliomas using diffusion tensor imaging. Magn Reson Med. 2005;54(3):616–24. doi: 10.1002/mrm.20625 16088879.

31. Freitag MT, Maier-Hein KH, Binczyk F, Laun FB, Weber C, Bonekamp D, et al. Early Detection of Malignant Transformation in Resected WHO II Low-Grade Glioma Using Diffusion Tensor-Derived Quantitative Measures. PLoS One. 2016;11(10):e0164679. doi: 10.1371/journal.pone.0164679 27741525.

32. Wang S, Kim SJ, Poptani H, Woo JH, Mohan S, Jin R, et al. Diagnostic utility of diffusion tensor imaging in differentiating glioblastomas from brain metastases. AJNR Am J Neuroradiol. 2014;35(5):928–34. doi: 10.3174/ajnr.A3871 24503556.

33. Abhinav K, Yeh FC, Mansouri A, Zadeh G, Fernandez-Miranda JC. High-definition fiber tractography for the evaluation of perilesional white matter tracts in high-grade glioma surgery. Neuro Oncol. 2015;17(9):1199–209. doi: 10.1093/neuonc/nov113 26117712.

34. Stieltjes B, Schluter M, Didinger B, Weber MA, Hahn HK, Parzer P, et al. Diffusion tensor imaging in primary brain tumors: reproducible quantitative analysis of corpus callosum infiltration and contralateral involvement using a probabilistic mixture model. Neuroimage. 2006;31(2):531–42. doi: 10.1016/j.neuroimage.2005.12.052 16478665.

35. Hart MG, Price SJ, Suckling J. Connectome analysis for pre-operative brain mapping in neurosurgery. Brit J Neurosurg. 2016;30(5):506–17. doi: 10.1080/02688697.2016.1208809 27447756

36. Alghamdi M, Hasan Y, Ruschin M, Atenafu EG, Myrehaug S, Tseng C-L, et al. Stereotactic radiosurgery for resected brain metastasis: Cavity dynamics and factors affecting its evolution. Journal of Radiosurgery and SBRT. 2018;5(3):191–200. 29988304

37. Liu M, Gross DW, Wheatley BM, Concha L, Beaulieu C. The acute phase of Wallerian degeneration: Longitudinal diffusion tensor imaging of the fornix following temporal lobe surgery. Neuroimage. 2013;74:128–39. https://doi.org/10.1016/j.neuroimage.2013.01.069 23396161

38. Beaulieu C, Does MD, Snyder RE, Allen PS. Changes in water diffusion due to Wallerian degeneration in peripheral nerve. Magn Reson Med. 1996;36(4):627–31. doi: 10.1002/mrm.1910360419 8892217

39. Cazzaniga LF, Marinoni MA, Bossi A, Bianchi E, Cagna E, Cosentino D, et al. Interphysician variability in defining the planning target volume in the irradiation of prostate and seminal vesicles. Radiother Oncol. 1998;47(3):293–96. doi: 10.1016/s0167-8140(98)00028-0 9681893

40. Kingsley PB. Introduction to diffusion tensor imaging mathematics: Part III. Tensor calculation, noise, simulations, and optimization. Concept Magn Reson A. 2006;28a(2):155–79. doi: 10.1002/cmr.a.20050

41. Jeurissen B, Leemans A, Jones DK, Tournier JD, Sijbers J. Probabilistic Fiber Tracking Using the Residual Bootstrap with Constrained Spherical Deconvolution. Hum Brain Mapp. 2011;32(3):461–79. doi: 10.1002/hbm.21032 21319270

42. Reddy CP, Rathi Y. Joint Multi-Fiber NODDI Parameter Estimation and Tractography Using the Unscented Information Filter. Front Neurosci-Switz. 2016;10. doi: 10.3389/fnins.2016.00166 27147956

43. Jayachandra MR, Rehbein N, Herweh C, Heiland S. Fiber Tracking of Human Brain Using Fourth-Order Tensor and High Angular Resolution Diffusion Imaging. Magnetic resonance in medicine. 2008;60(5):1207–17. doi: 10.1002/mrm.21775 18958858

44. Descoteaux M, Deriche R, Knosche TR, Anwander A. Deterministic and Probabilistic Tractography Based on Complex Fibre Orientation Distributions. Ieee T Med Imaging. 2009;28(2):269–86. doi: 10.1109/Tmi.2008.2004424 19188114

45. Polders DL, Leemans A, Hendrikse J, Donahue MJ, Luijten PR, Hoogduin JM. Signal to Noise Ratio and Uncertainty in Diffusion Tensor Imaging at 1.5, 3.0, and 7.0 Tesla. Journal of Magnetic Resonance Imaging. 2011;33(6):1456–63. doi: 10.1002/jmri.22554 21591016


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