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Differentiation of treatment-related changes from tumour progression: a direct comparison between dynamic FET PET and ADC values obtained from DWI MRI

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Abstract

Background

Following brain cancer treatment, the capacity of anatomical MRI to differentiate neoplastic tissue from treatment-related changes (e.g., pseudoprogression) is limited. This study compared apparent diffusion coefficients (ADC) obtained by diffusion-weighted MRI (DWI) with static and dynamic parameters of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET for the differentiation of treatment-related changes from tumour progression.

Patients and methods

Forty-eight pretreated high-grade glioma patients with anatomical MRI findings suspicious for progression (median time elapsed since last treatment was 16 weeks) were investigated using DWI and dynamic FET PET. Maximum and mean tumour-to-brain ratios (TBRmax, TBRmean) as well as dynamic parameters (time-to-peak and slope values) of FET uptake were calculated. For mean ADC calculation, regions-of-interest analyses were performed on ADC maps calculated from DWI coregistered with the contrast-enhanced MR image. Diagnoses were confirmed neuropathologically (21%) or clinicoradiologically. Diagnostic performance was evaluated using receiver-operating-characteristic analyses or Fisher’s exact test for a combinational approach.

Results

Ten of 48 patients had treatment-related changes (21%). The diagnostic performance of FET PET was significantly higher (threshold for both TBRmax and TBRmean, 1.95; accuracy, 83%; AUC, 0.89 ± 0.05; P < 0.001) than that of ADC values (threshold ADC, 1.09 × 10−3 mm2/s; accuracy, 69%; AUC, 0.73 ± 0.09; P = 0.13). The addition of static FET PET parameters to ADC values increased the latter’s accuracy to 89%. The highest accuracy was achieved by combining static and dynamic FET PET parameters (93%). Moreover, in contrast to ADC values, TBRs <1.95 at suspected progression predicted a significantly longer survival (P = 0.01).

Conclusions

Data suggest that static and dynamic FET PET provide valuable information concerning the differentiation of early treatment-related changes from tumour progression and outperform ADC measurement for this highly relevant clinical question.

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References

  1. Galldiks N, Dunkl V, Stoffels G, Hutterer M, Rapp M, Sabel M, et al. Diagnosis of pseudoprogression in patients with glioblastoma using O-(2-[18F]fluoroethyl)-L-tyrosine PET. Eur J Nucl Med Mol Imaging. 2015;42:685–95.

    Article  CAS  PubMed  Google Scholar 

  2. Langen KJ, Galldiks N, Hattingen E, Shah NJ. Advances in neuro-oncology imaging. Nat Rev Neurol. 2017;13:279–89.

    Article  PubMed  Google Scholar 

  3. Ahluwalia MS, Wen PY. Antiangiogenic therapy for patients with glioblastoma: current challenges in imaging and future directions. Expert Rev Anticancer Ther. 2011;11:653–6.

    Article  PubMed  Google Scholar 

  4. Dhermain FG, Hau P, Lanfermann H, Jacobs AH, van den Bent MJ. Advanced MRI and PET imaging for assessment of treatment response in patients with gliomas. Lancet Neurol. 2010;9:906–20.

    Article  PubMed  Google Scholar 

  5. Kumar AJ, Leeds NE, Fuller GN, Van Tassel P, Maor MH, Sawaya RE, et al. Malignant gliomas: MR imaging spectrum of radiation therapy- and chemotherapy-induced necrosis of the brain after treatment. Radiology. 2000;217:377–84.

    Article  CAS  PubMed  Google Scholar 

  6. Wen PY, Macdonald DR, Reardon DA, Cloughesy TF, Sorensen AG, Galanis E, et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol. 2010;28:1963–72.

    Article  PubMed  Google Scholar 

  7. Reardon DA, Weller M. Pseudoprogression: fact or wishful thinking in neuro-oncology? Lancet Oncol. 2018;19:1561–3.

    Article  PubMed  Google Scholar 

  8. Lee WJ, Choi SH, Park CK, Yi KS, Kim TM, Lee SH, et al. Diffusion-weighted MR imaging for the differentiation of true progression from pseudoprogression following concomitant radiotherapy with temozolomide in patients with newly diagnosed high-grade gliomas. Acad Radiol. 2012;19:1353–61.

    Article  PubMed  Google Scholar 

  9. Hein PA, Eskey CJ, Dunn JF, Hug EB. Diffusion-weighted imaging in the follow-up of treated high-grade gliomas: tumor recurrence versus radiation injury. AJNR Am J Neuroradiol. 2004;25:201–9.

    PubMed  PubMed Central  Google Scholar 

  10. Asao C, Korogi Y, Kitajima M, Hirai T, Baba Y, Makino K, et al. Diffusion-weighted imaging of radiation-induced brain injury for differentiation from tumor recurrence. AJNR Am J Neuroradiol. 2005;26:1455–60.

    PubMed  PubMed Central  Google Scholar 

  11. Chu HH, Choi SH, Ryoo I, Kim SC, Yeom JA, Shin H, et al. Differentiation of true progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant temozolomide: comparison study of standard and high-b-value diffusion-weighted imaging. Radiology. 2013;269:831–40.

    Article  PubMed  Google Scholar 

  12. Yoo RE, Choi SH, Kim TM, Lee SH, Park CK, Park SH, et al. Independent poor prognostic factors for true progression after radiation therapy and concomitant temozolomide in patients with glioblastoma: subependymal enhancement and low ADC value. AJNR Am J Neuroradiol. 2015;36:1846–52.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Kazda T, Bulik M, Pospisil P, Lakomy R, Smrcka M, Slampa P, et al. Advanced MRI increases the diagnostic accuracy of recurrent glioblastoma: single institution thresholds and validation of MR spectroscopy and diffusion weighted MR imaging. Neuroimage Clin. 2016;11:316–21.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Weller M, van den Bent M, Tonn JC, Stupp R, Preusser M, Cohen-Jonathan-Moyal E, et al. European Association for Neuro-Oncology (EANO) guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas. Lancet Oncol. 2017;18:e315–e29.

    Article  PubMed  Google Scholar 

  15. Padhani AR, Liu G, Koh DM, Chenevert TL, Thoeny HC, Takahara T, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia. 2009;11:102–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Prager AJ, Martinez N, Beal K, Omuro A, Zhang Z, Young RJ. Diffusion and perfusion MRI to differentiate treatment-related changes including pseudoprogression from recurrent tumors in high-grade gliomas with histopathologic evidence. AJNR Am J Neuroradiol. 2015;36:877–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Bulik M, Kazda T, Slampa P, Jancalek R. The diagnostic ability of follow-up imaging biomarkers after treatment of glioblastoma in the Temozolomide era: implications from proton MR spectroscopy and apparent diffusion coefficient mapping. Biomed Res Int. 2015;2015:641023.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Jena A, Taneja S, Gambhir A, Mishra AK, D’Souza MM, Verma SM, et al. Glioma recurrence versus radiation necrosis: single-session multiparametric approach using simultaneous O-(2-18F-Fluoroethyl)-L-tyrosine PET/MRI. Clin Nucl Med. 2016;41:e228–36.

    Article  PubMed  Google Scholar 

  19. Pyka T, Hiob D, Preibisch C, Gempt J, Wiestler B, Schlegel J, et al. Diagnosis of glioma recurrence using multiparametric dynamic 18F-fluoroethyl-tyrosine PET-MRI. Eur J Radiol. 2018;103:32–7.

    Article  PubMed  Google Scholar 

  20. Galldiks N, Law I, Pope WB, Arbizu J, Langen KJ. The use of amino acid PET and conventional MRI for monitoring of brain tumor therapy. Neuroimage Clin. 2017;13:386–94.

    Article  PubMed  Google Scholar 

  21. Ceccon G, Lohmann P, Stoffels G, Judov N, Filss CP, Rapp M, et al. Dynamic O-(2-18F-fluoroethyl)-L-tyrosine positron emission tomography differentiates brain metastasis recurrence from radiation injury after radiotherapy. Neuro-Oncology. 2017;19:281–8.

    CAS  PubMed  Google Scholar 

  22. Galldiks N, Langen KJ, Albert NL, Chamberlain M, Soffietti R, Kim MM, et al. PET imaging in patients with brain metastasis-report of the RANO/PET group. Neuro-Oncology. 2019;21:585–95.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Galldiks N, Albert NL, Sommerauer M, Grosu AL, Ganswindt U, Law I, et al. PET imaging in patients with meningioma-report of the RANO/PET group. Neuro-Oncology. 2017;19:1576–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Albert NL, Weller M, Suchorska B, Galldiks N, Soffietti R, Kim MM, et al. Response assessment in neuro-oncology working group and European Association for Neuro-Oncology recommendations for the clinical use of PET imaging in gliomas. Neuro-Oncology. 2016;18:1199–208.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Law I, Albert NL, Arbizu J, Boellaard R, Drzezga A, Galldiks N, et al. Joint EANM/EANO/RANO practice guidelines/SNMMI procedure standards for imaging of gliomas using PET with radiolabelled amino acids and [(18)F]FDG: version 1.0. Eur J Nucl Med Mol Imaging. 2019;46:540–57.

    Article  CAS  PubMed  Google Scholar 

  26. Sogani SK, Jena A, Taneja S, Gambhir A, Mishra AK, D’Souza MM, et al. Potential for differentiation of glioma recurrence from radionecrosis using integrated (18)F-fluoroethyl-L-tyrosine (FET) positron emission tomography/magnetic resonance imaging: a prospective evaluation. Neurol India. 2017;65:293–301.

    Article  PubMed  Google Scholar 

  27. Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 2005;352:987–96.

    Article  CAS  PubMed  Google Scholar 

  28. Young RJ, Gupta A, Shah AD, Graber JJ, Zhang Z, Shi W, et al. Potential utility of conventional MRI signs in diagnosing pseudoprogression in glioblastoma. Neurology. 2011;76:1918–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Hamacher K, Coenen HH. Efficient routine production of the 18F-labelled amino acid O-2-18F fluoroethyl-L-tyrosine. Appl Radiat Isot. 2002;57:853–6.

    Article  CAS  PubMed  Google Scholar 

  30. Langen KJ, Bartenstein P, Boecker H, Brust P, Coenen HH, Drzezga A, et al. German guidelines for brain tumour imaging by PET and SPECT using labelled amino acids. Nuklearmedizin. 2011;50:167–73.

    Article  PubMed  Google Scholar 

  31. Herzog H, Langen KJ, Weirich C, Rota Kops E, Kaffanke J, Tellmann L, et al. High resolution BrainPET combined with simultaneous MRI. Nuklearmedizin. 2011;50:74–82.

    Article  CAS  PubMed  Google Scholar 

  32. Lohmann P, Herzog H, Rota Kops E, Stoffels G, Judov N, Filss C, et al. Dual-time-point O-(2-[(18)F]fluoroethyl)-L-tyrosine PET for grading of cerebral gliomas. Eur Radiol. 2015;25:3017–24.

    Article  PubMed  Google Scholar 

  33. Galldiks N, Stoffels G, Filss C, Rapp M, Blau T, Tscherpel C, et al. The use of dynamic O-(2-18F-fluoroethyl)-l-tyrosine PET in the diagnosis of patients with progressive and recurrent glioma. Neuro-Oncology. 2015;17:1293–300.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Rapp M, Heinzel A, Galldiks N, Stoffels G, Felsberg J, Ewelt C, et al. Diagnostic performance of 18F-FET PET in newly diagnosed cerebral lesions suggestive of glioma. J Nucl Med. 2013;54:229–35.

    Article  CAS  PubMed  Google Scholar 

  35. Pauleit D, Floeth F, Hamacher K, Riemenschneider MJ, Reifenberger G, Müller HW, et al. O-(2-[18F]fluoroethyl)-L-tyrosine PET combined with MRI improves the diagnostic assessment of cerebral gliomas. Brain. 2005;128:678–87.

    Article  PubMed  Google Scholar 

  36. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 2016;131:803–20.

    Article  PubMed  Google Scholar 

  37. Felsberg J, Rapp M, Loeser S, Fimmers R, Stummer W, Goeppert M, et al. Prognostic significance of molecular markers and extent of resection in primary glioblastoma patients. Clin Cancer Res. 2009;15:6683–93.

    Article  CAS  PubMed  Google Scholar 

  38. Pöpperl G, Götz C, Rachinger W, Gildehaus FJ, Tonn JC, Tatsch K. Value of O-(2-[18F]fluoroethyl)-L-tyrosine PET for the diagnosis of recurrent glioma. Eur J Nucl Med Mol Imaging. 2004;31:1464–70.

    Article  CAS  PubMed  Google Scholar 

  39. Rachinger W, Goetz C, Pöpperl G, Gildehaus FJ, Kreth FW, Holtmannspotter M, et al. Positron emission tomography with O-(2-[18F]fluoroethyl)-l-tyrosine versus magnetic resonance imaging in the diagnosis of recurrent gliomas. Neurosurgery. 2005;57:505–11.

    Article  PubMed  Google Scholar 

  40. Mihovilovic MI, Kertels O, Hanscheid H, Lohr M, Monoranu CM, Kleinlein I, et al. O-(2-((18)F)fluoroethyl)-L-tyrosine PET for the differentiation of tumour recurrence from late pseudoprogression in glioblastoma. J Neurol Neurosurg Psychiatry. 2019;90:238–9.

    Article  PubMed  Google Scholar 

  41. Mehrkens JH, Pöpperl G, Rachinger W, Herms J, Seelos K, Tatsch K, et al. The positive predictive value of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET in the diagnosis of a glioma recurrence after multimodal treatment. J Neuro-Oncol. 2008;88:27–35.

    Article  CAS  Google Scholar 

  42. Kebir S, Fimmers R, Galldiks N, Schafer N, Mack F, Schaub C, et al. Late Pseudoprogression in glioblastoma: diagnostic value of dynamic O-(2-[18F]fluoroethyl)-L-tyrosine PET. Clin Cancer Res. 2016;22:2190–6.

    Article  CAS  PubMed  Google Scholar 

  43. Langen KJ, Stoffels G, Filss C, Heinzel A, Stegmayr C, Lohmann P, et al. Imaging of amino acid transport in brain tumours: positron emission tomography with O-(2-[(18)F]fluoroethyl)-L-tyrosine (FET). Methods. 2017;130:124–34.

    Article  CAS  PubMed  Google Scholar 

  44. Melguizo-Gavilanes I, Bruner JM, Guha-Thakurta N, Hess KR, Puduvalli VK. Characterization of pseudoprogression in patients with glioblastoma: is histology the gold standard? J Neuro-Oncol. 2015;123:141–50.

    Article  CAS  Google Scholar 

  45. Hutterer M, Nowosielski M, Putzer D, Jansen NL, Seiz M, Schocke M, et al. [18F]-fluoro-ethyl-L-tyrosine PET: a valuable diagnostic tool in neuro-oncology, but not all that glitters is glioma. Neuro-Oncology. 2013;15:341–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Filss CP, Galldiks N, Stoffels G, Sabel M, Wittsack HJ, Turowski B, et al. Comparison of 18F-FET PET and perfusion-weighted MR imaging: a PET/MR imaging hybrid study in patients with brain tumors. J Nucl Med. 2014;55:540–5.

    Article  CAS  PubMed  Google Scholar 

  47. Henriksen OM, Larsen VA, Muhic A, Hansen AE, Larsson HB, Poulsen HS, et al. Simultaneous evaluation of brain tumour metabolism, structure and blood volume using [(18)F]-fluoroethyltyrosine (FET) PET/MRI: feasibility, agreement and initial experience. Eur J Nucl Med Mol Imaging. 2016;43:103–12.

    Article  CAS  PubMed  Google Scholar 

  48. Göttler J, Lukas M, Kluge A, Kaczmarz S, Gempt J, Ringel F, et al. Intra-lesional spatial correlation of static and dynamic FET-PET parameters with MRI-based cerebral blood volume in patients with untreated glioma. Eur J Nucl Med Mol Imaging. 2017;44:392–7.

    Article  CAS  PubMed  Google Scholar 

  49. Zhang K, Langen KJ, Neuner I, Stoffels G, Filss C, Galldiks N, et al. Relationship of regional cerebral blood flow and kinetic behaviour of O-(2-(18)F-fluoroethyl)-L-tyrosine uptake in cerebral gliomas. Nucl Med Commun. 2014;35:245–51.

    Article  CAS  PubMed  Google Scholar 

  50. Stegmayr C, Schöneck M, Oliveira D, Willuweit A, Filss C, Galldiks N, et al. Reproducibility of O-(2-(18)F-fluoroethyl)-L-tyrosine uptake kinetics in brain tumors and influence of corticoid therapy: an experimental study in rat gliomas. Eur J Nucl Med Mol Imaging. 2016;43:1115–23.

    Article  CAS  PubMed  Google Scholar 

  51. Zhang H, Ma L, Shu C, Wang YB, Dong LQ. Diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating glioma recurrence from radiation necrosis. J Neurol Sci. 2015;351:65–71.

    Article  PubMed  Google Scholar 

  52. Bobek-Billewicz B, Stasik-Pres G, Majchrzak H, Zarudzki L. Differentiation between brain tumor recurrence and radiation injury using perfusion, diffusion-weighted imaging and MR spectroscopy. Folia Neuropathol. 2010;48:81–92.

    PubMed  Google Scholar 

  53. Huang RY, Neagu MR, Reardon DA, Wen PY. Pitfalls in the neuroimaging of glioblastoma in the era of antiangiogenic and immuno/targeted therapy—detecting illusive disease, defining response. Front Neurol. 2015;6:33.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Sundgren PC, Fan X, Weybright P, Welsh RC, Carlos RC, Petrou M, et al. Differentiation of recurrent brain tumor versus radiation injury using diffusion tensor imaging in patients with new contrast-enhancing lesions. Magn Reson Imaging. 2006;24:1131–42.

    Article  CAS  PubMed  Google Scholar 

  55. Lohmann P, Werner JM, Shah NJ, Fink GR, Langen KJ, Galldiks N. Combined amino acid positron emission tomography and advanced magnetic resonance imaging in glioma patients. Cancers (Basel). 2019;11.

  56. Sasaki M, Yamada K, Watanabe Y, Matsui M, Ida M, Fujiwara S, et al. Variability in absolute apparent diffusion coefficient values across different platforms may be substantial: a multivendor, multi-institutional comparison study. Radiology. 2008;249:624–30.

    Article  PubMed  Google Scholar 

  57. Ogura A, Tamura T, Ozaki M, Doi T, Fujimoto K, Miyati T, et al. Apparent diffusion coefficient value is not dependent on magnetic resonance systems and field strength under fixed imaging parameters in brain. J Comput Assist Tomogr. 2015;39:760–5.

    Article  PubMed  Google Scholar 

  58. Wu CC, Jain R, Radmanesh A, Poisson LM, Guo WY, Zagzag D, et al. Predicting genotype and survival in glioma using standard clinical MR imaging apparent diffusion coefficient images: a pilot study from the Cancer genome atlas. AJNR Am J Neuroradiol. 2018;39:1814–20.

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

The Wilhelm-Sander Stiftung, Germany, supported this work.

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Author notes

  1. Christoph Kabbasch and Norbert Galldiks contributed equally to this work.

Authors and Affiliations

  1. Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

    Jan-Michael Werner, Garry Ceccon, Gereon R. Fink & Norbert Galldiks

  2. Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany

    Gabriele Stoffels, Philipp Lohmann, Nadim J. Shah, Gereon R. Fink, Karl-Josef Langen & Norbert Galldiks

  3. Department of Neuroradiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

    Thorsten Lichtenstein, Jan Borggrefe & Christoph Kabbasch

  4. Department of Neurology, University Hospital Aachen, Aachen, Germany

    Nadim J. Shah

  5. Department of Nuclear Medicine, University Hospital Aachen, Aachen, Germany

    Karl-Josef Langen

  6. Center of Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne, and Düsseldorf, Cologne, Germany

    Norbert Galldiks

  7. Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Leo-Brandt-St. 5, 52425, Juelich, Germany

    Norbert Galldiks

  8. Department of Neurology, University Hospital Cologne, Kerpener St. 62, 50937, Cologne, Germany

    Norbert Galldiks

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  1. Jan-Michael Werner
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Correspondence to Norbert Galldiks.

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Werner, JM., Stoffels, G., Lichtenstein, T. et al. Differentiation of treatment-related changes from tumour progression: a direct comparison between dynamic FET PET and ADC values obtained from DWI MRI. Eur J Nucl Med Mol Imaging 46, 1889–1901 (2019). https://doi.org/10.1007/s00259-019-04384-7

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