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Radiation injury vs. recurrent brain metastasis: combining textural feature radiomics analysis and standard parameters may increase 18F-FET PET accuracy without dynamic scans

  • Nuclear Medicine
  • Published:
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Abstract

Objectives

We investigated the potential of textural feature analysis of O-(2-[18F]fluoroethyl)-L-tyrosine (18F-FET) PET to differentiate radiation injury from brain metastasis recurrence.

Methods

Forty-seven patients with contrast-enhancing brain lesions (n = 54) on MRI after radiotherapy of brain metastases underwent dynamic 18F-FET PET. Tumour-to-brain ratios (TBRs) of 18F-FET uptake and 62 textural parameters were determined on summed images 20-40 min post-injection. Tracer uptake kinetics, i.e., time-to-peak (TTP) and patterns of time-activity curves (TAC) were evaluated on dynamic PET data from 0-50 min post-injection. Diagnostic accuracy of investigated parameters and combinations thereof to discriminate between brain metastasis recurrence and radiation injury was compared.

Results

Diagnostic accuracy increased from 81 % for TBRmean alone to 85 % when combined with the textural parameter Coarseness or Short-zone emphasis. The accuracy of TBRmax alone was 83 % and increased to 85 % after combination with the textural parameters Coarseness, Short-zone emphasis, or Correlation. Analysis of TACs resulted in an accuracy of 70 % for kinetic pattern alone and increased to 83 % when combined with TBRmax.

Conclusions

Textural feature analysis in combination with TBRs may have the potential to increase diagnostic accuracy for discrimination between brain metastasis recurrence and radiation injury, without the need for dynamic 18F-FET PET scans.

Key points

• Textural feature analysis provides quantitative information about tumour heterogeneity

• Textural features help improve discrimination between brain metastasis recurrence and radiation injury

• Textural features might be helpful to further understand tumour heterogeneity

• Analysis does not require a more time consuming dynamic PET acquisition

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Abbreviations

11C-AMT:

α-[11C]-methyl-L-tryptophan

11C-MET:

[11C]-methyl-L-methionine

18F-FDG:

2-[18F]-fluoro-2-deoxy-D-glucose

18F-FDOPA:

L-3,4-dihydroxy-6-[18F]-fluoro-phenylalanine

18F-FET:

O-(2-[18F]fluoroethyl)-L-tyrosine

AUC:

Area under the receiver-operating-characteristic curve

CM:

Co-occurrence matrix

FWHM:

Full width at half maximum

MRS:

Magnetic resonance spectroscopy

NCM:

Normalized co-occurrence matrix

NGLD:

Neighbourhood grey level dependence

NIDM:

Neighbourhood intensity difference matrix

OSEM:

Ordered subset expectation maximisation

ROC:

Receiver-operating-characteristic

SRS:

Stereotactic radiosurgery

SUV:

Standardized uptake value

TAC:

Time-activity curve

TBR:

Tumour-to-brain ratio

TBRmean :

Mean tumour-to-brain ratio

TBRmax :

Maximum tumour-to-brain ratio

TFC:

Texture feature coding

TFCCM:

Texture feature coding co-occurrence matrix

TTP:

Time-to-peak

VOI:

Volume-of-interest

WBRT:

Whole-brain radiation therapy

References

  1. Platta CS, Khuntia D, Mehta MP, Suh JH (2010) Current Treatment Strategies for Brain Metastasis and Complications From Therapeutic Techniques. Am J Clin Oncol 33:398–407

    Article  PubMed  Google Scholar 

  2. Andrews DW, Scott CB, Sperduto PW et al (2004) Whole brain radiation therapy with or without stereotactic radiosurgery boost for patients with one to three brain metastases: Phase III results of the RTOG 9508 randomised trial. Lancet 363:1665–1672

    Article  PubMed  Google Scholar 

  3. Kondziolka D, Patel A, Lunsford LD et al (1999) Stereotactic radiosurgery plus whole brain radiotherapy versus radiotherapy alone for patients with multiple brain metastases. Int J Radiat Oncol Biol Phys 45:427–434

    Article  CAS  PubMed  Google Scholar 

  4. Kocher M, Soffietti R, Abacioglu U et al (2011) Adjuvant whole-brain radiotherapy versus observation after radiosurgery or surgical resection of one to three cerebral metastases: Results of the EORTC 22952-26001 study. J Clin Oncol 29:134–141

    Article  PubMed  Google Scholar 

  5. Lippitz B, Lindquist C, Paddick I et al (2014) Stereotactic radiosurgery in the treatment of brain metastases: The current evidence. Cancer Treat Rev 40:48–59

    Article  PubMed  Google Scholar 

  6. Chen W (2007) Clinical Applications of PET in Brain Tumors. J Nucl Med 48:1468–1481

    Article  PubMed  Google Scholar 

  7. Greene-Schloesser D, Robbins ME, Peiffer AM et al (2012) Radiation-induced brain injury: A review. Front Oncol 2:1–18

    Article  Google Scholar 

  8. Minniti G, Clarke E, Lanzetta G et al (2011) Stereotactic radiosurgery for brain metastases: analysis of outcome and risk of brain radionecrosis. Radiat Oncol 6:48

    Article  PubMed  PubMed Central  Google Scholar 

  9. Wang Y-XJ, King AD, Zhou H et al (2010) Evolution of radiation-induced brain injury: MR imaging-based study. Radiology 254:210–218

    Article  PubMed  Google Scholar 

  10. Patel TR, McHugh BJ, Bi WL et al (2011) A comprehensive review of MR imaging changes following radiosurgery to 500 brain metastases. Am J Neuroradiol 32:1885–1892

    Article  CAS  PubMed  Google Scholar 

  11. Kunz M, Thon N, Eigenbrod S et al (2011) Hot spots in dynamic (18)FET-PET delineate malignant tumor parts within suspected WHO grade II gliomas. Neuro Oncol 13:307–316

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Boström J, Hadizadeh DR, Block W et al (2013) Magnetic resonance spectroscopic study of radiogenic changes after radiosurgery of cerebral arteriovenous malformations with implications for the differential diagnosis of radionecrosis. Radiat Oncol 8:54

    Article  PubMed  PubMed Central  Google Scholar 

  13. Bélohlávek O, Šimonová G, Kantorová I et al (2003) Brain metastases after stereotactic radiosurgery using the Leksell gamma knife: Can FDG PET help to differentiate radionecrosis from tumour progression? Eur J Nucl Med Mol Imaging 30:96–100

    Article  PubMed  Google Scholar 

  14. Chao ST, Suh JH, Raja S et al (2001) The sensitivity and specificity of FDG PET in distinguishing recurrent brain tumor from radionecrosis in patients treated with stereotactic radiosurgery. Int J Cancer 96:191–197

    Article  CAS  PubMed  Google Scholar 

  15. Galldiks N, Langen K-J, Pope WB (2015) From the clinician’s point of view - What is the status quo of positron emission tomography in patients with brain tumors? Neuro Oncol 17:1434–1444

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Rottenburger C, Hentschel M, Kelly T et al (2011) Comparison of C-11 Methionine and C-11 Choline for PET Imaging of Brain Metastases. Clin Nucl Med 36:639–642

    Article  PubMed  Google Scholar 

  17. Terakawa Y, Tsuyuguchi N, Iwai Y et al (2008) Diagnostic accuracy of 11C-methionine PET for differentiation of recurrent brain tumors from radiation necrosis after radiotherapy. J Nucl Med 49:694–699

    Article  PubMed  Google Scholar 

  18. Galldiks N, Stoffels G, Filss CP et al (2012) Role of O-(2-(18)F-fluoroethyl)-L-tyrosine PET for differentiation of local recurrent brain metastasis from radiation necrosis. J Nucl Med 53:1367–1374

    Article  CAS  PubMed  Google Scholar 

  19. Lizarraga KJ, Allen-Auerbach M, Czernin J et al (2014) 18F-FDOPA PET for Differentiating Recurrent or Progressive Brain Metastatic Tumors from Late or Delayed Radiation Injury After Radiation Treatment. J Nucl Med 55:30–36

    Article  CAS  PubMed  Google Scholar 

  20. Alkonyi B, Barger GR, Mittal S et al (2012) Accurate Differentiation of Recurrent Gliomas from Radiation Injury by Kinetic Analysis of -11C-Methyl-L-Tryptophan PET. J Nucl Med 53:1058–1064

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Albert NL, Weller M, Suchorska B et al (2016) Response Assessment in Neuro-Oncology working group and European Association for Neuro-Oncology recommendations for the clinical use of PET imaging in gliomas. Neuro Oncol. doi:10.1093/neuonc/now058

    Google Scholar 

  22. Calcagni ML, Galli G, Giordano A et al (2011) Dynamic O-(2-[18F]fluoroethyl)-L-tyrosine (F-18 FET) PET for Glioma Grading. Clin Nucl Med 36:841–847

    Article  PubMed  Google Scholar 

  23. Pöpperl G, Kreth FW, Mehrkens JH et al (2007) FET PET for the evaluation of untreated gliomas: correlation of FET uptake and uptake kinetics with tumour grading. Eur J Nucl Med Mol Imaging 34:1933–1942

    Article  PubMed  Google Scholar 

  24. Lohmann P, Herzog H, Rota Kops E et al (2015) Dual-time-point O-(2-[18F]fluoroethyl)-L-tyrosine PET for grading of cerebral gliomas. Eur Radiol 25:3017–3024

    Article  PubMed  Google Scholar 

  25. Jansen NL, Graute V, Armbruster L et al (2012) MRI-suspected low-grade glioma: is there a need to perform dynamic FET PET? Eur J Nucl Med Mol Imaging 39:1021–1029

    Article  CAS  PubMed  Google Scholar 

  26. Jansen NL, Suchorska B, Wenter V et al (2014) Dynamic 18F-FET PET in Newly Diagnosed Astrocytic Low-Grade Glioma Identifies High-Risk Patients. J Nucl Med 55:198–203

    Article  CAS  PubMed  Google Scholar 

  27. Jansen NL, Suchorska B, Wenter V et al (2015) Prognostic significance of dynamic 18F-FET PET in newly diagnosed astrocytic high-grade glioma. J Nucl Med 56:9–15

    Article  CAS  PubMed  Google Scholar 

  28. Ceccon G, Lohmann P, Stoffels G et al (2016) Dynamic O-(2-18F-fluoroethyl)-L-tyrosine positron emission tomography differentiates brain metastasis recurrence from radiation injury after radiotherapy. Neuro Oncol. doi:10.1093/neuonc/now149

    Google Scholar 

  29. Galldiks N, Dunkl V, Stoffels G et al (2015) Diagnosis of pseudoprogression in patients with glioblastoma using O-(2-[18F]fluoroethyl)-l-tyrosine PET. Eur J Nucl Med Mol Imaging 42:685–695

    Article  CAS  PubMed  Google Scholar 

  30. Galldiks N, Stoffels G, Filss C et al (2015) The use of dynamic O-(2-18F-fluoroethyl)-L-tyrosine PET in the diagnosis of patients with progressive and recurrent glioma. Neuro Oncol 17:1293–1300

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Moulin-Romsée G, D’Hondt E, de Groot T et al (2007) Non-invasive grading of brain tumours using dynamic amino acid PET imaging: does it work for 11C-methionine? Eur J Nucl Med Mol Imaging 34:2082–2087

    Article  PubMed  Google Scholar 

  32. Kratochwil C, Combs SE, Leotta K et al (2014) Intra-individual comparison of 18F-FET and 18F-DOPA in PET imaging of recurrent brain tumors. Neuro Oncol 16:434–440

    Article  CAS  PubMed  Google Scholar 

  33. Marusyk A, Polyak K (2011) Tumor heterogeneity: causes and consequences. Biochim Biophys Acta 1805:1–28

    Google Scholar 

  34. Meacham CE, Morrison SJ (2013) Tumour heterogeneity and cancer cell plasticity. Nature 501:328–337

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Tixier F, Le Rest CC, Hatt M et al (2011) Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med 52:369–378

    Article  PubMed  PubMed Central  Google Scholar 

  36. Pyka T, Gempt J, Hiob D et al (2016) Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas. Eur J Nucl Med Mol Imaging 43:133–141

    Article  PubMed  Google Scholar 

  37. Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol 59:1061–1069

    Article  CAS  PubMed  Google Scholar 

  38. Chowdhury R, Ganeshan B, Irshad S et al (2014) The use of molecular imaging combined with genomic techniques to understand the heterogeneity in cancer metastasis. Br J Radiol 87:1–15

    Article  Google Scholar 

  39. Murrell DH, Hamilton AM, Mallett CL et al (2015) Understanding Heterogeneity and Permeability of Brain Metastases in Murine Models of HER2-Positive Breast Cancer Through Magnetic Resonance Imaging: Implications for Detection and Therapy. Transl Oncol 8:176–184

    Article  PubMed  PubMed Central  Google Scholar 

  40. Lin NU, Lee EQ, Aoyama H et al (2015) Response assessment criteria for brain metastases: Proposal from the RANO group. Lancet Oncol 16:270–278

    Article  Google Scholar 

  41. Hamacher K, Coenen HH (2002) Efficient routine production of the 18F-labelled amino acid O-2-18F fluoroethyl-L-tyrosine. Appl Radiat Isot 57:853–856

    Article  CAS  PubMed  Google Scholar 

  42. Langen K-J, Bartenstein P, Boecker H et al (2011) German guidelines for brain tumour imaging by PET and SPECT using labelled amino acids. Nuklearmedizin 50:167–173

    Article  PubMed  Google Scholar 

  43. Herzog H, Tellmann L, Hocke C et al (2004) NEMA NU2-2001 guided performance evaluation of four Siemens ECAT PET scanners. IEEE Trans Nucl Sci 51:2662–2669

    Article  Google Scholar 

  44. Pauleit D, Floeth F, Hamacher K et al (2005) O-(2-[18F]fluoroethyl)-L-tyrosine PET combined with MRI improves the diagnostic assessment of cerebral gliomas. Brain 128:678–687

    Article  PubMed  Google Scholar 

  45. Fang Y-HD, Lin C-Y, Shih M-J et al (2014) Development and evaluation of an open-source software package “CGITA” for quantifying tumor heterogeneity with molecular images. Biomed Res Int 2014:248505

    PubMed  PubMed Central  Google Scholar 

  46. Haralick RM, Shanmugam K, Dinstein I (1973) Textural Features for Image Classification. IEEE Trans Syst Man Cybern 3:610–621

  47. Loh H-H, Leu J-G, Luo RC (1988) The analysis of natural textures using run length features. IEEE Trans Ind Electron 35:323–328

    Article  Google Scholar 

  48. Amadasun M, King R (1989) Textural features corresponding to textural properties. IEEE Trans Syst Man Cybern 19:1264–1274

    Article  Google Scholar 

  49. Thibault G, Fertil B, Navarro C, et al (2009) Texture Indexes and Gray Level Size Zone Matrix Application to Cell Nuclei Classification. Pattern Recognit Inf Process 140–145.

  50. He D-C, Wang L (1991) Texture features based on texture spectrum. Pattern Recognit 24:391–399

    Article  Google Scholar 

  51. Horng MH, Sun YN, Lin XZ (2002) Texture feature coding method for classification of liver sonography. Comput Med Imaging Graph 26:33–42

    Article  PubMed  Google Scholar 

  52. Sun C, Wee WG (1983) Neighboring gray level dependence matrix for texture classification. Comput Vision, Graph Image Process 23:341–352

    Article  Google Scholar 

  53. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36

    Article  CAS  PubMed  Google Scholar 

  54. Geertzen J (2012) Inter-Rater Agreement with multiple raters and variables. https:/nlp-ml.io/jg/software/ira/. Accessed 27 Apr 2016

  55. Lowry R (1998) VassarStats: Website for Statistical Computation. http://vassarstats.net/roc_comp.html. Accessed 29 Sep 2016

  56. Cook GJR, Yip C, Siddique M et al (2012) Are Pretreatment 18F-FDG PET Tumor Textural Features in Non-Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy? J Nucl Med 54:19–26

    Article  PubMed  Google Scholar 

  57. Yang F, Thomas MA, Dehdashti F, Grigsby PW (2013) Temporal analysis of intratumoral metabolic heterogeneity characterized by textural features in cervical cancer. Eur J Nucl Med Mol Imaging 40:716–727

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Huang B, Chan T, Kwong DLW et al (2012) Nasopharyngeal carcinoma: Investigation of intratumoral heterogeneity with FDG PET/CT. Am J Roentgenol 199:169–174

    Article  Google Scholar 

  59. Salamon J, Derlin T, Bannas P et al (2013) Evaluation of intratumoural heterogeneity on 18F-FDG PET/CT for characterization of peripheral nerve sheath tumours in neurofibromatosis type 1. Eur J Nucl Med Mol Imaging 40:685–692

    Article  CAS  PubMed  Google Scholar 

  60. Galldiks N, Stoffels G, Ruge MI et al (2013) Role of O-(2-18F-fluoroethyl)-L-tyrosine PET as a diagnostic tool for detection of malignant progression in patients with low-grade glioma. J Nucl Med 54:2046–2054

    Article  CAS  PubMed  Google Scholar 

  61. Bailly C, Bodet-Milin C, Couespel S et al (2016) Revisiting the robustness of PET-based textural features in the context of multi-centric trials. PLoS One 11:1–16

    Google Scholar 

  62. Piroth MD, Liebenstund S, Galldiks N et al (2013) Monitoring of radiochemotherapy in patients with glioblastoma using O-(2-[18F]fluoroethyl)-L-tyrosine positron emission tomography: Is dynamic imaging helpful? Mol Imaging 12:1–8

    Google Scholar 

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Acknowledgments

The authors thank Suzanne Schaden, Elisabeth Theelen, Silke Frensch, Kornelia Frey and Lutz Tellmann for assistance in the patient studies; Johannes Ermert, Silke Grafmüller, Erika Wabbals and Sascha Rehbein for radiosynthesis of 18F-FET.

The scientific guarantor of this publication is Prof. Dr. Karl-Josef Langen. The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. The authors state that this work has not received any funding.

No complex statistical methods were necessary for this paper. Institutional Review Board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study.

Some study subjects or cohorts have been previously reported in Galldiks N, Stoffels G, Filss CP et al (2012) Role of O-(2-(18)F-fluoroethyl)-L-tyrosine PET for differentiation of local recurrent brain metastasis from radiation necrosis. J Nucl Med 53:1367–1374.

Methodology: retrospective, diagnostic study, performed at one institution.

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Authors and Affiliations

  1. Institute of Neuroscience and Medicine, Forschungszentrum Jülich, Wilhelm-Johnen-Str., 52428, Jülich, Germany

    Philipp Lohmann, Gabriele Stoffels, Christian P. Filss, Carina Stegmayr, Bernd Neumaier, Nadim J. Shah, Karl-Josef Langen & Norbert Galldiks

  2. Department of Neurology, University of Cologne, Cologne, Germany

    Garry Ceccon & Norbert Galldiks

  3. Department of Neurosurgery, Heinrich Heine University Düsseldorf, Düsseldorf, Germany

    Marion Rapp, Michael Sabel & Marcel A. Kamp

  4. Department of Nuclear Medicine, RWTH Aachen University Hospital, Aachen, Germany

    Christian P. Filss & Karl-Josef Langen

  5. Department of Neurology, RWTH Aachen University Hospital, Aachen, Germany

    Nadim J. Shah

  6. Department of Neurology, Jülich-Aachen Research Alliance (JARA) - Section JARA-Brain, Jülich, Germany

    Nadim J. Shah & Karl-Josef Langen

  7. Center of Integrated Oncology (CIO), University of Cologne, Cologne, Germany

    Norbert Galldiks

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  1. Philipp Lohmann
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  2. Gabriele Stoffels
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Correspondence to Philipp Lohmann.

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Lohmann, P., Stoffels, G., Ceccon, G. et al. Radiation injury vs. recurrent brain metastasis: combining textural feature radiomics analysis and standard parameters may increase 18F-FET PET accuracy without dynamic scans. Eur Radiol 27, 2916–2927 (2017). https://doi.org/10.1007/s00330-016-4638-2

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