European Radiology

, Volume 27, Issue 7, pp 2916–2927 | Cite as

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

  • Philipp Lohmann
  • Gabriele Stoffels
  • Garry Ceccon
  • Marion Rapp
  • Michael Sabel
  • Christian P. Filss
  • Marcel A. Kamp
  • Carina Stegmayr
  • Bernd Neumaier
  • Nadim J. Shah
  • Karl-Josef Langen
  • Norbert Galldiks
Nuclear Medicine



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.


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.


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.


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


FET PET Textural analysis Radiomics Brain metastasis Radiation injury 













Area under the receiver-operating-characteristic curve


Co-occurrence matrix


Full width at half maximum


Magnetic resonance spectroscopy


Normalized co-occurrence matrix


Neighbourhood grey level dependence


Neighbourhood intensity difference matrix


Ordered subset expectation maximisation




Stereotactic radiosurgery


Standardized uptake value


Time-activity curve


Tumour-to-brain ratio


Mean tumour-to-brain ratio


Maximum tumour-to-brain ratio


Texture feature coding


Texture feature coding co-occurrence matrix






Whole-brain radiation therapy



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.


  1. 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 33:398–407CrossRefPubMedGoogle Scholar
  2. 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 363:1665–1672CrossRefPubMedGoogle Scholar
  3. 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 45:427–434CrossRefPubMedGoogle Scholar
  4. 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 29:134–141CrossRefPubMedGoogle Scholar
  5. 5.
    Lippitz B, Lindquist C, Paddick I et al (2014) Stereotactic radiosurgery in the treatment of brain metastases: The current evidence. Cancer Treat Rev 40:48–59CrossRefPubMedGoogle Scholar
  6. 6.
    Chen W (2007) Clinical Applications of PET in Brain Tumors. J Nucl Med 48:1468–1481CrossRefPubMedGoogle Scholar
  7. 7.
    Greene-Schloesser D, Robbins ME, Peiffer AM et al (2012) Radiation-induced brain injury: A review. Front Oncol 2:1–18CrossRefGoogle Scholar
  8. 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 6:48CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Wang Y-XJ, King AD, Zhou H et al (2010) Evolution of radiation-induced brain injury: MR imaging-based study. Radiology 254:210–218CrossRefPubMedGoogle Scholar
  10. 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 32:1885–1892CrossRefPubMedGoogle Scholar
  11. 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 13:307–316CrossRefPubMedPubMedCentralGoogle Scholar
  12. 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 8:54CrossRefPubMedPubMedCentralGoogle Scholar
  13. 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 30:96–100CrossRefPubMedGoogle Scholar
  14. 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 96:191–197CrossRefPubMedGoogle Scholar
  15. 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 17:1434–1444CrossRefPubMedPubMedCentralGoogle Scholar
  16. 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 36:639–642CrossRefPubMedGoogle Scholar
  17. 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 49:694–699CrossRefPubMedGoogle Scholar
  18. 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 53:1367–1374CrossRefPubMedGoogle Scholar
  19. 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 55:30–36CrossRefPubMedGoogle Scholar
  20. 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 53:1058–1064CrossRefPubMedPubMedCentralGoogle Scholar
  21. 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. 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 36:841–847CrossRefPubMedGoogle Scholar
  23. 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 34:1933–1942CrossRefPubMedGoogle Scholar
  24. 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 25:3017–3024CrossRefPubMedGoogle Scholar
  25. 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 39:1021–1029CrossRefPubMedGoogle Scholar
  26. 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 55:198–203CrossRefPubMedGoogle Scholar
  27. 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 56:9–15CrossRefPubMedGoogle Scholar
  28. 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. 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 42:685–695CrossRefPubMedGoogle Scholar
  30. 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 17:1293–1300CrossRefPubMedPubMedCentralGoogle Scholar
  31. 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 34:2082–2087CrossRefPubMedGoogle Scholar
  32. 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 16:434–440CrossRefPubMedGoogle Scholar
  33. 33.
    Marusyk A, Polyak K (2011) Tumor heterogeneity: causes and consequences. Biochim Biophys Acta 1805:1–28Google Scholar
  34. 34.
    Meacham CE, Morrison SJ (2013) Tumour heterogeneity and cancer cell plasticity. Nature 501:328–337CrossRefPubMedPubMedCentralGoogle Scholar
  35. 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 52:369–378CrossRefPubMedPubMedCentralGoogle Scholar
  36. 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 43:133–141CrossRefPubMedGoogle Scholar
  37. 37.
    Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol 59:1061–1069CrossRefPubMedGoogle Scholar
  38. 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 87:1–15CrossRefGoogle Scholar
  39. 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 8:176–184CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Lin NU, Lee EQ, Aoyama H et al (2015) Response assessment criteria for brain metastases: Proposal from the RANO group. Lancet Oncol 16:270–278CrossRefGoogle Scholar
  41. 41.
    Hamacher K, Coenen HH (2002) Efficient routine production of the 18F-labelled amino acid O-2-18F fluoroethyl-L-tyrosine. Appl Radiat Isot 57:853–856CrossRefPubMedGoogle Scholar
  42. 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 50:167–173CrossRefPubMedGoogle Scholar
  43. 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 51:2662–2669CrossRefGoogle Scholar
  44. 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 128:678–687CrossRefPubMedGoogle Scholar
  45. 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 2014:248505PubMedPubMedCentralGoogle Scholar
  46. 46.
    Haralick RM, Shanmugam K, Dinstein I (1973) Textural Features for Image Classification. IEEE Trans Syst Man Cybern 3:610–621Google Scholar
  47. 47.
    Loh H-H, Leu J-G, Luo RC (1988) The analysis of natural textures using run length features. IEEE Trans Ind Electron 35:323–328CrossRefGoogle Scholar
  48. 48.
    Amadasun M, King R (1989) Textural features corresponding to textural properties. IEEE Trans Syst Man Cybern 19:1264–1274CrossRefGoogle Scholar
  49. 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.Google Scholar
  50. 50.
    He D-C, Wang L (1991) Texture features based on texture spectrum. Pattern Recognit 24:391–399CrossRefGoogle Scholar
  51. 51.
    Horng MH, Sun YN, Lin XZ (2002) Texture feature coding method for classification of liver sonography. Comput Med Imaging Graph 26:33–42CrossRefPubMedGoogle Scholar
  52. 52.
    Sun C, Wee WG (1983) Neighboring gray level dependence matrix for texture classification. Comput Vision, Graph Image Process 23:341–352CrossRefGoogle Scholar
  53. 53.
    Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36CrossRefPubMedGoogle Scholar
  54. 54.
    Geertzen J (2012) Inter-Rater Agreement with multiple raters and variables. https:/ Accessed 27 Apr 2016Google Scholar
  55. 55.
    Lowry R (1998) VassarStats: Website for Statistical Computation. Accessed 29 Sep 2016Google Scholar
  56. 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 54:19–26CrossRefPubMedGoogle Scholar
  57. 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 40:716–727CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Huang B, Chan T, Kwong DLW et al (2012) Nasopharyngeal carcinoma: Investigation of intratumoral heterogeneity with FDG PET/CT. Am J Roentgenol 199:169–174CrossRefGoogle Scholar
  59. 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 40:685–692CrossRefPubMedGoogle Scholar
  60. 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 54:2046–2054CrossRefPubMedGoogle Scholar
  61. 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 11:1–16Google Scholar
  62. 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 12:1–8Google Scholar

Copyright information

© European Society of Radiology 2016

Authors and Affiliations

  • Philipp Lohmann
    • 1
  • Gabriele Stoffels
    • 1
  • Garry Ceccon
    • 2
  • Marion Rapp
    • 3
  • Michael Sabel
    • 3
  • Christian P. Filss
    • 1
    • 4
  • Marcel A. Kamp
    • 3
  • Carina Stegmayr
    • 1
  • Bernd Neumaier
    • 1
  • Nadim J. Shah
    • 1
    • 5
    • 6
  • Karl-Josef Langen
    • 1
    • 4
    • 6
  • Norbert Galldiks
    • 1
    • 2
    • 7
  1. 1.Institute of Neuroscience and MedicineForschungszentrum JülichJülichGermany
  2. 2.Department of NeurologyUniversity of CologneCologneGermany
  3. 3.Department of NeurosurgeryHeinrich Heine University DüsseldorfDüsseldorfGermany
  4. 4.Department of Nuclear MedicineRWTH Aachen University HospitalAachenGermany
  5. 5.Department of NeurologyRWTH Aachen University HospitalAachenGermany
  6. 6.Department of NeurologyJülich-Aachen Research Alliance (JARA) - Section JARA-BrainJülichGermany
  7. 7.Center of Integrated Oncology (CIO)University of CologneCologneGermany

Personalised recommendations