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The new era of bio-molecular imaging with O-(2-18F-fluoroethyl)-L-tyrosine (18F-FET) in neurosurgery of gliomas

Abstract

Purpose

The possibility to provide answers from morphological imaging to neurosurgeon needs in patients with high- and low-grade gliomas relies on FLAIR images and percentage increase of contrast enhancement on T1-weighted images. Molecular imaging investigating the functionality of tumor cells has been progressively introduced in surgery planning to overcome limitations of conventional MRI. The purpose is to highlight the potential diagnostic and prognostic role of 18F-FET PET in gliomas.

Methods

We performed a literature review for articles on the topic in PubMed, Google Scholar, and Web of Science until January 2022. Search keywords included “glioma”, “glioblastoma”, “tumor biology” “18F-FET”, “MRI”, “surgery”.

Results

This review provides evidence of the potential of molecular imaging with 18F-FET to address the surgeon diagnostic and prognostic needs, including the knowledge of precise burden of cancer to guide biopsy or maximal safe resection, tumor grade and residual disease. The main cornerstones underlying the capacity of molecular imaging to provide information concerning tumor biology are deeply discussed to make the reader confident with the role of 18F-FET as a reliable imaging bio-marker in gliomas.

Conclusion

Although conventional MRI has been shown to be reliable technique to identify gliomas in terms of morphological concerns, some important limitations depending on the incapacity to reveal the tumor biology have emerged. The 18F-FET PET has been established to provide early information of functional nature including real tumor extension, grade, and metabolic residual disease. Therefore, the use of 18F-FET PET has progressively increased suggesting a potential shift from a morphological to functional plus morphological neurosurgery.

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References

  1. Ostrom QT, Bauchet L, Davis FG et al (2014) The epidemiology of glioma in adults: a “state of the science” review. Neuro Oncol 16(7):896–913. https://doi.org/10.1093/neuonc/nou087

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. Braunstein S, Raleigh D, Bindra R, Mueller S, Haas-Kogan D (2017) Pediatric high-grade glioma: current molecular landscape and therapeutic approaches. J Neurooncol. 134(3):541–549

    CAS  Article  Google Scholar 

  3. Minturn JE, Fisher MJ (2013) Gliomas in Children. Curr Treat Options Neurol 15(3):316–327. https://doi.org/10.1007/s11940-013-0225-x

    Article  PubMed  Google Scholar 

  4. Xu S, Tang L, Li X, Fan F, Liu Z (2020) Immunotherapy for glioma: current management and future application. Cancer Lett. 476:1–12

    CAS  Article  Google Scholar 

  5. Bush NAO, Chang SM, Berger MS (2017) Current and future strategies for treatment of glioma. Neurosurg Rev 40(1):1–14. https://doi.org/10.1007/s10143-016-0709-8

    Article  PubMed  Google Scholar 

  6. Stupp R, Mason WP, van den Bent MJ et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352(10):987–996. https://doi.org/10.1056/NEJMoa043330

    CAS  Article  PubMed  Google Scholar 

  7. Henriksson R, Asklund T, Poulsen HS (2011) Impact of therapy on quality of life, neurocognitive function and their correlates in glioblastoma multiforme: a review. J Neurooncol 104(3):639–646. https://doi.org/10.1007/s11060-011-0565-x

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. Korja M, Raj R, Seppä K et al (2019) Glioblastoma survival is improving despite increasing incidence rates: a nationwide study between 2000 and 2013 in Finland. Neuro Oncol 21(3):370–379. https://doi.org/10.1093/neuonc/noy164

    Article  PubMed  Google Scholar 

  9. Lattanzi W, Ripoli C, Greco V et al (2021) Basic and preclinical research for personalized medicine. J Pers Med 11(5):354. https://doi.org/10.3390/jpm11050354

    Article  PubMed  PubMed Central  Google Scholar 

  10. Ma R, Taphoorn MJB, Plaha P (2021) Advances in the management of glioblastoma. J Neurol Neurosurg Psychiatry 92(10):1103–1111. https://doi.org/10.1136/jnnp-2020-325334

    Article  PubMed  Google Scholar 

  11. Galldiks N, Langen KJ, 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(11):1434–1444. https://doi.org/10.1093/neuonc/nov118

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. Molinaro AM, Hervey-Jumper S, Morshed RA, Young J, Han SJ, Chunduru P, Zhang Y, Phillips JJ, Shai A, Lafontaine M, Crane J, Chandra A, Flanigan P, Jahangiri A, Cioffi G, Ostrom Q, Anderson JE, Badve C, Barnholtz-Sloan J, Sloan AE, Erickson BJ, Decker PAB (2020) Association of maximal extent of resection of contrast-enhanced and non-contrast-enhanced tumor with survival within molecular subgroups of patients with newly diagnosed glioblastoma. JAMA Oncol 6(4):495–503. https://doi.org/10.1001/jamaoncol.2019.6143

    Article  PubMed  PubMed Central  Google Scholar 

  13. Stupp R, Dietrich P-Y, Kraljevic SO et al (2002) Promising survival for patients with newly diagnosed glioblastoma multiforme treated with concomitant radiation plus temozolomide followed by adjuvant temozolomide. J Clin Oncol 20(5):1375–1382. https://doi.org/10.1200/JCO.2002.20.5.1375

    CAS  Article  PubMed  Google Scholar 

  14. Comeau ZJ, Lessard BH, Shuhendler AJ (2022) The need to pair molecular monitoring devices with molecular imaging to personalize health. Mol Imaging Biol. https://doi.org/10.1007/s11307-022-01714-4

    Article  PubMed  PubMed Central  Google Scholar 

  15. Zaccagna F, Grist JT, Quartuccio N et al (2021) Imaging and treatment of brain tumors through molecular targeting: recent clinical advances. Eur J Radiol 142:109842. https://doi.org/10.1016/j.ejrad.2021.109842

    Article  PubMed  Google Scholar 

  16. Chukwueke UN, Wen PY (2019) Use of the response assessment in neuro-oncology (RANO) criteria in clinical trials and clinical practice. CNS Oncol. 8(1):CNS28. https://doi.org/10.2217/cns-2018-0007

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. Wen PY, Macdonald DR, Reardon DA et al (2010) Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 28(11):1963–1972. https://doi.org/10.1200/JCO.2009.26.3541

    Article  PubMed  Google Scholar 

  18. Zhang J, Liu H, Tong H et al (2017) Clinical applications of contrast-enhanced perfusion MRI techniques in gliomas: recent advances and current challenges. Contrast Media Mol Imaging 2017:1–27. https://doi.org/10.1155/2017/7064120

    CAS  Article  Google Scholar 

  19. 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 18(9):1199–1208. https://doi.org/10.1093/neuonc/now058

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. Wen PY, Chang SM, Van den Bent MJ, Vogelbaum MA, Macdonald DR, Lee EQ (2017) Response assessment in neuro-oncology clinical trials. J Clin Oncol 35(21):2439–2449. https://doi.org/10.1200/JCO.2017.72.7511

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. Law I, Albert NL, Arbizu J et al (2019) Joint EANM/EANO/RANO practice guidelines/SNMMI procedure standards for imaging of gliomas using PET with radiolabelled amino acids and [18F]FDG: version 1.0. Eur J Nucl Med Mol Imaging. 46(3):540–557. https://doi.org/10.1007/s00259-018-4207-9

    CAS  Article  PubMed  Google Scholar 

  22. Louis DN, Perry A, Wesseling P et al (2021) The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol 23(8):1231–1251. https://doi.org/10.1093/neuonc/noab106

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. Miyake K, Ogawa D, Okada M, Hatakeyama T, Tamiya T (2016) Usefulness of positron emission tomographic studies for gliomas. Neurol Med Chir (Tokyo) 56(7):396–408. https://doi.org/10.2176/nmc.ra.2015-0305

    Article  Google Scholar 

  24. Alam IS, Arshad MA, Nguyen Q-D, Aboagye EO (2015) Radiopharmaceuticals as probes to characterize tumour tissue. Eur J Nucl Med Mol Imaging 42(4):537–561. https://doi.org/10.1007/s00259-014-2984-3

    CAS  Article  PubMed  Google Scholar 

  25. Muthu M, Nordström A (2019) Current status and future prospects of clinically exploiting cancer-specific metabolism-why is tumor metabolism not more extensively translated into clinical targets and biomarkers? Int J Mol Sci 20(6):1385. https://doi.org/10.3390/ijms20061385

    CAS  Article  PubMed Central  Google Scholar 

  26. Pavlova NN, Thompson CB (2016) The emerging hallmarks of cancer metabolism. Cell Metab 23(1):27–47. https://doi.org/10.1016/j.cmet.2015.12.006

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. Venneti S, Dunphy MP, Zhang H et al (2015) Glutamine-based PET imaging facilitates enhanced metabolic evaluation of gliomas in vivo. Sci Transl Med. https://doi.org/10.1126/scitranslmed.aaa1009

    Article  PubMed  PubMed Central  Google Scholar 

  28. Ju H-Q, Lin J-F, Tian T, Xie D, Xu R-H (2020) NADPH homeostasis in cancer: functions, mechanisms and therapeutic implications. Signal Transduct Target Ther 5(1):231. https://doi.org/10.1038/s41392-020-00326-0

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. Zhang J, Pavlova NN, Thompson CB (2017) Cancer cell metabolism: the essential role of the nonessential amino acid, glutamine. EMBO J 36(10):1302–1315. https://doi.org/10.15252/embj.201696151

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. Lin L, Yee SW, Kim RB, Giacomini KM (2015) SLC transporters as therapeutic targets: emerging opportunities. Nat Rev Drug Discov 14(8):543–560. https://doi.org/10.1038/nrd4626

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. Lopes C, Pereira C, Medeiros R (2021) ASCT2 and LAT1 contribution to the hallmarks of cancer: from a molecular perspective to clinical translation. Cancers (Basel) 13(2):203. https://doi.org/10.3390/cancers13020203

    CAS  Article  Google Scholar 

  32. Fotiadis D, Kanai Y, Palacín M (2013) The SLC3 and SLC7 families of amino acid transporters. Mol Aspects Med 34(2–3):139–158. https://doi.org/10.1016/j.mam.2012.10.007

    CAS  Article  PubMed  Google Scholar 

  33. Scalise M, Pochini L, Console L, Losso MA, Indiveri C (2018) The human SLC1A5 (ASCT2) amino acid transporter: from function to structure and role in cell biology. Front Cell Dev Biol. https://doi.org/10.3389/fcell.2018.00096

    Article  PubMed  PubMed Central  Google Scholar 

  34. Nałęcz KA (2020) Amino acid transporter SLC6A14 (ATB0,+) – a target in combined anti-cancer therapy. Front Cell Dev Biol. https://doi.org/10.3389/fcell.2020.594464

    Article  PubMed  PubMed Central  Google Scholar 

  35. Kovalchuk V, Samluk Ł, Juraszek B et al (2019) Trafficking of the amino acid transporter B0,+ (SLC6A14) to the plasma membrane involves an exclusive interaction with SEC24C for its exit from the endoplasmic reticulum. Biochim Biophys Acta Mol Cell Res 1866(2):252–263. https://doi.org/10.1016/j.bbamcr.2018.11.005

    CAS  Article  PubMed  Google Scholar 

  36. Daubner SC, Le T, Wang S (2011) Tyrosine hydroxylase and regulation of dopamine synthesis. Arch Biochem Biophys 508(1):1–12. https://doi.org/10.1016/j.abb.2010.12.017

    CAS  Article  PubMed  Google Scholar 

  37. Saxton RA, Sabatini DM (2017) mTOR signaling in growth, metabolism, and disease. Cell 168(6):960–976. https://doi.org/10.1016/j.cell.2017.02.004

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. McCracken AN, Edinger AL (2013) Nutrient transporters: the Achilles’ heel of anabolism. Trends Endocrinol Metab 24(4):200–208. https://doi.org/10.1016/j.tem.2013.01.002

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. Yoshida GJ (2021) The harmonious interplay of amino acid and monocarboxylate transporters induces the robustness of cancer cells. Metabolites 11(1):27. https://doi.org/10.3390/metabo11010027

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. Lewerenz J, Hewett SJ, Huang Y et al (2013) The cystine/glutamate antiporter system x c in health and disease: from molecular mechanisms to novel therapeutic opportunities. Antioxid Redox Signal 18(5):522–555. https://doi.org/10.1089/ars.2011.4391

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. Sontheimer H (2008) A role for glutamate in growth and invasion of primary brain tumors. J Neurochem 105(2):287–295. https://doi.org/10.1111/j.1471-4159.2008.05301.x

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. Kan LK, Drummond K, Hunn M, Williams D, O’Brien TJ, Monif M (2020) Potential biomarkers and challenges in glioma diagnosis, therapy and prognosis. BMJ Neurol Open 2(2):e000069. https://doi.org/10.1136/bmjno-2020-000069

    Article  PubMed  PubMed Central  Google Scholar 

  43. Kristensen BW, Priesterbach-Ackley LP, Petersen JK, Wesseling P (2019) Molecular pathology of tumors of the central nervous system. Ann Oncol 30(8):1265–1278. https://doi.org/10.1093/annonc/mdz164

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. Bailey P, Cushing H (1925) Microchemical color reactions as an aid to the identification and classification of brain tumors. Proc Natl Acad Sci 11(1):82–84. https://doi.org/10.1073/pnas.11.1.82

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  45. Dunbar E, Yachnis AT (2010) Glioma diagnosis: immunohistochemistry and beyond. Adv Anat Pathol 17(3):187–201. https://doi.org/10.1097/PAP.0b013e3181d98cd9

    Article  PubMed  Google Scholar 

  46. Jaiswal S (2016) Role of immunohistochemistry in the diagnosis of central nervous system tumors. Neurol India 64(3):502. https://doi.org/10.4103/0028-3886.181547

    Article  PubMed  Google Scholar 

  47. Masui K, Cloughesy TF, Mischel PS (2012) Molecular pathology in adult high-grade gliomas: from molecular diagnostics to target therapies. Neuropathol Appl Neurobiol 38(3):271–291. https://doi.org/10.1111/j.1365-2990.2011.01238.x

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. Li L, Wang Y, Li Y, Fang S, Jiang T (2020) Role of molecular biomarkers in glioma resection: a systematic review. Chin Neurosurg J 6(1):1–7. https://doi.org/10.1186/s41016-020-00198-x

    Article  Google Scholar 

  49. Elazab A, Bai H, Abdulazeem YM et al (2017) Post-surgery glioma growth modeling from magnetic resonance images for patients with treatment. Sci Rep 7(1):1222. https://doi.org/10.1038/s41598-017-01189-2

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. Kapur S (2001) Neuroimaging and drug development: an algorithm for decision making. J Clin Pharmacol 41(S7):64S-71S

    CAS  Article  Google Scholar 

  51. Wester H-J (2007) Nuclear imaging probes: from bench to bedside. Clin Cancer Res 13(12):3470–3481. https://doi.org/10.1158/1078-0432.CCR-07-0264

    CAS  Article  PubMed  Google Scholar 

  52. McColl J, Holmes A, Ford I (1994) Statistical methods in neuroimaging with particular application to emission tomography. Stat Methods Med Res 3(1):63–86. https://doi.org/10.1177/096228029400300105

    CAS  Article  PubMed  Google Scholar 

  53. Enderling H, Chaplain M (2014) Mathematical modeling of tumor growth and treatment. Curr Pharm Des 20(30):4934–4940. https://doi.org/10.2174/1381612819666131125150434

    CAS  Article  PubMed  Google Scholar 

  54. Taghizadeh H, Müllauer L, Furtner J et al (2019) Applied precision cancer medicine in neuro-oncology. Sci Rep 9(1):20139. https://doi.org/10.1038/s41598-019-56473-0

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  55. Patel V, Shah J (2021) The current and future aspects of glioblastoma: immunotherapy a new hope? Eur J Neurosci 54(3):5120–5142. https://doi.org/10.1111/ejn.15343

    Article  PubMed  Google Scholar 

  56. Smith JS, Chang EF, Lamborn KR et al (2008) Role of extent of resection in the long-term outcome of low-grade hemispheric gliomas. J Clin Oncol 26(8):1338–1345. https://doi.org/10.1200/JCO.2007.13.9337

    Article  PubMed  Google Scholar 

  57. Brown TJ, Brennan MC, Li M et al (2016) Association of the extent of resection with survival in glioblastoma. JAMA Oncol 2(11):1460. https://doi.org/10.1001/jamaoncol.2016.1373

    Article  PubMed  PubMed Central  Google Scholar 

  58. D’Amico RS, Englander ZK, Canoll P, Bruce JN (2017) Extent of resection in glioma–a review of the cutting edge. World Neurosurg 103:538–549. https://doi.org/10.1016/j.wneu.2017.04.041

    Article  PubMed  Google Scholar 

  59. Li YM, Suki D, Hess K, Sawaya R (2016) The influence of maximum safe resection of glioblastoma on survival in 1229 patients: can we do better than gross-total resection? J Neurosurg 124(4):977–988. https://doi.org/10.3171/2015.5.JNS142087

    Article  PubMed  Google Scholar 

  60. Kim Y-J, Lee DJ, Park C-K, Kim IA (2019) Optimal extent of resection for glioblastoma according to site, extension, and size: a population-based study in the temozolomide era. Neurosurg Rev 42(4):937–950. https://doi.org/10.1007/s10143-018-01071-3

    Article  PubMed  Google Scholar 

  61. Belhawi SMK, Hoefnagels FWA, Baaijen JC et al (2011) Early postoperative MRI overestimates residual tumour after resection of gliomas with no or minimal enhancement. Eur Radiol 21(7):1526–1534. https://doi.org/10.1007/s00330-011-2081-y

    Article  PubMed  PubMed Central  Google Scholar 

  62. Marner L, Henriksen OM, Lundemann M, Larsen VA, Law I (2017) Clinical PET/MRI in neurooncology: opportunities and challenges from a single-institution perspective. Clin Transl Imaging 5(2):135–149. https://doi.org/10.1007/s40336-016-0213-8

    Article  PubMed  Google Scholar 

  63. Chaichana KL, Jusue-Torres I, Navarro-Ramirez R et al (2014) Establishing percent resection and residual volume thresholds affecting survival and recurrence for patients with newly diagnosed intracranial glioblastoma. Neuro Oncol 16(1):113–122. https://doi.org/10.1093/neuonc/not137

    Article  PubMed  Google Scholar 

  64. Pessina F, Navarria P, Cozzi L et al (2017) Maximize surgical resection beyond contrast-enhancing boundaries in newly diagnosed glioblastoma multiforme: is it useful and safe? A single institution retrospective experience. J Neurooncol 135(1):129–139. https://doi.org/10.1007/s11060-017-2559-9

    Article  PubMed  Google Scholar 

  65. Bénard F, Romsa J, Hustinx R (2003) Imaging gliomas with positron emission tomography and single-photon emission computed tomography. Semin Nucl Med 33(2):148–162. https://doi.org/10.1053/snuc.2003.127304

    Article  PubMed  Google Scholar 

  66. Chen W (2007) Clinical applications of PET in brain tumors. J Nucl Med 48(9):1468–1481. https://doi.org/10.2967/jnumed.106.037689

    Article  PubMed  Google Scholar 

  67. Cuddapah VA, Robel S, Watkins S, Sontheimer H (2014) A neurocentric perspective on glioma invasion. Nat Rev Neurosci 15(7):455–465. https://doi.org/10.1038/nrn3765

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  68. Jain R, Gutierrez J, Narang J et al (2011) In vivo correlation of tumor blood volume and permeability with histologic and molecular angiogenic markers in gliomas. Am J Neuroradiol 32(2):388–394. https://doi.org/10.3174/ajnr.A2280

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  69. Eidel O, Burth S, Neumann J-O et al (2017) Tumor infiltration in enhancing and non-enhancing parts of glioblastoma: a correlation with histopathology. PLoS ONE 12(1):e0169292. https://doi.org/10.1371/journal.pone.0169292

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  70. Lasocki A, Gaillard F (2019) Non-contrast-enhancing tumor: a new frontier in glioblastoma research. Am J Neuroradiol 40(5):758–765. https://doi.org/10.3174/ajnr.A6025

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  71. Sharma M, Juthani RG, Vogelbaum MA (2017) Updated response assessment criteria for high-grade glioma: beyond the MacDonald criteria. Chin Clin Oncol 6(4):37–50. https://doi.org/10.21037/cco.2017.06.26

    Article  PubMed  Google Scholar 

  72. Song S, Cheng Y, Ma J et al (2020) Simultaneous FET-PET and contrast-enhanced MRI based on hybrid PET/MR improves delineation of tumor spatial biodistribution in gliomas: a biopsy validation study. Eur J Nucl Med Mol Imaging. https://doi.org/10.1007/s00259-019-04656-2

    Article  PubMed  PubMed Central  Google Scholar 

  73. Huang RY, Wen PY (2020) Indications and limitations of conventional imaging current clinical practice in the context of standard therapy. In: Pope W (ed) Glioma imaging. Springer, Cham, pp 1–15. https://doi.org/10.1007/978-3-030-27359-0_1

    Chapter  Google Scholar 

  74. Lemée J-M, Clavreul A, Menei P (2015) Intratumoral heterogeneity in glioblastoma: don’t forget the peritumoral brain zone. Neuro Oncol 17(10):1322–1332. https://doi.org/10.1093/neuonc/nov119

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  75. Senft C, Bink A, Franz K, Vatter H, Gasser T, Seifert V (2011) Intraoperative MRI guidance and extent of resection in glioma surgery: a randomised, controlled trial. Lancet Oncol 12(11):997–1003. https://doi.org/10.1016/S1470-2045(11)70196-6

    Article  PubMed  Google Scholar 

  76. Jenkinson MD, Barone DG, Bryant A, Vale L, Bulbeck H, Lawrie TA, Hart MG, Watts C (2018) Intraoperative imaging technology to maximize extent of resection for glioma. Cochrane Database Syst Rev. https://doi.org/10.1002/14651858.CD012788.pub2

    Article  PubMed  PubMed Central  Google Scholar 

  77. Zhang ZZ, Shields LBE, Sun DA, Zhang YP, Hunt MA, Shields CB (2015) The art of intraoperative glioma identification. Front Oncol. https://doi.org/10.3389/fonc.2015.00175

    Article  PubMed  PubMed Central  Google Scholar 

  78. Muragaki Y, Iseki H, Maruyama T, Kawamata T, Yamane F, Nakamura R, Kubo O, Takakura K, Hori T (2006) Usefulness of intraoperative magnetic resonance imaging for glioma surgery. Acta Neurochir Suppl 98:67–75. https://doi.org/10.1007/978-3-211-33303-7_10.79

    CAS  Article  PubMed  Google Scholar 

  79. Villanueva-Meyer JE, Mabray MC, Cha S (2017) Current clinical brain tumor imaging. Neurosurgery 81(3):397–415. https://doi.org/10.1093/neuros/nyx103

    Article  PubMed  PubMed Central  Google Scholar 

  80. Verburg N, de Witt Hamer PC (2021) State-of-the-art imaging for glioma surgery. Neurosurg Rev 44(3):1331–1343. https://doi.org/10.1007/s10143-020-01337-9

    Article  PubMed  Google Scholar 

  81. Forte E, Fiorenza D, Torino E et al (2019) Radiolabeled PET/MRI nanoparticles for tumor imaging. J Clin Med. https://doi.org/10.3390/jcm9010089

    Article  PubMed  PubMed Central  Google Scholar 

  82. Zhang-Yin JT, Girard A, Bertaux M (2022) What does PET imaging bring to neuro-oncology in 2022? A review. Cancers (Basel) 14(4):879. https://doi.org/10.3390/cancers14040879

    Article  Google Scholar 

  83. Wester HJ, Herz M, Weber W et al (1999) Synthesis and radiopharmacology of O-(2-[18F]fluoroethyl)-L-tyrosine for tumor imaging. J Nucl Med. 40(1):205–212

    CAS  PubMed  Google Scholar 

  84. Zuhayra M, Alfteimi A, Von FC, Lützen U, Meller B, Henze E (2009) New approach for the synthesis of [18F]fluoroethyltyrosine for cancer imaging: simple, fast, and high yielding automated synthesis. Bioorg Med Chem 17(21):7441–7448. https://doi.org/10.1016/j.bmc.2009.09.029

    CAS  Article  PubMed  Google Scholar 

  85. Lisova K, Chen BY, Wang J, Fong KM-M, Clark PM, van Dam RM (2020) Rapid, efficient, and economical synthesis of PET tracers in a droplet microreactor: application to O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET). EJNMMI Radiopharm Chem. 5(1):1. https://doi.org/10.1186/s41181-019-0082-3

    Article  Google Scholar 

  86. Bourdier T, Greguric I, Roselt P, Jackson T, Faragalla J, Katsifis A (2011) Fully automated one-pot radiosynthesis of O-(2-[18F]fluoroethyl)-l-tyrosine on the tracerlab FXFN module. Nucl Med Biol 38(5):645–651. https://doi.org/10.1016/j.nucmedbio.2011.01.001

    CAS  Article  PubMed  Google Scholar 

  87. Chao M, Chezal J-M, Debiton E et al (2022) A convenient route to new (radio)fluorinated and (radio)iodinated cyclic tyrosine analogs. Pharmaceuticals 15(2):162. https://doi.org/10.3390/ph15020162

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  88. Gladson CL, Prayson RA, Liu WM (2010) The pathobiology of glioma tumors. Annu Rev Pathol Mech Dis 5(1):33–50. https://doi.org/10.1146/annurev-pathol-121808-102109

    CAS  Article  Google Scholar 

  89. Vettermann FJ, Diekmann C, Weidner L et al (2021) L-type amino acid transporter (LAT) 1 expression in 18F-FET-negative gliomas. EJNMMI Res 11(1):124. https://doi.org/10.1186/s13550-021-00865-9

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  90. Zhang J, Xu Y, Li D et al (2020) Review of the correlation of LAT1 with diseases: mechanism and treatment. Front Chem. https://doi.org/10.3389/fchem.2020.564809

    Article  PubMed  PubMed Central  Google Scholar 

  91. Galldiks N, Lohmann P, Albert NL, Tonn JC, Langen K-J (2019) Current status of PET imaging in neuro-oncology. Neuro-Oncol Adv. https://doi.org/10.1093/noajnl/vdz010

    Article  Google Scholar 

  92. Arvanitis CD, Ferraro GB, Jain RK (2020) The blood–brain barrier and blood–tumour barrier in brain tumours and metastases. Nat Rev Cancer 20(1):26–41. https://doi.org/10.1038/s41568-019-0205-x

    CAS  Article  PubMed  Google Scholar 

  93. Fuenfgeld B, Mächler P, Fischer DR et al (2020) Reference values of physiological 18F-FET uptake: implications for brain tumor discrimination. PLoS ONE 15(4):1–16. https://doi.org/10.1371/journal.pone.0230618

    CAS  Article  Google Scholar 

  94. Law I, Albert NL, Arbizu J et al (2019) 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. 46(3):540–557. https://doi.org/10.1007/s00259-018-4207-9

    CAS  Article  PubMed  Google Scholar 

  95. Hua T, Zhou W, Zhou Z, Guan Y, Li M (2021) Heterogeneous parameters based on 18F-FET PET imaging can non-invasively predict tumor grade and isocitrate dehydrogenase gene 1 mutation in untreated gliomas. Quant Imaging Med Surg 11(1):317–327. https://doi.org/10.21037/QIMS-20-723

    Article  PubMed  PubMed Central  Google Scholar 

  96. Hadjipanayis CG, Widhalm G, Stummer W (2015) What is the surgical benefit of utilizing 5-aminolevulinic acid for fluorescence-guided surgery of malignant gliomas? Neurosurgery 77(5):663–673. https://doi.org/10.1227/NEU.0000000000000929

    Article  PubMed  Google Scholar 

  97. Lau D, Hervey-Jumper SL, Chang S et al (2016) A prospective Phase II clinical trial of 5-aminolevulinic acid to assess the correlation of intraoperative fluorescence intensity and degree of histologic cellularity during resection of high-grade gliomas. J Neurosurg 124(5):1300–1309. https://doi.org/10.3171/2015.5.JNS1577

    CAS  Article  PubMed  Google Scholar 

  98. Golub D, Hyde J, Dogra S et al (2021) Intraoperative MRI versus 5-ALA in high-grade glioma resection: a network meta-analysis. J Neurosurg 134(2):484–498. https://doi.org/10.3171/2019.12.JNS191203

    CAS  Article  Google Scholar 

  99. Schucht P, Knittel S, Slotboom J et al (2014) 5-ALA complete resections go beyond MR contrast enhancement: shift corrected volumetric analysis of the extent of resection in surgery for glioblastoma. Acta Neurochir (Wien) 156(2):305–312. https://doi.org/10.1007/s00701-013-1906-7

    Article  Google Scholar 

  100. Celli M, Caroli P, Amadori E et al (2021) Diagnostic and prognostic potential of 18F-FET PET in the differential diagnosis of glioma recurrence and treatment-induced changes after chemoradiation therapy. Front Oncol 11:1–10. https://doi.org/10.3389/fonc.2021.721821

    Article  Google Scholar 

  101. Dunet V, Rossier C, Buck A, Stupp R, Prior JO (2012) Performance of 18F-fluoro-ethyl-tyrosine (18F-FET) PET for the differential diagnosis of primary brain tumor: a systematic review and metaanalysis. J Nucl Med 53(2):207–214

    CAS  Article  Google Scholar 

  102. Näslund O, Smits A, Förander P, Laesser M, Bartek J Jr, Gempt J, Liljegren A, Daxberg ELJA (2018) Amino acid tracers in PET imaging of diffuse low-grade gliomas: a systematic review of preoperative applications. Acta Neurochir (Wien) 160(7):1451–1460

    Article  Google Scholar 

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Acknowledgements

We are grateful to Nuclear Medicine Staff of S. Stefano Hospital of Prato - Azienda USL Toscana Centro (physicians, technicians, nurses and administrative) for the fruitful collaboration, discussion and competence demonstrated in the implementation of 18F-FET PET in the clinical routine.

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This research received no external funding.

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Correspondence to Stelvio Sestini.

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Laghai, I., Muscas, G., Tardelli, E. et al. The new era of bio-molecular imaging with O-(2-18F-fluoroethyl)-L-tyrosine (18F-FET) in neurosurgery of gliomas. Clin Transl Imaging (2022). https://doi.org/10.1007/s40336-022-00509-5

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Keywords

  • 18F-FET PET
  • MRI
  • Surgery
  • Glioma
  • Glioblastoma
  • Tumor biology