Skip to main content

Advertisement

Log in

From FDG and beyond: the evolving potential of nuclear medicine

  • Invited Review Article
  • Published:
Annals of Nuclear Medicine Aims and scope Submit manuscript

Abstract

The radiopharmaceutical 2-[fluorine-18]fluoro-2-deoxy-d-glucose (FDG) has been dominantly used in positron emission tomography (PET) scans for over 20 years, and due to its vast utility its applications have expanded and are continuing to expand into oncology, neurology, cardiology, and infectious/inflammatory diseases. More recently, the addition of artificial intelligence (AI) has enhanced nuclear medicine diagnosis and imaging with FDG-PET, and new radiopharmaceuticals such as prostate-specific membrane antigen (PSMA) and fibroblast activation protein inhibitor (FAPI) have emerged. Nuclear medicine therapy using agents such as [177Lu]-dotatate surpasses conventional treatments in terms of efficacy and side effects. This article reviews recently established evidence of FDG and non-FDG drugs and anticipates the future trajectory of nuclear medicine.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Nutt R. The history of positron emission tomography. Mol Imaging Biol. 2002;4:11–26.

    Article  PubMed  Google Scholar 

  2. Fukuda H, Matsuzawa T, Abe Y, Endo S, Yamada K, Kubota K, et al. Experimental study for cancer diagnosis with positron-labeled fluorinated glucose analogs: [18F]-2-fluoro-2-deoxy-d-mannose: a new tracer for cancer detection. Eur J Nucl Med. 1982;7:294–7.

    Article  CAS  PubMed  Google Scholar 

  3. Nishiyama Y, Kinuya S, Kato T, Kayano D, Sato S, Tashiro M, et al. Nuclear medicine practice in Japan: a report of the eighth nationwide survey in 2017. Ann Nucl Med. 2019;33:725–32.

    Article  CAS  PubMed  Google Scholar 

  4. Nensa F, Demircioglu A, Rischpler C. Artificial intelligence in nuclear medicine. J Nucl Med. 2019;60:29S-37S.

    Article  PubMed  Google Scholar 

  5. Kratochwil C, Flechsig P, Lindner T, Abderrahim L, Altmann A, Mier W, et al. 68Ga-FAPI PET/CT: tracer uptake in 28 different kinds of cancer. J Nucl Med. 2019;60:801–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Maurer T, Eiber M, Schwaiger M, Gschwend JE. Current use of PSMA–PET in prostate cancer management. Nat Rev Urol. 2016;13:226–35.

    Article  CAS  PubMed  Google Scholar 

  7. Yan S, Zhang H, Wang J. Trends and hot topics in radiology, nuclear medicine and medical imaging from 2011–2021: a bibliometric analysis of highly cited papers. Jpn J Radiol. 2022;40:847–56.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Nakanishi K, Tanaka J, Nakaya Y, Maeda N, Sakamoto A, Nakayama A, et al. Whole-body MRI: detecting bone metastases from prostate cancer. Jpn J Radiol. 2022;40:229–44.

    Article  PubMed  Google Scholar 

  9. Wu Y, Feng T, Zhao Y, Xu T, Fu F, Huang Z, et al. Whole-body parametric imaging of 18F-FDG PET using uEXPLORER with reduced scanning time. J Nucl Med. 2022;63:622–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Herrmann K, Kraus BJ, Hadaschik B, Kunikowska J, van Poppel H, N’Dow J, et al. Nuclear medicine theranostics comes of age. Lancet Oncol. 2021;22:1497–8.

    Article  PubMed  Google Scholar 

  11. Strosberg J, El-Haddad G, Wolin E, Hendifar A, Yao J, Chasen B, et al. Phase 3 trial of 177Lu-dotatate for midgut neuroendocrine tumors. N Engl J Med. 2017;376:125–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kostakoglu L, Agress H Jr, Goldsmith SJ. Clinical role of FDG PET in evaluation of cancer patients. Radiographics. 2003;23:315–40 (quiz 533).

    Article  PubMed  Google Scholar 

  13. DeBerardinis RJ, Chandel NS. We need to talk about the Warburg effect. Nat Metab. 2020;2:127–9.

    Article  PubMed  Google Scholar 

  14. Ide M, Suzuki Y. Is whole-body FDG-PET valuable for health screening? For. Eur J Nucl Med Mol Imaging. 2005;32:339–41.

    Article  PubMed  Google Scholar 

  15. Groheux D, Cochet A, Humbert O, Alberini J-L, Hindié E, Mankoff D. 18F-FDG PET/CT for staging and restaging of breast cancer. J Nucl Med. 2016;57:17S-26S.

    Article  CAS  PubMed  Google Scholar 

  16. Kubicek GJ, Champ C, Fogh S, Wang F, Reddy E, Intenzo C, et al. FDG-PET staging and importance of lymph node SUV in head and neck cancer. Head Neck Oncol. 2010;2:19.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Cerfolio RJ, Ojha B, Bryant AS, Bass CS, Bartalucci AA, Mountz JM. The role of FDG-PET scan in staging patients with nonsmall cell carcinoma. Ann Thorac Surg. 2003;76:861–6.

    Article  PubMed  Google Scholar 

  18. Matsumoto Y. A pictorial essay on radiological changes after stereotactic body radiation therapy for lung tumors. Jpn J Radiol. 2022;40:647–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Hayashi K, Yamamoto N, Nakajima M, Nomoto A, Ishikawa H, Ogawa K, et al. Carbon-ion radiotherapy for octogenarians with locally advanced non-small-cell lung cancer. Jpn J Radiol. 2021;39:703–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Delbeke D, Rose DM, Chapman WC, Pinson CW, Wright JK, Beauchamp RD, et al. Optimal interpretation of FDG PET in the diagnosis, staging and management of pancreatic carcinoma. J Nucl Med. 1999;40:1784–91.

    CAS  PubMed  Google Scholar 

  21. Inoue A, Ota S, Yamasaki M, Batsaikhan B, Furukawa A, Watanabe Y. Gastrointestinal stromal tumors: a comprehensive radiological review. Jpn J Radiol. 2022;40:1105–20.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Tanaka T, Kawashima A, Marukawa Y, Kitayama T, Masaoka Y, Kojima K, et al. Imaging evaluation of hereditary renal tumors: a pictorial review. Jpn J Radiol. 2021;39:619–32.

    Article  PubMed  Google Scholar 

  23. Kitao T, Shiga T, Hirata K, Sekizawa M, Takei T, Yamashiro K, et al. Volume-based parameters on FDG PET may predict the proliferative potential of soft-tissue sarcomas. Ann Nucl Med. 2019;33:22–31.

    Article  PubMed  Google Scholar 

  24. Nishimori M, Iwasa H, Nakaji K, Nitta N, Miyatake K, Yoshimatsu R, et al. Predicting the pathological invasiveness of early lung adenocarcinoma prior to surgery using Deauville criteria: reliability and validity. Jpn J Radiol. 2023;41:768–76.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Uchiyama Y, Hirata K, Watanabe S, Okamoto S, Shiga T, Okada K, et al. Development and validation of a prediction model based on the organ-based metabolic tumor volume on FDG-PET in patients with differentiated thyroid carcinoma. Ann Nucl Med. 2021;35:1223–31.

    Article  CAS  PubMed  Google Scholar 

  26. Takenaka J, Watanabe S, Abe T, Hirata K, Uchiyama Y, Kimura R, et al. Prognostic value of [18F]FDG-PET prior to [131I]MIBG treatment for pheochromocytoma and paraganglioma (PPGL). Ann Nucl Med. 2023;37:10–7.

    Article  CAS  PubMed  Google Scholar 

  27. Bedmutha AS, Agrawal A, Rangarajan V, Goel M, Patkar S, Puranik AD, et al. Diagnostic performance of F-18 FDG PET/CT in recurrent adenocarcinoma gallbladder and its impact on post-recurrence survival. Jpn J Radiol. 2023;41:201–8.

    Article  PubMed  Google Scholar 

  28. Kitajima K, Watabe T, Nakajo M, Ishibashi M, Daisaki H, Soeda F, et al. Tumor response evaluation in patients with malignant melanoma undergoing immune checkpoint inhibitor therapy and prognosis prediction using 18F-FDG PET/CT: multicenter study for comparison of EORTC, PERCIST, and imPERCIST. Jpn J Radiol. 2022;40:75–85.

    Article  CAS  PubMed  Google Scholar 

  29. Ben-Haim S, Ell P. 18F-FDG PET and PET/CT in the evaluation of cancer treatment response. J Nucl Med. 2009;50:88–99.

    Article  PubMed  Google Scholar 

  30. Wong WL. PET-CT for staging and detection of recurrence of head and neck cancer. Semin Nucl Med. 2021;51:13–25.

    Article  PubMed  Google Scholar 

  31. Suga K. F-18-FDG PET/CT findings of paraneoplastic dermatoses. Jpn J Radiol. 2022;40:863–75.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Ito H, Koyama T, Kanie Y, Morioka K, Nakaya M, Mitsumori A, et al. MRI, CT and FDG-PET/CT findings of Wolffian tumor: four-case series. Jpn J Radiol. 2021;39:1009–16.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Albano D, Bonacina M, Savelli G, Ferro P, Busnardo E, Gianolli L, et al. Clinical and prognostic 18F-FDG PET/CT role in recurrent vulvar cancer: a multicentric experience. Jpn J Radiol. 2022;40:66–74.

    Article  PubMed  Google Scholar 

  34. Simsek FS, Yuksel D, Yaylali O, Aslan HS, Kılıçarslan E, Bir F, et al. Can PET/CT be used more effectively in pleural effusion evaluation? Jpn J Radiol. 2021;39:1186–94.

    Article  PubMed  Google Scholar 

  35. Oki N, Ikebe Y, Koike H, Ideguchi R, Niino D, Uetani M. FDG-PET vs. chemical shift MR imaging in differentiating intertrabecular metastasis from hematopoietic bone marrow hyperplasia. Jpn J Radiol. 2021;39:1077–85.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Yoldaş B, Gürsoy S, Budak E, Gülmez B, Ceylan KC, Çırak AK, et al. FDG PET/CT signs of proven pulmonary hydatid cyst: is there any clue? Jpn J Radiol. 2022;40:1194–200.

    Article  PubMed  Google Scholar 

  37. He L, Chen Y, Tan X, Sun X, Zhang Q, Luo H, et al. 18F-FDG PET/CT and contrast-enhanced CT in the diagnosis of Castleman disease. Jpn J Radiol. 2023;41:98–107.

    Article  CAS  PubMed  Google Scholar 

  38. Albini A, Pennesi G, Donatelli F, Cammarota R, De Flora S, Noonan DM. Cardiotoxicity of anticancer drugs: the need for cardio-oncology and cardio-oncological prevention. J Natl Cancer Inst. 2010;102:14–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Minamimoto R. Series of myocardial FDG uptake requiring considerations of myocardial abnormalities in FDG-PET/CT. Jpn J Radiol. 2021;39:540–57.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Manabe O, Tamaki N. The future of cardiac disease assessment using 18F-FDG PET/CT. Jpn J Radiol. 2021;39:511–3.

    Article  PubMed  Google Scholar 

  41. Vaidyanathan S, Patel CN, Scarsbrook AF, Chowdhury FU. FDG PET/CT in infection and inflammation—current and emerging clinical applications. Clin Radiol. 2015;70:787–800.

    Article  CAS  PubMed  Google Scholar 

  42. Minamimoto R, Hotta M, Ishikane M, Inagaki T. FDG-PET/CT images of COVID-19: a comprehensive review. Glob Health Med. 2020;2:221–6.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Minamimoto R. Optimal use of the FDG-PET/CT in the diagnostic process of fever of unknown origin (FUO): a comprehensive review. Jpn J Radiol. 2022;40:1121–37.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Wang S, Li B, Li P, Xie R, Wang Q, Shi H, et al. Feasibility of perfusion and early-uptake 18F-FDG PET/CT in primary hepatocellular carcinoma: a dual-input dual-compartment uptake model. Jpn J Radiol. 2021;39:1086–96.

    Article  PubMed  Google Scholar 

  45. Tamaki N, Hirata K, Kotani T, Nakai Y, Matsushima S, Yamada K. Four-dimensional quantitative analysis using FDG-PET in clinical oncology. Jpn J Radiol. 2023;41:831–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Xie Y, Teng Y, Jiang C, Ding C, Zhou Z. Prognostic value of 18F-FDG lesion dissemination features in patients with peripheral T-cell lymphoma (PTCL). Jpn J Radiol. 2023;41:777–86.

    Article  PubMed  Google Scholar 

  47. Ni M, Wang S, Liu X, Shi Q, Zhu X, Zhang Y, et al. Predictive value of intratumor metabolic and heterogeneity parameters on [18F]FDG PET/CT for EGFR mutations in patients with lung adenocarcinoma. Jpn J Radiol. 2023;41:209–18.

    Article  CAS  PubMed  Google Scholar 

  48. Iwasa H, Nagamachi S, Nakayama S, Yamamoto T, Yoshimitsu K. The reproducibility of MTV and TLG of soft tissue tumors calculated by FDG-PET: comparison between the lower limit by the fixed value SUV 2.5 and that value by 30% of SUVmax. Jpn J Radiol. 2023;41:531–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Mosleh-Shirazi MA, Nasiri-Feshani Z, Ghafarian P, Alavi M, Haddadi G, Ketabi A. Tumor volume-adapted SUVN as an alternative to SUVpeak for quantification of small lesions in PET/CT imaging: a proof-of-concept study. Jpn J Radiol. 2021;39:811–23.

    Article  CAS  PubMed  Google Scholar 

  50. Wei W, Jia G, Wu Z, Wang T, Wang H, Wei K, et al. A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based on 18F-FDG PET/CT images. Jpn J Radiol. 2023;41:417–27.

    Article  CAS  PubMed  Google Scholar 

  51. Nakajo M, Jinguji M, Ito S, Tani A, Hirahara M, Yoshiura T. Clinical application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography radiomics-based machine learning analyses in the field of oncology. Jpn J Radiol [Internet]. 2023. https://doi.org/10.1007/s11604-023-01476-1.

    Article  PubMed  Google Scholar 

  52. Fang Y-HD, Lin C-Y, Shih M-J, Wang H-M, Ho T-Y, Liao C-T, et al. Development and evaluation of an open-source software package “CGITA” for quantifying tumor heterogeneity with molecular images. Biomed Res Int. 2014;2014: 248505.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, et al. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018;78:4786–9.

    Article  CAS  PubMed  Google Scholar 

  54. Hirata K, Kobayashi K, Wong K-P, Manabe O, Surmak A, Tamaki N, et al. A semi-automated technique determining the liver standardized uptake value reference for tumor delineation in FDG PET-CT. PLoS One. 2014;9: e105682.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Larobina M, Megna R, Solla R. Comparison of three freeware software packages for 18F-FDG PET texture feature calculation. Jpn J Radiol. 2021;39:710–9.

    Article  CAS  PubMed  Google Scholar 

  56. van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77:e104–7.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Hirata K, Sugimori H, Fujima N, Toyonaga T, Kudo K. Artificial intelligence for nuclear medicine in oncology. Ann Nucl Med. 2022;36:123–32.

    Article  PubMed  Google Scholar 

  58. Otaki Y, Miller RJH, Slomka PJ. The application of artificial intelligence in nuclear cardiology. Ann Nucl Med. 2022;36:111–22.

    Article  PubMed  Google Scholar 

  59. Matsubara K, Ibaraki M, Nemoto M, Watabe H, Kimura Y. A review on AI in PET imaging. Ann Nucl Med. 2022;36:133–43.

    Article  PubMed  Google Scholar 

  60. Nakao T, Hanaoka S, Nomura Y, Hayashi N, Abe O. Anomaly detection in chest 18F-FDG PET/CT by Bayesian deep learning. Jpn J Radiol. 2022;40:730–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Nai Y-H, Loi HY, O’Doherty S, Tan TH, Reilhac A. Comparison of the performances of machine learning and deep learning in improving the quality of low dose lung cancer PET images. Jpn J Radiol. 2022;40:1290–9.

    Article  PubMed  Google Scholar 

  62. Li Q, Li Y, Yuan H, Yang F, Huang Y, Song X, et al. PET morphology helps distinguish solitary and solid pulmonary tuberculosis from non-small cell lung cancer. Jpn J Radiol. 2023;41:312–21.

    PubMed  Google Scholar 

  63. Okuyama C, Higashi T, Ishizu K, Saga T. FDG-PET findings associated with various medical procedures and treatments. Jpn J Radiol. 2023;41:459–76.

    Article  CAS  PubMed  Google Scholar 

  64. Morita Y, Nogami M, Sakaguchi K, Okada Y, Hirota Y, Sugawara K, et al. Enhanced release of glucose into the intraluminal space of the intestine associated with metformin treatment as revealed by [18F]fluorodeoxyglucose PET-MRI. Diabetes Care. 2020;43:1796–802.

    Article  CAS  PubMed  Google Scholar 

  65. Yamane T, Matsusaka Y, Fukushima K, Seto A, Matsunari I, Kuji I. Atlas of non-pathological solitary or asymmetrical skeletal muscle uptake in [18F]FDG-PET. Jpn J Radiol. 2022;40:755–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Yuan H, Tan X, Sun X, He L, Li D, Jiang L. Role of 18F-FDG PET/CT and sarcopenia in untreated non-small cell lung cancer with advanced stage. Jpn J Radiol. 2023;41:521–30.

    Article  PubMed  Google Scholar 

  67. Pijl JP, Nienhuis PH, Kwee TC, Glaudemans AWJM, Slart RHJA, Gormsen LC. Limitations and Pitfalls of FDG-PET/CT in Infection and Inflammation. Semin Nucl Med. 2021;51:633–45.

    Article  PubMed  Google Scholar 

  68. Surasi DS, Bhambhvani P, Baldwin JA, Almodovar SE, O’Malley JP. 18F-FDG PET and PET/CT patient preparation: a review of the literature. J Nucl Med Technol. 2014;42:5–13.

    Article  PubMed  Google Scholar 

  69. Krarup MMK, Fischer BM, Christensen TN. New PET tracers: current knowledge and perspectives in lung cancer. Semin Nucl Med. 2022;52:781–96.

    Article  PubMed  Google Scholar 

  70. Whiteside TL. The tumor microenvironment and its role in promoting tumor growth. Oncogene. 2008;27:5904–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. de Visser KE, Joyce JA. The evolving tumor microenvironment: from cancer initiation to metastatic outgrowth. Cancer Cell. 2023;41:374–403.

    Article  PubMed  Google Scholar 

  72. Loktev A, Lindner T, Mier W, Debus J, Altmann A, Jäger D, et al. A tumor-imaging method targeting cancer-associated fibroblasts. J Nucl Med. 2018;59:1423–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Mori Y, Dendl K, Cardinale J, Kratochwil C, Giesel FL, Haberkorn U. FAPI PET: fibroblast activation protein inhibitor use in oncologic and nononcologic disease. Radiology. 2023;306: e220749.

    Article  PubMed  Google Scholar 

  74. Zhang Z, Jia G, Pan G, Cao K, Yang Q, Meng H, et al. Comparison of the diagnostic efficacy of 68Ga-FAPI-04 PET/MR and 18F-FDG PET/CT in patients with pancreatic cancer. Eur J Nucl Med Mol Imaging. 2022;49:2877–88.

    Article  CAS  PubMed  Google Scholar 

  75. Lang M, Spektor A-M, Hielscher T, Hoppner J, Glatting FM, Bicu F, et al. Static and dynamic 68Ga-FAPI PET/CT for the detection of malignant transformation of intraductal papillary mucinous neoplasia of the pancreas. J Nucl Med. 2023;64:244–51.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Zhang J, He Q, Jiang S, Li M, Xue H, Zhang D, et al. [18F]FAPI PET/CT in the evaluation of focal liver lesions with [18F]FDG non-avidity. Eur J Nucl Med Mol Imaging. 2023;50:937–50.

    Article  CAS  PubMed  Google Scholar 

  77. Wei Y, Ma L, Li P, Lu J, Ren J, Yan S, et al. FAPI compared with FDG PET/CT for diagnosis of primary and metastatic lung cancer. Radiology. 2023;308: e222785.

    Article  PubMed  Google Scholar 

  78. Demmert TT, Maric I, Pomykala KL, Lueckerath K, Siveke J, Schaarschmidt BM, et al. Novel 68Ga-FAPI PET/CT offers oncologic staging without COVID-19 vaccine-related pitfalls. J Nucl Med. 2023;64:368–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Wass G, Clifford K, Subramaniam RM. Evaluation of the diagnostic accuracy of FAPI PET/CT in oncologic studies: systematic review and metaanalysis. J Nucl Med. 2023;64:1218–24.

    Article  CAS  PubMed  Google Scholar 

  80. Hotta M, Rieger AC, Jafarvand MG, Menon N, Farolfi A, Benz MR, et al. Non-oncologic incidental uptake on FAPI PET/CT imaging. Br J Radiol. 2023;96:20220463.

    Article  PubMed  Google Scholar 

  81. Bentestuen M, Al-Obaydi N, Zacho HD. FAPI-avid nonmalignant PET/CT findings: an expedited systematic review. Semin Nucl Med. 2023;53:694–705.

    Article  PubMed  Google Scholar 

  82. Luo Y, Pan Q, Zhou Z, Li M, Wei Y, Jiang X, et al. 68Ga-FAPI PET/CT for rheumatoid arthritis: a prospective study. Radiology. 2023;307: e222052.

    Article  PubMed  Google Scholar 

  83. Scharitzer M, Macher-Beer A, Mang T, Unger LW, Haug A, Reinisch W, et al. Evaluation of intestinal fibrosis with 68Ga-FAPI PET/MR enterography in Crohn disease. Radiology. 2023;307: e222389.

    Article  PubMed  Google Scholar 

  84. Song W, Zhang X, He S, Gai Y, Qin C, Hu F, et al. 68Ga-FAPI PET visualize heart failure: from mechanism to clinic. Eur J Nucl Med Mol Imaging. 2023;50:475–85.

    Article  CAS  PubMed  Google Scholar 

  85. Gu Y, Han K, Zhang Z, Zhao Z, Yan C, Wang L, et al. 68Ga-FAPI PET/CT for molecular assessment of fibroblast activation in right heart in pulmonary arterial hypertension: a single-center, pilot study. J Nucl Cardiol. 2023;30:495–503.

    Article  PubMed  Google Scholar 

  86. Song L, Zan C, Liang Z, Chen X, Li J, Ren N, et al. Potential value of FAPI PET/CT in the detection and treatment of fibrosing mediastinitis: preclinical and pilot clinical investigation. Mol Pharm. 2023;20:4307–18.

    Article  CAS  PubMed  Google Scholar 

  87. Liu Y, Watabe T, Kaneda-Nakashima K, Shirakami Y, Naka S, Ooe K, et al. Fibroblast activation protein targeted therapy using [177Lu]FAPI-46 compared with [225Ac]FAPI-46 in a pancreatic cancer model. Eur J Nucl Med Mol Imaging. 2022;49:871–80.

    Article  CAS  PubMed  Google Scholar 

  88. Farolfi A, Calderoni L, Mattana F, Mei R, Telo S, Fanti S, et al. Current and emerging clinical applications of PSMA PET diagnostic imaging for prostate cancer. J Nucl Med. 2021;62:596–604.

    Article  CAS  PubMed  Google Scholar 

  89. Swiha M, Ayati N, Oprea-Lager DE, Ceci F, Emmett L. How to report PSMA PET. Semin Nucl Med [Internet]. 2023. https://doi.org/10.1053/j.semnuclmed.2023.07.007.

    Article  PubMed  Google Scholar 

  90. Duan H, Iagaru A. PSMA PET for detection of recurrence. Semin Nucl Med [Internet]. 2023. https://doi.org/10.1053/j.semnuclmed.2023.07.002.

    Article  PubMed  Google Scholar 

  91. Lawal IO, Ndlovu H, Kgatle M, Mokoala KMG, Sathekge MM. Prognostic value of PSMA PET/CT in prostate cancer. Semin Nucl Med [Internet]. 2023. https://doi.org/10.1053/j.semnuclmed.2023.07.003.

    Article  PubMed  Google Scholar 

  92. Kind F, Eder A-C, Jilg CA, Hartrampf PE, Meyer PT, Ruf J, et al. Prognostic value of tumor volume assessment on PSMA PET after 177Lu-PSMA radioligand therapy evaluated by PSMA PET/CT consensus statement and RECIP 1.0. J Nucl Med. 2023;64:605–10.

    Article  CAS  PubMed  Google Scholar 

  93. Seifert R, Rasul S, Seitzer K, Eveslage M, Rahbar Nikoukar L, Kessel K, et al. A Prognostic risk score for prostate cancer based on PSMA PET-derived organ-specific tumor volumes. Radiology. 2023;307: e222010.

    Article  PubMed  Google Scholar 

  94. Gafita A, Djaileb L, Rauscher I, Fendler WP, Hadaschik B, Rowe SP, et al. Response evaluation criteria in PSMA PET/CT (RECIP 1.0) in metastatic castration-resistant prostate cancer. Radiology. 2023;308: e222148.

    Article  PubMed  Google Scholar 

  95. Holzgreve A, Unterrainer M, Calais J, Adams T, Oprea-Lager DE, Goffin K, et al. Is PSMA PET/CT cost-effective for the primary staging in prostate cancer? First results for European countries and the USA based on the proPSMA trial. Eur J Nucl Med Mol Imaging [Internet]. 2023. https://doi.org/10.1007/s00259-023-06332-y.

    Article  PubMed  Google Scholar 

  96. Vetrone L, Fortunati E, Castellucci P, Fanti S. Future imaging of prostate cancer: do we need more than PSMA PET/CT? Semin Nucl Med [Internet]. 2023. https://doi.org/10.1053/j.semnuclmed.2023.06.004.

    Article  PubMed  Google Scholar 

  97. Hirata K, Yamaguchi S, Shiga T, Kuge Y, Tamaki N. The roles of hypoxia imaging using 18F-fluoromisonidazole positron emission tomography in glioma treatment. J Clin Med Res. 2019;8:1088.

    CAS  Google Scholar 

  98. Hirata K, Terasaka S, Shiga T, Hattori N, Magota K, Kobayashi H, et al. 18F-Fluoromisonidazole positron emission tomography may differentiate glioblastoma multiforme from less malignant gliomas. Eur J Nucl Med Mol Imaging. 2012;39:760–70.

    Article  CAS  PubMed  Google Scholar 

  99. Watanabe S, Takayanagi A, Hirata K, Inoue T, Okamoto S, Toyonaga T, et al. Combination of FDG and FMISO may predict accurate PFS in patients with early-stage NSCLC after stereotactic radiotherapy. J Nucl Med. 2019;60:150–150.

    Google Scholar 

  100. Nakajima K, Homma M, Suzuki M, Yokouchi Y, Matsuda T, Takakura H, et al. Reduction of tumor hypoxia by anti-PD-1 therapy assessed using pimonidazole and [18F]FMISO. Nucl Med Biol. 2022;108–109:85–92.

    Article  PubMed  Google Scholar 

  101. Furuya S, Naya M, Manabe O, Hirata K, Ohira H, Aikawa T, et al. 18F-FMISO PET/CT detects hypoxic lesions of cardiac and extra-cardiac involvement in patients with sarcoidosis. J Nucl Cardiol. 2021;28:2141–8.

    Article  PubMed  Google Scholar 

  102. Kroenke M, Hirata K, Gafita A, Watanabe S, Okamoto S, Magota K, et al. Voxel based comparison and texture analysis of 18F-FDG and 18F-FMISO PET of patients with head-and-neck cancer. PLoS One. 2019;14: e0213111.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Wang Y, Fushimi Y, Arakawa Y, Shimizu Y, Sano K, Sakata A, et al. Evaluation of isocitrate dehydrogenase mutation in 2021 world health organization classification grade 3 and 4 glioma adult-type diffuse gliomas with 18F-fluoromisonidazole PET. Jpn J Radiol [Internet]. 2023. https://doi.org/10.1007/s11604-023-01450-x.

    Article  PubMed  Google Scholar 

  104. Saksø M, Mortensen LS, Primdahl H, Johansen J, Kallehauge J, Hansen CR, et al. Influence of FAZA PET hypoxia and HPV-status for the outcome of head and neck squamous cell carcinoma (HNSCC) treated with radiotherapy: long-term results from the DAHANCA 24 trial (NCT01017224). Radiother Oncol. 2020;151:126–33.

    Article  PubMed  Google Scholar 

  105. Watanabe S, Okamoto S, Hirata K, Shiga T, Toyonaga T, Kobayashi K, et al. First-in-Human Study of [18F] DiFA, an improved PET probe for tumor hypoxia, in six healthy volunteers. J Nucl Med. 2017;58:839–839.

    Google Scholar 

  106. Liu T, Karlsen M, Karlberg AM, Redalen KR. Hypoxia imaging and theranostic potential of [64Cu][Cu(ATSM)] and ionic Cu(II) salts: a review of current evidence and discussion of the retention mechanisms. EJNMMI Res. 2020;10:33.

    Article  PubMed  PubMed Central  Google Scholar 

  107. Dias AH, Hansen AK, Munk OL, Gormsen LC. Normal values for 18F-FDG uptake in organs and tissues measured by dynamic whole body multiparametric FDG PET in 126 patients. EJNMMI Res. 2022;12:1–14.

    Article  Google Scholar 

  108. Caobelli F, Seibel S, Krieger K, Bregenzer C, Viscione M, Silva Mendes AF, et al. First-time rest-stress dynamic whole-body 82Rb-PET imaging using a long axial field-of-view PET/CT scanner. Eur J Nucl Med Mol Imaging. 2023;50:2219–21.

    Article  PubMed  PubMed Central  Google Scholar 

  109. Reed MB, Ponce de León M, Vraka C, Rausch I, Godbersen GM, Popper V, et al. Whole-body metabolic connectivity framework with functional PET. Neuroimage. 2023;271: 120030.

    Article  CAS  PubMed  Google Scholar 

  110. Suzuki M, Fushimi Y, Okada T, Hinoda T, Nakamoto R, Arakawa Y, et al. Quantitative and qualitative evaluation of sequential PET/MRI using a newly developed mobile PET system for brain imaging. Jpn J Radiol. 2021;39:669–80.

    Article  PubMed  Google Scholar 

  111. Satoh Y, Imai M, Ikegawa C, Onishi H. Image quality evaluation of real low-dose breast PET. Jpn J Radiol. 2022;40:1186–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Nakaura T, Naganawa S. Writing medical papers using large-scale language models: a perspective from the Japanese Journal of Radiology. Jpn J Radiol. 2023;41:457–8.

    Article  PubMed  Google Scholar 

  113. Doi K, Takegawa H, Yui M, Anetai Y, Koike Y, Nakamura S, et al. Deep learning-based detection of patients with bone metastasis from Japanese radiology reports. Jpn J Radiol. 2023;41:900–8.

    Article  PubMed  Google Scholar 

  114. Alberts IL, Mercolli L, Pyka T, Prenosil G, Shi K, Rominger A, et al. Large language models (LLM) and ChatGPT: what will the impact on nuclear medicine be? Eur J Nucl Med Mol Imaging. 2023;50:1549–52.

    Article  PubMed  PubMed Central  Google Scholar 

  115. Beam AL, Drazen JM, Kohane IS, Leong T-Y, Manrai AK, Rubin EJ. Artificial intelligence in medicine. N Engl J Med. 2023;388:1220–1.

    Article  PubMed  Google Scholar 

  116. Finlayson SG, Subbaswamy A, Singh K, Bowers J, Kupke A, Zittrain J, et al. The clinician and dataset shift in artificial intelligence. N Engl J Med. 2021;385:283–6.

    Article  PubMed  PubMed Central  Google Scholar 

  117. Sjögreen Gleisner K, Chouin N, Gabina PM, Cicone F, Gnesin S, Stokke C, et al. EANM dosimetry committee recommendations for dosimetry of 177Lu-labelled somatostatin-receptor- and PSMA-targeting ligands. Eur J Nucl Med Mol Imaging. 2022;49:1778–809.

    Article  PubMed  PubMed Central  Google Scholar 

  118. Pouget J-P, Konijnenberg M, Eberlein U, Glatting G, Gabina PM, Herrmann K, et al. An EANM position paper on advancing radiobiology for shaping the future of nuclear medicine. Eur J Nucl Med Mol Imaging. 2023;50:242–6.

    Article  PubMed  Google Scholar 

Download references

Funding

This review article has not received any specific grant from any public, commercial, or nonprofit funding agency.

Author information

Authors and Affiliations

Authors

Contributions

The first draft of the manuscript was written by KH, and all authors edited or provided critical comments on previous versions of the manuscript. All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Kenji Hirata.

Ethics declarations

Conflict of interest

All authors declare no conflicts of interest with regard to this manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hirata, K., Kamagata, K., Ueda, D. et al. From FDG and beyond: the evolving potential of nuclear medicine. Ann Nucl Med 37, 583–595 (2023). https://doi.org/10.1007/s12149-023-01865-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12149-023-01865-6

Keywords

Navigation