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Comparison of parametric imaging and SUV imaging with [68 Ga]Ga-PSMA-11 using dynamic total-body PET/CT in prostate cancer

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Abstract

Purpose

Standardized uptake value (SUV) has been prevalently used to measure [68 Ga]Ga-PSMA-11 activity in prostate cancer, but it is susceptible to multiple factors. Parametric imaging allows for absolute quantification of tracer uptake and provides a better diagnostic accuracy that is crucial for lesion detection. However, the clinical significance of total-body parametric imaging of [68 Ga]Ga-PSMA-11 remains to be fully assessed. Therefore, the aim of our study is to delve into the diagnostic implications of total-body parametric imaging of [68 Ga]Ga-PSMA-11 PET/CT for patients with prostate cancer.

Methods

Twenty prostate cancer patients were included and underwent a dynamic total-body [68 Ga]Ga-PSMA-11 PET/CT scan. An irreversible two-tissue compartment model (2T3k) was fitted for each tissue time-to-activity curve, and the net influx rate (Ki) was obtained. The image quality and semi-quantitative analysis of lesion-to-background ratio (LBR), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were compared between parametric images and SUV images.

Results

Kinetic modeling using 2T3k demonstrated favorable model fitting in both normal organs and lesions. All of the lesions detected on SUV images (55–60 min) could be detected on Ki images. The correlation between Ki, SUVmean, and SUVmax in both normal organs and pathological lesions was found to be positive and statistically significant. Conversely, a moderate positive correlations were found between Ki and K1 (R = 0.69, P < 0.001; R = 0.61, P < 0.001) and Ki and k3 (R = 0.69, P < 0.001; R = 0.62, P < 0.001), in normal organs and pathological lesions, respectively. Visual assessment in Ki images showed less image noise and higher lesions conspicuity compared to SUV images. Ki image-derived LBR, SNR, and CBR of pathological lesions including primary tumors (PTs), lymph node metastases (LNMs) and bone metastases (BMs), exhibited remarkably higher folds (1.4–3.6 folds) compared to those derived from SUV of corresponding lesions.

Conclusions

Total-body parametric imaging of [68 Ga]Ga-PSMA-11 enhanced lesion contrast and improved lesion detectability compared to SUV images. This may potentially serve as an imaging biomarker and theranostic tool for precise diagnosis and treatment evaluation in prostate cancer patients.

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References

  1. Adams MC, Turkington TG, Wilson JM, Wong TZ. A systematic review of the factors affecting accuracy of SUV measurements. AJR Am J Roentgenol. 2010;195:310–20. https://doi.org/10.2214/AJR.10.4923.

    Article  PubMed  Google Scholar 

  2. Bertoldo A, Rizzo G, Veronese M. Deriving physiological information from PET images: from SUV to compartmental modelling. Clin Translat Imaging. 2014;2:239–51. https://doi.org/10.1007/s40336-014-0067-x.

    Article  Google Scholar 

  3. Dimitrakopoulou-Strauss A, Pan L, Sachpekidis C. Kinetic modeling and parametric imaging with dynamic PET for oncological applications: general considerations, current clinical applications, and future perspectives. Eur J Nucl Med Mol Imaging. 2021;48:21–39. https://doi.org/10.1007/s00259-020-04843-6.

    Article  PubMed  Google Scholar 

  4. Wang G, Rahmim A, Gunn RN. PET Parametric Imaging: Past, Present, and Future. IEEE Transact Radiat Plasma Med Sci. 2020;4:663–75. https://doi.org/10.1109/TRPMS.2020.3025086.

    Article  Google Scholar 

  5. Rahmim A, Lodge MA, Karakatsanis NA, Panin VY, Zhou Y, McMillan A, et al. Dynamic whole-body PET imaging: principles, potentials and applications. Eur J Nucl Med Mol Imaging. 2019;46:501–18. https://doi.org/10.1007/s00259-018-4153-6.

    Article  PubMed  Google Scholar 

  6. Zhang X, Cherry SR, Xie Z, Shi H, Badawi RD, Qi J. Subsecond total-body imaging using ultrasensitive positron emission tomography. Proc Natl Acad Sci U S A. 2020;117:2265–7. https://doi.org/10.1073/pnas.1917379117.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Zhang X, Xie Z, Berg E, Judenhofer MS, Liu W, Xu T, et al. Total-Body Dynamic Reconstruction and Parametric Imaging on the uEXPLORER. J Nucl Med. 2020;61:285–91. https://doi.org/10.2967/jnumed.119.230565.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Zhang YQ, Hu PC, Wu RZ, Gu YS, Chen SG, Yu HJ, et al. The image quality, lesion detectability, and acquisition time of (18)F-FDG total-body PET/CT in oncological patients. Eur J Nucl Med Mol Imaging. 2020;47:2507–15. https://doi.org/10.1007/s00259-020-04823-w.

    Article  CAS  PubMed  Google Scholar 

  9. Wen J, Zhu Y, Li L, Liu J, Chen Y, Chen R. Determination of optimal 68 Ga-PSMA PET/CT imaging time in prostate cancers by total-body dynamic PET/CT. Eur J Nucl Med Mol Imaging. 2022;49:2086–95. https://doi.org/10.1007/s00259-021-05659-8.

    Article  CAS  PubMed  Google Scholar 

  10. Chen R, Wang Y, Zhu Y, Shi Y, Xu L, Huang G, et al. The Added Value of (18)F-FDG PET/CT Compared with (68)Ga-PSMA PET/CT in Patients with Castration-Resistant Prostate Cancer. J Nucl Med. 2022;63:69–75. https://doi.org/10.2967/jnumed.120.262250.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Yu H, Gu Y, Fan W, Gao Y, Wang M, Zhu X, et al. Expert consensus on oncological [(18)F]FDG total-body PET/CT imaging (version 1). Eur Radiol. 2023;33:615–26. https://doi.org/10.1007/s00330-022-08960-8.

    Article  PubMed  Google Scholar 

  12. Eiber M, Herrmann K, Calais J, Hadaschik B, Giesel FL, Hartenbach M, et al. Prostate Cancer Molecular Imaging Standardized Evaluation (PROMISE): Proposed miTNM Classification for the Interpretation of PSMA-Ligand PET/CT. J Nucl Med. 2018;59:469–78. https://doi.org/10.2967/jnumed.117.198119.

    Article  PubMed  Google Scholar 

  13. Wang Y, Li E, Cherry SR, Wang G. Total-Body PET Kinetic Modeling and Potential Opportunities Using Deep Learning. PET Clin. 2021;16:613–25. https://doi.org/10.1016/j.cpet.2021.06.009.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Ringheim A, Campos Neto GC, Anazodo U, Cui L, da Cunha ML, Vitor T, et al. Kinetic modeling of (68)Ga-PSMA-11 and validation of simplified methods for quantification in primary prostate cancer patients. EJNMMI Res. 2020;10:12. https://doi.org/10.1186/s13550-020-0594-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Patlak CS, Blasberg RG, Fenstermacher JD. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J Cereb Blood Flow Metab. 1983;3:1–7. https://doi.org/10.1038/jcbfm.1983.1.

    Article  CAS  PubMed  Google Scholar 

  16. Sachpekidis C, Kopka K, Eder M, Hadaschik BA, Freitag MT, Pan L, et al. 68Ga-PSMA-11 Dynamic PET/CT Imaging in Primary Prostate Cancer. Clin Nucl Med. 2016;41:e473–9. https://doi.org/10.1097/RLU.0000000000001349.

    Article  PubMed  Google Scholar 

  17. Sachpekidis C, Eder M, Kopka K, Mier W, Hadaschik BA, Haberkorn U, et al. (68)Ga-PSMA-11 dynamic PET/CT imaging in biochemical relapse of prostate cancer. Eur J Nucl Med Mol Imaging. 2016;43:1288–99. https://doi.org/10.1007/s00259-015-3302-4.

    Article  CAS  PubMed  Google Scholar 

  18. Huang X, Zhou Y, Bao S, Huang SC. Clustering-based linear least square fitting method for generation of parametric images in dynamic FDG PET studies. Int J Biomed Imaging. 2007;2007:65641. https://doi.org/10.1155/2007/65641.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Blomqvist G. On the construction of functional maps in positron emission tomography. J Cereb Blood Flow Metab. 1984;4:629–32. https://doi.org/10.1038/jcbfm.1984.89.

    Article  CAS  PubMed  Google Scholar 

  20. Zhou Y, Flores S, Mansor S, Hornbeck RC, Tu Z, Perlmutter JS, et al. Spatially constrained kinetic modeling with dual reference tissues improves (18)F-flortaucipir PET in studies of Alzheimer disease. Eur J Nucl Med Mol Imaging. 2021;48:3172–86. https://doi.org/10.1007/s00259-020-05134-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Gjedde A. High- and low-affinity transport of D-glucose from blood to brain. J Neurochem. 1981;36:1463–71. https://doi.org/10.1111/j.1471-4159.1981.tb00587.x.

    Article  CAS  Google Scholar 

  22. Patlak CS, Blasberg RG. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Generalizations J Cereb Blood Flow Metab. 1985;5:584–90. https://doi.org/10.1038/jcbfm.1985.87.

    Article  CAS  PubMed  Google Scholar 

  23. Wong DF, Gjedde A, Wagner HN Jr. Quantification of neuroreceptors in the living human brain. I. Irreversible binding of ligands. J Cereb Blood Flow Metab. 1986;6:137–46. https://doi.org/10.1038/jcbfm.1986.27.

    Article  CAS  PubMed  Google Scholar 

  24. Landis JR, Koch GG. An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics. 1977;33:363–74. https://doi.org/10.2307/2529786.

    Article  CAS  PubMed  Google Scholar 

  25. Strauss DS, Sachpekidis C, Kopka K, Pan L, Haberkorn U, Dimitrakopoulou-Strauss A. Pharmacokinetic studies of [(68) Ga]Ga-PSMA-11 in patients with biochemical recurrence of prostate cancer: detection, differences in temporal distribution and kinetic modelling by tissue type. Eur J Nucl Med Mol Imaging. 2021;48:4472–82. https://doi.org/10.1007/s00259-021-05420-1.

    Article  CAS  PubMed Central  Google Scholar 

  26. Sari H, Mingels C, Alberts I, Hu J, Buesser D, Shah V, et al. First results on kinetic modelling and parametric imaging of dynamic (18)F-FDG datasets from a long axial FOV PET scanner in oncological patients. Eur J Nucl Med Mol Imaging. 2022;49:1997–2009. https://doi.org/10.1007/s00259-021-05623-6.

    Article  CAS  Google Scholar 

  27. Sari H, Eriksson L, Mingels C, Alberts I, Casey ME, Afshar-Oromieh A, et al. Feasibility of using abbreviated scan protocols with population-based input functions for accurate kinetic modeling of [(18)F]-FDG datasets from a long axial FOV PET scanner. Eur J Nucl Med Mol Imaging. 2023;50:257–65. https://doi.org/10.1007/s00259-022-05983-7.

    Article  CAS  Google Scholar 

  28. Dias AH, Jochumsen MR, Zacho HD, Munk OL, Gormsen LC. Multiparametric dynamic whole-body PSMA PET/CT using [(68)Ga]Ga-PSMA-11 and [(18)F]PSMA-1007. EJNMMI Res. 2023;13:31. https://doi.org/10.1186/s13550-023-00981-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Lu M, Lindenberg L, Mena E, Turkbey B, Seidel J, Ton A, et al. A Pilot Study of Dynamic (18)F-DCFPyL PET/CT Imaging of Prostate Adenocarcinoma in High-Risk Primary Prostate Cancer Patients. Mol Imaging Biol. 2022;24:444–52. https://doi.org/10.1007/s11307-021-01670-5.

    Article  CAS  Google Scholar 

  30. Prasad V, Steffen IG, Diederichs G, Makowski MR, Wust P, Brenner W. Biodistribution of [(68)Ga]PSMA-HBED-CC in Patients with Prostate Cancer: Characterization of Uptake in Normal Organs and Tumour Lesions. Mol Imaging Biol. 2016;18:428–36. https://doi.org/10.1007/s11307-016-0945-x.

    Article  CAS  PubMed  Google Scholar 

  31. Hofman MS, Hicks RJ, Maurer T, Eiber M. Prostate-specific Membrane Antigen PET: Clinical Utility in Prostate Cancer, Normal Patterns, Pearls, and Pitfalls. Radiographics. 2018;38:200–17. https://doi.org/10.1148/rg.2018170108.

    Article  PubMed  Google Scholar 

  32. Rosar F, Wenner F, Khreish F, Dewes S, Wagenpfeil G, Hoffmann MA, et al. Early molecular imaging response assessment based on determination of total viable tumor burden in [68Ga]Ga-PSMA-11 PET/CT independently predicts overall survival in [177Lu]Lu-PSMA-617 radioligand therapy. Eur J Nucl Med Mol Imaging. 2022;49:1584–94. https://doi.org/10.1007/s00259-021-05594-8.

    Article  CAS  PubMed  Google Scholar 

  33. Seifert R, Kessel K, Schlack K, Weber M, Herrmann K, Spanke M, et al. PSMA PET total tumor volume predicts outcome of patients with advanced prostate cancer receiving [(177)Lu]Lu-PSMA-617 radioligand therapy in a bicentric analysis. Eur J Nucl Med Mol Imaging. 2021;48:1200–10. https://doi.org/10.1007/s00259-020-05040-1.

    Article  CAS  PubMed  Google Scholar 

  34. Unterrainer LM, Beyer L, Zacherl MJ, Gildehaus FJ, Todica A, Kunte SC, et al. Total Tumor Volume on (18)F-PSMA-1007 PET as Additional Imaging Biomarker in mCRPC Patients Undergoing PSMA-Targeted Alpha Therapy with (225)Ac-PSMA-I&T. Biomedicines. 2022;10:946. https://doi.org/10.3390/biomedicines10050946.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Cunningham VJ, Jones T. Spectral analysis of dynamic PET studies. J Cereb Blood Flow Metab. 1993;13:15–23. https://doi.org/10.1038/jcbfm.1993.5.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We express our appreciation to Wenjian Gu (Central Research Institute, UIH Group) for his assistance in preparing the code for the generating parametric image presented in this manuscript.

Funding

The study was supported by National Natural Science Foundation of China (No. 92259103, 82171972); National Key R&D Program of China (No. 2021YFA0910004); Clinical Research Project of Health Industry of Shanghai Municipal Health Commission (20214Y0438); Nurture projects for the Youth Medical Talents-Medical Imaging Practitioners Program (SHWRS(2021)_099).

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Author notes

  1. Ruohua Chen and Yee Ling Ng contributed equally to this article.

Authors and Affiliations

  1. Department of Nuclear Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, China

    Ruohua Chen, Lianghua Li, Haitao Zhao, Gang Huang & Jianjun Liu

  2. Institute of Clinical Nuclear Medicine, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, China

    Ruohua Chen, Lianghua Li, Haitao Zhao, Gang Huang & Jianjun Liu

  3. Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China

    Ruohua Chen, Lianghua Li, Haitao Zhao, Gang Huang & Jianjun Liu

  4. Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China

    Yee Ling Ng, Xinlan Yang & Yun Zhou

  5. Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, China

    Yinjie Zhu

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Chen, R., Ng, Y.L., Yang, X. et al. Comparison of parametric imaging and SUV imaging with [68 Ga]Ga-PSMA-11 using dynamic total-body PET/CT in prostate cancer. Eur J Nucl Med Mol Imaging 51, 568–580 (2024). https://doi.org/10.1007/s00259-023-06456-1

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