Skip to main content
Log in

The role of 18F-FDG PET/CT radiomics in lymphoma

  • Systematic Review
  • Published:
Clinical and Translational Imaging Aims and scope Submit manuscript

Abstract

Purpose

To systematically review the applications of texture analysis and radiomics applied to 18F-FDG PET/CT in lymphoma.

Methods

According to the PRISMA statement, a comprehensive research of the literature was performed to find relevant articles on the applications of texture analysis and radiomics to 18F-FDG PET/CT in lymphomas. Information on the general, methodological and clinical aspects of all included studies was collected. Studies were divided into three groups depending on their clinical aim: (1) outcome prediction; (2) histological differentiation from other malignancies; (3) assessment of bone marrow involvement.

Results

Twenty-seven full-text papers were selected for final review, 17 of which aimed to predict outcome, prognosis or survival, 7 tried to differentiate lymphoma from other malignancies and 3 studies aimed to assess bone marrow involvement.

Conclusions

18F-FDG PET/CT textural and radiomic features may be useful tools in lymphoma for histological prediction, prognostic assessment and bone marrow involvement definition. Further studies are needed to integrate radiomics in clinical multi-omic models.

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

Similar content being viewed by others

References

  1. Cheson BD (2018) PET/CT in lymphoma: current overview and future directions. Semin Nucl Med 48(1):76–81. https://doi.org/10.1053/j.semnuclmed.2017.09.007

    Article  PubMed  Google Scholar 

  2. Cheson BD, Fisher RI, Barrington SF et al (2014) Recommendations for initial evaluation, staging and response assessment of Hodgkin and non-Hodgkin lymphoma—The Lugano Classification. J Clin Oncol 32:3059–3068. https://doi.org/10.1200/JCO.2013.54.8800

    Article  PubMed  PubMed Central  Google Scholar 

  3. Frood R, Burton C, Tsoumpas C et al (2021) Baseline PET/CT imaging parameters for prediction of treatment outcome in Hodgkin and diffuse large B cell lymphoma: a systematic review. Eur J Nucl Med Mol Imaging. https://doi.org/10.1007/s00259-021-05233-2

    Article  PubMed  PubMed Central  Google Scholar 

  4. Zwanenburg A (2019) Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis. Eur J Nucl Med Mol Imaging 46(13):2638–2655. https://doi.org/10.1007/s00259-019-04391-8

    Article  PubMed  Google Scholar 

  5. Arabi H, AkhavanAllaf A, Sanaat A et al (2021) The promise of artificial intelligence and deep learning in PET and SPECT imaging. Phys Med 22(83):122–137. https://doi.org/10.1016/j.ejmp.2021.03.008

    Article  Google Scholar 

  6. Ko K-Y, Liu C-J, Ko C-L, Yen R-F et al (2016) Intratumoral heterogeneity of pretreatment 18F-FDG PET images predict disease progression in patients with nasal type extranodal natural killer/T-cell lymphoma. Clin Nucl Med 41(12):922–926. https://doi.org/10.1097/RLU.0000000000001375

    Article  PubMed  Google Scholar 

  7. Bouallègue FV, Tabaa YA, Kafrouni M et al (2017) Association between textural and morphological tumor indices on baseline PET-CT and early metabolic response on interim PET-CT in bulky malignant lymphomas. Med Phys 44(9):4608–4619. https://doi.org/10.1002/mp.12349 (Epub 2017 Aug 2)

    Article  PubMed  Google Scholar 

  8. Parvez A, Tau N, Hussey D et al (2018) 18 F-FDG PET/CT metabolic tumor parameters and radiomics features in aggressive non-Hodgkin’s lymphoma as predictors of treatment outcome and survival. Ann Nucl Med 32(6):410–416. https://doi.org/10.1007/s12149-018-1260-1 (Epub 2018 May 12)

    Article  CAS  PubMed  Google Scholar 

  9. Lue KH, Wu YF, Liu SH et al (2020) Intratumor heterogeneity assessed by 18 F-FDG PET/CT predicts treatment response and survival outcomes in patients with hodgkin lymphoma. Acad Radiol 27(8):e183–e192. https://doi.org/10.1016/j.acra.2019.10.015

    Article  PubMed  Google Scholar 

  10. Lue KH, Wu YF, Liu SH et al (2019) Prognostic value of pretreatment radiomic features of 18F-FDG PET in patients with Hodgkin lymphoma. Clin Nucl Med 44(10):e559–e565. https://doi.org/10.1097/RLU.0000000000002732

    Article  PubMed  Google Scholar 

  11. Mayerhoefer ME, Riedl CC, Kumar A et al (2019) Radiomic features of glucose metabolism enable prediction of outcome in mantle cell lymphoma. Eur J Nucl Med Mol Imaging 46(13):2760–2769. https://doi.org/10.1007/s00259-019-04420-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Milgrom SA, Elhalawani H, Lee J et al (2019) A PET radiomics model to predict refractory mediastinal Hodgkin Lymphoma. Sci Rep 9(1):1322. https://doi.org/10.1038/s41598-018-37197-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Tatsumi M, Isohashi K, Matsunaga K et al (2019) Volumetric and texture analysis on FDG PET in evaluating and predicting treatment response and recurrence after chemotherapy in follicular lymphoma. Int J Clin Oncol 24(10):1292–1300. https://doi.org/10.1007/s10147-019-01482-2

    Article  PubMed  Google Scholar 

  14. Wang H, Zhao S, Li L et al (2020) Development and validation of an 18F-FDG PET radiomic model for prognosis prediction in patients with nasal-type extranodal natural killer/T cell lymphoma. Eur Radiol 30(10):5578–5587. https://doi.org/10.1007/s00330-020-069431

    Article  CAS  PubMed  Google Scholar 

  15. Wu J, Lian C, Ruan S et al (2019) Treatment outcome prediction for cancer patients based on radiomics and belief function theory. IEEE Trans Radiat Plasma Med Sci 3(2):216–224. https://doi.org/10.1109/TRPMS.2018.2872406

    Article  PubMed  Google Scholar 

  16. Zhou Y, Ma XL, Pu LT et al (2019) Prediction of overall survival and progression-free survival by the 18 F-FDG PET/CT radiomic features in patients with primary gastric diffuse large B-cell lymphoma. Contrast Media Mol Imaging 2019:5963607. https://doi.org/10.1155/2019/5963607

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Aide N, Fruchart C, Nganoa C et al (2020) Baseline 18 F-FDG PET radiomic features as predictors of 2-year event-free survival in diffuse large B cell lymphomas treated with immunochemotherapy. Eur Radiol 30(8):4623–4632. https://doi.org/10.1007/s00330-020-06815-8

    Article  PubMed  Google Scholar 

  18. Cottereau AS, Nioche C, Dirand AS et al (2020) 18 F-FDG PET Dissemination features in diffuse large B-cell lymphoma are predictive of outcome. J Nucl Med 61(1):40–45. https://doi.org/10.2967/jnumed.119.229450

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Rodríguez Taroco MG, Cuña EG, Pages C et al (2021) Prognostic value of imaging markers from 18FDG-PET/CT in paediatric patients with Hodgkin lymphoma. Nucl Med Commun 42(3):306–314. https://doi.org/10.1097/MNM.0000000000001337

    Article  CAS  PubMed  Google Scholar 

  20. Sun Y, Qiao X, Jiang C et al (2020) Texture analysis improves the value of pretreatment 18 F-FDG PET/CT in predicting interim response of primary gastrointestinal diffuse large B-Cell lymphoma. Contrast Media Mol Imaging 21(2020):2981585. https://doi.org/10.1155/2020/2981585

    Article  CAS  Google Scholar 

  21. Wang M, Xu H, Xiao L et al (2019) Prognostic value of functional parameters of 18 F-FDG-PET images in patients with primary renal/adrenal lymphoma. Contrast Media Mol Imaging 25(2019):2641627. https://doi.org/10.1155/2019/2641627

    Article  CAS  Google Scholar 

  22. Lue KH, Wu YF, Lin HH et al (2020) Prognostic value of baseline radiomic features of 18F-FDG PET in patients with diffuse large B-cell lymphoma. Diagnostics (Basel) 11(1):36. https://doi.org/10.3390/diagnostics11010036

    Article  CAS  Google Scholar 

  23. Lartizien C, Rogez M, Niaf E et al (2014) Computer-aided staging of lymphoma patients with FDG PET/CT imaging based on textural information. IEEE J Biomed Health Inform 18(3):946–955. https://doi.org/10.1109/JBHI.2013.2283658

    Article  CAS  PubMed  Google Scholar 

  24. Kong Z, Jiang C, Zhu R et al (2019) 18F-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma. Neuroimage Clin 23:101912. https://doi.org/10.1016/j.nicl.2019.101912

    Article  PubMed  PubMed Central  Google Scholar 

  25. Lippi M, Gianotti S, Fama A et al (2020) Texture analysis and multiple-instance learning for the classification of malignant lymphomas. Comput Methods Programs Biomed 185:105153. https://doi.org/10.1016/j.cmpb.2019.105153

    Article  PubMed  Google Scholar 

  26. Ou X, Wang J, Zhou R et al (2019) Ability of 18 F-FDG PET/CT radiomic features to distinguish breast carcinoma from breast lymphoma. Contrast Media Mol Imaging 25(2019):4507694. https://doi.org/10.1155/2019/4507694

    Article  CAS  Google Scholar 

  27. Xu H, Guo W, Cui X et al (2019) Three-dimensional texture analysis based on PET/CT images to distinguish hepatocellular carcinoma and hepatic lymphoma. Front Oncol 3(9):844. https://doi.org/10.3389/fonc.2019.00844

    Article  Google Scholar 

  28. Ou X, Zhang J, Wang J et al (2020) Radiomics based on 18 F-FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine-learning approach: A preliminary study. Cancer Med 9(2):496–506. https://doi.org/10.1002/cam4.2711

    Article  PubMed  Google Scholar 

  29. Sun YW, Ji CF, Wang H et al (2020) Differentiating gastric cancer and gastric lymphoma using texture analysis (TA) of positron emission tomography (PET. Chin Med J (Engl) 134(4):439–447. https://doi.org/10.1097/CM9.0000000000001206

    Article  PubMed  PubMed Central  Google Scholar 

  30. Aide N, Talbot M, Fruchart C et al (2018) Diagnostic and prognostic value of baseline FDG PET/CT skeletal textural features in diffuse large B cell lymphoma. Eur J Nucl Med Mol Imaging 45(5):699–711. https://doi.org/10.1007/s00259-017-3899-6

    Article  PubMed  Google Scholar 

  31. Mayerhoefer ME, Riedl CC, Kumar A et al (2020) [18F] FDG-PET/CT radiomics for prediction of bone marrow involvement in mantle cell lymphoma: a retrospective study in 97 patients. Cancers (Basel) 12(5):1138. https://doi.org/10.3390/cancers12051138

    Article  CAS  Google Scholar 

  32. Kenawy MA, Khalil MM, Abdelgawad MH et al (2020) Correlation of texture feature analysis with bone marrow infiltration in initial staging of patients with lymphoma using 18 F-fluorodeoxyglucose positron emission tomography combined with computed tomography. Pol J Radiol 19(85):e586–e594. https://doi.org/10.5114/pjr.2020.99833

    Article  Google Scholar 

  33. Annunziata S, Cuccaro A, Tisi MC et al (2018) FDG-PET/CT at the end of immuno-chemotherapy in follicular lymphoma: the prognostic role of the ratio between target lesion and liver SUV(max) (rPET). Ann Nucl Med 32:372–377

    Article  CAS  PubMed  Google Scholar 

  34. Annunziata S, Pelliccioni A, Hohaus S, Maiolo E, Cuccaro A, Giordano A (2021) The prognostic role of end-of-treatment FDG-PET/CT in diffuse large B cell lymphoma: a pilot study application of neural networks to predict time-to-event. Ann Nucl Med 35(1):102–110. https://doi.org/10.1007/s12149-020-01542-y

    Article  PubMed  Google Scholar 

  35. Whiting PF, Rutjes AWS, Westwood ME et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155:529–536

    Article  PubMed  Google Scholar 

  36. Treglia G, Sadeghi R, Annunziata S, Lococo F, Cafarotti S, Prior JO, Bertagna F, Ceriani L, Giovanella L (2014) Diagnostic performance of fluorine-18-fluorodeoxyglucose positron emission tomography in the assessment of pleural abnormalities in cancer patients: a systematic review and a meta-analysis. Lung Cancer 83(1):1–7. https://doi.org/10.1016/j.lungcan.2013.11.002 (Epub 2013 Nov 13 PMID: 24290256)

    Article  PubMed  Google Scholar 

Download references

Funding

Salvatore Annunziata is funded by Ministero della Salute through Ricerca Finalizzata 2019, for a research project about a large retrospective ontology about PET/CT radiomics in lymphoma (PERL, 2021–2024; grant number: GR-2019-12370372).

Author information

Authors and Affiliations

Authors

Contributions

AR and EKT literature search and review, writing; MR and MM writing and editing; RG, LB, SA content planning and editing.

Corresponding author

Correspondence to Salvatore Annunziata.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rizzo, A., Triumbari, E.K.A., Gatta, R. et al. The role of 18F-FDG PET/CT radiomics in lymphoma. Clin Transl Imaging 9, 589–598 (2021). https://doi.org/10.1007/s40336-021-00451-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40336-021-00451-y

Keywords

Navigation