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International Journal of Clinical Oncology

, Volume 24, Issue 10, pp 1292–1300 | Cite as

Volumetric and texture analysis on FDG PET in evaluating and predicting treatment response and recurrence after chemotherapy in follicular lymphoma

  • Mitsuaki TatsumiEmail author
  • Kayako Isohashi
  • Keiko Matsunaga
  • Tadashi Watabe
  • Hiroki Kato
  • Yuzuru Kanakura
  • Jun Hatazawa
Original Article

Abstract

Purpose

The purpose of this study was to determine if quantitative SUV-related, volumetric FDG PET parameters, and texture features (SPs, VPs, and TFs, respectively) were useful to evaluate and predict response and recurrence after chemotherapy in follicular lymphoma (FL).

Methods

Pre- and posttreatment FDG PET examinations in 45 FL patients were analyzed retrospectively. In addition to SPs in the representative lesion, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were calculated as VPs for the representative and whole-body lesions. Six TFs were calculated in the pretreatment representative lesion. Response results with reduction of SPs or VPs after treatment (Δ) were compared to the Lugano classification based on visual assessment. SPs, VPs, and Δ of them as well as TFs were also evaluated if they allow prediction of response and recurrence after chemotherapy.

Results

Quantitative assessment with SPs and VPs provided 89% and 93–96% concordant results, respectively, with Lugano classification. Among pretreatment PET parameters, low gray-level zone emphasis (LGZE) in TFs solely showed statistical significance to predict complete response. All of posttreatment and Δ of SPs and VPs were considered as the predictors of progression free survival in the univariate Cox regression analysis, but none of them was the predictor in the multivariate analysis.

Conclusion

This study demonstrated that quantitative PET parameters were applicable to evaluate treatment response in FL. Texture analysis showed promise in predicting treatment response. Although posttreatment and Δ of PET parameters were the candidates, all of them proved to have limited value in predicting recurrence after chemotherapy.

Keywords

Volumetric parameters Texture analysis Quantitative evaluation FDG PET Follicular lymphoma 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Dreyling M, Ghielmini M, Rule S et al (2016) Newly diagnosed and relapsed follicular lymphoma: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol 27(suppl 5):v83–v90CrossRefGoogle Scholar
  2. 2.
    Pyo J, Won Kim K, Jacene HA et al (2013) End-therapy positron emission tomography for treatment response assessment in follicular lymphoma: a systematic review and meta-analysis. Clin Cancer Res 19(23):6566–6577CrossRefGoogle Scholar
  3. 3.
    Schöder H, Noy A, Gönen ML et al (2005) Intensity of 18fluorodeoxyglucose uptake in positron emission tomography distinguishes between indolent and aggressive non-Hodgkin’s lymphoma. J Clin Oncol 23(21):4643–4651CrossRefGoogle Scholar
  4. 4.
    Wu X, Pertovaara H, Korkola P et al (2014) Correlations between functional imaging markers derived from PET/CT and diffusion-weighted MRI in diffuse large B-cell lymphoma and follicular lymphoma. PLoS One 9(1):e84999CrossRefGoogle Scholar
  5. 5.
    Cheson BD, Pfistner B, Juweid ME et al (2007) Revised response criteria for malignant lymphoma. J Clin Oncol 25(5):579–586CrossRefGoogle Scholar
  6. 6.
    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(27):3059–3067CrossRefGoogle Scholar
  7. 7.
    Pak K, Cheon GJ, Nam H-Y et al (2014) Prognostic value of metabolic tumor volume and total lesion glycolysis in head and neck cancer: a systematic review and meta-analysis. J Nucl Med 55(6):884–890CrossRefGoogle Scholar
  8. 8.
    Im H-J, Pak K, Cheon GJ et al (2015) Prognostic value of volumetric parameters of 18 F-FDG PET in non-small-cell lung cancer: a meta-analysis. European journal of nuclear medicine and molecular imaging 42(2):241–251CrossRefGoogle Scholar
  9. 9.
    Li Y-J, Dai Y-L, Cheng Y-S et al (2016) Positron emission tomography (18) F-fluorodeoxyglucose uptake and prognosis in patients with bone and soft tissue sarcoma: a meta-analysis. Eur J Surg Oncol 42Google Scholar
  10. 10.
    Chicklore S, Goh V, Siddique M et al (2013) Quantifying tumour heterogeneity in 18 F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging 40(1):133–140CrossRefGoogle Scholar
  11. 11.
    Orlhac F, Soussan M, Maisonobe J-A et al (2014) Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis. J Nucl Med 55(3):414–422CrossRefGoogle Scholar
  12. 12.
    Hatt M, Tixier F, Pierce L et al (2017) Characterization of PET/CT images using texture analysis: the past, the present … any future? Eur J Nucl Med Mol Imaging 44(1):151–165CrossRefGoogle Scholar
  13. 13.
    Sharma P, Gupta A, Patel C et al (2012) Pediatric lymphoma: metabolic tumor burden as a quantitative index for treatment response evaluation. Ann Nucl Med 26(1):58–66CrossRefGoogle Scholar
  14. 14.
    Song M, Chung J, Lee J et al (2013) Metabolic tumor volume by positron emission tomography/computed tomography as a clinical parameter to determine therapeutic modality for early stage Hodgkin's lymphoma. Cancer Sci 104(12):1656–1661CrossRefGoogle Scholar
  15. 15.
    Kim C-Y, Hong CM, Kim D-H et al (2013) Prognostic value of whole-body metabolic tumour volume and total lesion glycolysis measured on 18 F-FDG PET/CT in patients with extranodal NK/T-cell lymphoma. Eur J Nucl Med Mol Imaging 40(9):1321–1329CrossRefGoogle Scholar
  16. 16.
    Ko K-Y, Liu C-J, Ko C-L 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):922–926CrossRefGoogle Scholar
  17. 17.
    Wahl RL, Jacene H, Kasamon Y et al (2009) From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med 50(Suppl 1):122S–150SCrossRefGoogle Scholar
  18. 18.
    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(3):369–378CrossRefGoogle Scholar
  19. 19.
    Tateishi U, Tatsumi M, Terauchi T et al (2015) Prognostic significance of metabolic tumor burden by positron emission tomography/computed tomography in patients with relapsed/refractory diffuse large B-cell lymphoma. Cancer Sci 106(2):186–193CrossRefGoogle Scholar
  20. 20.
    Hanaoka K, Hosono M, Tatsumi Y et al (2015) Heterogeneity of intratumoral 111 In-ibritumomab tiuxetan and 18 F-FDG distribution in association with therapeutic response in radioimmunotherapy for B-cell non-Hodgkin's lymphoma. EJNMMI Res 5(1):10CrossRefGoogle Scholar
  21. 21.
    Orlhac F, Nioche C, Soussan M et al (2017) Understanding changes in tumor texture indices in PET: a comparison between visual assessment and index values in simulated and patient data. J Nucl Med 58(3):387–392CrossRefGoogle Scholar
  22. 22.
    Meignan M, Cottereau AS, Versari A et al (2016) Baseline metabolic tumor volume predicts outcome in high-tumor-burden follicular lymphoma: a pooled analysis of three multicenter studies. J Clin Oncol 34(30):3618–3626CrossRefGoogle Scholar
  23. 23.
    Nakajo M, Jinguji M, Nakabeppu Y et al (2017) Texture analysis of 18 F-FDG PET/CT to predict tumour response and prognosis of patients with esophageal cancer treated by chemoradiotherapy. Eur J Nucl Med Mol Imaging 44(2):206–214CrossRefGoogle Scholar
  24. 24.
    Larson SM, Erdi Y, Akhurst T et al (1999) Tumor treatment response based on visual and quantitative changes in global tumor glycolysis using PET-FDG imaging: the visual response score and the change in total lesion glycolysis. Clin Positron Imaging 2(3):159–171CrossRefGoogle Scholar
  25. 25.
    Miyabe J, Hanamoto A, Tatsumi M et al (2017) Metabolic tumor volume of primary tumor predicts survival better than T classification in the larynx preservation approach. Cancer Sci 108(10):2030–2038CrossRefGoogle Scholar

Copyright information

© Japan Society of Clinical Oncology 2019

Authors and Affiliations

  • Mitsuaki Tatsumi
    • 1
    • 2
    Email author
  • Kayako Isohashi
    • 2
    • 3
  • Keiko Matsunaga
    • 2
  • Tadashi Watabe
    • 2
  • Hiroki Kato
    • 2
  • Yuzuru Kanakura
    • 4
  • Jun Hatazawa
    • 2
  1. 1.Department of RadiologyOsaka University HospitalSuitaJapan
  2. 2.Department of Nuclear Medicine and Tracer KineticsOsaka University Graduate School of MedicineSuitaJapan
  3. 3.Department of RadiologyOsaka Medical CollegeTakatsukiJapan
  4. 4.Department of Hematology and OncologyOsaka University Graduate School of MedicineSuitaJapan

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