Skip to main content

Advertisement

Log in

Diffusion-weighted MRI of lymphoma: prognostic utility and implications for PET/MRI?

  • Original Article
  • Published:
European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

With the recent introduction of PET/MRI, we investigated whether diffusion-weighted imaging (DWI) can complement PET for predicting local treatment response in Hodgkin lymphoma.

Methods

This retrospective study included 39 patients selected from a hospital database with a histological diagnosis of Hodgkin lymphoma undergoing whole-body MRI (supplemented by DWI) and PET/CT before and after two cycles of vincristine, etoposide, prednisolone and doxorubicin (OEPA). The pretreatment volume, MRI apparent diffusion coefficient (ADC) and PET maximum standardized uptake value (SUVmax) of the largest nodal mass were determined quantitatively for evaluation of the local response following two cycles of OEPA. Quantitative pretreatment imaging biomarkers (disease volume, ADC, SUVmax) were compared between sites with an adequate and those with an inadequate response using Fisher’s exact test and Mann Whitney statistics. Multivariate models predictive of an inadequate response based on demographic/clinical features, pretreatment disease volume and SUVmax without (model 1) and with (model 2) the addition of ADC were derived and crossvalidated. The ROC area under curve (AUC) was calculated for both models using the full dataset (training) and the crossvalidation (test) data.

Results

Sites with an adequate response had a significantly lower median pretreatment ADC (1.0 × 10−3mm2s−1) than those with an inadequate response (1.26 × 10−3mm2s−1; p < 0.01). There were no significant differences in patient demographic/clinical parameters, pretreatment SUVmax or pretreatment nodal volume between sites with inadequate and adequate response. The ROC-AUCs for prediction of an inadequate response for the training and test data for model 1 were 0.90 and 0.53, and for model 2 were 0.84 and 0.71, respectively.

Conclusion

DWI complements PET for prediction of site-specific interim response to chemotherapy.

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.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Delbeke D, Stroobants S, de Kerviler E, Gisselbrecht C, Meignan M, Conti PS. Expert opinions on positron emission tomography and computed tomography imaging in lymphoma. Oncologist. 2009;14 Suppl 2:30–40.

    Article  PubMed  Google Scholar 

  2. Kwee TC, van Ufford HM, Beek FJ, Takahara T, Uiterwaal CS, Bierings MB, et al. Whole-body MRI, including diffusion-weighted imaging, for the initial staging of malignant lymphoma: comparison to computed tomography. Investig Radiol. 2009;44(10):683–90.

    Article  Google Scholar 

  3. Kauczor HU, Zechmann C, Stieltjes B, Weber MA. Functional magnetic resonance imaging for defining the biological target volume. Cancer Imaging. 2006;6:51–5.

    Article  PubMed  Google Scholar 

  4. Koh DM, Collins DJ. Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am J Roentgenol. 2007;188(6):1622–35.

    Article  PubMed  Google Scholar 

  5. Szafer A, Zhong J, Anderson AW, Gore JC. Diffusion-weighted imaging in tissues: theoretical models. NMR Biomed. 1995;8(7–8):289–96.

    Article  PubMed  CAS  Google Scholar 

  6. Lyng H, Haraldseth O, Rofstad EK. Measurement of cell density and necrotic fraction in human melanoma xenografts by diffusion weighted magnetic resonance imaging. Magn Reson Med. 2000;43(6):828–36.

    Article  PubMed  CAS  Google Scholar 

  7. Barajas Jr RF, Rubenstein JL, Chang JS, Hwang J, Cha S. Diffusion-weighted MR imaging derived apparent diffusion coefficient is predictive of clinical outcome in primary central nervous system lymphoma. AJNR Am J Neuroradiol. 2010;31(1):60–6.

    Article  PubMed  Google Scholar 

  8. Gu J, Chan T, Zhang J, Leung AY, Kwong YL, Khong PL. Whole-body diffusion-weighted imaging: the added value to whole-body MRI at initial diagnosis of lymphoma. AJR Am J Roentgenol. 2011;197(3):W384–91.

    Article  PubMed  Google Scholar 

  9. Abdulqadhr G, Molin D, Astrom G, Suurküla M, Johansson L, Hagberg H, et al. Whole-body diffusion-weighted imaging compared with FDG-PET/CT in staging of lymphoma patients. Acta Radiol. 2011;52(2):173–80.

    Article  PubMed  Google Scholar 

  10. van Ufford HM, Kwee TC, Beek FJ, van Leeuwen MS, Takahara T, Fijnheer R, et al. Newly diagnosed lymphoma: initial results with whole-body T1-weighted, STIR, and diffusion-weighted MRI compared with 18F-FDG PET/CT. AJR Am J Roentgenol. 2011;196(3):662–9.

    Article  PubMed  Google Scholar 

  11. Punwani S, Prakash V, Bainbridge A, Taylor SA, Bandula B, Olsen OE, et al. Quantitative diffusion weighted MRI: a functional biomarker of nodal disease in Hodgkin lymphoma? Cancer Biomark. 2010;7(4):249–59.

    Google Scholar 

  12. Palumbo B, Angotti F, Marano GD. Relationship between PET-FDG and MRI apparent diffusion coefficients in brain tumors. Q J Nucl Med Mol Imaging. 2009;53(1):17–22.

    PubMed  CAS  Google Scholar 

  13. Ho KC, Lin G, Wang JJ, Lai CH, Chang CJ, Yen TC. Correlation of apparent diffusion coefficients measured by 3T diffusion-weighted MRI and SUV from FDG PET/CT in primary cervical cancer. Eur J Nucl Med Mol Imaging. 2009;36(2):200–8.

    Article  PubMed  Google Scholar 

  14. Beer AJ, Eiber M, Souvatzoglou M, Holzapfel K, Ganter C, Weirich G, et al. Restricted water diffusibility as measured by diffusion-weighted MR imaging and choline uptake in (11)C-choline PET/CT are correlated in pelvic lymph nodes in patients with prostate cancer. Mol Imaging Biol. 2011;13(2):352–61.

    Article  PubMed  Google Scholar 

  15. Pichler BJ, Kolb A, Nagele T, Schlemmer HP. PET/MRI: paving the way for the next generation of clinical multimodality imaging applications. J Nucl Med. 2010;51(3):333–6.

    Article  PubMed  Google Scholar 

  16. Hasenclever D, Diehl V. A prognostic score for advanced Hodgkin’s disease. International Prognostic Factors Project on Advanced Hodgkin’s Disease. N Engl J Med. 1998;339(21):1506–14.

    Article  PubMed  CAS  Google Scholar 

  17. Ramsdale E, van Besien K, Smith SM. Personalized treatment of lymphoma: promise and reality. Semin Oncol. 2011;38(2):225–35.

    Article  PubMed  Google Scholar 

  18. Cazaentre T, Morschhauser F, Vermandel M, Betrouni N, Prangère T, Steinling M, et al. Pre-therapy 18F-FDG PET quantitative parameters help in predicting the response to radioimmunotherapy in non-Hodgkin lymphoma. Eur J Nucl Med Mol Imaging. 2010;37(3):494–504.

    Article  PubMed  CAS  Google Scholar 

  19. Torizuka T, Zasadny KR, Kison PV, Rommelfanger SG, Kaminski MS, Wahl RL. Metabolic response of non-Hodgkin’s lymphoma to 131I-anti-B1 radioimmunotherapy: evaluation with FDG PET. J Nucl Med. 2000;41(6):999–1005.

    PubMed  CAS  Google Scholar 

  20. Koh DM, Scurr E, Collins D, Kanber B, Norman A, Leach MO, et al. Predicting response of colorectal hepatic metastasis: value of pretreatment apparent diffusion coefficients. AJR Am J Roentgenol. 2007;188(4):1001–8.

    Article  PubMed  Google Scholar 

  21. Dzik-Jurasz A, Domenig C, George M, Wolber J, Padhani A, Brown G, et al. Diffusion MRI for prediction of response of rectal cancer to chemoradiation. Lancet. 2002;360(9329):307–8.

    Article  PubMed  Google Scholar 

  22. Liu Y, Bai R, Sun H, Liu H, Zhao X, Li Y. Diffusion-weighted imaging in predicting and monitoring the response of uterine cervical cancer to combined chemoradiation. Clin Radiol. 2009;64(11):1067–74.

    Article  PubMed  CAS  Google Scholar 

  23. Vandecaveye V, Dirix P, De Keyzer F, de Beeck KO, Vander Poorten V, Roebben I, et al. Predictive value of diffusion-weighted magnetic resonance imaging during chemoradiotherapy for head and neck squamous cell carcinoma. Eur Radiol. 2010;20(7):1703–14.

    Article  PubMed  Google Scholar 

  24. Hutchings M, Mikhaeel NG, Fields PA, Nunan T, Timothy AR. Prognostic value of interim FDG-PET after two or three cycles of chemotherapy in Hodgkin lymphoma. Ann Oncol. 2005;16(7):1160–8.

    Article  PubMed  CAS  Google Scholar 

  25. Zinzani PL, Tani M, Fanti S, Alinari L, Musuraca G, Marchi E, et al. Early positron emission tomography (PET) restaging: a predictive final response in Hodgkin’s disease patients. Ann Oncol. 2006;17(8):1296–300.

    Article  PubMed  CAS  Google Scholar 

  26. Gallamini A, Rigacci L, Merli F, Nassi L, Bosi A, Capodanno I, et al. The predictive value of positron emission tomography scanning performed after two courses of standard therapy on treatment outcome in advanced stage Hodgkin’s disease. Haematologica. 2006;91(4):475–81.

    PubMed  Google Scholar 

  27. Gallamini A, Hutchings M, Rigacci L, Specht L, Merli F, Hansen M, et al. Early interim 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography is prognostically superior to international prognostic score in advanced-stage Hodgkin’s lymphoma: a report from a joint Italian-Danish study. J Clin Oncol. 2007;25(24):3746–52.

    Article  PubMed  CAS  Google Scholar 

  28. Advani R, Maeda L, Lavori P, Quon A, Hoppe R, Breslin S, et al. Impact of positive positron emission tomography on prediction of freedom from progression after Stanford V chemotherapy in Hodgkin’s disease. J Clin Oncol. 2007;25(25):3902–7.

    Article  PubMed  Google Scholar 

  29. Cerci JJ, Pracchia LF, Linardi CC, Pitella FA, Delbeke D, Izaki M, et al. 18F-FDG PET after 2 cycles of ABVD predicts event-free survival in early and advanced Hodgkin lymphoma. J Nucl Med. 2010;51(9):1337–43.

    Article  PubMed  CAS  Google Scholar 

  30. Gallamini A, Patti C, Viviani S, Rossi A, Fiore F, Di Raimondo F, et al. Early chemotherapy intensification with BEACOPP in advanced-stage Hodgkin lymphoma patients with a interim-PET positive after two ABVD courses. Br J Haematol. 2011;152(5):551–60.

    Article  PubMed  Google Scholar 

  31. Stauss J, Franzius C, Pfluger T, Juergens KU, Biassoni L, Begent J, et al. Guidelines for 18F-FDG PET and PET-CT imaging in paediatric oncology. Eur J Nucl Med Mol Imaging. 2008;35(8):1581–8.

    Article  PubMed  CAS  Google Scholar 

  32. Erdi YE, Rosenzweig K, Erdi AK, Macapinlac HA, Hu YC, Braban LE, et al. Radiotherapy treatment planning for patients with non-small cell lung cancer using positron emission tomography (PET). Radiother Oncol. 2002;62(1):51–60.

    Article  PubMed  Google Scholar 

  33. Meignan M, Gallamini A, Haioun C. Report on the First International Workshop on Interim-PET-Scan in Lymphoma. Leuk Lymphoma. 2009;50(8):1257–60.

    Article  PubMed  Google Scholar 

  34. Furth C, Amthauer H, Hautzel H, Steffen IG, Ruf J, Schiefer J, et al. Evaluation of interim PET response criteria in paediatric Hodgkin’s lymphoma – results for dedicated assessment criteria in a blinded dual-centre read. Ann Oncol. 2011;22(5):1198–203.

    Article  PubMed  CAS  Google Scholar 

  35. Kostakoglu L, Schoder H, Johnson JL, Hall NC, Schwartz LH, Straus DJ, et al. Interim [(18)F]fluorodeoxyglucose positron emission tomography imaging in stage I-II non-bulky Hodgkin lymphoma: would using combined positron emission tomography and computed tomography criteria better predict response than each test alone? Leuk Lymphoma. 2012;53(11):2143–50.

    Article  PubMed  CAS  Google Scholar 

  36. Humphries PD, Sebire NJ, Siegel MJ, Olsen OE. Tumors in pediatric patients at diffusion-weighted MR imaging: apparent diffusion coefficient and tumor cellularity. Radiology. 2007;245(3):848–54.

    Article  PubMed  Google Scholar 

  37. Fodale V, Pierobon M, Liotta L, Petricoin E. Mechanism of cell adaptation: when and how do cancer cells develop chemoresistance? Cancer J. 2011;17(2):89–95.

    Article  PubMed  CAS  Google Scholar 

  38. Rajan R, Poniecka A, Smith TL, Yang Y, Frye D, Pusztai L, et al. Change in tumor cellularity of breast carcinoma after neoadjuvant chemotherapy as a variable in the pathologic assessment of response. Cancer. 2004;100(7):1365–73.

    Article  PubMed  Google Scholar 

  39. Wu X, Kellokumpu-Lehtinen PL, Pertovaara H, Korkola P, Soimakallio S, Eskola H, et al. Diffusion-weighted MRI in early chemotherapy response evaluation of patients with diffuse large B-cell lymphoma – a pilot study: comparison with 2-deoxy-2-fluoro-D-glucose-positron emission tomography/computed tomography. NMR Biomed. 2011;24(10):1181–90.

    Article  PubMed  CAS  Google Scholar 

  40. Dieckmann K, Potter R, Hofmann J, Heinzl H, Wagner W, Schellong G. Does bulky disease at diagnosis influence outcome in childhood Hodgkin’s disease and require higher radiation doses? Results from the German-Austrian Pediatric Multicenter Trial DAL-HD-90. Int J Radiat Oncol Biol Phys. 2003;56(3):644–52.

    Article  PubMed  Google Scholar 

  41. Ali A, Sayed H, Farrag A, El-Sayed M. Risk-based combined-modality therapy of pediatric Hodgkin’s lymphoma: a retrospective study. Leuk Res. 2010;34(11):1447–52.

    Article  PubMed  Google Scholar 

  42. Palumbo B, Fravolini ML, Nuvoli S, Spanu A, Paulus KS, Schillaci O, et al. Comparison of two neural network classifiers in the differential diagnosis of essential tremor and Parkinson’s disease by (123)I-FP-CIT brain SPECT. Eur J Nucl Med Mol Imaging. 2010;37(11):2146–53.

    Article  PubMed  Google Scholar 

  43. Taylor A, Manatunga A, Garcia EV. Decision support systems in diuresis renography. Semin Nucl Med. 2008;38(1):67–81.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This work was undertaken at the Comprehensive Biomedical Centre, University College Hospital, London, which received a proportion of its funding from the National Institute for Health Research. The views expressed in this publication are those of the authors and not necessarily those of the UK Department of Health.

Funding

The authors are grateful to the Royal College of Radiologists and the Radiological Research Trust for grants in support of this work.

Conflicts of interest

None.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shonit Punwani.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Punwani, S., Taylor, S.A., Saad, Z.Z. et al. Diffusion-weighted MRI of lymphoma: prognostic utility and implications for PET/MRI?. Eur J Nucl Med Mol Imaging 40, 373–385 (2013). https://doi.org/10.1007/s00259-012-2293-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00259-012-2293-7

Keywords

Navigation