Top

European Journal of Nuclear Medicine and Molecular Imaging

Published in:

Open Access 01-12-2021 | Glioma | Original Article

Fully automated analysis combining [¹⁸F]-FET-PET and multiparametric MRI including DSC perfusion and APTw imaging: a promising tool for objective evaluation of glioma progression

Authors: K. J. Paprottka, S. Kleiner, C. Preibisch, F. Kofler, F. Schmidt-Graf, C. Delbridge, D. Bernhardt, S. E. Combs, J. Gempt, B. Meyer, C. Zimmer, B. H. Menze, I. Yakushev, J. S. Kirschke, B. Wiestler

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 13/2021

Abstract

Purpose

To evaluate diagnostic accuracy of fully automated analysis of multimodal imaging data using [¹⁸F]-FET-PET and MRI (including amide proton transfer-weighted (APTw) imaging and dynamic-susceptibility-contrast (DSC) perfusion) in differentiation of tumor progression from treatment-related changes in patients with glioma.

Material and methods

At suspected tumor progression, MRI and [¹⁸F]-FET-PET data as part of a retrospective analysis of an observational cohort of 66 patients/74 scans (51 glioblastoma and 23 lower-grade-glioma, 8 patients included at two different time points) were automatically segmented into necrosis, FLAIR-hyperintense, and contrast-enhancing areas using an ensemble of deep learning algorithms. In parallel, previous MR exam was processed in a similar way to subtract preexisting tumor areas and focus on progressive tumor only. Within these progressive areas, intensity statistics were automatically extracted from [¹⁸F]-FET-PET, APTw, and DSC-derived cerebral-blood-volume (CBV) maps and used to train a Random Forest classifier with threefold cross-validation. To evaluate contribution of the imaging modalities to the classifier’s performance, impurity-based importance measures were collected. Classifier performance was compared with radiology reports and interdisciplinary tumor board assessments.

Results

In 57/74 cases (77%), tumor progression was confirmed histopathologically (39 cases) or via follow-up imaging (18 cases), while remaining 17 cases were diagnosed as treatment-related changes. The classification accuracy of the Random Forest classifier was 0.86, 95% CI 0.77–0.93 (sensitivity 0.91, 95% CI 0.81–0.97; specificity 0.71, 95% CI 0.44–0.9), significantly above the no-information rate of 0.77 (p = 0.03), and higher compared to an accuracy of 0.82 for MRI (95% CI 0.72–0.9), 0.81 for [¹⁸F]-FET-PET (95% CI 0.7–0.89), and 0.81 for expert consensus (95% CI 0.7–0.89), although these differences were not statistically significant (p > 0.1 for all comparisons, McNemar test). [¹⁸F]-FET-PET hot-spot volume was single-most important variable, with relevant contribution from all imaging modalities.

Conclusion

Automated, joint image analysis of [¹⁸F]-FET-PET and advanced MR imaging techniques APTw and DSC perfusion is a promising tool for objective response assessment in gliomas.

Available only for authorised users

Brandsma D, Stalpers L, Taal W, Sminia P, van den Bent MJ. Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol. 2008;9:453–61. https://doi.org/10.1016/S1470-2045(08)70125-6.CrossRefPubMed

Topkan E, Topuk S, Oymak E, Parlak C, Pehlivan B. Pseudoprogression in patients with glioblastoma multiforme after concurrent radiotherapy and temozolomide. Am J Clin Oncol. 2012;35:284–9. https://doi.org/10.1097/COC.0b013e318210f54a.CrossRefPubMed

Jang BS, Jeon SH, Kim IH, Kim IA. Prediction of pseudoprogression versus progression using machine learning algorithm in glioblastoma. Sci Rep. 2018;8:12516. https://doi.org/10.1038/s41598-018-31007-2.CrossRefPubMedPubMedCentral

Chu HH, Choi SH, Ryoo I, Kim SC, Yeom JA, Shin H, et al. Differentiation of true progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant temozolomide: comparison study of standard and high-b-value diffusion-weighted imaging. Radiology. 2013;269:831–40. https://doi.org/10.1148/radiol.13122024.CrossRefPubMed

Abdulla S, Saada J, Johnson G, Jefferies S, Ajithkumar T. Tumour progression or pseudoprogression? A review of post-treatment radiological appearances of glioblastoma. Clin Radiol. 2015;70:1299–312. https://doi.org/10.1016/j.crad.2015.06.096.CrossRefPubMed

Le Fevre C, Constans JM, Chambrelant I, Antoni D, Bund C, Leroy-Freschini B, et al. Pseudoprogression versus true progression in glioblastoma patients: a multiapproach literature review. Part 2 - Radiological features and metric markers. Crit Rev Oncol Hematol. 2021;159:103230. https://doi.org/10.1016/j.critrevonc.2021.103230.

Jain R, Gutierrez J, Narang J, Scarpace L, Schultz LR, Lemke N, et al. In vivo correlation of tumor blood volume and permeability with histologic and molecular angiogenic markers in gliomas. AJNR Am J Neuroradiol. 2011;32:388–94. https://doi.org/10.3174/ajnr.A2280.CrossRefPubMedPubMedCentral

Kim HS, Kim JH, Kim SH, Cho KG, Kim SY. Posttreatment high-grade glioma: usefulness of peak height position with semiquantitative MR perfusion histogram analysis in an entire contrast-enhanced lesion for predicting volume fraction of recurrence. Radiology. 2010;256:906–15. https://doi.org/10.1148/radiol.10091461.CrossRefPubMed

Mangla R, Singh G, Ziegelitz D, Milano MT, Korones DN, Zhong J, et al. Changes in relative cerebral blood volume 1 month after radiation-temozolomide therapy can help predict overall survival in patients with glioblastoma. Radiology. 2010;256:575–84. https://doi.org/10.1148/radiol.10091440.CrossRefPubMed

10.

Choi YS, Ahn SS, Lee SK, Chang JH, Kang SG, Kim SH, et al. Amide proton transfer imaging to discriminate between low- and high-grade gliomas: added value to apparent diffusion coefficient and relative cerebral blood volume. Eur Radiol. 2017;27:3181–9. https://doi.org/10.1007/s00330-017-4732-0.CrossRefPubMedPubMedCentral

11.

Zhou J, Tryggestad E, Wen Z, Lal B, Zhou T, Grossman R, et al. Differentiation between glioma and radiation necrosis using molecular magnetic resonance imaging of endogenous proteins and peptides. Nat Med. 2011;17:130–4. https://doi.org/10.1038/nm.2268.CrossRefPubMed

12.

Jiang S, Eberhart CG, Lim M, Heo HY, Zhang Y, Blair L, et al. Identifying recurrent malignant glioma after treatment using amide proton transfer-weighted MR imaging: a validation study with image-guided stereotactic biopsy. Clin Cancer Res. 2019;25:552–61. https://doi.org/10.1158/1078-0432.CCR-18-1233.CrossRefPubMed

13.

Heiss P, Mayer S, Herz M, Wester HJ, Schwaiger M, Senekowitsch-Schmidtke R. Investigation of transport mechanism and uptake kinetics of O-(2-[18F]fluoroethyl)-L-tyrosine in vitro and in vivo. J Nucl Med. 1999;40:1367–73.PubMed

14.

Jansen NL, Schwartz C, Graute V, Eigenbrod S, Lutz J, Egensperger R, et al. Prediction of oligodendroglial histology and LOH 1p/19q using dynamic [(18)F]FET-PET imaging in intracranial WHO grade II and III gliomas. Neuro Oncol. 2012;14:1473–80. https://doi.org/10.1093/neuonc/nos259.CrossRefPubMedPubMedCentral

15.

Kunz M, Thon N, Eigenbrod S, Hartmann C, Egensperger R, Herms J, et al. Hot spots in dynamic (18)FET-PET delineate malignant tumor parts within suspected WHO grade II gliomas. Neuro Oncol. 2011;13:307–16. https://doi.org/10.1093/neuonc/noq196.CrossRefPubMedPubMedCentral

16.

Galldiks N, Dunkl V, Stoffels G, Hutterer M, Rapp M, Sabel M, et al. Diagnosis of pseudoprogression in patients with glioblastoma using O-(2-[18F]fluoroethyl)-L-tyrosine PET. Eur J Nucl Med Mol Imaging. 2015;42:685–95. https://doi.org/10.1007/s00259-014-2959-4.CrossRefPubMed

17.

Togao O, Hiwatashi A, Yamashita K, Kikuchi K, Keupp J, Yoshimoto K, et al. Grading diffuse gliomas without intense contrast enhancement by amide proton transfer MR imaging: comparisons with diffusion- and perfusion-weighted imaging. Eur Radiol. 2017;27:578–88. https://doi.org/10.1007/s00330-016-4328-0.CrossRefPubMed

18.

Park JE, Lee JY, Kim HS, Oh JY, Jung SC, Kim SJ, et al. Amide proton transfer imaging seems to provide higher diagnostic performance in post-treatment high-grade gliomas than methionine positron emission tomography. Eur Radiol. 2018;28:3285–95. https://doi.org/10.1007/s00330-018-5341-2.CrossRefPubMed

19.

Unterrainer M, Vettermann F, Brendel M, Holzgreve A, Lifschitz M, Zahringer M, et al. Towards standardization of (18)F-FET PET imaging: do we need a consistent method of background activity assessment? EJNMMI Res. 2017;7:48. https://doi.org/10.1186/s13550-017-0295-y.CrossRefPubMedPubMedCentral

20.

Wiestler B, Menze B. Deep learning for medical image analysis: a brief introduction. Neurooncol Adv. 2020;2:iv35-iv41. https://doi.org/10.1093/noajnl/vdaa092.

21.

Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 2016;131:803–20. https://doi.org/10.1007/s00401-016-1545-1.CrossRefPubMed

22.

Wen PY, Macdonald DR, Reardon DA, Cloughesy TF, Sorensen AG, Galanis E, et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol. 2010;28:1963–72. https://doi.org/10.1200/JCO.2009.26.3541.CrossRefPubMed

23.

Togao O, Keupp J, Hiwatashi A, Yamashita K, Kikuchi K, Yoneyama M, et al. Amide proton transfer imaging of brain tumors using a self-corrected 3D fast spin-echo dixon method: comparison with separate B0 correction. Magn Reson Med. 2017;77:2272–9. https://doi.org/10.1002/mrm.26322.CrossRefPubMed

24.

Kluge A, Lukas M, Toth V, Pyka T, Zimmer C, Preibisch C. Analysis of three leakage-correction methods for DSC-based measurement of relative cerebral blood volume with respect to heterogeneity in human gliomas. Magn Reson Imaging. 2016;34:410–21. https://doi.org/10.1016/j.mri.2015.12.015.CrossRefPubMed

25.

Hedderich D, Kluge A, Pyka T, Zimmer C, Kirschke JS, Wiestler B, et al. Consistency of normalized cerebral blood volume values in glioblastoma using different leakage correction algorithms on dynamic susceptibility contrast magnetic resonance imaging data without and with preload. J Neuroradiol. 2019;46:44–51. https://doi.org/10.1016/j.neurad.2018.04.006.CrossRefPubMed

26.

Boxerman JL, Schmainda KM, Weisskoff RM. Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am J Neuroradiol. 2006;27:859–67.PubMedPubMedCentral

27.

Leenders KL. PET: blood flow and oxygen consumption in brain tumors. J Neurooncol. 1994;22:269–73. https://doi.org/10.1007/BF01052932.CrossRefPubMed

28.

Rohlfing T, Zahr NM, Sullivan EV, Pfefferbaum A. The SRI24 multichannel atlas of normal adult human brain structure. Hum Brain Mapp. 2010;31:798–819. https://doi.org/10.1002/hbm.20906.CrossRefPubMed

29.

Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage. 2011;54:2033–44. https://doi.org/10.1016/j.neuroimage.2010.09.025.CrossRefPubMed

30.

Kofler F, Berger C, Waldmannstetter D, Lipkova J, Ezhov I, Tetteh G, et al. BraTS toolkit: translating BraTS brain tumor segmentation algorithms into clinical and scientific practice. Front Neurosci. 2020;14:125. https://doi.org/10.3389/fnins.2020.00125.CrossRefPubMedPubMedCentral

31.

Langerak TR, van der Heide UA, Kotte AN, Viergever MA, van Vulpen M, Pluim JP. Label fusion in atlas-based segmentation using a selective and iterative method for performance level estimation (SIMPLE). IEEE Trans Med Imaging. 2010;29:2000–8. https://doi.org/10.1109/TMI.2010.2057442.CrossRefPubMed

32.

Galldiks N, Stoffels G, Filss C, Rapp M, Blau T, Tscherpel C, et al. The use of dynamic O-(2–18F-fluoroethyl)-l-tyrosine PET in the diagnosis of patients with progressive and recurrent glioma. Neuro Oncol. 2015;17:1293–300. https://doi.org/10.1093/neuonc/nov088.CrossRefPubMedPubMedCentral

33.

Gottler J, Lukas M, Kluge A, Kaczmarz S, Gempt J, Ringel F, et al. Intra-lesional spatial correlation of static and dynamic FET-PET parameters with MRI-based cerebral blood volume in patients with untreated glioma. Eur J Nucl Med Mol Imaging. 2017;44:392–7. https://doi.org/10.1007/s00259-016-3585-0.CrossRefPubMed

34.

Breiman L. Random Forest Machine learning. 2001;45:5–32.CrossRef

35.

Sharma M, Juthani RG, Vogelbaum MA. Updated response assessment criteria for high-grade glioma: beyond the MacDonald criteria. Chin Clin Oncol. 2017;6:37. https://doi.org/10.21037/cco.2017.06.26.

36.

Yang D. Standardized MRI assessment of high-grade glioma response: a review of the essential elements and pitfalls of the RANO criteria. Neurooncol Pract. 2016;3:59–67. https://doi.org/10.1093/nop/npv023.CrossRefPubMed

37.

Lutz K, Radbruch A, Wiestler B, Baumer P, Wick W, Bendszus M. Neuroradiological response criteria for high-grade gliomas. Clin Neuroradiol. 2011;21:199–205. https://doi.org/10.1007/s00062-011-0080-7.CrossRefPubMed

38.

Kickingereder P, Isensee F, Tursunova I, Petersen J, Neuberger U, Bonekamp D, et al. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol. 2019;20:728–40. https://doi.org/10.1016/S1470-2045(19)30098-1.CrossRefPubMed

39.

Sotoudeh H, Shafaat O, Bernstock JD, Brooks MD, Elsayed GA, Chen JA, et al. Artificial intelligence in the management of glioma: era of personalized medicine. Front Oncol. 2019;9:768. https://doi.org/10.3389/fonc.2019.00768.CrossRefPubMedPubMedCentral

40.

Hutterer M, Hattingen E, Palm C, Proescholdt MA, Hau P. Current standards and new concepts in MRI and PET response assessment of antiangiogenic therapies in high-grade glioma patients. Neuro Oncol. 2015;17:784–800. https://doi.org/10.1093/neuonc/nou322.CrossRefPubMed

41.

Lohmeier J, Bohner G, Siebert E, Brenner W, Hamm B, Makowski MR. Quantitative biparametric analysis of hybrid (18)F-FET PET/MR-neuroimaging for differentiation between treatment response and recurrent glioma. Sci Rep. 2019;9:14603. https://doi.org/10.1038/s41598-019-50182-4.CrossRefPubMedPubMedCentral

42.

Jena A, Taneja S, Gambhir A, Mishra AK, D’Souza MM, Verma SM, et al. Glioma recurrence versus radiation necrosis: single-session multiparametric approach using simultaneous O-(2–18F-fluoroethyl)-L-tyrosine PET/MRI. Clin Nucl Med. 2016;41:e228–36. https://doi.org/10.1097/RLU.0000000000001152.CrossRefPubMed

43.

Chang K, Beers AL, Bai HX, Brown JM, Ly KI, Li X, et al. Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement. Neuro Oncol. 2019;21:1412–22. https://doi.org/10.1093/neuonc/noz106.CrossRefPubMedPubMedCentral

44.

Ellingson BM. On the promise of artificial intelligence for standardizing radiographic response assessment in gliomas. Neuro Oncol. 2019;21:1346–7. https://doi.org/10.1093/neuonc/noz162.CrossRefPubMedPubMedCentral

45.

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. 2019.

46.

Park KJ, Kim HS, Park JE, Shim WH, Kim SJ, Smith SA. Added value of amide proton transfer imaging to conventional and perfusion MR imaging for evaluating the treatment response of newly diagnosed glioblastoma. Eur Radiol. 2016;26:4390–403. https://doi.org/10.1007/s00330-016-4261-2.CrossRefPubMed

47.

Liesche F, Lukas M, Preibisch C, Shi K, Schlegel J, Meyer B, et al. (18)F-fluoroethyl-tyrosine uptake is correlated with amino acid transport and neovascularization in treatment-naive glioblastomas. Eur J Nucl Med Mol Imaging. 2019;46:2163–8. https://doi.org/10.1007/s00259-019-04407-3.CrossRefPubMed

48.

Schon S, Cabello J, Liesche-Starnecker F, Molina-Romero M, Eichinger P, Metz M, et al. Imaging glioma biology: spatial comparison of amino acid PET, amide proton transfer, and perfusion-weighted MRI in newly diagnosed gliomas. Eur J Nucl Med Mol Imaging. 2020;47:1468–75. https://doi.org/10.1007/s00259-019-04677-x.CrossRefPubMedPubMedCentral

49.

Wiestler B, Kluge A, Lukas M, Gempt J, Ringel F, Schlegel J, et al. Multiparametric MRI-based differentiation of WHO grade II/III glioma and WHO grade IV glioblastoma. Sci Rep. 2016;6:35142. https://doi.org/10.1038/srep35142.CrossRefPubMedPubMedCentral

50.

Pyka T, Hiob D, Preibisch C, Gempt J, Wiestler B, Schlegel J, et al. Diagnosis of glioma recurrence using multiparametric dynamic 18F-fluoroethyl-tyrosine PET-MRI. Eur J Radiol. 2018;103:32–7. https://doi.org/10.1016/j.ejrad.2018.04.003.CrossRefPubMed

51.

Hu X, Wong KK, Young GS, Guo L, Wong ST. Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma. J Magn Reson Imaging. 2011;33:296–305. https://doi.org/10.1002/jmri.22432.CrossRefPubMedPubMedCentral

Title: Fully automated analysis combining [18F]-FET-PET and multiparametric MRI including DSC perfusion and APTw imaging: a promising tool for objective evaluation of glioma progression
Authors: K. J. Paprottka
S. Kleiner
C. Preibisch
F. Kofler
F. Schmidt-Graf
C. Delbridge
D. Bernhardt
S. E. Combs
J. Gempt
B. Meyer
C. Zimmer
B. H. Menze
I. Yakushev
J. S. Kirschke
B. Wiestler
Publication date: 01-12-2021
Publisher: Springer Berlin Heidelberg
Keywords: Glioma
Magnetic Resonance Imaging
Magnetic Resonance Imaging
Glioma
Glioma
Published in: European Journal of Nuclear Medicine and Molecular Imaging / Issue 13/2021
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-021-05427-8

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Fully automated analysis combining [¹⁸F]-FET-PET and multiparametric MRI including DSC perfusion and APTw imaging: a promising tool for objective evaluation of glioma progression

Abstract

Purpose

Material and methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Material and methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 13/2021

Head-to-head intra-individual comparison of biodistribution and tumor uptake of 68Ga-FAPI and 18F-FDG PET/CT in cancer patients

Yes, the Holy Gray exists. Learn from modern radioembolisation

Feasibility of late acquisition [68Ga]Ga-PSMA-11 PET/CT using a long axial field-of-view PET/CT scanner for the diagnosis of recurrent prostate cancer—first clinical experiences

68Ga-DOTA-FAPI-04 PET mimicking whole body bone scan in a patient with metabolic bone disease

Both [68Ga]Ga-FAPI and [18F]FDG PET/CT missed bone metastasis in a patient with breast cancer

Correction to: The Strategic Biomarker Roadmap for the validation of Alzheimer’s diagnostic biomarkers: methodological update