Fully automated analysis combining [
APTw
B. coshared last
DSC perfusion
Fully automated
Glioma progression
J. S. and Wiestler
Kirschke
Multiparametric MRI
[18F]-FET-PET
Journal
European journal of nuclear medicine and molecular imaging
ISSN: 1619-7089
Titre abrégé: Eur J Nucl Med Mol Imaging
Pays: Germany
ID NLM: 101140988
Informations de publication
Date de publication:
12 2021
12 2021
Historique:
received:
05
03
2021
accepted:
24
05
2021
pubmed:
27
6
2021
medline:
12
11
2021
entrez:
26
6
2021
Statut:
ppublish
Résumé
To evaluate diagnostic accuracy of fully automated analysis of multimodal imaging data using [ At suspected tumor progression, MRI and [ 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 [ Automated, joint image analysis of [
Identifiants
pubmed: 34173008
doi: 10.1007/s00259-021-05427-8
pii: 10.1007/s00259-021-05427-8
pmc: PMC8566389
doi:
Substances chimiques
Amides
0
Protons
0
Tyrosine
42HK56048U
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4445-4455Informations de copyright
© 2021. The Author(s).
Références
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 .
doi: 10.1016/S1470-2045(08)70125-6
pubmed: 18452856
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 .
doi: 10.1097/COC.0b013e318210f54a
pubmed: 21399487
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 .
doi: 10.1038/s41598-018-31007-2
pubmed: 30131513
pmcid: 6104063
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 .
doi: 10.1148/radiol.13122024
pubmed: 23771912
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 .
doi: 10.1016/j.crad.2015.06.096
pubmed: 26272530
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 .
doi: 10.3174/ajnr.A2280
pubmed: 21071537
pmcid: 7965727
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 .
doi: 10.1148/radiol.10091461
pubmed: 20634429
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 .
doi: 10.1148/radiol.10091440
pubmed: 20529987
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 .
doi: 10.1007/s00330-017-4732-0
pubmed: 28116517
pmcid: 5746027
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 .
doi: 10.1038/nm.2268
pubmed: 21170048
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 .
doi: 10.1158/1078-0432.CCR-18-1233
pubmed: 30366937
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: 10450690
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 .
doi: 10.1093/neuonc/nos259
pubmed: 23090986
pmcid: 3499015
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 .
doi: 10.1093/neuonc/noq196
pubmed: 21292686
pmcid: 3064604
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 .
doi: 10.1007/s00259-014-2959-4
pubmed: 25411133
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 .
doi: 10.1007/s00330-016-4328-0
pubmed: 27003139
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 .
doi: 10.1007/s00330-018-5341-2
pubmed: 29488086
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 .
doi: 10.1186/s13550-017-0295-y
pubmed: 28560582
pmcid: 5449315
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 .
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 .
doi: 10.1007/s00401-016-1545-1
pubmed: 27157931
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 .
doi: 10.1200/JCO.2009.26.3541
pubmed: 20231676
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 .
doi: 10.1002/mrm.26322
pubmed: 27385636
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 .
doi: 10.1016/j.mri.2015.12.015
pubmed: 26708034
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 .
doi: 10.1016/j.neurad.2018.04.006
pubmed: 29753641
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.
pubmed: 16611779
pmcid: 8134002
Leenders KL. PET: blood flow and oxygen consumption in brain tumors. J Neurooncol. 1994;22:269–73. https://doi.org/10.1007/BF01052932 .
doi: 10.1007/BF01052932
pubmed: 7760106
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 .
doi: 10.1002/hbm.20906
pubmed: 20017133
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 .
doi: 10.1016/j.neuroimage.2010.09.025
pubmed: 20851191
pmcid: 20851191
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 .
doi: 10.3389/fnins.2020.00125
pubmed: 32410929
pmcid: 7201293
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 .
doi: 10.1109/TMI.2010.2057442
pubmed: 20667809
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 .
doi: 10.1093/neuonc/nov088
pubmed: 26008606
pmcid: 4588758
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 .
doi: 10.1007/s00259-016-3585-0
pubmed: 27913827
Breiman L. Random Forest Machine learning. 2001;45:5–32.
doi: 10.1023/A:1010933404324
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 .
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 .
doi: 10.1093/nop/npv023
pubmed: 31579522
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 .
doi: 10.1007/s00062-011-0080-7
pubmed: 21681688
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 .
doi: 10.1016/S1470-2045(19)30098-1
pubmed: 30952559
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 .
doi: 10.3389/fonc.2019.00768
pubmed: 31475111
pmcid: 6702305
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 .
doi: 10.1093/neuonc/nou322
pubmed: 25543124
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 .
doi: 10.1038/s41598-019-50182-4
pubmed: 31601829
pmcid: 6787240
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 .
doi: 10.1097/RLU.0000000000001152
pubmed: 26859208
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 .
doi: 10.1093/neuonc/noz106
pubmed: 31190077
pmcid: 6827825
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 .
doi: 10.1093/neuonc/noz162
pubmed: 31504809
pmcid: 6827830
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. 2019.
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 .
doi: 10.1007/s00330-016-4261-2
pubmed: 26883333
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 .
doi: 10.1007/s00259-019-04407-3
pubmed: 31289907
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 .
doi: 10.1007/s00259-019-04677-x
pubmed: 31953672
pmcid: 7188730
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 .
doi: 10.1038/srep35142
pubmed: 27739434
pmcid: 5064384
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 .
doi: 10.1016/j.ejrad.2018.04.003
pubmed: 29803382
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 .
doi: 10.1002/jmri.22432
pubmed: 21274970
pmcid: 3273302