A multi-label CNN model for the automatic detection and segmentation of gliomas using [
CNN
Gliomas
Lesion detection
Lesion segmentation
Quantification
[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:
Jul 2023
Jul 2023
Historique:
received:
07
07
2022
accepted:
07
03
2023
medline:
12
6
2023
pubmed:
19
3
2023
entrez:
18
3
2023
Statut:
ppublish
Résumé
The aim of this study was to develop a convolutional neural network (CNN) for the automatic detection and segmentation of gliomas using [ Ninety-three patients (84 in-house/7 external) who underwent a 20-40-min static [ Based on the threefold CV, the multi-label CNN model achieved 88.9% sensitivity and 96.5% precision for discriminating between positive and negative [ The proposed multi-label CNN model detected positive [
Identifiants
pubmed: 36933075
doi: 10.1007/s00259-023-06193-5
pii: 10.1007/s00259-023-06193-5
doi:
Substances chimiques
Tyrosine
42HK56048U
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2441-2452Subventions
Organisme : H2020 Marie Skłodowska-Curie Actions
ID : 764458
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Albert NL, Weller M, Suchorska B, Galldiks N, Soffietti R, Kim MM, et al. Response Assessment in Neuro-Oncology working group and European Association for Neuro-Oncology recommendations for the clinical use of PET imaging in gliomas. Neuro-Oncol. 2016;18(9):1199–208.
doi: 10.1093/neuonc/now058
pubmed: 27106405
pmcid: 4999003
Galldiks N, Langen KJ, Pope WB. From the clinician’s point of view-what is the status quo of positron emission tomography in patients with brain tumors? Neuro Oncol. 2015;17(11):1434–44.
doi: 10.1093/neuonc/nov118
pubmed: 26130743
pmcid: 4648307
Kobayashi K, Ohnishi A, Promsuk J, Shimizu S, Kanai Y, Shiokawa Y, et al. Enhanced tumor growth elicited by L-type amino acid transporter 1 in human malignant glioma cells. Neurosurgery. 2008;62(2):493–504.
doi: 10.1227/01.neu.0000316018.51292.19
pubmed: 18382329
Stöber B, Tanase U, Herz M, Seidl C, Schwaiger M, Senekowitsch-Schmidtke R. Differentiation of tumour and inflammation: characterisation of [methyl-3H] methionine (MET) and O-(2-[
doi: 10.1007/s00259-005-0047-5
pubmed: 16604346
Lahoutte T, Caveliers V, Camargo SM, Franca R, Ramadan T, Veljkovic E, et al. SPECT and PET amino acid tracer influx via system L (h4F2hc-hLAT1) and its transstimulation. J Nucl Med. 2004;45(9):1591–6.
pubmed: 15347729
Verger A, Arbizu J, Law I. Role of amino-acid PET in high-grade gliomas: limitations and perspectives. Q J Nucl Med Mol Imaging. 2018;62(3):254–66.
doi: 10.23736/S1824-4785.18.03092-3
pubmed: 29696948
Dunet V, Pomoni A, Hottinger A, Nicod-Lalonde M, Prior JO. Performance of [
Weckesser M, Langen KJ, Rickert CH, Kloska S, Straeter R, Hamacher K, et al. O-(2-[
Langen KJ, Stoffels G, Filss C, Heinzel A, Stegmayr C, Lohmann P, Willuweit A, Neumaier B, Mottaghy FM, Galldiks N. Imaging of amino acid transport in brain tumours: positron emission tomography with O-(2-[
doi: 10.1016/j.ymeth.2017.05.019
Ahmed R, Oborski MJ, Hwang M, Lieberman FS, Mountz JM. Malignant gliomas: current perspectives in diagnosis, treatment, and early response assessment using advanced quantitative imaging methods. Cancer management and research. 2014;6:149.
pubmed: 24711712
pmcid: 3969256
Weber DC, Zilli T, Buchegger F, Casanova N, Haller G, Rouzaud M, et al. [(18) F] Fluoroethyltyrosine-positron emission tomography-guided radiotherapy for high-grade glioma. Radiat Oncol. 2008;3(1):1–11.
doi: 10.1186/1748-717X-3-44
Verma N, Cowperthwaite MC, Burnett MG, Markey MK. Differentiating tumor recurrence from treatment necrosis: a review of neuro-oncologic imaging strategies. Neuro Oncol. 2013;15(5):515–34.
doi: 10.1093/neuonc/nos307
pubmed: 23325863
pmcid: 3635510
Bolcaen J, Descamps B, Deblaere K, Boterberg T, Pharm FDV, Kalala JP, et al. 18F-fluoromethylcholine (FCho), 18F-fluoroethyltyrosine (FET), and 18F-fluorodeoxyglucose (FDG) for the discrimination between high-grade glioma and radiation necrosis in rats: a PET study. Nucl Med Biol. 2015;42(1):38–45.
doi: 10.1016/j.nucmedbio.2014.07.006
pubmed: 25218024
Pöpperl G, Kreth FW, Mehrkens JH, Herms J, Seelos K, Koch W, et al. FET PET for the evaluation of untreated gliomas: correlation of FET uptake and uptake kinetics with tumour grading. Eur J Nucl Med Mol Imaging. 2007;34(12):1933–42.
doi: 10.1007/s00259-007-0534-y
pubmed: 17763848
Floeth FW, Pauleit D, Sabel M, Stoffels G, Reifenberger G, Riemenschneider MJ, et al. Prognostic value of O-(2–18F-fluoroethyl)-L-tyrosine PET and MRI in low-grade glioma. J Nucl Med. 2007;48(4):519–27.
doi: 10.2967/jnumed.106.037895
pubmed: 17401087
Floeth FW, Sabel M, Stoffels G, Pauleit D, Hamacher K, Steiger HJ, et al. Prognostic value of 18F-fluoroethyl-L-tyrosine PET and MRI in small nonspecific incidental brain lesions. J Nucl Med. 2008;49(5):730–7.
doi: 10.2967/jnumed.107.050005
pubmed: 18413396
Celli M, Caroli P, Amadori E, Arpa D, Gurrieri L, Ghigi G, et al. Diagnostic and prognostic potential of 18F-FET PET in the differential diagnosis of glioma recurrence and treatment-induced changes after chemoradiation therapy. Front Oncol. 2021;11:721–821.
Blanc-Durand P, Van Der Gucht A, Verger A, Langen KJ, Dunet V, Bloch J, et al. Voxel-based 18F-FET PET segmentation and automatic clustering of tumor voxels: a significant association with IDH1 mutation status and survival in patients with gliomas. PLoS ONE. 2018;13(6): e0199379.
doi: 10.1371/journal.pone.0199379
pubmed: 29953478
pmcid: 6023198
Debus C, Waltenberger M, Floca R, Afshar-Oromieh A, Bougatf N, et al. Impact of 18F-FET PET on target volume definition and tumor progression of recurrent high grade glioma treated with carbon-ion radiotherapy. Sci Rep. 2018;8(1):1–13.
doi: 10.1038/s41598-018-25350-7
Andrearczyk V, Oreiller V, Boughdad S, Rest CCL, Elhalawani H, Jreige M, et al. Overview of the HECKTOR challenge at MICCAI 2021: automatic head and neck tumor segmentation and outcome prediction in PET/CT images. In 3D Head and Neck Tumor Segmentation in PET/CT Challenge. Springer Cham. 2021;1–37.
Hatt M, Laurent B, Ouahabi A, Fayad H, Tan S, Li L, et al. The first MICCAI challenge on PET tumor segmentation. Med Image Anal. 2018;44:177–95.
doi: 10.1016/j.media.2017.12.007
pubmed: 29268169
Blanc-Durand P, Van Der Gucht A, Schaefer N, Itti E, Prior JO. Automatic lesion detection and segmentation of 18F-FET PET in gliomas: a full 3D U-Net convolutional neural network study. PLoS ONE. 2018;13(4): e0195798.
doi: 10.1371/journal.pone.0195798
pubmed: 29652908
pmcid: 5898737
Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM, Reifenberger G, Soffietti R. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol. 2021;23(8):1231–51.
doi: 10.1093/neuonc/noab106
pubmed: 34185076
pmcid: 8328013
Unterrainer M, Vettermann F, Brendel M, Holzgreve A, Lifschitz M, Zähringer M, et al. Towards standardization of 18F-FET PET imaging: do we need a consistent method of background activity assessment? Eur J Nucl Med Mol Imaging Res. 2017;7(1):1–8.
Koopman T, Verburg N, Schuit RC, Pouwels PJ, Wesseling P, Windhorst AD, Hoekstra OS, de Witt Hamer PC, Lammertsma AA, Boellaard R, Yaqub M. Quantification of O-(2-[
doi: 10.1186/s13550-018-0418-0
Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In International conference on medical image computing and computer-assisted intervention. Springer Cham. 2016; 424–32.
Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203–11.
doi: 10.1038/s41592-020-01008-z
pubmed: 33288961
Rahimpour M, Bertels J, Radwan A, Vandermeulen H, Sunaert S, Vandermeulen D, Maes F, Goffin K, Koole M. Cross-modal distillation to improve MRI-based brain tumor segmentation with missing MRI sequences. IEEE Transactions on Biomedical Engineering. 2021 Dec 23.
Rahimpour M, Saint Martin MJ, Frouin F, Akl P, Orlhac F, Koole M, Malhaire C. Visual ensemble selection of deep convolutional neural networks for 3D segmentation of breast tumors on dynamic contrast enhanced MRI. Eur Radiol. 2022;8:1–1.
Rahimpour M, Radwan A, Vandermeulen H, Sunaert S, Goffin K, Koole M. Investigating certain choices of CNN configurations for brain lesion segmentation. arXiv preprint arXiv:2212.01235 . Accessed 2 Dec 2022.
Rahimpour M, Boellaard R, Deckers W, Goffin K, Koole M. Kinetic filtering and deep learning for the automatic detection and quantification of primary brain tumors using dynamic 18F-FET PET imaging. InEuropean Association of Nuclear Medicine, Location: Barcelona, Spain 2022 Sep 1.
Lee YS, Kim JS, Kim KM, Kang JH, Lim SM, Kim HJ. Performance measurement of PSF modeling reconstruction (true X) on Siemens Biograph TruePoint TrueV PET/CT. Ann Nucl Med. 2014;28(4):340–8.
doi: 10.1007/s12149-014-0815-z
pubmed: 24504938
Rapp M, Heinzel A, Galldiks N, Stoffels G, Felsberg J, Ewelt C, et al. Diagnostic performance of 18F-FET PET in newly diagnosed cerebral lesions suggestive of glioma. J Nucl Med. 2013;54(2):229–35.
doi: 10.2967/jnumed.112.109603
pubmed: 23232275