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Larsson HBW, Law I, Andersen TL, Andersen FL, Fischer BM, Vestergaard MB, Larsson TSW, Lindberg U. Brain perfusion estimation by Tikhonov model-free deconvolution in a long axial field of view PET/CT scanner exploring five different PET tracers. Eur J Nucl Med Mol Imaging 2024; 51:707-720. [PMID: 37843600 PMCID: PMC10796558 DOI: 10.1007/s00259-023-06469-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 10/03/2023] [Indexed: 10/17/2023]
Abstract
PURPOSE New total-body PET scanners with a long axial field of view (LAFOV) allow for higher temporal resolution due to higher sensitivity, which facilitates perfusion estimation by model-free deconvolution. Fundamental tracer kinetic theory predicts that perfusion can be estimated for all tracers despite their different fates given sufficiently high temporal resolution of 1 s or better, bypassing the need for compartment modelling. The aim of this study was to investigate whether brain perfusion could be estimated using model-free Tikhonov generalized deconvolution for five different PET tracers, [15O]H2O, [11C]PIB, [18F]FE-PE2I, [18F]FDG and [18F]FET. To our knowledge, this is the first example of a general model-free approach to estimate cerebral blood flow (CBF) from PET data. METHODS Twenty-five patients underwent dynamic LAFOV PET scanning (Siemens, Quadra). PET images were reconstructed with an isotropic voxel resolution of 1.65 mm3. Time framing was 40 × 1 s during bolus passage followed by increasing framing up to 60 min. AIF was obtained from the descending aorta. Both voxel- and region-based calculations of perfusion in the thalamus were performed using the Tikhonov method. The residue impulse response function was used to estimate the extraction fraction of tracer leakage across the blood-brain barrier. RESULTS CBF ranged from 37 to 69 mL blood min-1 100 mL of tissue-1 in the thalamus. Voxelwise calculation of CBF resulted in CBF maps in the physiologically normal range. The extraction fractions of [15O]H2O, [18F]FE-PE2I, [11C]PIB, [18F]FDG and [18F]FET in the thalamus were 0.95, 0.78, 0.62, 0.19 and 0.03, respectively. CONCLUSION The high temporal resolution and sensitivity associated with LAFOV PET scanners allow for noninvasive perfusion estimation of multiple tracers. The method provides an estimation of the residue impulse response function, from which the fate of the tracer can be studied, including the extraction fraction, influx constant, volume of distribution and transit time distribution, providing detailed physiological insight into normal and pathologic tissue.
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Affiliation(s)
- Henrik Bo Wiberg Larsson
- Functional Imaging Unit, Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital-Rigshospitalet, Valdemar Hansens Vej 13, 2600, Glostrup, Denmark.
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Ian Law
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Thomas L Andersen
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Flemming L Andersen
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Barbara M Fischer
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Mark B Vestergaard
- Functional Imaging Unit, Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital-Rigshospitalet, Valdemar Hansens Vej 13, 2600, Glostrup, Denmark
| | - Tanne S W Larsson
- Functional Imaging Unit, Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital-Rigshospitalet, Valdemar Hansens Vej 13, 2600, Glostrup, Denmark
| | - Ulrich Lindberg
- Functional Imaging Unit, Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital-Rigshospitalet, Valdemar Hansens Vej 13, 2600, Glostrup, Denmark
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Rahimpour M, Boellaard R, Jentjens S, Deckers W, Goffin K, Koole M. A multi-label CNN model for the automatic detection and segmentation of gliomas using [ 18F]FET PET imaging. Eur J Nucl Med Mol Imaging 2023; 50:2441-2452. [PMID: 36933075 DOI: 10.1007/s00259-023-06193-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 03/07/2023] [Indexed: 03/19/2023]
Abstract
PURPOSE The aim of this study was to develop a convolutional neural network (CNN) for the automatic detection and segmentation of gliomas using [18F]fluoroethyl-L-tyrosine ([18F]FET) PET. METHODS Ninety-three patients (84 in-house/7 external) who underwent a 20-40-min static [18F]FET PET scan were retrospectively included. Lesions and background regions were defined by two nuclear medicine physicians using the MIM software, such that delineations by one expert reader served as ground truth for training and testing the CNN model, while delineations by the second expert reader were used to evaluate inter-reader agreement. A multi-label CNN was developed to segment the lesion and background region while a single-label CNN was implemented for a lesion-only segmentation. Lesion detectability was evaluated by classifying [18F]FET PET scans as negative when no tumor was segmented and vice versa, while segmentation performance was assessed using the dice similarity coefficient (DSC) and segmented tumor volume. The quantitative accuracy was evaluated using the maximal and mean tumor to mean background uptake ratio (TBRmax/TBRmean). CNN models were trained and tested by a threefold cross-validation (CV) using the in-house data, while the external data was used for an independent evaluation to assess the generalizability of the two CNN models. RESULTS Based on the threefold CV, the multi-label CNN model achieved 88.9% sensitivity and 96.5% precision for discriminating between positive and negative [18F]FET PET scans compared to a 35.3% sensitivity and 83.1% precision obtained with the single-label CNN model. In addition, the multi-label CNN allowed an accurate estimation of the maximal/mean lesion and mean background uptake, resulting in an accurate TBRmax/TBRmean estimation compared to a semi-automatic approach. In terms of lesion segmentation, the multi-label CNN model (DSC = 74.6 ± 23.1%) demonstrated equal performance as the single-label CNN model (DSC = 73.7 ± 23.2%) with tumor volumes estimated by the single-label and multi-label model (22.9 ± 23.6 ml and 23.1 ± 24.3 ml, respectively) closely approximating the tumor volumes estimated by the expert reader (24.1 ± 24.4 ml). DSCs of both CNN models were in line with the DSCs by the second expert reader compared with the lesion segmentations by the first expert reader, while detection and segmentation performance of both CNN models as determined with the in-house data were confirmed by the independent evaluation using external data. CONCLUSION The proposed multi-label CNN model detected positive [18F]FET PET scans with high sensitivity and precision. Once detected, an accurate tumor segmentation and estimation of background activity was achieved resulting in an automatic and accurate TBRmax/TBRmean estimation, such that user interaction and potential inter-reader variability can be minimized.
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Affiliation(s)
- Masoomeh Rahimpour
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, UZ, KU Leuven, Herestraat 49 - Box 7003, 3000, Leuven, Belgium.
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | | | - Wies Deckers
- Division of Nuclear Medicine, UZ Leuven, Leuven, Belgium
| | - Karolien Goffin
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, UZ, KU Leuven, Herestraat 49 - Box 7003, 3000, Leuven, Belgium
- Division of Nuclear Medicine, UZ Leuven, Leuven, Belgium
| | - Michel Koole
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, UZ, KU Leuven, Herestraat 49 - Box 7003, 3000, Leuven, Belgium
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Tumor-to-blood ratio for assessment of fibroblast activation protein receptor density in pancreatic cancer using [ 68Ga]Ga-FAPI-04. Eur J Nucl Med Mol Imaging 2023; 50:929-936. [PMID: 36334106 DOI: 10.1007/s00259-022-06010-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 10/12/2022] [Indexed: 11/08/2022]
Abstract
PURPOSE [68Ga]Ga-FAPI PET/CT has been widely used in clinical diagnosis and radiopharmaceutical therapy. In this study, tumor-to-blood ratio (TBR) was evaluated as a powerful tool for semiquantitative assessment of [68Ga]Ga-FAPI-04 tumor uptake and as an effective index for tumors with high FAP expression in theranostics. METHODS Nine patients with pancreatic cancer underwent a 60-min dynamic PET/CT scan by total-body PET/CT (with a long AFOV of 194 cm) after injection of [68Ga]Ga-FAPI-04. After dynamic PET/CT scan, three patients received chemotherapy and underwent the second dynamic scan to evaluate treatment response. Time-activity curves (TACs) were obtained by drawing regions of interest for primary pancreatic lesions and metastatic lesions. The lesion TACs were fitted using four compartment models by the software PMOD PKIN kinetic modeling. The preferred pharmacokinetic model for [68Ga]Ga-FAPI-04 was evaluated based on the Akaike information criterion. The correlations between simplified methods for quantification of [68Ga]Ga-FAPI-04 (SUVs; tumor-to-blood ratios [TBRs]) and the total distribution volume (Vt) estimates obtained from pharmacokinetic analysis were calculated. RESULTS In total, 9 primary lesions and 25 metastatic lesions were evaluated. The reversible two-tissue compartment model (2TCM) was the most appropriate model among the four compartment models. The total distribution volume Vt values derived from 2TCM varied significantly in pathological lesions and background regions. A strong positive correlation was observed between TBRmean and Vt from the 2TCM model in pathological lesions (R2=0.92, P<0.001). The relative difference range for TBRmean was 2.1% compared to the reduction rate of Vt in the patients who were treated with chemotherapy. CONCLUSIONS A strong positive correlation was observed between TBRmean and Vt for [68Ga]Ga-FAPI-04. TBRmean reflects FAP receptor density better than SUVmean and SUVmax, and would be the preferred measurement tool for semiquantitative assessment of [68Ga]Ga-FAPI-04 tumor uptake and as a means for evaluating treatment response.
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Lindemann M, Oteiza A, Martin-Armas M, Guttormsen Y, Moldes-Anaya A, Berzaghi R, Bogsrud TV, Bach-Gansmo T, Sundset R, Kranz M. Glioblastoma PET/MRI: kinetic investigation of [ 18F]rhPSMA-7.3, [ 18F]FET and [ 18F]fluciclovine in an orthotopic mouse model of cancer. Eur J Nucl Med Mol Imaging 2023; 50:1183-1194. [PMID: 36416908 PMCID: PMC9931868 DOI: 10.1007/s00259-022-06040-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/11/2022] [Indexed: 11/24/2022]
Abstract
PURPOSE Glioblastoma multiforme (GBM) is the most common glioma and standard therapies can only slightly prolong the survival. Neo-vascularization is a potential target to image tumor microenvironment, as it defines its brain invasion. We investigate [18F]rhPSMA-7.3 with PET/MRI for quantitative imaging of neo-vascularization in GBM bearing mice and human tumor tissue and compare it to [18F]FET and [18F]fluciclovine using PET pharmacokinetic modeling (PKM). METHODS [18F]rhPSMA-7.3, [18F]FET, and [18F]fluciclovine were i.v. injected with 10.5 ± 3.1 MBq, 8.0 ± 2.2 MBq, 11.5 ± 1.9 MBq (n = 28, GL261-luc2) and up to 90 min PET/MR imaged 21/28 days after surgery. Regions of interest were delineated on T2-weighted MRI for (i) tumor, (ii) brain, and (iii) the inferior vena cava. Time-activity curves were expressed as SUV mean, SUVR and PKM performed using 1-/2-tissue-compartment models (1TCM, 2TCM), Patlak and Logan analysis (LA). Immunofluorescent staining (IFS), western blotting, and autoradiography of tumor tissue were performed for result validation. RESULTS [18F]rhPSMA-7.3 showed a tumor uptake with a tumor-to-background-ratio (TBR) = 2.1-2.5, in 15-60 min. PKM (2TCM) confirmed higher K1 (0.34/0.08, p = 0.0012) and volume of distribution VT (0.24/0.1, p = 0.0017) in the tumor region compared to the brain. Linearity in LA and similar k3 = 0.6 and k4 = 0.47 (2TCM, tumor, p = ns) indicated reversible binding. K1, an indicator for vascularization, increased (0.1/0.34, 21 to 28 days, p < 0.005). IFS confirmed co-expression of PSMA and tumor vascularization. [18F]fluciclovine showed higher TBR (2.5/1.8, p < 0.001, 60 min) and VS (1.3/0.7, p < 0.05, tumor) compared to [18F]FET and LA indicated reversible binding. VT increased (p < 0.001, tumor, 21 to 28 days) for [18F]FET (0.5-1.4) and [18F]fluciclovine (0.84-1.5). CONCLUSION [18F]rhPSMA-7.3 showed to be a potential candidate to investigate the tumor microenvironment of GBM. Following PKM, this uptake was associated with tumor vascularization. In contrast to what is known from PSMA-PET in prostate cancer, reversible binding was found for [18F]rhPSMA-7.3 in GBM, contradicting cellular trapping. Finally, [18F]fluciclovine was superior to [18F]FET rendering it more suitable for PET imaging of GBM.
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Affiliation(s)
- Marcel Lindemann
- PET Imaging Center Tromsø, University Hospital of North Norway (UNN), Tromsø, Norway
- Nuclear Medicine and Radiation Biology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Ana Oteiza
- PET Imaging Center Tromsø, University Hospital of North Norway (UNN), Tromsø, Norway
- Nuclear Medicine and Radiation Biology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Montserrat Martin-Armas
- PET Imaging Center Tromsø, University Hospital of North Norway (UNN), Tromsø, Norway
- Nuclear Medicine and Radiation Biology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Yngve Guttormsen
- PET Imaging Center Tromsø, University Hospital of North Norway (UNN), Tromsø, Norway
- Nuclear Medicine and Radiation Biology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Angel Moldes-Anaya
- PET Imaging Center Tromsø, University Hospital of North Norway (UNN), Tromsø, Norway
- Nuclear Medicine and Radiation Biology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Rodrigo Berzaghi
- Nuclear Medicine and Radiation Biology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Trond Velde Bogsrud
- PET Imaging Center Tromsø, University Hospital of North Norway (UNN), Tromsø, Norway
- PET Center, Aarhus University Hospital, Aarhus, Denmark
| | - Tore Bach-Gansmo
- PET Imaging Center Tromsø, University Hospital of North Norway (UNN), Tromsø, Norway
| | - Rune Sundset
- PET Imaging Center Tromsø, University Hospital of North Norway (UNN), Tromsø, Norway
- Nuclear Medicine and Radiation Biology, UiT The Arctic University of Norway, Tromsø, Norway
| | - Mathias Kranz
- PET Imaging Center Tromsø, University Hospital of North Norway (UNN), Tromsø, Norway.
- Nuclear Medicine and Radiation Biology, UiT The Arctic University of Norway, Tromsø, Norway.
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Brighi C, Puttick S, Li S, Keall P, Neville K, Waddington D, Bourgeat P, Gillman A, Fay M. A novel semiautomated method for background activity and biological tumour volume definition to improve standardisation of 18F-FET PET imaging in glioblastoma. EJNMMI Phys 2022; 9:9. [PMID: 35122529 PMCID: PMC8818070 DOI: 10.1186/s40658-022-00438-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 01/24/2022] [Indexed: 11/10/2022] Open
Abstract
Background Multicentre clinical trials evaluating the role of 18F-Fluoroethyl-l-tyrosine (18F-FET) PET as a diagnostic biomarker in glioma management have highlighted a need for standardised methods of data analysis. 18F-FET uptake normalised against background in the contralateral brain is a standard imaging technique to delineate the biological tumour volume (BTV). Quantitative analysis of 18F-FET PET images requires a consistent and robust background activity. Currently, defining background activity involves the manual selection of an arbitrary region of interest, a process that is subject to large variability. This study aims to eliminate methodological errors in background activity definition through the introduction of a semiautomated method for region of interest selection. A new method for background activity definition, involving the semiautomated generation of mirror-image (MI) reference regions, was compared with the current state-of-the-art method, involving manually drawing crescent-shape (gCS) reference regions. The MI and gCS methods were tested by measuring values of background activity and resulting BTV of 18F-FET PET scans of ten patients with recurrent glioblastoma multiforme generated from inputs provided by seven readers. To assess intra-reader variability, each scan was evaluated six times by each reader. Intra- and inter-reader variability in background activity and BTV definition was assessed by means of coefficient of variation. Results Compared to the gCS method, the MI method showed significantly lower intra- and inter-reader variability both in background activity and in BTV definition. Conclusions The proposed semiautomated MI method minimises intra- and inter-reader variability, providing a valuable approach for standardisation of 18F-FET PET quantitative parameters. Trial registration ANZCTR, ACTRN12618001346268. Registered 9 August 2018, https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374253 Supplementary Information The online version contains supplementary material available at 10.1186/s40658-022-00438-2.
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Affiliation(s)
- Caterina Brighi
- ACRF Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
| | - Simon Puttick
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organization, Royal Brisbane and Women's Hospital, Brisbane, Australia
| | - Shenpeng Li
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organization, Royal Brisbane and Women's Hospital, Brisbane, Australia
| | - Paul Keall
- ACRF Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | | | - David Waddington
- ACRF Image X Institute, Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Pierrick Bourgeat
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organization, Royal Brisbane and Women's Hospital, Brisbane, Australia
| | - Ashley Gillman
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organization, Royal Brisbane and Women's Hospital, Brisbane, Australia
| | - Michael Fay
- GenesisCare, Newcastle, Australia.,School of Medicine and Public Health, The University of Newcastle, Newcastle, Australia
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Lerche CW, Radomski T, Lohmann P, Caldeira L, Brambilla CR, Tellmann L, Scheins J, Kops ER, Galldiks N, Langen KJ, Herzog H, Jon Shah N. A Linearized Fit Model for Robust Shape Parameterization of FET-PET TACs. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:1852-1862. [PMID: 33735076 DOI: 10.1109/tmi.2021.3067169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The kinetic analysis of [Formula: see text]-FET time-activity curves (TAC) can provide valuable diagnostic information in glioma patients. The analysis is most often limited to the average TAC over a large tissue volume and is normally assessed by visual inspection or by evaluating the time-to-peak and linear slope during the late uptake phase. Here, we derived and validated a linearized model for TACs of [Formula: see text]-FET in dynamic PET scans. Emphasis was put on the robustness of the numerical parameters and how reliably automatic voxel-wise analysis of TAC kinetics was possible. The diagnostic performance of the extracted shape parameters for the discrimination between isocitrate dehydrogenase (IDH) wildtype (wt) and IDH-mutant (mut) glioma was assessed by receiver-operating characteristic in a group of 33 adult glioma patients. A high agreement between the adjusted model and measured TACs could be obtained and relative, estimated parameter uncertainties were small. The best differentiation between IDH-wt and IDH-mut gliomas was achieved with the linearized model fitted to the averaged TAC values from dynamic FET PET data in the time interval 4-50 min p.i.. When limiting the acquisition time to 20-40 min p.i., classification accuracy was only slightly lower (-3%) and was comparable to classification based on linear fits in this time interval. Voxel-wise fitting was possible within a computation time ≈ 1 min per image slice. Parameter uncertainties smaller than 80% for all fits with the linearized model were achieved. The agreement of best-fit parameters when comparing voxel-wise fits and fits of averaged TACs was very high (p < 0.001).
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Martens C, Debeir O, Decaestecker C, Metens T, Lebrun L, Leurquin-Sterk G, Trotta N, Goldman S, Van Simaeys G. Voxelwise Principal Component Analysis of Dynamic [S-Methyl- 11C]Methionine PET Data in Glioma Patients. Cancers (Basel) 2021; 13:cancers13102342. [PMID: 34066294 PMCID: PMC8152079 DOI: 10.3390/cancers13102342] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 04/30/2021] [Accepted: 05/09/2021] [Indexed: 01/08/2023] Open
Abstract
Recent works have demonstrated the added value of dynamic amino acid positron emission tomography (PET) for glioma grading and genotyping, biopsy targeting, and recurrence diagnosis. However, most of these studies are based on hand-crafted qualitative or semi-quantitative features extracted from the mean time activity curve within predefined volumes. Voxelwise dynamic PET data analysis could instead provide a better insight into intra-tumor heterogeneity of gliomas. In this work, we investigate the ability of principal component analysis (PCA) to extract relevant quantitative features from a large number of motion-corrected [S-methyl-11C]methionine ([11C]MET) PET frames. We first demonstrate the robustness of our methodology to noise by means of numerical simulations. We then build a PCA model from dynamic [11C]MET acquisitions of 20 glioma patients. In a distinct cohort of 13 glioma patients, we compare the parametric maps derived from our PCA model to these provided by the classical one-compartment pharmacokinetic model (1TCM). We show that our PCA model outperforms the 1TCM to distinguish characteristic dynamic uptake behaviors within the tumor while being less computationally expensive and not requiring arterial sampling. Such methodology could be valuable to assess the tumor aggressiveness locally with applications for treatment planning and response evaluation. This work further supports the added value of dynamic over static [11C]MET PET in gliomas.
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Affiliation(s)
- Corentin Martens
- Department of Nuclear Medicine, Hôpital Erasme, Université libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium; (G.L.-S.); (N.T.); (S.G.); (G.V.S.)
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (O.D.); (C.D.); (T.M.)
- Correspondence:
| | - Olivier Debeir
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (O.D.); (C.D.); (T.M.)
| | - Christine Decaestecker
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (O.D.); (C.D.); (T.M.)
| | - Thierry Metens
- Laboratory of Image Synthesis and Analysis (LISA), École Polytechnique de Bruxelles, Université libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium; (O.D.); (C.D.); (T.M.)
- Department of Radiology, Hôpital Erasme, Université libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium
| | - Laetitia Lebrun
- Department of Pathology, Hôpital Erasme, Université libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium;
| | - Gil Leurquin-Sterk
- Department of Nuclear Medicine, Hôpital Erasme, Université libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium; (G.L.-S.); (N.T.); (S.G.); (G.V.S.)
| | - Nicola Trotta
- Department of Nuclear Medicine, Hôpital Erasme, Université libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium; (G.L.-S.); (N.T.); (S.G.); (G.V.S.)
| | - Serge Goldman
- Department of Nuclear Medicine, Hôpital Erasme, Université libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium; (G.L.-S.); (N.T.); (S.G.); (G.V.S.)
| | - Gaetan Van Simaeys
- Department of Nuclear Medicine, Hôpital Erasme, Université libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium; (G.L.-S.); (N.T.); (S.G.); (G.V.S.)
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Verburg N, Koopman T, Yaqub MM, Hoekstra OS, Lammertsma AA, Barkhof F, Pouwels PJW, Reijneveld JC, Heimans JJ, Rozemuller AJM, Bruynzeel AME, Lagerwaard F, Vandertop WP, Boellaard R, Wesseling P, de Witt Hamer PC. Improved detection of diffuse glioma infiltration with imaging combinations: a diagnostic accuracy study. Neuro Oncol 2021; 22:412-422. [PMID: 31550353 PMCID: PMC7058442 DOI: 10.1093/neuonc/noz180] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 09/13/2019] [Indexed: 11/22/2022] Open
Abstract
Background Surgical resection and irradiation of diffuse glioma are guided by standard MRI: T2/fluid attenuated inversion recovery (FLAIR)–weighted MRI for non-enhancing and T1-weighted gadolinium-enhanced (T1G) MRI for enhancing gliomas. Amino acid PET has been suggested as the new standard. Imaging combinations may improve standard MRI and amino acid PET. The aim of the study was to determine the accuracy of imaging combinations to detect glioma infiltration. Methods We included 20 consecutive adults with newly diagnosed non-enhancing glioma (7 diffuse astrocytomas, isocitrate dehydrogenase [IDH] mutant; 1 oligodendroglioma, IDH mutant and 1p/19q codeleted; 1 glioblastoma IDH wildtype) or enhancing glioma (glioblastoma, 9 IDH wildtype and 2 IDH mutant). Standardized preoperative imaging (T1-, T2-, FLAIR-weighted, and T1G MRI, perfusion and diffusion MRI, MR spectroscopy and O-(2-[18F]-fluoroethyl)-L-tyrosine ([18F]FET) PET) was co-localized with multiregion stereotactic biopsies preceding resection. Tumor presence in the biopsies was assessed by 2 neuropathologists. Diagnostic accuracy was determined using receiver operating characteristic analysis. Results A total of 174 biopsies were obtained (63 from 9 non-enhancing and 111 from 11 enhancing gliomas), of which 129 contained tumor (50 from non-enhancing and 79 from enhancing gliomas). In enhancing gliomas, the combination of apparent diffusion coefficient (ADC) with [18F]FET PET (area under the curve [AUC], 95% CI: 0.89, 0.79‒0.99) detected tumor better than T1G MRI (0.56, 0.39‒0.72; P < 0.001) and [18F]FET PET (0.76, 0.66‒0.86; P = 0.001). In non-enhancing gliomas, no imaging combination detected tumor significantly better than standard MRI. FLAIR-weighted MRI had an AUC of 0.81 (0.65–0.98) compared with 0.69 (0.56–0.81; P = 0.019) for [18F]FET PET. Conclusion Combining ADC and [18F]FET PET detects glioma infiltration better than standard MRI and [18F]FET PET in enhancing gliomas, potentially enabling better guidance of local therapy.
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Affiliation(s)
- Niels Verburg
- Brain Tumor Center Amsterdam, Amsterdam University Medical Center (UMC), Amsterdam, Netherlands.,Neurosurgical Center Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Thomas Koopman
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location Free University Medical Center (VUmc), Amsterdam, Netherlands
| | - Maqsood M Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location Free University Medical Center (VUmc), Amsterdam, Netherlands
| | - Otto S Hoekstra
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location Free University Medical Center (VUmc), Amsterdam, Netherlands
| | - Adriaan A Lammertsma
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location Free University Medical Center (VUmc), Amsterdam, Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location Free University Medical Center (VUmc), Amsterdam, Netherlands.,University College London Institute of Neurology and Healthcare Engineering, London, UK
| | - Petra J W Pouwels
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location Free University Medical Center (VUmc), Amsterdam, Netherlands
| | - Jaap C Reijneveld
- Brain Tumor Center Amsterdam, Amsterdam University Medical Center (UMC), Amsterdam, Netherlands.,Department of Neurology, Amsterdam UMC, VUmc, Amsterdam, Netherlands
| | - Jan J Heimans
- Department of Neurology, Amsterdam UMC, VUmc, Amsterdam, Netherlands
| | | | | | - Frank Lagerwaard
- Department of Radiotherapy, Amsterdam UMC, VUmc, Amsterdam, Netherlands
| | - William P Vandertop
- Brain Tumor Center Amsterdam, Amsterdam University Medical Center (UMC), Amsterdam, Netherlands.,Neurosurgical Center Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, location Free University Medical Center (VUmc), Amsterdam, Netherlands
| | - Pieter Wesseling
- Brain Tumor Center Amsterdam, Amsterdam University Medical Center (UMC), Amsterdam, Netherlands.,Neurosurgical Center Amsterdam, Amsterdam UMC, Amsterdam, Netherlands.,Department of Pathology, Amsterdam UMC, VUmc, Amsterdam, Netherlands.,Princess Máxima Center for Pediatric Oncology and Department of Pathology, UMC Utrecht, Utrecht, Netherlands
| | - Philip C de Witt Hamer
- Brain Tumor Center Amsterdam, Amsterdam University Medical Center (UMC), Amsterdam, Netherlands.,Neurosurgical Center Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
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9
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Koopman T, Verburg N, Pouwels PJ, Wesseling P, Hoekstra OS, De Witt Hamer PC, Lammertsma AA, Yaqub M, Boellaard R. Quantitative parametric maps of O-(2-[ 18F]fluoroethyl)-L-tyrosine kinetics in diffuse glioma. J Cereb Blood Flow Metab 2020; 40:895-903. [PMID: 31122112 PMCID: PMC7074601 DOI: 10.1177/0271678x19851878] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Quantitative parametric images of O-(2-[18F]fluoroethyl)-L-tyrosine kinetics in diffuse gliomas could be used to improve glioma grading, tumour delineation or the assessment of the uptake distribution of this positron emission tomography tracer. In this study, several parametric images and tumour-to-normal maps were compared in terms of accuracy of region averages (when compared to results from nonlinear regression of a reversible two-tissue compartment plasma input model) and image noise using 90 min of dynamic scan data acquired in seven patients with diffuse glioma. We included plasma input methods (the basis function implementation of the single-tissue compartment model, spectral analysis and Logan graphical analysis) and reference tissue methods (basis function implementations of the simplified reference tissue model, variations of the multilinear reference tissue model and non-invasive Logan graphical analysis) as well as tumour-to-normal ratio maps at three intervals. (Non-invasive) Logan graphical analysis provided volume of distribution maps and distribution volume ratio maps with the lowest level of noise, while the basis function implementations provided the best accuracy. Tumour-to-normal ratio maps provided better results if later interval times were used, i.e. 60-90 min instead of 20-40 min, leading to lower bias (2.9% vs. 10.8%, respectively) and less noise (12.8% vs. 14.4%).
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Affiliation(s)
- Thomas Koopman
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Niels Verburg
- Neurosurgical Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Brain Tumor Center Amsterdam, Amsterdam, The Netherlands
| | - Petra Jw Pouwels
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Pieter Wesseling
- Department of Pathology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.,Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Otto S Hoekstra
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Philip C De Witt Hamer
- Neurosurgical Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Brain Tumor Center Amsterdam, Amsterdam, The Netherlands
| | - Adriaan A Lammertsma
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Maqsood Yaqub
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Nuclear Medicine & Molecular Imaging, University Medical Center Groningen, Groningen, The Netherlands
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10
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Verburg N, Koopman T, Yaqub M, Hoekstra OS, Lammertsma AA, Schwarte LA, Barkhof F, Pouwels PJW, Heimans JJ, Reijneveld JC, Rozemuller AJM, Vandertop WP, Wesseling P, Boellaard R, de Witt Hamer PC. Direct comparison of [ 11C] choline and [ 18F] FET PET to detect glioma infiltration: a diagnostic accuracy study in eight patients. EJNMMI Res 2019; 9:57. [PMID: 31254208 PMCID: PMC6598977 DOI: 10.1186/s13550-019-0523-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 05/28/2019] [Indexed: 02/07/2023] Open
Abstract
Background Positron emission tomography (PET) is increasingly used to guide local treatment in glioma. The purpose of this study was a direct comparison of two potential tracers for detecting glioma infiltration, O-(2-[18F]-fluoroethyl)-l-tyrosine ([18F] FET) and [11C] choline. Methods Eight consecutive patients with newly diagnosed diffuse glioma underwent dynamic [11C] choline and [18F] FET PET scans. Preceding craniotomy, multiple stereotactic biopsies were obtained from regions inside and outside PET abnormalities. Biopsies were assessed independently for tumour presence by two neuropathologists. Imaging measurements were derived at the biopsy locations from 10 to 40 min [11C] choline and 20–40, 40–60 and 60–90 min [18F] FET intervals, as standardized uptake value (SUV) and tumour-to-brain ratio (TBR). Diagnostic accuracies of both tracers were compared using receiver operating characteristic analysis and generalized linear mixed modelling with consensus histopathological assessment as reference. Results Of the 74 biopsies, 54 (73%) contained tumour. [11C] choline SUV and [18F] FET SUV and TBR at all intervals were higher in tumour than in normal samples. For [18F] FET, the diagnostic accuracy of TBR was higher than that of SUV for intervals 40–60 min (area under the curve: 0.88 versus 0.81, p = 0.026) and 60–90 min (0.90 versus 0.81, p = 0.047). The diagnostic accuracy of [18F] FET TBR 60–90 min was higher than that of [11C] choline SUV 20–40 min (0.87 versus 0.67, p = 0.005). Conclusions [18F] FET was more accurate than [11C] choline for detecting glioma infiltration. Highest accuracy was found for [18F] FET TBR for the interval 60–90 min post-injection. Electronic supplementary material The online version of this article (10.1186/s13550-019-0523-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Niels Verburg
- Neurosurgical Center Amsterdam, Brain Tumour Center Amsterdam, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Thomas Koopman
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Maqsood Yaqub
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Otto S Hoekstra
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Adriaan A Lammertsma
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Lothar A Schwarte
- Department of Anaesthesiology, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.,UCL institutes of Neurology & Healthcare Engineering, Gower St, Bloomsbury, London, WC1E 6BT, UK
| | - Petra J W Pouwels
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Jan J Heimans
- Department of Neurology, Brain Tumour Center Amsterdam, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Jaap C Reijneveld
- Department of Neurology, Brain Tumour Center Amsterdam, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Annemieke J M Rozemuller
- Department of Pathology, Brain Tumour Center Amsterdam, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - William P Vandertop
- Neurosurgical Center Amsterdam, Brain Tumour Center Amsterdam, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Pieter Wesseling
- Department of Pathology, Brain Tumour Center Amsterdam, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.,Princess Máxima Center for Paediatric Oncology, and Department of Pathology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Ronald Boellaard
- Department of Radiology & Nuclear Medicine, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Philip C de Witt Hamer
- Neurosurgical Center Amsterdam, Brain Tumour Center Amsterdam, Amsterdam UMC, location VUmc, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
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11
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Ishiwata K. 4-Borono-2- 18F-fluoro-L-phenylalanine PET for boron neutron capture therapy-oriented diagnosis: overview of a quarter century of research. Ann Nucl Med 2019; 33:223-236. [PMID: 30820862 PMCID: PMC6450856 DOI: 10.1007/s12149-019-01347-8] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 02/17/2019] [Indexed: 11/29/2022]
Abstract
4-10B-Borono-2-18F-fluoro-L-phenylalanine (18F-FBPA) was developed for monitoring the pharmacokinetics of 4-10B-borono-L-phenylalanine (10B-BPA) used in boron neutron capture therapy (BNCT) with positron emission tomography (PET). The tumor-imaging potential of 18F-FBPA was demonstrated in various animal models. Accumulation of 18F-FBPA was higher in melanomas than in non-melanoma tumors in animal models and cell cultures. 18F-FBPA was incorporated into tumors mediated mainly by L-type amino acid transporters in in vitro and in vivo models. Tumoral distribution of 18F-FBPA was primarily related to the activity of DNA synthesis. 18F-FBPA is metabolically stable but is incorporated into melanogenesis non-enzymatically. These in vitro and in vivo characteristics of 18F-FBPA corresponded well to those of 10B-BPA. Nuclear magnetic resonance and other studies using non-radioactive 19F-10/11B-FBPA also contributed to characterization. The validity and reliability of 18/19F-FBPA as an in vivo probe of 10B-BPA were confirmed by comparison of the pharmacokinetics of 18F-FBPA and 10B-BPA and direct measurement of both 18F and 10B in tumors with various doses of both probes administered by different routes and methods. Clinically, based on the kinetic parameters of dynamic 18F-FBPA PET, the estimated 10B-concentrations in tumors with continuous 10B-BPA infusion were similar to those measured directly in surgical specimens. The significance of 18F-FBPA PET was verified for the estimation of 10B-concentration and planning of BNCT. Later 18F-FBPA PET has been involved in 10B-BPA BNCT of patients with intractable tumors such as malignant brain tumors, head and neck tumors, and melanoma. Usually a static PET scan is used for screening patients for BNCT, prediction of the distribution and accumulation of 10B-BPA, and evaluation of treatment after BNCT. In some clinical trials, a tumor-to-normal tissue ratio of 18F-FBPA > 2.5 was an inclusion criterion for BNCT. Apart from BNCT, 18F-FBPA was demonstrated to be a useful PET probe for tumor diagnosis in nuclear medicine: better tumor-to-normal brain contrast compared with 11C-methionine, differentiation of recurrent and radiation necrosis after radiotherapy, and melanoma-preferential uptake. Further progress in 18F-FBPA studies is expected for more elaborate evaluation of 10B-concentrations in tumors and normal tissues for successful 10B-BPA BNCT and for radiosynthesis of 18F-FBPA to enable higher 18F-activity amounts and higher molar activities.
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Affiliation(s)
- Kiichi Ishiwata
- Southern TOHOKU Drug Discovery and Cyclotron Research Center, Southern TOHOKU Research Institute for Neuroscience, 7-61-2 Yatsuyamada, Koriyama, 963-8052, Japan. .,Department of Biofunctional Imaging, Fukushima Medical University, Fukushima, Japan.
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12
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Voxel-wise analysis of dynamic 18F-FET PET: a novel approach for non-invasive glioma characterisation. EJNMMI Res 2018; 8:91. [PMID: 30203138 PMCID: PMC6131687 DOI: 10.1186/s13550-018-0444-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 08/26/2018] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Glioma grading with dynamic 18F-FET PET (0-40 min p.i.) is typically performed by analysing the mean time-activity curve of the entire tumour or a suspicious area within a heterogeneous tumour. This work aimed to ensure a reader-independent glioma characterisation and identification of aggressive sub-volumes by performing a voxel-based analysis with diagnostically relevant kinetic and static 18F-FET PET parameters. One hundred sixty-two patients with a newly diagnosed glioma classified according to histologic and molecular genetic properties were evaluated. The biological tumour volume (BTV) was segmented in static 20-40 min p.i. 18F-FET PET images using the established threshold of 1.6 × background activity. For each enclosed voxel, the time-to-peak (TTP), the late slope (Slope15-40), and the tumour-to-background ratios (TBR5-15, TBR20-40) obtained from 5 to 15 min p.i. and 20 to 40 min p.i. images were determined. The percentage portion of these values within the BTV was evaluated with percentage volume fractions (PVFs) and cumulated percentage volume histograms (PVHs). The ability to differentiate histologic and molecular genetic classes was assessed and compared to volume-of-interest (VOI)-based parameters. RESULTS Aggressive WHO grades III and IV and IDH-wildtype gliomas were dominated by a high proportion of voxels with an early peak, negative slope, and high TBR, whereby the PVHs with TTP < 20 min p.i., Slope15-40 < 0 SUV/h, and TBR5-15 and TBR20-40 > 2 yielded the most significant differences between glioma grades. We found significant differences of the parameters between WHO grades and IDH mutation status, where the effect size was predominantly higher for voxel-based PVHs compared to the corresponding VOI-based parameters. A low overlap of BTV sub-volumes defined by TTP < 20 min p.i. and negative Slope15-40 with TBR5-15 > 2- and TBR20-40 > 2-defined hotspots was observed. CONCLUSIONS The presented approach applying voxel-wise analysis of dynamic 18F-FET PET enables an enhanced characterisation of gliomas and might potentially provide a fast identification of aggressive sub-volumes within the BTV. Parametric 3D 18F-FET PET information as investigated in this study has the potential to guide individual therapy instrumentation and may be included in future biopsy studies.
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