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Hua T, Chen M, Fu P, Zhou W, Zhao W, Li M, Zuo C, Guan Y, Xu H. Heterogeneity of fibroblast activation protein expression in the microenvironment of an intracranial tumor cohort: head-to-head comparison of gallium-68 FAP inhibitor-04 ( 68Ga-FAPi-04) and fluoride-18 fluoroethyl-L-tyrosine ( 18F-FET) in positron emission tomography-computed tomography imaging. Quant Imaging Med Surg 2024; 14:4450-4463. [PMID: 39022225 PMCID: PMC11250301 DOI: 10.21037/qims-24-82] [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: 01/15/2024] [Accepted: 05/14/2024] [Indexed: 07/20/2024]
Abstract
Background Cancer-associated fibroblasts (CAFs) within the tumor microenvironment (TME) can interact with tumor parenchymal cells to promote tumor growth and migration. Fibroblast activation protein (FAP) expressed by CAFs can be targeted with positron emission tomography (PET) tracers, but studies on FAP expression patterns in intracranial tumors remain scarce. We aimed to evaluate FAP expression patterns in intracranial tumors with gallium-68 FAP inhibitor-04 (68Ga-FAPi-04) and immunohistochemical staining and to observe the interactions between CAFs and tumor cells with a head-to-head comparison of 68Ga-FAPi-04 and fluoride-18 fluoroethyl-L-tyrosine (18F-FET) for PET quantification analysis. Methods We prospectively enrolled 22 adult patients with intracranial mass lesions. 68Ga-FAPi-04 and 18F-FET PET-computed tomography (PET/CT) brain imaging were applied before surgery. Maximal tumor-to-brain ratio (TBRmax), metabolic tumor volume (MTV), and total lesion tracer uptake (TLU) was obtained, and different thresholds were used for 68Ga-FAPi-04-positive lesion delineation owing to the lack of relevant guidelines. The MTV and TLU ratios of both tracers were calculated. Linear regression was applied to observe the differential efficacy of semiquantitative PET parameters. Results A total of 22 patients with a mean age of 50±13 years (range, 27-69 years) were enrolled. Heterogeneous patterns of 68Ga-FAPi-04 uptake [median of maximal standardized uptake value (SUVmax) =3.8; range, 0.1-19.1] were found. More malignant tumors, including brain metastasis, glioblastoma, and medulloblastoma, generally exhibited more significant 68Ga-FAPi-04 uptake than did the less malignant tumors, while the SUVmax and TBRmax exhibited nonsignificant differences across three intracranial lesion groups of primary brain tumor, brain metastasis, and noncancerous disease (SUVmax: P=0.092; TBRmax: P=0.189). Immunohistochemistry staining showed different stromal FAP expression status in various intracranial lesions. In 15 patients with positive 68Ga-FAPi-04 intracranial tumor uptake, the MTVFAPi:MTVFET ratio had differential efficacy in various types of intracranial tumors [95% confidence interval (CI): 0.572-7.712; P=0.027], and further quantification analyses confirmed the differential ability of the MTVFAPi:MTVFET ratio (95% CI: -0.045 to 11.013, P=0.052; 95% CI: 0.044-17.903, P=0.049; 95% CI: -1.131 to 30.596, P=0.065) with different isocontour volumetric thresholds. Conclusions This head-to-head study demonstrated heterogeneous FAP expression in intracranial tumors. The FAP expression volume percentage in tumor parenchyma may therefore offer benefit with respect to differentiating between intracranial tumor types.
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Affiliation(s)
- Tao Hua
- Department of Nuclear Medicine & Positron Emission Tomography Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Mingyu Chen
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
| | - Pengfei Fu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
| | - Weiyan Zhou
- Department of Nuclear Medicine & Positron Emission Tomography Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Wen Zhao
- Department of Anesthesiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Ming Li
- Department of Nuclear Medicine & Positron Emission Tomography Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Chuantao Zuo
- Department of Nuclear Medicine & Positron Emission Tomography Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Yihui Guan
- Department of Nuclear Medicine & Positron Emission Tomography Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Hongzhi Xu
- Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Shanghai, China
- Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, China
- Neurosurgical Institute of Fudan University, Shanghai, China
- Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China
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Ziegenfeuter J, Delbridge C, Bernhardt D, Gempt J, Schmidt-Graf F, Hedderich D, Griessmair M, Thomas M, Meyer HS, Zimmer C, Meyer B, Combs SE, Yakushev I, Metz MC, Wiestler B. Resolving spatial response heterogeneity in glioblastoma. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06782-y. [PMID: 38837060 DOI: 10.1007/s00259-024-06782-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 05/30/2024] [Indexed: 06/06/2024]
Abstract
PURPOSE Spatial intratumoral heterogeneity poses a significant challenge for accurate response assessment in glioblastoma. Multimodal imaging coupled with advanced image analysis has the potential to unravel this response heterogeneity. METHODS Based on automated tumor segmentation and longitudinal registration with follow-up imaging, we categorized contrast-enhancing voxels of 61 patients with suspected recurrence of glioblastoma into either true tumor progression (TP) or pseudoprogression (PsP). To allow the unbiased analysis of semantically related image regions, adjacent voxels with similar values of cerebral blood volume (CBV), FET-PET, and contrast-enhanced T1w were automatically grouped into supervoxels. We then extracted first-order statistics as well as texture features from each supervoxel. With these features, a Random Forest classifier was trained and validated employing a 10-fold cross-validation scheme. For model evaluation, the area under the receiver operating curve, as well as classification performance metrics were calculated. RESULTS Our image analysis pipeline enabled reliable spatial assessment of tumor response. The predictive model reached an accuracy of 80.0% and a macro-weighted AUC of 0.875, which takes class imbalance into account, in the hold-out samples from cross-validation on supervoxel level. Analysis of feature importances confirmed the significant role of FET-PET-derived features. Accordingly, TP- and PsP-labeled supervoxels differed significantly in their 10th and 90th percentile, as well as the median of tumor-to-background normalized FET-PET. However, CBV- and T1c-related features also relevantly contributed to the model's performance. CONCLUSION Disentangling the intratumoral heterogeneity in glioblastoma holds immense promise for advancing precise local response evaluation and thereby also informing more personalized and localized treatment strategies in the future.
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Affiliation(s)
- Julian Ziegenfeuter
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany.
| | - Claire Delbridge
- Department of Pathology, Technical University of Munich, 81675, München, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Jens Gempt
- Department of Neurosurgery, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany
| | - Friederike Schmidt-Graf
- Department of Neurology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Dennis Hedderich
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Michael Griessmair
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Marie Thomas
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Hanno S Meyer
- Department of Neurosurgery, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, 20251, Hamburg, Germany
| | - Claus Zimmer
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Bernhard Meyer
- Department of Neurosurgery, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Igor Yakushev
- Department of Nuclear Medicine, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Marie-Christin Metz
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, 81675, München, Germany
- TranslaTUM, Technical University of Munich, 81675, München, Germany
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Eisazadeh R, Shahbazi-Akbari M, Mirshahvalad SA, Pirich C, Beheshti M. Application of Artificial Intelligence in Oncologic Molecular PET-Imaging: A Narrative Review on Beyond [ 18F]F-FDG Tracers Part II. [ 18F]F-FLT, [ 18F]F-FET, [ 11C]C-MET and Other Less-Commonly Used Radiotracers. Semin Nucl Med 2024; 54:293-301. [PMID: 38331629 DOI: 10.1053/j.semnuclmed.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 01/11/2024] [Indexed: 02/10/2024]
Abstract
Following the previous part of the narrative review on artificial intelligence (AI) applications in positron emission tomography (PET) using tracers rather than 18F-fluorodeoxyglucose ([18F]F-FDG), in this part we review the impact of PET-derived radiomics data on the diagnostic performance of other PET radiotracers, 18F-O-(2-fluoroethyl)-L-tyrosine ([18F]F-FET), 18F-Fluorothymidine ([18F]F-FLT) and 11C-Methionine ([11C]C-MET). [18F]F-FET-PET, using an artificial amino acid taken up into upregulated tumoral cells, showed potential in lesion detection and tumor characterization, especially with its ability to reflect glioma heterogeneity. [18F]F-FET-PET-derived textural features appeared to have the potential to reveal considerable information for accurate delineation for guiding biopsy and treatment, differentiate between low-grade and high-grade glioma and related wild-type genotypes, and distinguish pseudoprogression from true progression. In addition, models built using clinical parameters and [18F]F-FET-PET-derived radiomics features showed acceptable results for survival stratification of glioblastoma patients. [18F]F-FLT-PET-based characteristics also showed potential in evaluating glioma patients, correlating with Ki-67 and patient prognosis. AI-based PET-volumetry using this radiotracer as a proliferation marker also revealed promising preliminary results in terms of guide-targeting bone marrow-preserving adaptive radiation therapy. Similar to [18F]F-FET, the other amino acid tracer which reflects cellular proliferation, [11C]C-MET, has also shown acceptable performance in predicting tumor grade, distinguishing brain tumor recurrence from radiation necrosis, and treatment monitoring by PET-derived radiomics models. In addition, PET-derived radiomics features of various radiotracers such as [18F]F-DOPA, [18F]F-FACBC, [18F]F-NaF, [68Ga]Ga-CXCR-4 and [18F]F-FMISO may also provide useful information for tumor characterization and predict of disease outcome. In conclusion, AI using tracers beyond [18F]F-FDG could improve the diagnostic performance of PET-imaging for specific indications and help clinicians in their daily routine by providing features that are often not detectable by the naked eye.
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Affiliation(s)
- Roya Eisazadeh
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Malihe Shahbazi-Akbari
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Research center for Nuclear Medicine, Department of Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Seyed Ali Mirshahvalad
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Research center for Nuclear Medicine, Department of Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran; Joint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, Ontario, Canada
| | - Christian Pirich
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Mohsen Beheshti
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria.
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Filss CP, Cramer J, Löher S, Lohmann P, Stoffels G, Stegmayr C, Kocher M, Heinzel A, Galldiks N, Wittsack HJ, Sabel M, Neumaier B, Scheins J, Shah NJ, Meyer PT, Mottaghy FM, Langen KJ. Assessment of Brain Tumour Perfusion Using Early-Phase 18F-FET PET: Comparison with Perfusion-Weighted MRI. Mol Imaging Biol 2024; 26:36-44. [PMID: 37848641 PMCID: PMC10827807 DOI: 10.1007/s11307-023-01861-2] [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: 07/12/2023] [Revised: 09/10/2023] [Accepted: 09/19/2023] [Indexed: 10/19/2023]
Abstract
PURPOSE Morphological imaging using MRI is essential for brain tumour diagnostics. Dynamic susceptibility contrast (DSC) perfusion-weighted MRI (PWI), as well as amino acid PET, may provide additional information in ambiguous cases. Since PWI is often unavailable in patients referred for amino acid PET, we explored whether maps of relative cerebral blood volume (rCBV) in brain tumours can be extracted from the early phase of PET using O-(2-18F-fluoroethyl)-L-tyrosine (18F-FET). PROCEDURE Using a hybrid brain PET/MRI scanner, PWI and dynamic 18F-FET PET were performed in 33 patients with cerebral glioma and four patients with highly vascularized meningioma. The time interval from 0 to 2 min p.i. was selected to best reflect the blood pool phase in 18F-FET PET. For each patient, maps of MR-rCBV, early 18F-FET PET (0-2 min p.i.) and late 18F-FET PET (20-40 min p.i.) were generated and coregistered. Volumes of interest were placed on the tumour (VOI-TU) and normal-appearing brain (VOI-REF). The correlation between tumour-to-brain ratios (TBR) of the different parameters was analysed. In addition, three independent observers evaluated MR-rCBV and early 18F-FET maps (18F-FET-rCBV) for concordance in signal intensity, tumour extent and intratumoural distribution. RESULTS TBRs calculated from MR-rCBV and 18F-FET-rCBV showed a significant correlation (r = 0.89, p < 0.001), while there was no correlation between late 18F-FET PET and MR-rCBV (r = 0.24, p = 0.16) and 18F-FET-rCBV (r = 0.27, p = 0.11). Visual rating yielded widely agreeing findings or only minor differences between MR-rCBV maps and 18F-FET-rCBV maps in 93 % of the tumours (range of three independent raters 91-94%, kappa among raters 0.78-1.0). CONCLUSION Early 18F-FET maps (0-2 min p.i.) in gliomas provide similar information to MR-rCBV maps and may be helpful when PWI is not possible or available. Further studies in gliomas are needed to evaluate whether 18F-FET-rCBV provides the same clinical information as MR-rCBV.
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Affiliation(s)
- Christian P Filss
- Department of Nuclear Medicine, RWTH University Hospital, Aachen, Germany.
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-5, INM-11), Forschungszentrum Jülich, Jülich, Germany.
- Center of Integrated Oncology (CIO), University of Aachen, Bonn, Cologne and Düsseldorf, Germany.
| | - Julian Cramer
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-5, INM-11), Forschungszentrum Jülich, Jülich, Germany
- Faculty of Medical Engineering and Technomathematics, FH Aachen University of Applied Sciences, Campus Juelich, Jülich, Germany
| | - Saskia Löher
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-5, INM-11), Forschungszentrum Jülich, Jülich, Germany
- Faculty of Medical Engineering and Technomathematics, FH Aachen University of Applied Sciences, Campus Juelich, Jülich, Germany
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-5, INM-11), Forschungszentrum Jülich, Jülich, Germany
| | - Gabriele Stoffels
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-5, INM-11), Forschungszentrum Jülich, Jülich, Germany
| | - Carina Stegmayr
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-5, INM-11), Forschungszentrum Jülich, Jülich, Germany
| | - Martin Kocher
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-5, INM-11), Forschungszentrum Jülich, Jülich, Germany
- Center of Integrated Oncology (CIO), University of Aachen, Bonn, Cologne and Düsseldorf, Germany
- Department of Stereotactic and Functional Neurosurgery, Center for Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Alexander Heinzel
- Department of Nuclear Medicine, RWTH University Hospital, Aachen, Germany
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-5, INM-11), Forschungszentrum Jülich, Jülich, Germany
- Center of Integrated Oncology (CIO), University of Aachen, Bonn, Cologne and Düsseldorf, Germany
- Department of Nuclear Medicine, University Hospital Halle (Saale), Halle (Saale), Germany
| | - Norbert Galldiks
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-5, INM-11), Forschungszentrum Jülich, Jülich, Germany
- Center of Integrated Oncology (CIO), University of Aachen, Bonn, Cologne and Düsseldorf, Germany
- Department of Neurology, University Hospital Cologne, Cologne, Germany
| | - Hans J Wittsack
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Düsseldorf, Düsseldorf, Germany
| | - Michael Sabel
- Center of Integrated Oncology (CIO), University of Aachen, Bonn, Cologne and Düsseldorf, Germany
- Department of Neurosurgery, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Bernd Neumaier
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-5, INM-11), Forschungszentrum Jülich, Jülich, Germany
- Institute of Radiochemistry and Experimental Molecular Imaging, University Hospital Cologne, Cologne, Germany
| | - Jürgen Scheins
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-5, INM-11), Forschungszentrum Jülich, Jülich, Germany
| | - N Jon Shah
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-5, INM-11), Forschungszentrum Jülich, Jülich, Germany
- JARA - BRAIN - Translational Medicine, RWTH Aachen University, Aachen, Germany
- Department of Neurology, RWTH Aachen University Hospital, Aachen, Germany
| | - Philipp T Meyer
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Felix M Mottaghy
- Department of Nuclear Medicine, RWTH University Hospital, Aachen, Germany
- Center of Integrated Oncology (CIO), University of Aachen, Bonn, Cologne and Düsseldorf, Germany
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center (MUMC+), Maastricht, Netherlands
| | - Karl-Josef Langen
- Department of Nuclear Medicine, RWTH University Hospital, Aachen, Germany
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-5, INM-11), Forschungszentrum Jülich, Jülich, Germany
- Center of Integrated Oncology (CIO), University of Aachen, Bonn, Cologne and Düsseldorf, Germany
- JARA - BRAIN - Translational Medicine, RWTH Aachen University, Aachen, Germany
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Alongi P, Arnone A, Vultaggio V, Fraternali A, Versari A, Casali C, Arnone G, DiMeco F, Vetrano IG. Artificial Intelligence Analysis Using MRI and PET Imaging in Gliomas: A Narrative Review. Cancers (Basel) 2024; 16:407. [PMID: 38254896 PMCID: PMC10814838 DOI: 10.3390/cancers16020407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 01/10/2024] [Accepted: 01/14/2024] [Indexed: 01/24/2024] Open
Abstract
The lack of early detection and a high rate of recurrence/progression after surgery are defined as the most common causes of a very poor prognosis of Gliomas. The developments of quantification systems with special regards to artificial intelligence (AI) on medical images (CT, MRI, PET) are under evaluation in the clinical and research context in view of several applications providing different information related to the reconstruction of imaging, the segmentation of tissues acquired, the selection of features, and the proper data analyses. Different approaches of AI have been proposed as the machine and deep learning, which utilize artificial neural networks inspired by neuronal architectures. In addition, new systems have been developed using AI techniques to offer suggestions or make decisions in medical diagnosis, emulating the judgment of radiologist experts. The potential clinical role of AI focuses on the prediction of disease progression in more aggressive forms in gliomas, differential diagnosis (pseudoprogression vs. proper progression), and the follow-up of aggressive gliomas. This narrative Review will focus on the available applications of AI in brain tumor diagnosis, mainly related to malignant gliomas, with particular attention to the postoperative application of MRI and PET imaging, considering the current state of technical approach and the evaluation after treatment (including surgery, radiotherapy/chemotherapy, and prognostic stratification).
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Affiliation(s)
- Pierpaolo Alongi
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90127 Palermo, Italy; (P.A.); (V.V.); (G.A.)
| | - Annachiara Arnone
- Nuclear Medicine Unit, Azienda Unità Sanitaria Locale IRCCS, 42122 Reggio Emilia, Italy; (A.A.); (A.F.); (A.V.)
| | - Viola Vultaggio
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90127 Palermo, Italy; (P.A.); (V.V.); (G.A.)
| | - Alessandro Fraternali
- Nuclear Medicine Unit, Azienda Unità Sanitaria Locale IRCCS, 42122 Reggio Emilia, Italy; (A.A.); (A.F.); (A.V.)
| | - Annibale Versari
- Nuclear Medicine Unit, Azienda Unità Sanitaria Locale IRCCS, 42122 Reggio Emilia, Italy; (A.A.); (A.F.); (A.V.)
| | - Cecilia Casali
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (C.C.); (F.D.)
| | - Gaspare Arnone
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90127 Palermo, Italy; (P.A.); (V.V.); (G.A.)
| | - Francesco DiMeco
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (C.C.); (F.D.)
- Department of Oncology and Onco-Hematology, Università di Milano, 20122 Milan, Italy
- Department of Neurological Surgery, Johns Hopkins Medical School, Baltimore, MD 21218, USA
| | - Ignazio Gaspare Vetrano
- Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, Italy; (C.C.); (F.D.)
- Department of Biomedical Sciences for Health, Università di Milano, 20122 Milan, Italy
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Yu P, Wang Y, Su F, Chen Y. Comparing [18F]FET PET and [18F]FDOPA PET for glioma recurrence diagnosis: a systematic review and meta-analysis. Front Oncol 2024; 13:1346951. [PMID: 38269019 PMCID: PMC10805829 DOI: 10.3389/fonc.2023.1346951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 12/20/2023] [Indexed: 01/26/2024] Open
Abstract
Purpose The purpose of our meta-analysis and systematic review was to evaluate and compare the diagnostic effectiveness of [18F]FET PET and [18F]FDOPA PET in detecting glioma recurrence. Methods Sensitivities and specificities were assessed using the DerSimonian and Laird methodology, and subsequently transformed using the Freeman-Tukey double inverse sine transformation. Confidence intervals were computed employing the Jackson method, while heterogeneity within and between groups was evaluated through the Cochrane Q and I² statistics. If substantial heterogeneity among the studies was observed (P < 0.10 or I² > 50%), we conducted meta-regression and sensitivity analyses. Publication bias was assessed through the test of a funnel plot and the application of Egger's test. For all statistical tests, except for assessing heterogeneity (P < 0.10), statistical significance was determined when the two-tailed P value fell below 0.05. Results Initially, 579 publications were identified, and ultimately, 22 studies, involving 1514 patients(1226 patients for [18F]FET PET and 288 patients for [18F]FDOPA PET), were included in the analysis. The sensitivity and specificity of [18F]FET PET were 0.84 (95% CI, 0.75-0.90) and 0.86 (95% CI, 0.80-0.91), respectively, while for [18F]FDOPA PET, the values were 0.95 (95% CI, 0.86-1.00) for sensitivity and 0.90 (95% CI, 0.77-0.98) for specificity. A statistically significant difference in sensitivity existed between these two radiotracers (P=0.04), while no significant difference was observed in specificity (P=0.58). Conclusion It seems that [18F]FDOPA PET demonstrates superior sensitivity and similar specificity to [18F] FET PET. Nevertheless, it's crucial to emphasize that [18F]FDOPA PET results were obtained from studies with limited sample sizes. Further larger prospective studies, especially head-to-head comparisons, are needed in this issue. Systematic Review Registration identifier CRD42023463476.
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Affiliation(s)
| | | | | | - Yan Chen
- Department of Neurosurgery, The Second Hospital of Jilin University, Changchun, China
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Kas A, Rozenblum L, Pyatigorskaya N. Clinical Value of Hybrid PET/MR Imaging: Brain Imaging Using PET/MR Imaging. Magn Reson Imaging Clin N Am 2023; 31:591-604. [PMID: 37741643 DOI: 10.1016/j.mric.2023.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/25/2023]
Abstract
Hybrid PET/MR imaging offers a unique opportunity to acquire MR imaging and PET information during a single imaging session. PET/MR imaging has numerous advantages, including enhanced diagnostic accuracy, improved disease characterization, and better treatment planning and monitoring. It enables the immediate integration of anatomic, functional, and metabolic imaging information, allowing for personalized characterization and monitoring of neurologic diseases. This review presents recent advances in PET/MR imaging and highlights advantages in clinical practice for neuro-oncology, epilepsy, and neurodegenerative disorders. PET/MR imaging provides valuable information about brain tumor metabolism, perfusion, and anatomic features, aiding in accurate delineation, treatment response assessment, and prognostication.
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Affiliation(s)
- Aurélie Kas
- Department of Nuclear Medicine, Pitié-Salpêtrière Hospital, APHP Sorbonne Université, Paris, France; Sorbonne Université, INSERM, CNRS, Laboratoire d'Imagerie Biomédicale, LIB, Paris F-75006, France.
| | - Laura Rozenblum
- Department of Nuclear Medicine, Pitié-Salpêtrière Hospital, APHP Sorbonne Université, Paris, France; Sorbonne Université, INSERM, CNRS, Laboratoire d'Imagerie Biomédicale, LIB, Paris F-75006, France
| | - Nadya Pyatigorskaya
- Neuroradiology Department, Pitié-Salpêtrière Hospital, APHP Sorbonne Université, Paris, France; Sorbonne Université, UMR S 1127, CNRS UMR 722, Institut du Cerveau, Paris, France
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8
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Ouyang ZQ, Zheng GR, Duan XR, Zhang XR, Ke TF, Bao SS, Yang J, He B, Liao CD. Diagnostic accuracy of glioma pseudoprogression identification with positron emission tomography imaging: a systematic review and meta-analysis. Quant Imaging Med Surg 2023; 13:4943-4959. [PMID: 37581048 PMCID: PMC10423382 DOI: 10.21037/qims-22-1340] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 05/15/2023] [Indexed: 08/16/2023]
Abstract
Background Positron emission tomography (PET) imaging is a promising molecular neuroimaging technique and has been proposed as one of the criteria for glioma management. However, there is some controversy concerning the diagnostic accuracy of PET using different radiotracers to differentiate between glioma pseudoprogression (PsP) and true progression (TPR). The purpose of this meta-analysis was to systematically evaluate the methodological quality and clinical value of original studies for distinguishing PsP from TPR in glioma. Methods The Medline, Web of Science, Embase, Cochrane Library, and ClinicalTrials.gov were searched from inception until September 1, 2022. Retrieved clinical studies only investigated the PsP cases but did not include the cases of radiation necrosis or other treatment-related changes. Eligible studies were screened for data extraction and evaluated by 2 independent reviewers using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. A random effects model was used to describe summary receiver operating characteristics. Meta-regression and subgroup analyses were applied to identify any sources of heterogeneity. Results The meta-analysis included 20 studies, comprising 317 (30.9%) patients with PsP and 708 (69.1%) with TPR. The summary sensitivity and specificity of general PET for identifying PsP were 0.86 [95% confidence interval (CI): 0.77-0.91] and 0.84 (95% CI: 0.79-0.88), respectively. The statistical heterogeneity was explained by sample size, study design, World Health Organization (WHO) grade, gold standard, and radiotracer type. The summary sensitivity and specificity of O-(2-18F-fluoroethyl)-L-tyrosine (18F-FET PET) were 0.80 (95% CI: 0.68-0.88) and 0.81 (95% CI: 0.75-0.85), respectively. The maximum tumor-to-brain ratio (TBRmax) and the mean tumor-to-brain ratio (TBRmean) both showed excellent diagnostic performance in 18F-FET studies, the summary sensitivity was 0.83 (95% CI: 0.72-0.91) and 0.79 (95% CI: 0.65-0.98), respectively, and the specificity was 0.76 (95% CI: 0.68-0.84) and 0.78 (95% CI: 0.64-0.88), respectively. Conclusions PET imaging is generally accurate in identifying glioma PsP. Considering the credibility of meta-evidence and the practicability of using radiotracer, 18F-FET PET holds the highest clinical value, while TBRmax and TBRmean should be regarded as reliable parameters. PET used with the radiotracers and multiple-parameter combinations of PET with magnetic resonance imaging (MRI) and radiomics analysis have broad research and application prospects, whose diagnostic values for identifying glioma PsP warrant further investigation.
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Affiliation(s)
- Zhi-Qiang Ouyang
- Department of Radiology, Yan’an Hospital of Kunming City (Yan’an Hospital Affiliated to Kunming Medical University), Kunming, China
| | - Guang-Rong Zheng
- Department of Radiology, Yan’an Hospital of Kunming City (Yan’an Hospital Affiliated to Kunming Medical University), Kunming, China
| | - Xi-Rui Duan
- Department of Radiology, Yunnan Cancer Hospital (the Third Affiliated Hospital of Kunming Medical University), Kunming, China
| | - Xue-Rong Zhang
- Department of Radiology, Yunnan Cancer Hospital (the Third Affiliated Hospital of Kunming Medical University), Kunming, China
| | - Teng-Fei Ke
- Department of Radiology, Yunnan Cancer Hospital (the Third Affiliated Hospital of Kunming Medical University), Kunming, China
| | - Sha-Sha Bao
- Department of Radiology, Yunnan Cancer Hospital (the Third Affiliated Hospital of Kunming Medical University), Kunming, China
| | - Jun Yang
- Department of Radiology, Yunnan Cancer Hospital (the Third Affiliated Hospital of Kunming Medical University), Kunming, China
| | - Bin He
- Department of Neurosurgery, the Second Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Cheng-De Liao
- Department of Radiology, Yan’an Hospital of Kunming City (Yan’an Hospital Affiliated to Kunming Medical University), Kunming, China
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9
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Langen KJ, Galldiks N, Mauler J, Kocher M, Filß CP, Stoffels G, Régio Brambilla C, Stegmayr C, Willuweit A, Worthoff WA, Shah NJ, Lerche C, Mottaghy FM, Lohmann P. Hybrid PET/MRI in Cerebral Glioma: Current Status and Perspectives. Cancers (Basel) 2023; 15:3577. [PMID: 37509252 PMCID: PMC10377176 DOI: 10.3390/cancers15143577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/06/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
Advanced MRI methods and PET using radiolabelled amino acids provide valuable information, in addition to conventional MR imaging, for brain tumour diagnostics. These methods are particularly helpful in challenging situations such as the differentiation of malignant processes from benign lesions, the identification of non-enhancing glioma subregions, the differentiation of tumour progression from treatment-related changes, and the early assessment of responses to anticancer therapy. The debate over which of the methods is preferable in which situation is ongoing, and has been addressed in numerous studies. Currently, most radiology and nuclear medicine departments perform these examinations independently of each other, leading to multiple examinations for the patient. The advent of hybrid PET/MRI allowed a convergence of the methods, but to date simultaneous imaging has reached little relevance in clinical neuro-oncology. This is partly due to the limited availability of hybrid PET/MRI scanners, but is also due to the fact that PET is a second-line examination in brain tumours. PET is only required in equivocal situations, and the spatial co-registration of PET examinations of the brain to previous MRI is possible without disadvantage. A key factor for the benefit of PET/MRI in neuro-oncology is a multimodal approach that provides decisive improvements in the diagnostics of brain tumours compared with a single modality. This review focuses on studies investigating the diagnostic value of combined amino acid PET and 'advanced' MRI in patients with cerebral gliomas. Available studies suggest that the combination of amino acid PET and advanced MRI improves grading and the histomolecular characterisation of newly diagnosed tumours. Few data are available concerning the delineation of tumour extent. A clear additive diagnostic value of amino acid PET and advanced MRI can be achieved regarding the differentiation of tumour recurrence from treatment-related changes. Here, the PET-guided evaluation of advanced MR methods seems to be helpful. In summary, there is growing evidence that a multimodal approach can achieve decisive improvements in the diagnostics of cerebral gliomas, for which hybrid PET/MRI offers optimal conditions.
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Affiliation(s)
- Karl-Josef Langen
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-11), Forschungszentrum Juelich, 52425 Juelich, Germany
- Department of Nuclear Medicine, RWTH Aachen University Hospital, 52074 Aachen, Germany
- Center of Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne and Duesseldorf, 53127 Bonn, Germany
| | - Norbert Galldiks
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-11), Forschungszentrum Juelich, 52425 Juelich, Germany
- Center of Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne and Duesseldorf, 53127 Bonn, Germany
- Department of Neurology, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
| | - Jörg Mauler
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-11), Forschungszentrum Juelich, 52425 Juelich, Germany
| | - Martin Kocher
- Department of Stereotaxy and Functional Neurosurgery, Center for Neurosurgery, Faculty of Medicine, University Hospital Cologne, 50931 Cologne, Germany
| | - Christian Peter Filß
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-11), Forschungszentrum Juelich, 52425 Juelich, Germany
- Department of Nuclear Medicine, RWTH Aachen University Hospital, 52074 Aachen, Germany
| | - Gabriele Stoffels
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-11), Forschungszentrum Juelich, 52425 Juelich, Germany
| | - Cláudia Régio Brambilla
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-11), Forschungszentrum Juelich, 52425 Juelich, Germany
| | - Carina Stegmayr
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-11), Forschungszentrum Juelich, 52425 Juelich, Germany
| | - Antje Willuweit
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-11), Forschungszentrum Juelich, 52425 Juelich, Germany
| | - Wieland Alexander Worthoff
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-11), Forschungszentrum Juelich, 52425 Juelich, Germany
| | - Nadim Jon Shah
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-11), Forschungszentrum Juelich, 52425 Juelich, Germany
- Department of Neurology, RWTH Aachen University Hospital, 52074 Aachen, Germany
| | - Christoph Lerche
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-11), Forschungszentrum Juelich, 52425 Juelich, Germany
| | - Felix Manuel Mottaghy
- Department of Nuclear Medicine, RWTH Aachen University Hospital, 52074 Aachen, Germany
- Center of Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne and Duesseldorf, 53127 Bonn, Germany
- Department of Neurology, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50931 Cologne, Germany
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center (MUMC+), 6229 HX Maastricht, The Netherlands
| | - Philipp Lohmann
- Institute of Neuroscience and Medicine (INM-3, INM-4, INM-11), Forschungszentrum Juelich, 52425 Juelich, Germany
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10
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Smith NJ, Deaton TK, Territo W, Graner B, Gauger A, Snyder SE, Schulte ML, Green MA, Hutchins GD, Veronesi MC. Hybrid 18F-Fluoroethyltyrosine PET and MRI with Perfusion to Distinguish Disease Progression from Treatment-Related Change in Malignant Brain Tumors: The Quest to Beat the Toughest Cases. J Nucl Med 2023; 64:1087-1092. [PMID: 37116915 PMCID: PMC10315704 DOI: 10.2967/jnumed.122.265149] [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: 11/16/2022] [Revised: 02/16/2023] [Indexed: 04/30/2023] Open
Abstract
Conventional MRI has important limitations when assessing for progression of disease (POD) versus treatment-related changes (TRC) in patients with malignant brain tumors. We describe the observed impact and pitfalls of implementing 18F-fluoroethyltyrosine (18F-FET) perfusion PET/MRI into routine clinical practice. Methods: Through expanded-access investigational new drug use of 18F-FET, hybrid 18F-FET perfusion PET/MRI was performed during clinical management of 80 patients with World Health Organization central nervous system grade 3 or 4 gliomas or brain metastases of 6 tissue origins for which the prior brain MRI results were ambiguous. The diagnostic performance with 18F-FET PET/MRI was dually evaluated within routine clinical service and for retrospective parametric evaluation. Various 18F-FET perfusion PET/MRI parameters were assessed, and patients were monitored for at least 6 mo to confirm the diagnosis using pathology, imaging, and clinical progress. Results: Hybrid 18F-FET perfusion PET/MRI had high overall accuracy (86%), sensitivity (86%), and specificity (87%) for difficult diagnostic cases for which conventional MRI accuracy was poor (66%). 18F-FET tumor-to-brain ratio static metrics were highly reliable for distinguishing POD from TRC (area under the curve, 0.90). Dynamic tumor-to-brain intercept was more accurate (85%) than SUV slope (73%) or time to peak (73%). Concordant PET/MRI findings were 89% accurate. When PET and MRI conflicted, 18F-FET PET was correct in 12 of 15 cases (80%), whereas MRI was correct in 3 of 15 cases (20%). Clinical management changed after 88% (36/41) of POD diagnoses, whereas management was maintained after 87% (34/39) of TRC diagnoses. Conclusion: Hybrid 18F-FET PET/MRI positively impacted the routine clinical care of challenging malignant brain tumor cases at a U.S. institution. The results add to a growing body of literature that 18F-FET PET complements MRI, even rescuing MRI when it fails.
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Affiliation(s)
- Nathaniel J Smith
- School of Medicine, Indiana University, Indianapolis, Indiana
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana; and
| | | | - Wendy Territo
- School of Medicine, Indiana University, Indianapolis, Indiana
| | - Brian Graner
- School of Medicine, Indiana University, Indianapolis, Indiana
| | - Andrew Gauger
- School of Medicine, Indiana University, Indianapolis, Indiana
| | - Scott E Snyder
- School of Medicine, Indiana University, Indianapolis, Indiana
| | | | - Mark A Green
- School of Medicine, Indiana University, Indianapolis, Indiana
| | - Gary D Hutchins
- School of Medicine, Indiana University, Indianapolis, Indiana
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11
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Peira E, Sensi F, Rei L, Gianeri R, Tortora D, Fiz F, Piccardo A, Bottoni G, Morana G, Chincarini A. Towards an Automated Approach to the Semi-Quantification of [ 18F]F-DOPA PET in Pediatric-Type Diffuse Gliomas. J Clin Med 2023; 12:jcm12082765. [PMID: 37109101 PMCID: PMC10142802 DOI: 10.3390/jcm12082765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 03/28/2023] [Accepted: 04/04/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND This study aims to evaluate the use of a computer-aided, semi-quantification approach to [18F]F-DOPA positron emission tomography (PET) in pediatric-type diffuse gliomas (PDGs) to calculate the tumor-to-background ratio. METHODS A total of 18 pediatric patients with PDGs underwent magnetic resonance imaging and [18F]F-DOPA PET, which were analyzed using both manual and automated procedures. The former provided a tumor-to-normal-tissue ratio (TN) and tumor-to-striatal-tissue ratio (TS), while the latter provided analogous scores (tn, ts). We tested the correlation, consistency, and ability to stratify grading and survival between these methods. RESULTS High Pearson correlation coefficients resulted between the ratios calculated with the two approaches: ρ = 0.93 (p < 10-4) and ρ = 0.814 (p < 10-4). The analysis of the residuals suggested that tn and ts were more consistent than TN and TS. Similarly to TN and TS, the automatically computed scores showed significant differences between low- and high-grade gliomas (p ≤ 10-4, t-test) and the overall survival was significantly shorter in patients with higher values when compared to those with lower ones (p < 10-3, log-rank test). CONCLUSIONS This study suggested that the proposed computer-aided approach could yield similar results to the manual procedure in terms of diagnostic and prognostic information.
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Affiliation(s)
- Enrico Peira
- Istituto Nazionale di Fisica Nucleare (INFN), 16146 Genoa, Italy
| | - Francesco Sensi
- Istituto Nazionale di Fisica Nucleare (INFN), 16146 Genoa, Italy
| | - Luca Rei
- Istituto Nazionale di Fisica Nucleare (INFN), 16146 Genoa, Italy
| | - Ruben Gianeri
- Istituto Nazionale di Fisica Nucleare (INFN), 16146 Genoa, Italy
| | - Domenico Tortora
- Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy
| | - Francesco Fiz
- S.C. di Medicina Nucleare, E.O. Ospedali Galliera, 16128 Genoa, Italy
| | - Arnoldo Piccardo
- S.C. di Medicina Nucleare, E.O. Ospedali Galliera, 16128 Genoa, Italy
| | - Gianluca Bottoni
- S.C. di Medicina Nucleare, E.O. Ospedali Galliera, 16128 Genoa, Italy
| | - Giovanni Morana
- Department of Neurosciences, University of Turin, 10124 Turin, Italy
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12
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Li Z, Holzgreve A, Unterrainer LM, Ruf VC, Quach S, Bartos LM, Suchorska B, Niyazi M, Wenter V, Herms J, Bartenstein P, Tonn JC, Unterrainer M, Albert NL, Kaiser L. Combination of pre-treatment dynamic [ 18F]FET PET radiomics and conventional clinical parameters for the survival stratification in patients with IDH-wildtype glioblastoma. Eur J Nucl Med Mol Imaging 2023; 50:535-545. [PMID: 36227357 PMCID: PMC9816231 DOI: 10.1007/s00259-022-05988-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 10/03/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE The aim of this study was to build and evaluate a prediction model which incorporates clinical parameters and radiomic features extracted from static as well as dynamic [18F]FET PET for the survival stratification in patients with newly diagnosed IDH-wildtype glioblastoma. METHODS A total of 141 patients with newly diagnosed IDH-wildtype glioblastoma and dynamic [18F]FET PET prior to surgical intervention were included. Patients with a survival time ≤ 12 months were classified as short-term survivors. First order, shape, and texture radiomic features were extracted from pre-treatment static (tumor-to-background ratio; TBR) and dynamic (time-to-peak; TTP) images, respectively, and randomly divided into a training (n = 99) and a testing cohort (n = 42). After feature normalization, recursive feature elimination was applied for feature selection using 5-fold cross-validation on the training cohort, and a machine learning model was constructed to compare radiomic models and combined clinical-radiomic models with selected radiomic features and clinical parameters. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were calculated to assess the predictive performance for identifying short-term survivors in both the training and testing cohort. RESULTS A combined clinical-radiomic model comprising six clinical parameters and six selected dynamic radiomic features achieved highest predictability of short-term survival with an AUC of 0.74 (95% confidence interval, 0.60-0.88) in the independent testing cohort. CONCLUSIONS This study successfully built and evaluated prediction models using [18F]FET PET-based radiomic features and clinical parameters for the individualized assessment of short-term survival in patients with a newly diagnosed IDH-wildtype glioblastoma. The combination of both clinical parameters and dynamic [18F]FET PET-based radiomic features reached highest accuracy in identifying patients at risk. Although the achieved accuracy level remained moderate, our data shows that the integration of dynamic [18F]FET PET radiomic data into clinical prediction models may improve patient stratification beyond established prognostic markers.
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Affiliation(s)
- Zhicong Li
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
| | - Adrien Holzgreve
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Lena M Unterrainer
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Viktoria C Ruf
- Center for Neuropathology and Prion Research, LMU Munich, Munich, Germany
| | - Stefanie Quach
- Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany
| | - Laura M Bartos
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Bogdana Suchorska
- Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany
- Department of Neurosurgery, Sana Hospital, Duisburg, Germany
| | - Maximilian Niyazi
- Department of Radiotherapy, University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Vera Wenter
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Jochen Herms
- Center for Neuropathology and Prion Research, LMU Munich, Munich, Germany
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Joerg-Christian Tonn
- Department of Neurosurgery, University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Marcus Unterrainer
- Department of Radiology, University Hospital, LMU Munich, Munich, Germany
| | - Nathalie L Albert
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
- German Cancer Consortium (DKTK), Partner Site Munich, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lena Kaiser
- Department of Nuclear Medicine, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
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13
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Sequential and Hybrid PET/MRI Acquisition in Follow-Up Examination of Glioblastoma Show Similar Diagnostic Performance. Cancers (Basel) 2022; 15:cancers15010083. [PMID: 36612079 PMCID: PMC9817523 DOI: 10.3390/cancers15010083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/09/2022] [Accepted: 12/20/2022] [Indexed: 12/25/2022] Open
Abstract
Both positron emission tomography (PET) and magnetic resonance imaging (MRI), including dynamic susceptibility contrast perfusion (DSC-PWI), are crucial for treatment monitoring of patients with high-grade gliomas. In clinical practice, they are usually conducted at separate time points. Whether this affects their diagnostic performance is presently unclear. To this end, we retrospectively reviewed 38 patients with pathologically confirmed glioblastoma (IDH wild-type) and suspected tumor recurrence after radiotherapy. Only patients who received both a PET−MRI (where DSC perfusion was acquired simultaneously with a FET-PET) and a separate MRI exam (including DSC perfusion) were included. Tumors were automatically segmented into contrast-enhancing tumor (CET), necrosis, and edema. To compare the simultaneous as well as the sequential DSC perfusion to the FET-PET, we calculated Dice overlap, global mutual information as well as voxel-wise Spearman correlation of hotspot areas. For the joint assessment of PET and MRI, we computed logistic regression models for the differentiation between true progression (PD) and treatment-related changes (TRC) using simultaneously or sequentially acquired images as input data. We observed no significant differences between Dice overlap (p = 0.17; paired t-test), mutual information (p = 0.18; paired t-test) and Spearman correlation (p = 0.90; paired t-test) when comparing simultaneous PET−MRI and sequential PET/MRI acquisition. This also held true for the subgroup of patients with >14 days between exams. Importantly, for the diagnostic performance, ROC analysis showed similar AUCs for differentiation of PD and TRC (AUC simultaneous PET: 0.77; AUC sequential PET: 0.78; p = 0.83, DeLong’s test). We found no relevant differences between simultaneous and sequential acquisition of FET-PET and DSC perfusion, also regarding their diagnostic performance. Given the increasing attention to multi-parametric assessment of glioma treatment response, our results reassuringly suggest that sequential acquisition is clinically and scientifically acceptable.
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14
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Jackson LR, Masi MR, Selman BM, Sandusky GE, Zarrinmayeh H, Das SK, Maharjan S, Wang N, Zheng QH, Pollok KE, Snyder SE, Sun PZ, Hutchins GD, Butch ER, Veronesi MC. Use of multimodality imaging, histology, and treatment feasibility to characterize a transgenic Rag2-null rat model of glioblastoma. Front Oncol 2022; 12:939260. [PMID: 36483050 PMCID: PMC9722958 DOI: 10.3389/fonc.2022.939260] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 10/20/2022] [Indexed: 11/23/2022] Open
Abstract
Many drugs that show potential in animal models of glioblastoma (GBM) fail to translate to the clinic, contributing to a paucity of new therapeutic options. In addition, animal model development often includes histologic assessment, but multiparametric/multimodality imaging is rarely included despite increasing utilization in patient cancer management. This study developed an intracranial recurrent, drug-resistant, human-derived glioblastoma tumor in Sprague-Dawley Rag2-Rag2 tm1Hera knockout rat and was characterized both histologically and using multiparametric/multimodality neuroimaging. Hybrid 18F-fluoroethyltyrosine positron emission tomography and magnetic resonance imaging, including chemical exchange saturation transfer (18F-FET PET/CEST MRI), was performed for full tumor viability determination and characterization. Histological analysis demonstrated human-like GBM features of the intracranially implanted tumor, with rapid tumor cell proliferation (Ki67 positivity: 30.5 ± 7.8%) and neovascular heterogeneity (von Willebrand factor VIII:1.8 to 5.0% positivity). Early serial MRI followed by simultaneous 18F-FET PET/CEST MRI demonstrated consistent, predictable tumor growth, with exponential tumor growth most evident between days 35 and 49 post-implantation. In a second, larger cohort of rats, 18F-FET PET/CEST MRI was performed in mature tumors (day 49 post-implantation) for biomarker determination, followed by evaluation of single and combination therapy as part of the model development and validation. The mean percentage of the injected dose per mL of 18F-FET PET correlated with the mean %CEST (r = 0.67, P < 0.05), but there was also a qualitative difference in hot spot location within the tumor, indicating complementary information regarding the tumor cell demand for amino acids and tumor intracellular mobile phase protein levels. Finally, the use of this glioblastoma animal model for therapy assessment was validated by its increased overall survival after treatment with combination therapy (temozolomide and idasanutlin) (P < 0.001). Our findings hold promise for a more accurate tumor viability determination and novel therapy assessment in vivo in a recently developed, reproducible, intracranial, PDX GBM.
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Affiliation(s)
- Luke R. Jackson
- Department of Radiology and Imaging Sciences, Indiana University (IU) School of Medicine, Indianapolis, IN, United States
| | - Megan R. Masi
- Department of Radiology and Imaging Sciences, Indiana University (IU) School of Medicine, Indianapolis, IN, United States
| | - Bryce M. Selman
- Department of Pathology and Laboratory Medicine, Indiana University (IU) School of Medicine, Indianapolis, IN, United States
| | - George E. Sandusky
- Department of Pathology and Laboratory Medicine, Indiana University (IU) School of Medicine, Indianapolis, IN, United States
| | - Hamideh Zarrinmayeh
- Department of Radiology and Imaging Sciences, Indiana University (IU) School of Medicine, Indianapolis, IN, United States
| | - Sudip K. Das
- Department of Pharmaceutical Sciences, Butler University, Indianapolis, IN, United States
| | - Surendra Maharjan
- Department of Radiology and Imaging Sciences, Indiana University (IU) School of Medicine, Indianapolis, IN, United States
| | - Nian Wang
- Department of Radiology and Imaging Sciences, Indiana University (IU) School of Medicine, Indianapolis, IN, United States
| | - Qi-Huang Zheng
- Department of Radiology and Imaging Sciences, Indiana University (IU) School of Medicine, Indianapolis, IN, United States
| | - Karen E. Pollok
- Department of Pediatrics, Indiana University (IU) School of Medicine, Indianapolis, IN, United States
| | - Scott E. Snyder
- Department of Radiology and Imaging Sciences, Indiana University (IU) School of Medicine, Indianapolis, IN, United States
| | - Phillip Zhe Sun
- Department of Radiology and Imaging Sciences, Emory School of Medicine, Atlanta, GA, United States
| | - Gary D. Hutchins
- Department of Radiology and Imaging Sciences, Indiana University (IU) School of Medicine, Indianapolis, IN, United States
| | - Elizabeth R. Butch
- Department of Radiology and Imaging Sciences, Indiana University (IU) School of Medicine, Indianapolis, IN, United States
| | - Michael C. Veronesi
- Department of Radiology and Imaging Sciences, Indiana University (IU) School of Medicine, Indianapolis, IN, United States,*Correspondence: Michael C. Veronesi,
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15
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Canalini L, Klein J, Waldmannstetter D, Kofler F, Cerri S, Hering A, Heldmann S, Schlaeger S, Menze BH, Wiestler B, Kirschke J, Hahn HK. Quantitative evaluation of the influence of multiple MRI sequences and of pathological tissues on the registration of longitudinal data acquired during brain tumor treatment. FRONTIERS IN NEUROIMAGING 2022; 1:977491. [PMID: 37555157 PMCID: PMC10406206 DOI: 10.3389/fnimg.2022.977491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 08/15/2022] [Indexed: 08/10/2023]
Abstract
Registration methods facilitate the comparison of multiparametric magnetic resonance images acquired at different stages of brain tumor treatments. Image-based registration solutions are influenced by the sequences chosen to compute the distance measure, and the lack of image correspondences due to the resection cavities and pathological tissues. Nonetheless, an evaluation of the impact of these input parameters on the registration of longitudinal data is still missing. This work evaluates the influence of multiple sequences, namely T1-weighted (T1), T2-weighted (T2), contrast enhanced T1-weighted (T1-CE), and T2 Fluid Attenuated Inversion Recovery (FLAIR), and the exclusion of the pathological tissues on the non-rigid registration of pre- and post-operative images. We here investigate two types of registration methods, an iterative approach and a convolutional neural network solution based on a 3D U-Net. We employ two test sets to compute the mean target registration error (mTRE) based on corresponding landmarks. In the first set, markers are positioned exclusively in the surroundings of the pathology. The methods employing T1-CE achieves the lowest mTREs, with a improvement up to 0.8 mm for the iterative solution. The results are higher than the baseline when using the FLAIR sequence. When excluding the pathology, lower mTREs are observable for most of the methods. In the second test set, corresponding landmarks are located in the entire brain volumes. Both solutions employing T1-CE obtain the lowest mTREs, with a decrease up to 1.16 mm for the iterative method, whereas the results worsen using the FLAIR. When excluding the pathology, an improvement is observable for the CNN method using T1-CE. Both approaches utilizing the T1-CE sequence obtain the best mTREs, whereas the FLAIR is the least informative to guide the registration process. Besides, the exclusion of pathology from the distance measure computation improves the registration of the brain tissues surrounding the tumor. Thus, this work provides the first numerical evaluation of the influence of these parameters on the registration of longitudinal magnetic resonance images, and it can be helpful for developing future algorithms.
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Affiliation(s)
- Luca Canalini
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Jan Klein
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Diana Waldmannstetter
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Florian Kofler
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
- Department of Neuroradiology, Technical University of Munich (TUM) School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
- Helmholtz AI, Helmholtz Zentrum Munich, Munich, Germany
| | - Stefano Cerri
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Alessa Hering
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, Netherlands
| | - Stefan Heldmann
- Fraunhofer Institute for Digital Medicine MEVIS, Lübeck, Germany
| | - Sarah Schlaeger
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Bjoern H. Menze
- Image-Based Biomedical Modeling, Department of Informatics, Technical University of Munich, Munich, Germany
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Benedikt Wiestler
- Department of Neuroradiology, Technical University of Munich (TUM) School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Jan Kirschke
- Department of Neuroradiology, Technical University of Munich (TUM) School of Medicine, Klinikum Rechts Der Isar, Technical University of Munich, Munich, Germany
- TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Horst K. Hahn
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
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DEGRO practical guideline for central nervous system radiation necrosis part 1: classification and a multistep approach for diagnosis. Strahlenther Onkol 2022; 198:873-883. [PMID: 36038669 PMCID: PMC9515024 DOI: 10.1007/s00066-022-01994-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 07/19/2022] [Indexed: 10/31/2022]
Abstract
PURPOSE The Working Group for Neuro-Oncology of the German Society for Radiation Oncology in cooperation with members of the Neuro-Oncology Working Group of the German Cancer Society aimed to define a practical guideline for the diagnosis and treatment of radiation-induced necrosis (RN) of the central nervous system (CNS). METHODS Panel members of the DEGRO working group invited experts, participated in a series of conferences, supplemented their clinical experience, performed a literature review, and formulated recommendations for medical treatment of RN including bevacizumab in clinical routine. CONCLUSION Diagnosis and treatment of RN requires multidisciplinary structures of care and defined processes. Diagnosis has to be made on an interdisciplinary level with the joint knowledge of a neuroradiologist, radiation oncologist, neurosurgeon, neuropathologist, and neuro-oncologist. A multistep approach as an opportunity to review as many characteristics as possible to improve diagnostic confidence is recommended. Additional information about radiotherapy (RT) techniques is crucial for the diagnosis of RN. Misdiagnosis of untreated and progressive RN can lead to severe neurological deficits. In this practice guideline, we propose a detailed nomenclature of treatment-related changes and a multistep approach for their diagnosis.
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Chen K, Jiang XW, Deng LJ, She HL. Differentiation between glioma recurrence and treatment effects using amide proton transfer imaging: A mini-Bayesian bivariate meta-analysis. Front Oncol 2022; 12:852076. [PMID: 35978813 PMCID: PMC9376615 DOI: 10.3389/fonc.2022.852076] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 06/29/2022] [Indexed: 11/24/2022] Open
Abstract
Background Amide proton transfer (APT) imaging as an emerging MRI approach has been used for distinguishing tumor recurrence (TR) and treatment effects (TEs) in glioma patients, but the initial results from recent studies are different. Aim The aim of this study is to systematically review and quantify the diagnostic performance of APT in assessing treatment response in patients with post-treatment gliomas. Methods A systematic search in PubMed, EMBASE, and the Web of Science was performed to retrieve related original studies. For the single and added value of APT imaging in distinguishing TR from TEs, we calculated pooled sensitivity and specificity by using Bayesian bivariate meta-analyses. Results Six studies were included, five of which reported on single APT imaging parameters and four of which reported on multiparametric MRI combined with APT imaging parameters. For single APT imaging parameters, the pooled sensitivity and specificity were 0.85 (95% CI: 0.75–0.92) and 0.88 (95% CI: 0.74–0.97). For multiparametric MRI including APT, the pooled sensitivity and specificity were 0.92 (95% CI: 0.85–0.97) and 0.83 (95% CI: 0.55–0.97), respectively. In addition, in the three studies reported on both single and added value of APT imaging parameters, the combined imaging parameters further improved diagnostic performance, yielding pooled sensitivity and specificity of 0.91 (95% CI: 0.80–0.97) and 0.92 (95% CI: 0.79–0.98), respectively, but the pooled sensitivity was 0.81 (95% CI: 0.65-0.93) and specificity was 0.82 (95% CI: 0.61–0.94) for single APT imaging parameters. Conclusion APT imaging showed high diagnostic performance in assessing treatment response in patients with post-treatment gliomas, and the addition of APT imaging to other advanced MRI techniques can improve the diagnostic accuracy for distinguishing TR from TE.
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Affiliation(s)
- Kai Chen
- Department of Medical Imaging, Shenzhen Samii Medical Center, Shenzhen, China
| | - Xi-Wen Jiang
- Department of Medical Imaging, Affiliated Hospital of Xiangnan University (Clinical College), Chenzhou, China
| | - Li-jing Deng
- Department of Neonatology, Shenzhen Third People’s Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
| | - Hua-Long She
- Department of Medical Imaging, Affiliated Hospital of Xiangnan University (Clinical College), Chenzhou, China
- *Correspondence: Hua-Long She,
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Seo M, Ahn KJ, Choi Y, Shin NY, Jang J, Kim BS. Volumetric Measurement of Relative CBV Using T1-Perfusion-Weighted MRI with High Temporal Resolution Compared with Traditional T2*-Perfusion-Weighted MRI in Postoperative Patients with High-Grade Gliomas. AJNR Am J Neuroradiol 2022; 43:864-871. [PMID: 35618428 DOI: 10.3174/ajnr.a7527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 04/08/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE T1-PWI with high temporal resolution may provide a reliable relative CBV value as a valid alternative to T2*-PWI under increased susceptibility. The purpose of this study was to assess the technical and clinical performance of T1-relative CBV in patients with postoperative high-grade gliomas. MATERIALS AND METHODS Forty-five MRIs of 34 patients with proved high-grade gliomas were included. In all MRIs, T1- and T2*-PWIs were both acquired and processed semiautomatically to generate relative CBV maps using a released commercial software. Lesion masks were overlaid on the relative CBV maps, followed by a histogram of the whole VOI. The intraclass correlation coefficient and Bland-Altman plots were used for quantitative and qualitative comparisons. Signal loss from both methods was compared using the Wilcoxon signed-rank test of zero voxel percentage. The MRIs were divided into a progression group (n = 20) and a nonprogression group (n = 14) for receiver operating characteristic curve analysis. RESULTS Fair intertechnique consistency was observed between the 90th percentiles of the T1- and T2*-relative CBV values (intraclass correlation coefficient = 0.558, P < .001). T2*-PWI revealed a significantly higher percentage of near-zero voxels than T1-PWI (17.7% versus 3.1%, P < .001). There was no statistically significant difference between the area under the curve of T1- and T2*-relative CBV (0.811 versus 0.793, P = .835). T1-relative CBV showed 100% sensitivity and 57.1% specificity for the detection of progressive lesions. CONCLUSIONS T1-relative CBV demonstrated exquisite diagnostic performance for detecting progressive lesions in postoperative patients with high-grade gliomas, suggesting the potential role of T1-PWI as a valid alternative to the traditional T2*-PWI.
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Affiliation(s)
- M Seo
- From the Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, Republic of Korea
| | - K-J Ahn
- From the Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, Republic of Korea
| | - Y Choi
- From the Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, Republic of Korea
| | - N-Y Shin
- From the Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, Republic of Korea
| | - J Jang
- From the Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, Republic of Korea
| | - B-S Kim
- From the Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, the Catholic University of Korea, Seoul, Republic of Korea
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Santo G, Laudicella R, Linguanti F, Nappi AG, Abenavoli E, Vergura V, Rubini G, Sciagrà R, Arnone G, Schillaci O, Minutoli F, Baldari S, Quartuccio N, Bisdas S. The Utility of Conventional Amino Acid PET Radiotracers in the Evaluation of Glioma Recurrence also in Comparison with MRI. Diagnostics (Basel) 2022; 12:diagnostics12040844. [PMID: 35453892 PMCID: PMC9027186 DOI: 10.3390/diagnostics12040844] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/24/2022] [Accepted: 03/28/2022] [Indexed: 02/07/2023] Open
Abstract
AIM In this comprehensive review we present an update on the most relevant studies evaluating the utility of amino acid PET radiotracers for the evaluation of glioma recurrence as compared to magnetic resonance imaging (MRI). METHODS A literature search extended until June 2020 on the PubMed/MEDLINE literature database was conducted using the terms "high-grade glioma", "glioblastoma", "brain tumors", "positron emission tomography", "PET", "amino acid PET", "[11C]methyl-l-methionine", "[18F]fluoroethyl-tyrosine", "[18F]fluoro-l-dihydroxy-phenylalanine", "MET", "FET", "DOPA", "magnetic resonance imaging", "MRI", "advanced MRI", "magnetic resonance spectroscopy", "perfusion-weighted imaging", "diffusion-weighted imaging", "MRS", "PWI", "DWI", "hybrid PET/MR", "glioma recurrence", "pseudoprogression", "PSP", "treatment-related change", and "radiation necrosis" alone and in combination. Only original articles edited in English and about humans with at least 10 patients were included. RESULTS Forty-four articles were finally selected. Conventional amino acid PET tracers were demonstrated to be reliable diagnostic techniques in differentiating tumor recurrence thanks to their high uptake from tumor tissue and low background in normal grey matter, giving additional and early information to standard modalities. Among them, MET-PET seems to present the highest diagnostic value but its use is limited to on-site cyclotron facilities. [18F]labelled amino acids, such as FDOPA and FET, were developed to provide a more suitable PET tracer for routine clinical applications, and demonstrated similar diagnostic performance. When compared to the gold standard MRI, amino acid PET provides complementary and comparable information to standard modalities and seems to represent an essential tool in the differentiation between tumor recurrence and other entities such as pseudoprogression, radiation necrosis, and pseudoresponse. CONCLUSIONS Despite the introduction of new advanced imaging techniques, the diagnosis of glioma recurrence remains challenging. In this scenario, the growing knowledge about imaging techniques and analysis, such as the combined PET/MRI and the application of artificial intelligence (AI) and machine learning (ML), could represent promising tools to face this difficult and debated clinical issue.
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Affiliation(s)
- Giulia Santo
- Nuclear Medicine Unit, Department of Interdisciplinary Medicine, University of Bari Aldo Moro, 70124 Bari, Italy; (G.S.); (A.G.N.); (G.R.)
| | - Riccardo Laudicella
- Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging, University of Messina, 98125 Messina, Italy; (R.L.); (F.M.); (S.B.)
| | - Flavia Linguanti
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy; (F.L.); (E.A.); (V.V.); (R.S.)
| | - Anna Giulia Nappi
- Nuclear Medicine Unit, Department of Interdisciplinary Medicine, University of Bari Aldo Moro, 70124 Bari, Italy; (G.S.); (A.G.N.); (G.R.)
| | - Elisabetta Abenavoli
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy; (F.L.); (E.A.); (V.V.); (R.S.)
| | - Vittoria Vergura
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy; (F.L.); (E.A.); (V.V.); (R.S.)
| | - Giuseppe Rubini
- Nuclear Medicine Unit, Department of Interdisciplinary Medicine, University of Bari Aldo Moro, 70124 Bari, Italy; (G.S.); (A.G.N.); (G.R.)
| | - Roberto Sciagrà
- Nuclear Medicine Unit, Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy; (F.L.); (E.A.); (V.V.); (R.S.)
| | - Gaspare Arnone
- Nuclear Medicine Unit, A.R.N.A.S. Ospedali Civico, Di Cristina e Benfratelli, 90127 Palermo, Italy; (G.A.); (N.Q.)
| | - Orazio Schillaci
- Department of Biomedicine and Prevention, University of Tor Vergata, 00133 Rome, Italy;
| | - Fabio Minutoli
- Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging, University of Messina, 98125 Messina, Italy; (R.L.); (F.M.); (S.B.)
| | - Sergio Baldari
- Nuclear Medicine Unit, Department of Biomedical and Dental Sciences and Morpho-Functional Imaging, University of Messina, 98125 Messina, Italy; (R.L.); (F.M.); (S.B.)
| | - Natale Quartuccio
- Nuclear Medicine Unit, A.R.N.A.S. Ospedali Civico, Di Cristina e Benfratelli, 90127 Palermo, Italy; (G.A.); (N.Q.)
| | - Sotirios Bisdas
- Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London WC1N 3BG, UK
- Correspondence:
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Abstract
PURPOSE OF REVIEW This review aims to cover current MRI techniques for assessing treatment response in brain tumors, with a focus on radio-induced lesions. RECENT FINDINGS Pseudoprogression and radionecrosis are common radiological entities after brain tumor irradiation and are difficult to distinguish from real progression, with major consequences on daily patient care. To date, shortcomings of conventional MRI have been largely recognized but morphological sequences are still used in official response assessment criteria. Several complementary advanced techniques have been proposed but none of them have been validated, hampering their clinical use. Among advanced MRI, brain perfusion measures increase diagnostic accuracy, especially when added with spectroscopy and susceptibility-weighted imaging. However, lack of reproducibility, because of several hard-to-control variables, is still a major limitation for their standardization in routine protocols. Amide Proton Transfer is an emerging molecular imaging technique that promises to offer new metrics by indirectly quantifying intracellular mobile proteins and peptide concentration. Preliminary studies suggest that this noncontrast sequence may add key biomarkers in tumor evaluation, especially in posttherapeutic settings. SUMMARY Benefits and pitfalls of conventional and advanced imaging on posttreatment assessment are discussed and the potential added value of APT in this clinicoradiological evolving scenario is introduced.
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Affiliation(s)
- Lucia Nichelli
- Department of Neuroradiology, Sorbonne Université, Assistance Publique-Hôpitaux de Paris, Groupe Hospitalier Pitié-Salpêtrière-Charles Foix
- Sorbonne Université, INSERM, CNRS, Assistance Publique-Hôpitaux de Paris, Institut du Cerveau et de la Moelle épinière, boulevard de l’Hôpital, Paris
| | - Stefano Casagranda
- Department of Research & Innovation, Olea Medical, avenue des Sorbiers, La Ciotat, France
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