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Nienhuis PH, van Nieuwland M, van Praagh GD, Markusiewicz K, Colin EM, van der Geest KSM, Wagenaar N, Brouwer E, Alves C, Slart RHJA. Comparing Diagnostic Performance of Short and Long [ 18F]FDG-PET Acquisition Times in Giant Cell Arteritis. Diagnostics (Basel) 2023; 14:62. [PMID: 38201371 PMCID: PMC10802840 DOI: 10.3390/diagnostics14010062] [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: 11/14/2023] [Revised: 12/14/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
(1) Background: In giant cell arteritis (GCA), the assessment of cranial arteries using [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) combined with low-dose computed tomography (CT) may be challenging due to low image quality. This study aimed to investigate the effect of prolonged acquisition time on the diagnostic performance of [18F]FDG PET/CT in GCA. (2) Methods: Patients with suspected GCA underwent [18F]FDG-PET imaging with a short acquisition time (SAT) and long acquisition time (LAT). Two nuclear medicine physicians (NMPs) reported the presence or absence of GCA according to the overall image impression (gestalt) and total vascular score (TVS) of the cranial arteries. Inter-observer agreement and intra-observer agreement were assessed. (3) Results: In total, 38 patients were included, of whom 20 were diagnosed with GCA and 18 were without it. Sensitivity and specificity for GCA on SAT scans were 80% and 72%, respectively, for the first NMP, and 55% and 89% for the second NMP. On the LAT scans, these values were 65% and 83%, and 75% and 83%, respectively. When using the TVS, LAT scans showed especially increased specificity (94% for both NMPs). Observer agreement was higher on the LAT scans compared with that on the SAT scan. (4) Conclusions: LAT combined with the use of the TVS may decrease the number of false-positive assessments of [18F]FDG PET/CT. Additionally, LAT and TVS may increase both inter and intra-observer agreement.
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Affiliation(s)
- Pieter H. Nienhuis
- University of Groningen, University Medical Center Groningen, Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, 9713 GZ Groningen, The Netherlands
| | - Marieke van Nieuwland
- Hospital Group Twente, Department of Rheumatology and Clinical Immunology, 7600 SZ Almelo, The Netherlands; (M.v.N.); (C.A.)
- University of Groningen, University Medical Center Groningen, Department of Rheumatology and Clinical Immunology, 9713 GZ Groningen, The Netherlands
| | - Gijs D. van Praagh
- University of Groningen, University Medical Center Groningen, Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, 9713 GZ Groningen, The Netherlands
| | | | - Edgar M. Colin
- Hospital Group Twente, Department of Rheumatology and Clinical Immunology, 7600 SZ Almelo, The Netherlands; (M.v.N.); (C.A.)
| | - Kornelis S. M. van der Geest
- University of Groningen, University Medical Center Groningen, Department of Rheumatology and Clinical Immunology, 9713 GZ Groningen, The Netherlands
| | - Nils Wagenaar
- Hospital Group Twente, Department of Nuclear Medicine, 7555 DL Hengelo, The Netherlands
| | - Elisabeth Brouwer
- University of Groningen, University Medical Center Groningen, Department of Rheumatology and Clinical Immunology, 9713 GZ Groningen, The Netherlands
| | - Celina Alves
- Hospital Group Twente, Department of Rheumatology and Clinical Immunology, 7600 SZ Almelo, The Netherlands; (M.v.N.); (C.A.)
| | - Riemer H. J. A. Slart
- University of Groningen, University Medical Center Groningen, Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, 9713 GZ Groningen, The Netherlands
- University of Twente, Faculty of Science and Technology, Department of Biomedical Photonic Imaging, 7522 NB Enschede, The Netherlands
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Tingen HSA, van Praagh GD, Nienhuis PH, Tubben A, van Rijsewijk ND, ten Hove D, Mushari NA, Martinez-Lucio TS, Mendoza-Ibañez OI, van Sluis J, Tsoumpas C, Glaudemans AW, Slart RH. The clinical value of quantitative cardiovascular molecular imaging: a step towards precision medicine. Br J Radiol 2023; 96:20230704. [PMID: 37786997 PMCID: PMC10646628 DOI: 10.1259/bjr.20230704] [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: 07/31/2023] [Revised: 09/05/2023] [Accepted: 09/05/2023] [Indexed: 10/04/2023] Open
Abstract
Cardiovascular diseases (CVD) are the leading cause of death worldwide and have an increasing impact on society. Precision medicine, in which optimal care is identified for an individual or a group of individuals rather than for the average population, might provide significant health benefits for this patient group and decrease CVD morbidity and mortality. Molecular imaging provides the opportunity to assess biological processes in individuals in addition to anatomical context provided by other imaging modalities and could prove to be essential in the implementation of precision medicine in CVD. New developments in single-photon emission computed tomography (SPECT) and positron emission tomography (PET) systems, combined with rapid innovations in promising and specific radiopharmaceuticals, provide an impressive improvement of diagnostic accuracy and therapy evaluation. This may result in improved health outcomes in CVD patients, thereby reducing societal impact. Furthermore, recent technical advances have led to new possibilities for accurate image quantification, dynamic imaging, and quantification of radiotracer kinetics. This potentially allows for better evaluation of disease activity over time and treatment response monitoring. However, the clinical implementation of these new methods has been slow. This review describes the recent advances in molecular imaging and the clinical value of quantitative PET and SPECT in various fields in cardiovascular molecular imaging, such as atherosclerosis, myocardial perfusion and ischemia, infiltrative cardiomyopathies, systemic vascular diseases, and infectious cardiovascular diseases. Moreover, the challenges that need to be overcome to achieve clinical translation are addressed, and future directions are provided.
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Affiliation(s)
- Hendrea Sanne Aletta Tingen
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Gijs D. van Praagh
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Pieter H. Nienhuis
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Alwin Tubben
- Department of Cardiology, University Medical Centre Groningen, Groningen, The Netherlands
| | - Nick D. van Rijsewijk
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Derk ten Hove
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Nouf A. Mushari
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | - T. Samara Martinez-Lucio
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Oscar I. Mendoza-Ibañez
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Joyce van Sluis
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | | | - Andor W.J.M. Glaudemans
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
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Duff LM, Scarsbrook AF, Ravikumar N, Frood R, van Praagh GD, Mackie SL, Bailey MA, Tarkin JM, Mason JC, van der Geest KSM, Slart RHJA, Morgan AW, Tsoumpas C. An Automated Method for Artifical Intelligence Assisted Diagnosis of Active Aortitis Using Radiomic Analysis of FDG PET-CT Images. Biomolecules 2023; 13:343. [PMID: 36830712 PMCID: PMC9953018 DOI: 10.3390/biom13020343] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/30/2023] [Accepted: 02/01/2023] [Indexed: 02/12/2023] Open
Abstract
The aim of this study was to develop and validate an automated pipeline that could assist the diagnosis of active aortitis using radiomic imaging biomarkers derived from [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET-CT) images. The aorta was automatically segmented by convolutional neural network (CNN) on FDG PET-CT of aortitis and control patients. The FDG PET-CT dataset was split into training (43 aortitis:21 control), test (12 aortitis:5 control) and validation (24 aortitis:14 control) cohorts. Radiomic features (RF), including SUV metrics, were extracted from the segmented data and harmonized. Three radiomic fingerprints were constructed: A-RFs with high diagnostic utility removing highly correlated RFs; B used principal component analysis (PCA); C-Random Forest intrinsic feature selection. The diagnostic utility was evaluated with accuracy and area under the receiver operating characteristic curve (AUC). Several RFs and Fingerprints had high AUC values (AUC > 0.8), confirmed by balanced accuracy, across training, test and external validation datasets. Good diagnostic performance achieved across several multi-centre datasets suggests that a radiomic pipeline can be generalizable. These findings could be used to build an automated clinical decision tool to facilitate objective and standardized assessment regardless of observer experience.
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Affiliation(s)
- Lisa M. Duff
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Institute of Medical and Biological Engineering, University of Leeds, Leeds LS2 9JT, UK
| | - Andrew F. Scarsbrook
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Department of Radiology, St. James University Hospital, Leeds LS9 7TF, UK
| | - Nishant Ravikumar
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Center for Computational Imaging and Simulation Technologies in Biomedicine, University of Leeds, Leeds LS2 9JT, UK
| | - Russell Frood
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Department of Radiology, St. James University Hospital, Leeds LS9 7TF, UK
| | - Gijs D. van Praagh
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
| | - Sarah L. Mackie
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- NIHR Leeds Biomedical Research Centre and NIHR Leeds MedTech and In Vitro Diagnostics Co-Operative, Leeds Teaching Hospitals NHS Trust, Leeds LS7 4SA, UK
| | - Marc A. Bailey
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- The Leeds Vascular Institute, Leeds General Infirmary, Leeds LS2 9NS, UK
| | - Jason M. Tarkin
- Division of Cardiovascular Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Justin C. Mason
- National Heart and Lung Institute, Imperial College London, London SW3 6LY, UK
| | - Kornelis S. M. van der Geest
- Department of Rheumatology and Clinical Immunology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
| | - Riemer H. J. A. Slart
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
- Department of Biomedical Photonic Imaging, Faculty of Science and Technology, University of Twente, 7522 NB Enschede, The Netherlands
| | - Ann W. Morgan
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- NIHR Leeds Biomedical Research Centre and NIHR Leeds MedTech and In Vitro Diagnostics Co-Operative, Leeds Teaching Hospitals NHS Trust, Leeds LS7 4SA, UK
| | - Charalampos Tsoumpas
- School of Medicine, University of Leeds, Leeds LS2 9JT, UK
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, The Netherlands
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Satoh Y, Funayama S, Onishi H, Kirito K. Semi-automated histogram analysis of normal bone marrow using 18F-FDG PET/CT: correlation with clinical indicators. BMC Med Imaging 2022; 22:31. [PMID: 35197004 PMCID: PMC8867739 DOI: 10.1186/s12880-022-00757-x] [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: 09/10/2021] [Accepted: 02/11/2022] [Indexed: 11/10/2022] Open
Abstract
Background 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is increasingly applied to the diagnosis of bone marrow failure such as myeloproliferative neoplasm, aplastic anemia, and myelodysplastic syndrome, as well as malignant lymphoma and multiple myeloma. However, few studies have shown a normal FDG uptake pattern. This study aimed to establish a standard of bone marrow FDG uptake by a reproducible quantitative method with fewer steps using deep learning-based organ segmentation. Methods Bone marrow PET images were obtained using segmented whole-spine and pelvic bone marrow cavity CT as mask images using a commercially available imaging workstation that implemented an automatic organ segmentation algorithm based on deep learning. The correlation between clinical indicators and quantitative PET parameters, including histogram features, was evaluated. Results A total of 98 healthy adults were analyzed. The volume of bone marrow PET extracted in men was significantly higher than that in women (p < 0.0001). Univariate and multivariate regression analyses showed that mean of standardized uptake value corrected by lean body mass (SULmean) and entropy in both men and women were inversely correlated with age (all p < 0.0001), and SULmax in women were also inversely correlated with age (p = 0.011). Conclusion A normal FDG uptake pattern was demonstrated by simplified FDG PET/CT bone marrow quantification.
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Affiliation(s)
- Yoko Satoh
- Yamanashi PET Imaging Clinic, Shimokato 3046-2, Chuo City, Yamanashi Prefecture, 409-3821, Japan. .,Department of Radiology, University of Yamanashi, Shimokato 1110, Chuo City, Yamanashi Prefecture, 409-3898, Japan.
| | - Satoshi Funayama
- Department of Radiology, University of Yamanashi, Shimokato 1110, Chuo City, Yamanashi Prefecture, 409-3898, Japan
| | - Hiroshi Onishi
- Department of Radiology, University of Yamanashi, Shimokato 1110, Chuo City, Yamanashi Prefecture, 409-3898, Japan
| | - Keita Kirito
- Department of Hematology and Oncology, University of Yamanashi, Shimokato 1110, Chuo City, Yamanashi Prefecture, 409-3898, Japan
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