1
|
Cook EL, Su KH, Higgins GS, Johnsen R, Bouhnik JP, McGowan DR. Data-driven gating (DDG)-based motion match for improved CTAC registration. EJNMMI Phys 2024; 11:42. [PMID: 38691232 DOI: 10.1186/s40658-024-00644-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 04/24/2024] [Indexed: 05/03/2024] Open
Abstract
BACKGROUND Respiratory motion artefacts are a pitfall in thoracic PET/CT imaging. A source of these motion artefacts within PET images is the CT used for attenuation correction of the images. The arbitrary respiratory phase in which the helical CT ( CT helical ) is acquired often causes misregistration between PET and CT images, leading to inaccurate attenuation correction of the PET image. As a result, errors in tumour delineation or lesion uptake values can occur. To minimise the effect of motion in PET/CT imaging, a data-driven gating (DDG)-based motion match (MM) algorithm has been developed that estimates the phase of the CT helical , and subsequently warps this CT to a given phase of the respiratory cycle, allowing it to be phase-matched to the PET. A set of data was used which had four-dimensional CT (4DCT) acquired alongside PET/CT. The 4DCT allowed ground truth CT phases to be generated and compared to the algorithm-generated motion match CT (MMCT). Measurements of liver and lesion margin positions were taken across CT images to determine any differences and establish how well the algorithm performed concerning warping the CT helical to a given phase (end-of-expiration, EE). RESULTS Whilst there was a minor significance in the liver measurement between the 4DCT and MMCT ( p = 0.045 ), no significant differences were found between the 4DCT or MMCT for lesion measurements ( p = 1.0 ). In all instances, the CT helical was found to be significantly different from the 4DCT ( p < 0.001 ). Consequently, the 4DCT and MMCT can be considered equivalent with respect to warped CT generation, showing the DDG-based MM algorithm to be successful. CONCLUSION The MM algorithm successfully enables the phase-matching of a CT helical to the EE of a ground truth 4DCT. This would reduce the motion artefacts caused by PET/CT registration without requiring additional patient dose (required for a 4DCT).
Collapse
Affiliation(s)
- Ella L Cook
- Department of Oncology, University of Oxford, Oxford, UK
| | | | | | | | | | - Daniel R McGowan
- Department of Oncology, University of Oxford, Oxford, UK.
- Department of Medical Physics and Clinical Engineering, Oxford University Hospitals Foundation Trust, Oxford, UK.
| |
Collapse
|
2
|
Dizdarevic S, Mccready VR, Blower PJ, Vöö SA, Wadsley J, McGowan DR, Roldão Pereira L, Eccles A, Prakash VS, Abreu C, Jessop M, Weston CJ. The British nuclear medicine research strategy - the framework. Nucl Med Commun 2024; 45:347-351. [PMID: 38372041 DOI: 10.1097/mnm.0000000000001827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
The British Nuclear Medicine Society (BNMS) has developed a Research Strategy framework led by the Research Champions of the BNMS and overseen by the BNMS Research and Innovation Committee. The objectives of the Research Strategy are to improve translation of cutting-edge nuclear medicine research from bench to bedside, the implementation of state-of-the-art multimodality technologies and to enhance multicentre radionuclide research in the UK. It strives to involve patients and the public in radionuclide research and to contribute to and work with the multi-professional national and international organisations involved in research with an ultimate aim to improve nuclear medicine services, and patients' outcomes and care.
Collapse
Affiliation(s)
- Sabina Dizdarevic
- Department of Nuclear Medicine, Royal Sussex Hospital, University Hospitals Sussex Foundation Trust,
- Brighton & Sussex Medical School, University of Sussex and Brighton, Brighton,
| | - V Ralph Mccready
- Department of Nuclear Medicine, Royal Sussex Hospital, University Hospitals Sussex Foundation Trust,
| | - Philip J Blower
- School of Biomedical Engineering and Imaging Sciences, King's College London,
| | - Stefan A Vöö
- Institute of Nuclear Medicine, University College Hospital, London,
| | | | - Daniel R McGowan
- Department of Medical Physics and Clinical Engineering, Oxford University Hospitals NHS FT,
- Department of Oncology, University of Oxford, Oxford,
| | | | - Amy Eccles
- Department of Nuclear Medicine, Charing Cross Hospital, London,
| | - Vineet S Prakash
- Department of Nuclear Medicine, Royal Surrey County Hospital, Guildford,
| | - Carla Abreu
- Department of Nuclear Medicine, Royal Marsden NHS Foundation Trust, London and
| | - Maryam Jessop
- Department of Nuclear Medicine, Royal Sussex Hospital, University Hospitals Sussex Foundation Trust,
| | | |
Collapse
|
3
|
Dedja M, Mehranian A, Bradley KM, Walker MD, Fielding PA, Wollenweber SD, Johnsen R, McGowan DR. Sequential deep learning image enhancement models improve diagnostic confidence, lesion detectability, and image reconstruction time in PET. EJNMMI Phys 2024; 11:28. [PMID: 38488923 PMCID: PMC10942956 DOI: 10.1186/s40658-024-00632-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 03/07/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Investigate the potential benefits of sequential deployment of two deep learning (DL) algorithms namely DL-Enhancement (DLE) and DL-based time-of-flight (ToF) (DLT). DLE aims to enhance the rapidly reconstructed ordered-subset-expectation-maximisation algorithm (OSEM) images towards block-sequential-regularised-expectation-maximisation (BSREM) images, whereas DLT aims to improve the quality of BSREM images reconstructed without ToF. As the algorithms differ in their purpose, sequential application may allow benefits from each to be combined. 20 FDG PET-CT scans were performed on a Discovery 710 (D710) and 20 on Discovery MI (DMI; both GE HealthCare). PET data was reconstructed using five combinations of algorithms:1. ToF-BSREM, 2. ToF-OSEM + DLE, 3. OSEM + DLE + DLT, 4. ToF-OSEM + DLE + DLT, 5. ToF-BSREM + DLT. To assess image noise, 30 mm-diameter spherical VOIs were drawn in both lung and liver to measure standard deviation of voxels within the volume. In a blind clinical reading, two experienced readers rated the images on a five-point Likert scale based on lesion detectability, diagnostic confidence, and image quality. RESULTS Applying DLE + DLT reduced noise whilst improving lesion detectability, diagnostic confidence, and image reconstruction time. ToF-OSEM + DLE + DLT reconstructions demonstrated an increase in lesion SUVmax of 28 ± 14% (average ± standard deviation) and 11 ± 5% for data acquired on the D710 and DMI, respectively. The same reconstruction scored highest in clinical readings for both lesion detectability and diagnostic confidence for D710. CONCLUSIONS The combination of DLE and DLT increased diagnostic confidence and lesion detectability compared to ToF-BSREM images. As DLE + DLT used input OSEM images, and because DL inferencing was fast, there was a significant decrease in overall reconstruction time. This could have applications to total body PET.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Daniel R McGowan
- Oxford University Hospitals, Oxford, UK.
- University of Oxford, Oxford, UK.
| |
Collapse
|
4
|
Rojas B, McGowan DR, Gear J, Smith AL, Scott C, Craig AJ, Scuffham J, Towey D, Aldridge M, Tipping J. Nearly double the patients and dramatic changes over 14 years of UK MRT: Internal Dosimetry Users Group survey results from 2007 to 2021. Nucl Med Commun 2024; 45:16-23. [PMID: 37901930 PMCID: PMC10718208 DOI: 10.1097/mnm.0000000000001780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 09/27/2023] [Indexed: 10/31/2023]
Affiliation(s)
- Bruno Rojas
- Joint Department of Physics, The Royal Marsden NHS FT and Institute of Cancer Research, Sutton
| | - Daniel R. McGowan
- Department of Medical Physics and Clinical Engineering, Oxford University Hospitals NHS FT
- Department of Oncology, University of Oxford, Oxford
| | - Jonathan Gear
- Joint Department of Physics, The Royal Marsden NHS FT and Institute of Cancer Research, Sutton
| | - April-Louise Smith
- Institute of Nuclear Medicine, University College London Hospitals NHS FT, London
| | - Catherine Scott
- Institute of Nuclear Medicine, University College London Hospitals NHS FT, London
| | - Allison J. Craig
- Joint Department of Physics, The Royal Marsden NHS FT and Institute of Cancer Research, Sutton
| | - James Scuffham
- Nuclear Medicine Physics, Royal Surrey NHS FT, Guildford
| | - David Towey
- Nuclear Medicine, Royal Berkshire NHS FT, Reading
| | - Matthew Aldridge
- Nuclear Medicine Physics, Maidstone and Royal Tunbridge Wells NHS Trust, Maidstone and
| | - Jill Tipping
- Department of Nuclear Medicine, The Christie NHS FT, Manchester, UK
| |
Collapse
|
5
|
Tran-Gia J, Denis-Bacelar AM, Ferreira KM, Robinson AP, Bobin C, Bonney LM, Calvert N, Collins SM, Fenwick AJ, Finocchiaro D, Fioroni F, Giannopoulou K, Grassi E, Heetun W, Jewitt SJ, Kotzasarlidou M, Ljungberg M, Lourenço V, McGowan DR, Mewburn-Crook J, Sabot B, Scuffham J, Sjögreen Gleisner K, Solc J, Thiam C, Tipping J, Wevrett J, Lassmann M. On the use of solid 133Ba sources as surrogate for liquid 131I in SPECT/CT calibration: a European multi-centre evaluation. EJNMMI Phys 2023; 10:73. [PMID: 37993667 PMCID: PMC10665282 DOI: 10.1186/s40658-023-00582-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 09/25/2023] [Indexed: 11/24/2023] Open
Abstract
INTRODUCTION Commissioning, calibration, and quality control procedures for nuclear medicine imaging systems are typically performed using hollow containers filled with radionuclide solutions. This leads to multiple sources of uncertainty, many of which can be overcome by using traceable, sealed, long-lived surrogate sources containing a radionuclide of comparable energies and emission probabilities. This study presents the results of a quantitative SPECT/CT imaging comparison exercise performed within the MRTDosimetry consortium to assess the feasibility of using 133Ba as a surrogate for 131I imaging. MATERIALS AND METHODS Two sets of four traceable 133Ba sources were produced at two National Metrology Institutes and encapsulated in 3D-printed cylinders (volume range 1.68-107.4 mL). Corresponding hollow cylinders to be filled with liquid 131I and a mounting baseplate for repeatable positioning within a Jaszczak phantom were also produced. A quantitative SPECT/CT imaging comparison exercise was conducted between seven members of the consortium (eight SPECT/CT systems from two major vendors) based on a standardised protocol. Each site had to perform three measurements with the two sets of 133Ba sources and liquid 131I. RESULTS As anticipated, the 131I pseudo-image calibration factors (cps/MBq) were higher than those for 133Ba for all reconstructions and systems. A site-specific cross-calibration reduced the performance differences between both radionuclides with respect to a cross-calibration based on the ratio of emission probabilities from a median of 12-1.5%. The site-specific cross-calibration method also showed agreement between 133Ba and 131I for all cylinder volumes, which highlights the potential use of 133Ba sources to calculate recovery coefficients for partial volume correction. CONCLUSION This comparison exercise demonstrated that traceable solid 133Ba sources can be used as surrogate for liquid 131I imaging. The use of solid surrogate sources could solve the radiation protection problem inherent in the preparation of phantoms with 131I liquid activity solutions as well as reduce the measurement uncertainties in the activity. This is particularly relevant for stability measurements, which have to be carried out at regular intervals.
Collapse
Affiliation(s)
- Johannes Tran-Gia
- Department of Nuclear Medicine, University Hospital Würzburg, Oberdürrbacher Str. 6, 97080, Würzburg, Germany.
| | | | | | | | - Christophe Bobin
- Université Paris-Saclay, CEA, List, Laboratoire National Henri Becquerel (LNE-LNHB), 91120, Palaiseau, France
| | - Lara M Bonney
- Department of Medical Physics and Clinical Engineering, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Nicholas Calvert
- Christie Medical Physics and Engineering (CMPE), The Christie NHS Foundation Trust, Manchester, UK
| | - Sean M Collins
- National Physical Laboratory, Hampton Road, Teddington, UK
- School of Mathematics and Physics, University of Surrey, Guildford, UK
| | | | - Domenico Finocchiaro
- Medical Physics Unit, Azienda Ospedaliero-Universitaria Policlinico di Modena, Modena, Italy
| | - Federica Fioroni
- Medical Physics Unit, Azienda USL-IRCCS Di Reggio Emilia, Reggio Emilia, Italy
| | | | - Elisa Grassi
- Medical Physics Unit, Azienda USL-IRCCS Di Reggio Emilia, Reggio Emilia, Italy
| | - Warda Heetun
- National Physical Laboratory, Hampton Road, Teddington, UK
| | - Stephanie J Jewitt
- Department of Medical Physics and Clinical Engineering, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Maria Kotzasarlidou
- Nuclear Medicine Department, "THEAGENIO" Anticancer Hospital, Thessaloniki, Greece
| | | | - Valérie Lourenço
- Université Paris-Saclay, CEA, List, Laboratoire National Henri Becquerel (LNE-LNHB), 91120, Palaiseau, France
| | - Daniel R McGowan
- Department of Medical Physics and Clinical Engineering, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Department of Oncology, University of Oxford, Oxford, UK
| | | | - Benoit Sabot
- Université Paris-Saclay, CEA, List, Laboratoire National Henri Becquerel (LNE-LNHB), 91120, Palaiseau, France
| | - James Scuffham
- Royal Surrey County Hospital, Royal Surrey NHS Foundation Trust, Guildford, UK
| | | | - Jaroslav Solc
- Czech Metrology Institute, Okruzni 31, 638 00, Brno, Czech Republic
| | - Cheick Thiam
- Université Paris-Saclay, CEA, List, Laboratoire National Henri Becquerel (LNE-LNHB), 91120, Palaiseau, France
| | - Jill Tipping
- Department of Medical Physics and Clinical Engineering, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Jill Wevrett
- Royal Surrey County Hospital, Royal Surrey NHS Foundation Trust, Guildford, UK
| | - Michael Lassmann
- Department of Nuclear Medicine, University Hospital Würzburg, Oberdürrbacher Str. 6, 97080, Würzburg, Germany
| |
Collapse
|
6
|
Robinson M, Muirhead R, McGowan DR, Chu KY, Jacobs C, Hawkins MA. Differential Response of Pelvic Bone Marrow Fluorodeoxyglucose Uptake in Patients Receiving Concurrent Chemoradiotherapy. Clin Oncol (R Coll Radiol) 2023; 35:e622-e627. [PMID: 37339923 DOI: 10.1016/j.clon.2023.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 05/01/2023] [Accepted: 06/01/2023] [Indexed: 06/22/2023]
Abstract
AIMS Irradiation of pelvic bone marrow (PBM) at the level of the typical low dose bath of intensity-modulated radiotherapy delivery (10-20 Gy) is associated with an increased risk of haematological toxicity, particularly when combined with concurrent chemotherapy. Although sparing of the whole of the PBM at a 10-20 Gy dose level is unachievable, it is known that PBM is divided into haematopoietically active and inactive regions that are identifiable based on the threshold uptake of [18F]-fluorodeoxyglucose (FDG) seen on positron emission tomography-computed tomography (PET-CT). In published studies to date, the definition of active PBM widely used is that of a standardised uptake value (SUV) greater than the mean SUV of the whole PBM prior to the start of chemoradiation. These studies include those looking at developing an atlas-based approach to contouring active PBM. Using baseline and mid-treatment FDG PET scans acquired as part of a prospective clinical trial we sought to determine the suitability of the current definition of active bone marrow as representative of differential underlying cell physiology. MATERIALS AND METHODS Active and inactive PBM were contoured on baseline PET-CT and using deformable registration mapped onto mid-treatment PET-CT. Volumes were cropped to exclude definitive bone, voxel SUV extracted and the change between scans calculated. Change was compared using Mann-Whitney U testing. RESULTS Active and inactive PBM were shown to respond differentially to concurrent chemoradiotherapy. The median absolute response of active PBM for all patients was -0.25 g/ml, whereas the median inactive PBM response was -0.02 g/ml. Significantly, the inactive PBM median absolute response was shown to be near zero with a relatively unskewed distribution (0.12). CONCLUSIONS These results would support the definition of active PBM as FDG uptake greater than the mean of the whole structure as being representative of underlying cell physiology. This work would support the development of atlas-based approaches published in the literature to contour active PBM based on the current definition as being suitable.
Collapse
Affiliation(s)
- M Robinson
- Department of Clinical Oncology, Oxford University Hospitals NHS Trust, Oxford, UK; Department of Oncology, University of Oxford, Oxford, UK.
| | - R Muirhead
- Department of Clinical Oncology, Oxford University Hospitals NHS Trust, Oxford, UK
| | - D R McGowan
- Department of Oncology, University of Oxford, Oxford, UK; Department of Medical Physics and Clinical Engineering, Oxford University Hospitals NHS Trust, Oxford, UK
| | - K-Y Chu
- Department of Clinical Oncology, Oxford University Hospitals NHS Trust, Oxford, UK; Department of Oncology, University of Oxford, Oxford, UK
| | - C Jacobs
- Department of Clinical Oncology, Oxford University Hospitals NHS Trust, Oxford, UK
| | - M A Hawkins
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| |
Collapse
|
7
|
Barber TR, Muhammed K, Drew D, Bradley KM, McGowan DR, Klein JC, Manohar SG, Hu MTM, Husain M. Reward insensitivity is associated with dopaminergic deficit in rapid eye movement sleep behaviour disorder. Brain 2023; 146:2502-2511. [PMID: 36395092 PMCID: PMC10232265 DOI: 10.1093/brain/awac430] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 10/18/2022] [Accepted: 11/06/2022] [Indexed: 11/18/2022] Open
Abstract
Idiopathic rapid eye movement sleep behaviour disorder (iRBD) has now been established as an important marker of the prodromal stage of Parkinson's disease and related synucleinopathies. However, although dopamine transporter single photon emission computed tomography (SPECT) has been used to demonstrate the presence of nigro-striatal deficit in iRBD, quantifiable correlates of this are currently lacking. Sensitivity to rewarding stimuli is reduced in some people with Parkinson's disease, potentially contributing to aspects of the neuropsychiatric phenotype in these individuals. Furthermore, a role for dopaminergic degeneration is suggested by the fact that reward insensitivity can be improved by dopaminergic medications. Patients with iRBD present a unique opportunity to study the relationship between reward sensitivity and early dopaminergic deficit in the unmedicated state. Here, we investigate whether a non-invasive, objective measure of reward sensitivity might be a marker of dopaminergic status in prodromal Parkinson's disease by comparing with SPECT/CT measurement of dopaminergic loss in the basal ganglia. Striatal dopaminergic deficits in iRBD are associated with progression to Parkinsonian disorders. Therefore, identification of a clinically measurable correlate of this degenerative process might provide a basis for the development of novel risk stratification tools. Using a recently developed incentivized eye-tracking task, we quantified reward sensitivity in a cohort of 41 patients with iRBD and compared this with data from 40 patients with Parkinson's disease and 41 healthy controls. Patients with iRBD also underwent neuroimaging with dopamine transporter SPECT/CT. Overall, reward sensitivity, indexed by pupillary response to monetary incentives, was reduced in iRBD cases compared with controls and was not significantly different to that in patients with Parkinson's disease. However, in iRBD patients with normal dopamine transporter SPECT/CT imaging, reward sensitivity was not significantly different from healthy controls. Across all iRBD cases, a positive association was observed between reward sensitivity and dopaminergic SPECT/CT signal in the putamen. These findings demonstrate a direct relationship between dopaminergic deficit and reward sensitivity in patients with iRBD and suggest that measurement of pupillary responses could be of value in models of risk stratification and disease progression in these individuals.
Collapse
Affiliation(s)
- Thomas R Barber
- Nuffield Department of Clinical Neurosciences, University of Oxford, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Kinan Muhammed
- Nuffield Department of Clinical Neurosciences, University of Oxford, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK
- Department of Experimental Psychology, University of Oxford, Anna Watts Building, Radcliffe Observatory Quarter, Oxford, OX2 6GG, UK
| | - Daniel Drew
- Nuffield Department of Clinical Neurosciences, University of Oxford, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK
- Department of Experimental Psychology, University of Oxford, Anna Watts Building, Radcliffe Observatory Quarter, Oxford, OX2 6GG, UK
| | - Kevin M Bradley
- Wales Research and Diagnostic PET Imaging Centre, Cardiff University, School of Medicine, University Hospital Wales, Cardiff CF14 4XN, UK
| | - Daniel R McGowan
- Department of Medical Physics and Clinical Engineering, Oxford University Hospitals NHS Trust, Churchill Hospital, Oxford, OX3 7LE, UK
- Department of Oncology, University of Oxford, Oxford OX3 7DQ, UK
| | - Johannes C Klein
- Nuffield Department of Clinical Neurosciences, University of Oxford, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Sanjay G Manohar
- Nuffield Department of Clinical Neurosciences, University of Oxford, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK
- Department of Experimental Psychology, University of Oxford, Anna Watts Building, Radcliffe Observatory Quarter, Oxford, OX2 6GG, UK
| | - Michele T M Hu
- Nuffield Department of Clinical Neurosciences, University of Oxford, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Masud Husain
- Nuffield Department of Clinical Neurosciences, University of Oxford, Level 6, West Wing, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| |
Collapse
|
8
|
Deidda D, Denis-Bacelar AM, Fenwick AJ, Ferreira KM, Heetun W, Hutton BF, McGowan DR, Robinson AP, Scuffham J, Thielemans K, Twyman R. Triple modality image reconstruction of PET data using SPECT, PET, CT information increases lesion uptake in images of patients treated with radioembolization with [Formula: see text] micro-spheres. EJNMMI Phys 2023; 10:30. [PMID: 37133766 PMCID: PMC10156904 DOI: 10.1186/s40658-023-00549-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 04/13/2023] [Indexed: 05/04/2023] Open
Abstract
PURPOSE Nuclear medicine imaging modalities like computed tomography (CT), single photon emission CT (SPECT) and positron emission tomography (PET) are employed in the field of theranostics to estimate and plan the dose delivered to tumors and the surrounding tissues and to monitor the effect of the therapy. However, therapeutic radionuclides often provide poor images, which translate to inaccurate treatment planning and inadequate monitoring images. Multimodality information can be exploited in the reconstruction to enhance image quality. Triple modality PET/SPECT/CT scanners are particularly useful in this context due to the easier registration process between images. In this study, we propose to include PET, SPECT and CT information in the reconstruction of PET data. The method is applied to Yttrium-90 ([Formula: see text]Y) data. METHODS Data from a NEMA phantom filled with [Formula: see text]Y were used for validation. PET, SPECT and CT data from 10 patients treated with Selective Internal Radiation Therapy (SIRT) were used. Different combinations of prior images using the Hybrid kernelized expectation maximization were investigated in terms of VOI activity and noise suppression. RESULTS Our results show that triple modality PET reconstruction provides significantly higher uptake when compared to the method used as standard in the hospital and OSEM. In particular, using CT-guided SPECT images, as guiding information in the PET reconstruction significantly increases uptake quantification on tumoral lesions. CONCLUSION This work proposes the first triple modality reconstruction method and demonstrates up to 69% lesion uptake increase over standard methods with SIRT [Formula: see text]Y patient data. Promising results are expected for other radionuclide combination used in theranostic applications using PET and SPECT.
Collapse
Affiliation(s)
- Daniel Deidda
- National Physical Laboratory, Teddington, UK
- Nuclear Medicine Institute, University College London, London, UK
| | | | | | | | | | - Brian F. Hutton
- Nuclear Medicine Institute, University College London, London, UK
| | - Daniel R. McGowan
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- University of Oxford, Oxford, UK
| | | | | | - Kris Thielemans
- Nuclear Medicine Institute, University College London, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - Robert Twyman
- Nuclear Medicine Institute, University College London, London, UK
| |
Collapse
|
9
|
Bonney LM, McGowan DR. Variability of estimated glomerular filtration rate and 99m Tc-DTPA glomerular filtration rate: implications for a single time-point sampling regime. Nucl Med Commun 2023; 44:351-357. [PMID: 36826407 PMCID: PMC10069751 DOI: 10.1097/mnm.0000000000001674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 02/02/2023] [Indexed: 02/25/2023]
Abstract
BACKGROUND This work aimed to determine the implications of the variability in estimated glomerular filtration rate (eGFR) for the prediction of measured GFR (mGFR) for selection of sampling time-point in single-sample 99m Tc-diethylene-triamine-pentaacetate (DTPA) mGFR. METHODS Patient studies were used to compare eGFR and mGFR ( n = 282). The eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration 2009 equation, from serum creatinine values measured in the laboratory ( n = 27) or using a point-of-care testing device ( n = 255). The mGFR was taken as the true value, and the root mean square error (RMS err ) in eGFR was calculated. Receiver operator characteristic curves were generated comparing the sensitivity and specificity of eGFR for the prediction of mGFR within the British Nuclear Medicine Society (BNMS) 2018 guideline ranges. RESULTS The overall eGFR RMS err was 19.3 mL/min/1.73 m 2 . Use of eGFR to predict mGFR in the ranges specified in the BNMS 2018 guidelines (25-50; 50-70; 70-100; and >100) achieved the following specificity and sensitivity for each individual range (97%, 71%; 92%, 47%; 81%, 48%; and 74%, 90%). For the middle ranges (50-70 and 70-100) the sensitivity is very low, less than 50%; more studies are classified incorrectly on the basis of eGFR in these ranges than correctly. CONCLUSION This work shows that serum creatinine eGFR is not sufficiently accurate to predict the optimum single-sample time-point for 99m Tc-DTPA mGFR prior to measurement. It is the recommendation of this study that a single sampling time-point should be chosen for studies eGFR > 40 ml/min/1.73 m 2 as opposed to the use of eGFR to determine the sampling time-point.
Collapse
Affiliation(s)
- Lara M. Bonney
- Department of Medical Physics and Clinical Engineering, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust
| | - Daniel R. McGowan
- Department of Medical Physics and Clinical Engineering, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust
- Department of Oncology, University of Oxford, Oxford, UK
| |
Collapse
|
10
|
Bourigault P, Skwarski M, Macpherson RE, Higgins GS, McGowan DR. Timing of hypoxia PET/CT imaging after 18F-fluoromisonidazole injection in non-small cell lung cancer patients. Sci Rep 2022; 12:21746. [PMID: 36526815 PMCID: PMC9758119 DOI: 10.1038/s41598-022-26199-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
Positron emission tomography (PET)/computed tomography (CT) using the radiotracer 18F-Fluoromisonidazole (FMISO) has been widely employed to image tumour hypoxia and is of interest to help develop novel hypoxia modifiers and guide radiation treatment planning. Yet, the optimal post-injection (p.i.) timing of hypoxic imaging remains questionable. Therefore, we investigated the correlation between hypoxia-related quantitative values in FMISO-PET acquired at 2 and 4 h p.i. in patients with non-small cell lung cancer (NSCLC). Patients with resectable NSCLC participated in the ATOM clinical trial (NCT02628080) which investigated the hypoxia modifying effects of atovaquone. Two-hour and four-hour FMISO PET/CT images acquired at baseline and pre-surgery visits (n = 58) were compared. Cohort 1 (n = 14) received atovaquone treatment, while cohort 2 (n = 15) did not. Spearman's rank correlation coefficients (ρ) assessed the relationship between hypoxia-related metrics, including standardised uptake value (SUV), tumour-to-blood ratio (TBR), and tumour hypoxic volume (HV) defined by voxels with TBR ≥ 1.4. As the primary imaging-related trial endpoint used to evaluate the action of atovaquone on tumour hypoxia in patients with NSCLC was change in tumour HV from baseline, this was also assessed in patients (n = 20) with sufficient baseline 2- and 4-h scan HV to reliably measure change (predefined as ≥ 1.5 mL). Tumours were divided into four subregions or distance categories: edge, outer, inner, and centre, using MATLAB. In tumours overall, strong correlation (P < 0.001) was observed for SUVmax ρ = 0.87, SUVmean ρ = 0.91, TBRmax ρ = 0.83 and TBRmean ρ = 0.81 between 2- and 4-h scans. Tumour HV was moderately correlated (P < 0.001) with ρ = 0.69 between 2- and 4-h scans. Yet, in tumour subregions, the correlation of HV decreased from the centre ρ = 0.71 to the edge ρ = 0.45 (P < 0.001). SUV, TBR, and HV values were consistently higher on 4-h scans than on 2-h scans, indicating better tracer-to-background contrast. For instance, for TBRmax, the mean, median, and interquartile range were 1.9, 1.7, and 1.6-2.0 2-h p.i., and 2.6, 2.4, and 2.0-3.0 4-h p.i., respectively. Our results support that FMISO-PET scans should be performed at 4 h p.i. to evaluate tumour hypoxia in NSCLC.Trial registration: ClinicalTrials.gov, NCT02628080. Registered 11/12/2015, https://clinicaltrials.gov/ct2/show/NCT02628080 .
Collapse
Affiliation(s)
| | - Michael Skwarski
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
- Department of Oncology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Department of Clinical Oncology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Ruth E Macpherson
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Geoff S Higgins
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
- Department of Oncology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Daniel R McGowan
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK.
- Department of Medical Physics and Clinical Engineering, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
| |
Collapse
|
11
|
Roach JR, Plaha P, McGowan DR, Higgins GS. The role of [ 18F]fluorodopa positron emission tomography in grading of gliomas. J Neurooncol 2022; 160:577-589. [PMID: 36434486 PMCID: PMC9758109 DOI: 10.1007/s11060-022-04177-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 10/19/2022] [Indexed: 11/27/2022]
Abstract
PURPOSE Gliomas are the most commonly occurring brain tumour in adults and there remains no cure for these tumours with treatment strategies being based on tumour grade. All treatment options aim to prolong survival, maintain quality of life and slow the inevitable progression from low-grade to high-grade. Despite imaging advancements, the only reliable method to grade a glioma is to perform a biopsy, and even this is fraught with errors associated with under grading. Positron emission tomography (PET) imaging with amino acid tracers such as [18F]fluorodopa (18F-FDOPA), [11C]methionine (11C-MET), [18F]fluoroethyltyrosine (18F-FET), and 18F-FDOPA are being increasingly used in the diagnosis and management of gliomas. METHODS In this review we discuss the literature available on the ability of 18F-FDOPA-PET to distinguish low- from high-grade in newly diagnosed gliomas. RESULTS In 2016 the Response Assessment in Neuro-Oncology (RANO) and European Association for Neuro-Oncology (EANO) published recommendations on the clinical use of PET imaging in gliomas. However, since these recommendations there have been a number of studies performed looking at whether 18F-FDOPA-PET can identify areas of high-grade transformation before the typical radiological features of transformation such as contrast enhancement are visible on standard magnetic resonance imaging (MRI). CONCLUSION Larger studies are needed to validate 18F-FDOPA-PET as a non-invasive marker of glioma grade and prediction of tumour molecular characteristics which could guide decisions surrounding surgical resection.
Collapse
Affiliation(s)
- Joy R. Roach
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ UK
- Department of Neurosurgery, Oxford University Hospital NHS FT, John Radcliffe Hospital, L3 West Wing, Oxford, OX3 9DU UK
| | - Puneet Plaha
- Department of Neurosurgery, Oxford University Hospital NHS FT, John Radcliffe Hospital, L3 West Wing, Oxford, OX3 9DU UK
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, OX3 7DQ UK
| | - Daniel R. McGowan
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ UK
- Department of Medical Physics and Clinical Engineering, Oxford University Hospital NHS FT, Churchill Hospital, Oxford, OX3 7LE UK
| | - Geoff S. Higgins
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ UK
- Department of Oncology, Oxford University Hospitals NHS FT, Oxford, UK
| |
Collapse
|
12
|
McMeekin H, Townrow S, Barnfield M, Bradley A, Fongenie B, McGowan DR, Memmott M, Porter CA, Wickham F, Vennart N, Burniston M. Tailoring the sampling time of single-sample GFR measurement according to expected renal function: a multisite audit. EJNMMI Phys 2022; 9:73. [PMID: 36289135 PMCID: PMC9606161 DOI: 10.1186/s40658-022-00500-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 10/03/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The 2018 BNMS Glomerular Filtration Rate (GFR) guidelines recommend a single-sample technique with the sampling time dictated by the expected renal function, but this is not known with any accuracy before the test. We aimed to assess whether the sampling regime suggested in the guidelines is optimal and determine the error in GFR result if the sample time is chosen incorrectly. We can then infer the degree of flexibility in the sampling regime. METHODS Data from 6328 patients referred for GFR assessment at 6 different hospitals for a variety of indications were reviewed. The difference between the single-sample (Fleming) GFR result at each sample time and the slope-intercept GFR result at each hospital was calculated. A second dataset of 777 studies from one hospital with nine samples collected from 5 min to 8 h post-injection was analysed to provide a reference GFR to which the single-sample results were compared. RESULTS Recommended single-sample times have been revised: for an expected GFR above 90 ml/min/1.73m2 a 2-h sample is recommended; between 50 and 90 ml/min/1.73m2 a 3-h sample is recommended; and between 30 and 50 ml/min/1.73m2 a 4-h sample is recommended. Root mean square error in single-sample GFR result compared with slope-intercept can be kept less than or equal to 3.30 ml/min/1.73m2 by following these recommendations. CONCLUSION The results of this multisite study demonstrate a reassuringly wide range of sample times for an acceptably accurate single-sample GFR result. Modified recommended single-sample times have been proposed in line with the results, and a lookup table has been produced of rms errors across the full range of GFR results for the three sample times which can be used for error reporting of a mistimed sample.
Collapse
Affiliation(s)
| | | | | | | | - Ben Fongenie
- Barts Health NHS Trust, London, UK.,Royal Free London NHS FT, London, UK
| | - Daniel R McGowan
- Oxford University Hospitals NHS FT, Oxford, UK.,University of Oxford, Oxford, UK
| | | | - Charlotte A Porter
- Leeds Teaching Hospitals NHS Trust, Leeds, UK.,Oxford University Hospitals NHS FT, Oxford, UK
| | | | - Nick Vennart
- South Tyneside and Sunderland NHS FT, Gateshead, UK
| | | |
Collapse
|
13
|
Mehranian A, Wollenweber SD, Walker MD, Bradley KM, Fielding PA, Huellner M, Kotasidis F, Su KH, Johnsen R, Jansen FP, McGowan DR. Deep learning-based time-of-flight (ToF) image enhancement of non-ToF PET scans. Eur J Nucl Med Mol Imaging 2022; 49:3740-3749. [PMID: 35507059 PMCID: PMC9399038 DOI: 10.1007/s00259-022-05824-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 04/26/2022] [Indexed: 12/02/2022]
Abstract
PURPOSE To improve the quantitative accuracy and diagnostic confidence of PET images reconstructed without time-of-flight (ToF) using deep learning models trained for ToF image enhancement (DL-ToF). METHODS A total of 273 [18F]-FDG PET scans were used, including data from 6 centres equipped with GE Discovery MI ToF scanners. PET data were reconstructed using the block-sequential-regularised-expectation-maximisation (BSREM) algorithm with and without ToF. The images were then split into training (n = 208), validation (n = 15), and testing (n = 50) sets. Three DL-ToF models were trained to transform non-ToF BSREM images to their target ToF images with different levels of DL-ToF strength (low, medium, high). The models were objectively evaluated using the testing set based on standardised uptake value (SUV) in 139 identified lesions, and in normal regions of liver and lungs. Three radiologists subjectively rated the models using testing sets based on lesion detectability, diagnostic confidence, and image noise/quality. RESULTS The non-ToF, DL-ToF low, medium, and high methods resulted in - 28 ± 18, - 28 ± 19, - 8 ± 22, and 1.7 ± 24% differences (mean; SD) in the SUVmax for the lesions in testing set, compared to ToF-BSREM image. In background lung VOIs, the SUVmean differences were 7 ± 15, 0.6 ± 12, 1 ± 13, and 1 ± 11% respectively. In normal liver, SUVmean differences were 4 ± 5, 0.7 ± 4, 0.8 ± 4, and 0.1 ± 4%. Visual inspection showed that our DL-ToF improved feature sharpness and convergence towards ToF reconstruction. Blinded clinical readings of testing sets for diagnostic confidence (scale 0-5) showed that non-ToF, DL-ToF low, medium, and high, and ToF images scored 3.0, 3.0, 4.1, 3.8, and 3.5 respectively. For this set of images, DL-ToF medium therefore scored highest for diagnostic confidence. CONCLUSION Deep learning-based image enhancement models may provide converged ToF-equivalent image quality without ToF reconstruction. In clinical scoring DL-ToF-enhanced non-ToF images (medium and high) on average scored as high as, or higher than, ToF images. The model is generalisable and hence, could be applied to non-ToF images from BGO-based PET/CT scanners.
Collapse
Affiliation(s)
| | | | - Matthew D Walker
- Department of Medical Physics and Clinical Engineering, Oxford University Hospitals NHS FT, Oxford, UK
| | - Kevin M Bradley
- Wales Research and Diagnostic PET Imaging Centre, University Hospital of Wales, Cardiff, UK
| | | | | | | | | | | | | | - Daniel R McGowan
- Department of Medical Physics and Clinical Engineering, Oxford University Hospitals NHS FT, Oxford, UK.
- Department of Oncology, University of Oxford, Oxford, UK.
| |
Collapse
|
14
|
Ralli GP, Carter RD, McGowan DR, Cheng WC, Liu D, Teoh EJ, Patel N, Gleeson F, Harris AL, Lord SR, Buffa FM, Fenwick JD. Radiogenomic analysis of primary breast cancer reveals [18F]-fluorodeoxglucose dynamic flux-constants are positively associated with immune pathways and outperform static uptake measures in associating with glucose metabolism. Breast Cancer Res 2022; 24:34. [PMID: 35581637 PMCID: PMC9115966 DOI: 10.1186/s13058-022-01529-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 05/11/2022] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND PET imaging of 18F-fluorodeoxygucose (FDG) is used widely for tumour staging and assessment of treatment response, but the biology associated with FDG uptake is still not fully elucidated. We therefore carried out gene set enrichment analyses (GSEA) of RNA sequencing data to find KEGG pathways associated with FDG uptake in primary breast cancers. METHODS Pre-treatment data were analysed from a window-of-opportunity study in which 30 patients underwent static and dynamic FDG-PET and tumour biopsy. Kinetic models were fitted to dynamic images, and GSEA was performed for enrichment scores reflecting Pearson and Spearman coefficients of correlations between gene expression and imaging. RESULTS A total of 38 pathways were associated with kinetic model flux-constants or static measures of FDG uptake, all positively. The associated pathways included glycolysis/gluconeogenesis ('GLYC-GLUC') which mediates FDG uptake and was associated with model flux-constants but not with static uptake measures, and 28 pathways related to immune-response or inflammation. More pathways, 32, were associated with the flux-constant K of the simple Patlak model than with any other imaging index. Numbers of pathways categorised as being associated with individual micro-parameters of the kinetic models were substantially fewer than numbers associated with flux-constants, and lay around levels expected by chance. CONCLUSIONS In pre-treatment images GLYC-GLUC was associated with FDG kinetic flux-constants including Patlak K, but not with static uptake measures. Immune-related pathways were associated with flux-constants and static uptake. Patlak K was associated with more pathways than were the flux-constants of more complex kinetic models. On the basis of these results Patlak analysis of dynamic FDG-PET scans is advantageous, compared to other kinetic analyses or static imaging, in studies seeking to infer tumour-to-tumour differences in biology from differences in imaging. Trial registration NCT01266486, December 24th 2010.
Collapse
Affiliation(s)
- G P Ralli
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
| | - R D Carter
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
- Doctoral Training Centre, University of Oxford, Keble Road, Oxford, OX1 3NP, UK
- Department of Physiology, Anatomy and Genetics, University of Oxford, Sherrington Road, Oxford, OX1 3PT, UK
| | - D R McGowan
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK.
- Department of Medical Physics and Clinical Engineering, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Oxford, OX3 7LE, UK.
| | - W-C Cheng
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
| | - D Liu
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
| | - E J Teoh
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
- Department of Nuclear Medicine, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Oxford, OX3 7LE, UK
- Molecular Oncology Laboratories, Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - N Patel
- Department of Nuclear Medicine, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Oxford, OX3 7LE, UK
| | - F Gleeson
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
- Department of Nuclear Medicine, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Oxford, OX3 7LE, UK
| | - A L Harris
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
- Molecular Oncology Laboratories, Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - S R Lord
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
- Molecular Oncology Laboratories, Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - F M Buffa
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
| | - J D Fenwick
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Daulby Street, Liverpool, L69 3GA, UK
| |
Collapse
|
15
|
Scott NP, Teoh EJ, Flight H, Jones BE, Niederer J, Mustata L, MacLean GM, Roy PG, Remoundos DD, Snell C, Liu C, Gleeson FV, Harris AL, Lord SR, McGowan DR. Characterising 18F-fluciclovine uptake in breast cancer through the use of dynamic PET/CT imaging. Br J Cancer 2022; 126:598-605. [PMID: 34795409 PMCID: PMC8854436 DOI: 10.1038/s41416-021-01623-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 10/25/2021] [Accepted: 10/29/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND 18F-fluciclovine is a synthetic amino acid positron emission tomography (PET) radiotracer that is approved for use in prostate cancer. In this clinical study, we characterised the kinetic model best describing the uptake of 18F-fluciclovine in breast cancer and assessed differences in tracer kinetics and static parameters for different breast cancer receptor subtypes and tumour grades. METHODS Thirty-nine patients with pathologically proven breast cancer underwent 20-min dynamic PET/computed tomography imaging following the administration of 18F-fluciclovine. Uptake into primary breast tumours was evaluated using one- and two-tissue reversible compartmental kinetic models and static parameters. RESULTS A reversible one-tissue compartment model was shown to best describe tracer uptake in breast cancer. No significant differences were seen in kinetic or static parameters for different tumour receptor subtypes or grades. Kinetic and static parameters showed a good correlation. CONCLUSIONS 18F-fluciclovine has potential in the imaging of primary breast cancer, but kinetic analysis may not have additional value over static measures of tracer uptake. CLINICAL TRIAL REGISTRATION NCT03036943.
Collapse
Affiliation(s)
- N P Scott
- Department of Oncology, University of Oxford, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - E J Teoh
- Department of Oncology, University of Oxford, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Blue Earth Diagnostics Ltd, Oxford Science Park, Oxford, UK
| | - H Flight
- Department of Oncology, University of Oxford, Oxford, UK
| | - B E Jones
- Royal Berkshire NHS Foundation Trust, Reading, UK
| | - J Niederer
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - L Mustata
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - G M MacLean
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - P G Roy
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - D D Remoundos
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - C Snell
- Mater Research, University of Queensland, Brisbane, QLD, Australia
- Mater Pathology, Mater Hospital Brisbane, Brisbane, QLD, Australia
| | - C Liu
- Mater Pathology, Mater Hospital Brisbane, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - F V Gleeson
- Department of Oncology, University of Oxford, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - A L Harris
- Department of Oncology, University of Oxford, Oxford, UK
- MRC Weatherall Institute of Molecular Medicine, Oxford, UK
| | - S R Lord
- Department of Oncology, University of Oxford, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - D R McGowan
- Department of Oncology, University of Oxford, Oxford, UK.
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
| |
Collapse
|
16
|
Mehranian A, Wollenweber SD, Walker MD, Bradley KM, Fielding PA, Su KH, Johnsen R, Kotasidis F, Jansen FP, McGowan DR. Image enhancement of whole-body oncology [ 18F]-FDG PET scans using deep neural networks to reduce noise. Eur J Nucl Med Mol Imaging 2022; 49:539-549. [PMID: 34318350 PMCID: PMC8803788 DOI: 10.1007/s00259-021-05478-x] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 06/20/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE To enhance the image quality of oncology [18F]-FDG PET scans acquired in shorter times and reconstructed by faster algorithms using deep neural networks. METHODS List-mode data from 277 [18F]-FDG PET/CT scans, from six centres using GE Discovery PET/CT scanners, were split into ¾-, ½- and ¼-duration scans. Full-duration datasets were reconstructed using the convergent block sequential regularised expectation maximisation (BSREM) algorithm. Short-duration datasets were reconstructed with the faster OSEM algorithm. The 277 examinations were divided into training (n = 237), validation (n = 15) and testing (n = 25) sets. Three deep learning enhancement (DLE) models were trained to map full and partial-duration OSEM images into their target full-duration BSREM images. In addition to standardised uptake value (SUV) evaluations in lesions, liver and lungs, two experienced radiologists scored the quality of testing set images and BSREM in a blinded clinical reading (175 series). RESULTS OSEM reconstructions demonstrated up to 22% difference in lesion SUVmax, for different scan durations, compared to full-duration BSREM. Application of the DLE models reduced this difference significantly for full-, ¾- and ½-duration scans, while simultaneously reducing the noise in the liver. The clinical reading showed that the standard DLE model with full- or ¾-duration scans provided an image quality substantially comparable to full-duration scans with BSREM reconstruction, yet in a shorter reconstruction time. CONCLUSION Deep learning-based image enhancement models may allow a reduction in scan time (or injected activity) by up to 50%, and can decrease reconstruction time to a third, while maintaining image quality.
Collapse
Affiliation(s)
| | | | | | - Kevin M Bradley
- Wales Research and Diagnostic PET Imaging Centre, University Hospital of Wales, Cardiff, UK
| | | | | | | | | | | | - Daniel R McGowan
- Oxford University Hospitals NHS FT, Oxford, UK.
- Department of Oncology, University of Oxford, Oxford, UK.
| |
Collapse
|
17
|
Bourigault P, Skwarski M, Macpherson RE, Higgins GS, McGowan DR. Investigation of atovaquone-induced spatial changes in tumour hypoxia assessed by hypoxia PET/CT in non-small cell lung cancer patients. EJNMMI Res 2021; 11:130. [PMID: 34964932 PMCID: PMC8716680 DOI: 10.1186/s13550-021-00871-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 12/03/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Tumour hypoxia promotes an aggressive tumour phenotype and enhances resistance to anticancer treatments. Following the recent observation that the mitochondrial inhibitor atovaquone increases tumour oxygenation in NSCLC, we sought to assess whether atovaquone affects tumour subregions differently depending on their level of hypoxia. METHODS Patients with resectable NSCLC participated in the ATOM trial (NCT02628080). Cohort 1 (n = 15) received atovaquone treatment, whilst cohort 2 (n = 15) did not. Hypoxia-related metrics, including change in mean tumour-to-blood ratio, tumour hypoxic volume, and fraction of hypoxic voxels, were assessed using hypoxia PET imaging. Tumours were divided into four subregions or distance categories: edge, outer, inner, and centre, using MATLAB. RESULTS Atovaquone-induced reduction in tumour hypoxia mostly occurred in the inner and outer tumour subregions, and to a lesser extent in the centre subregion. Atovaquone did not seem to act in the edge subregion, which was the only tumour subregion that was non-hypoxic at baseline. Notably, the most intensely hypoxic tumour voxels, and therefore the most radiobiologically resistant areas, were subject to the most pronounced decrease in hypoxia in the different subregions. CONCLUSIONS This study provides insights into the action of atovaquone in tumour subregions that help to better understand its role as a novel tumour radiosensitiser. TRIAL REGISTRATION ClinicalTrials.gov, NCT0262808. Registered 11th December 2015, https://clinicaltrials.gov/ct2/show/NCT02628080.
Collapse
Affiliation(s)
| | - Michael Skwarski
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
- Department of Oncology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Department of Clinical Oncology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Ruth E Macpherson
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Geoff S Higgins
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
- Department of Oncology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Daniel R McGowan
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK.
- Department of Medical Physics and Clinical Engineering, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
| |
Collapse
|
18
|
Aide N, Lasnon C, Desmonts C, Armstrong IS, Walker MD, McGowan DR. Advances in PET-CT technology: An update. Semin Nucl Med 2021; 52:286-301. [PMID: 34823841 DOI: 10.1053/j.semnuclmed.2021.10.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 10/18/2021] [Accepted: 10/19/2021] [Indexed: 11/11/2022]
Abstract
This article reviews the current evolution and future directions in PET-CT technology focusing on three areas: time of flight, image reconstruction, and data-driven gating. Image reconstruction is considered with advances in point spread function modelling, Bayesian penalised likelihood reconstruction, and artificial intelligence approaches. Data-driven gating is examined with reference to respiratory motion, cardiac motion, and head motion. For each of these technological advancements, theory will be briefly discussed, benefits of their use in routine practice will be detailed and potential future developments will be discussed. Representative clinical cases will be presented, demonstrating the huge opportunities given to the PET community by hardware and software advances in PET technology when it comes to lesion detection, disease characterization, accurate quantitation and quicker scans. Through this review, hospitals are encouraged to embrace, evaluate and appropriately implement the wide range of new PET technologies that are available now or in the near future, for the improvement of patient care.
Collapse
Affiliation(s)
- Nicolas Aide
- Nuclear Medicine, Caen University Hospital, Caen, France; INSERM ANTICIPE, Normandie University, Caen, France.
| | - Charline Lasnon
- INSERM ANTICIPE, Normandie University, Caen, France; François Baclesse Cancer Center, Caen, France
| | - Cedric Desmonts
- Nuclear Medicine, Caen University Hospital, Caen, France; INSERM ANTICIPE, Normandie University, Caen, France
| | - Ian S Armstrong
- Nuclear Medicine, Manchester University NHS Foundation Trust, Manchester
| | - Matthew D Walker
- Department of Medical Physics and Clinical Engineering, Oxford University Hospitals NHS FT, Oxford
| | - Daniel R McGowan
- Department of Medical Physics and Clinical Engineering, Oxford University Hospitals NHS FT, Oxford; Department of Oncology, University of Oxford, Oxford
| |
Collapse
|
19
|
Aide N, Lasnon C, Kesner A, Levin CS, Buvat I, Iagaru A, Hermann K, Badawi RD, Cherry SR, Bradley KM, McGowan DR. New PET technologies - embracing progress and pushing the limits. Eur J Nucl Med Mol Imaging 2021; 48:2711-2726. [PMID: 34081153 PMCID: PMC8263417 DOI: 10.1007/s00259-021-05390-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 04/25/2021] [Indexed: 12/11/2022]
Affiliation(s)
- Nicolas Aide
- Nuclear medicine Department, University Hospital, Caen, France.
- INSERM ANTICIPE, Normandie University, Caen, France.
| | - Charline Lasnon
- INSERM ANTICIPE, Normandie University, Caen, France
- François Baclesse Cancer Centre, Caen, France
| | - Adam Kesner
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Craig S Levin
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, Stanford, CA, 94305, USA
| | - Irene Buvat
- Institut Curie, Université PLS, Inserm, U1288 LITO, Orsay, France
| | - Andrei Iagaru
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Stanford University, Stanford, CA, 94305, USA
| | - Ken Hermann
- Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | - Ramsey D Badawi
- Departments of Radiology and Biomedical Engineering, University of California, Davis, CA, USA
| | - Simon R Cherry
- Departments of Radiology and Biomedical Engineering, University of California, Davis, CA, USA
| | - Kevin M Bradley
- Wales Research and Diagnostic PET Imaging Centre, Cardiff University, Cardiff, UK
| | - Daniel R McGowan
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS FT, Oxford, UK.
- Department of Oncology, University of Oxford, Oxford, UK.
| |
Collapse
|
20
|
Tran-Gia J, Denis-Bacelar AM, Ferreira KM, Robinson AP, Calvert N, Fenwick AJ, Finocchiaro D, Fioroni F, Grassi E, Heetun W, Jewitt SJ, Kotzassarlidou M, Ljungberg M, McGowan DR, Scott N, Scuffham J, Gleisner KS, Tipping J, Wevrett J, Lassmann M. A multicentre and multi-national evaluation of the accuracy of quantitative Lu-177 SPECT/CT imaging performed within the MRTDosimetry project. EJNMMI Phys 2021; 8:55. [PMID: 34297218 PMCID: PMC8302709 DOI: 10.1186/s40658-021-00397-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 06/21/2021] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Patient-specific dosimetry is required to ensure the safety of molecular radiotherapy and to predict response. Dosimetry involves several steps, the first of which is the determination of the activity of the radiopharmaceutical taken up by an organ/lesion over time. As uncertainties propagate along each of the subsequent steps (integration of the time-activity curve, absorbed dose calculation), establishing a reliable activity quantification is essential. The MRTDosimetry project was a European initiative to bring together expertise in metrology and nuclear medicine research, with one main goal of standardizing quantitative 177Lu SPECT/CT imaging based on a calibration protocol developed and tested in a multicentre inter-comparison. This study presents the setup and results of this comparison exercise. METHODS The inter-comparison included nine SPECT/CT systems. Each site performed a set of three measurements with the same setup (system, acquisition and reconstruction): (1) Determination of an image calibration for conversion from counts to activity concentration (large cylinder phantom), (2) determination of recovery coefficients for partial volume correction (IEC NEMA PET body phantom with sphere inserts), (3) validation of the established quantitative imaging setup using a 3D printed two-organ phantom (ICRP110-based kidney and spleen). In contrast to previous efforts, traceability of the activity measurement was required for each participant, and all participants were asked to calculate uncertainties for their SPECT-based activities. RESULTS Similar combinations of imaging system and reconstruction lead to similar image calibration factors. The activity ratio results of the anthropomorphic phantom validation demonstrate significant harmonization of quantitative imaging performance between the sites with all sites falling within one standard deviation of the mean values for all inserts. Activity recovery was underestimated for total kidney, spleen, and kidney cortex, while it was overestimated for the medulla. CONCLUSION This international comparison exercise demonstrates that harmonization of quantitative SPECT/CT is feasible when following very specific instructions of a dedicated calibration protocol, as developed within the MRTDosimetry project. While quantitative imaging performance demonstrates significant harmonization, an over- and underestimation of the activity recovery highlights the limitations of any partial volume correction in the presence of spill-in and spill-out between two adjacent volumes of interests.
Collapse
Affiliation(s)
- Johannes Tran-Gia
- Department of Nuclear Medicine, University of Würzburg, Oberdürrbacher Str. 6, 97080, Würzburg, Germany.
| | | | | | - Andrew P Robinson
- National Physical Laboratory, Teddington, UK
- Christie Medical Physics and Engineering (CMPE), The Christie NHS Foundation Trust, Manchester, UK
- The University of Manchester, Manchester, UK
| | - Nicholas Calvert
- Christie Medical Physics and Engineering (CMPE), The Christie NHS Foundation Trust, Manchester, UK
| | - Andrew J Fenwick
- National Physical Laboratory, Teddington, UK
- Cardiff University, Cardiff, UK
| | - Domenico Finocchiaro
- Medical Physics Unit, Azienda Unità Sanitaria Locale di Reggio Emilia-IRCCS, Reggio Emilia, Italy
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy
| | - Federica Fioroni
- Medical Physics Unit, Azienda Unità Sanitaria Locale di Reggio Emilia-IRCCS, Reggio Emilia, Italy
| | - Elisa Grassi
- Medical Physics Unit, Azienda Unità Sanitaria Locale di Reggio Emilia-IRCCS, Reggio Emilia, Italy
| | | | - Stephanie J Jewitt
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Maria Kotzassarlidou
- Nuclear Medicine Department, "THEAGENIO" Anticancer Hospital, Thessaloniki, Greece
| | | | - Daniel R McGowan
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Department of Oncology, University of Oxford, Oxford, UK
| | - Nathaniel Scott
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - James Scuffham
- National Physical Laboratory, Teddington, UK
- Royal Surrey County Hospital, Guildford, UK
- Department of Physics, University of Surrey, Guildford, UK
| | | | - Jill Tipping
- Christie Medical Physics and Engineering (CMPE), The Christie NHS Foundation Trust, Manchester, UK
| | - Jill Wevrett
- National Physical Laboratory, Teddington, UK
- Royal Surrey County Hospital, Guildford, UK
- Department of Physics, University of Surrey, Guildford, UK
| | - Michael Lassmann
- Department of Nuclear Medicine, University of Würzburg, Oberdürrbacher Str. 6, 97080, Würzburg, Germany
| |
Collapse
|
21
|
Bradley KM, Deller TW, Spangler-Bickell MG, Jansen FP, McGowan DR. Correction to: A solution to PET brain motion artefact. J Neurol 2021; 268:3478-3479. [PMID: 34125268 DOI: 10.1007/s00415-021-10653-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Kevin M Bradley
- Oxford University Hospitals NHS FT, Oxford, UK
- Wales Research and Diagnostic PET Imaging Centre, University Hospital of Wales, Cardiff, UK
| | | | | | | | - Daniel R McGowan
- Oxford University Hospitals NHS FT, Oxford, UK.
- Department of Oncology, University of Oxford, Oxford, UK.
| |
Collapse
|
22
|
Bradley KM, Deller TW, Spangler-Bickell MG, Jansen FP, McGowan DR. A solution to PET brain motion artefact. J Neurol 2021; 268:3476-3477. [PMID: 34091746 DOI: 10.1007/s00415-021-10632-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 05/27/2021] [Accepted: 05/28/2021] [Indexed: 11/27/2022]
Affiliation(s)
- Kevin M Bradley
- Oxford University Hospitals NHS FT, Oxford, UK.,Wales Research and Diagnostic PET Imaging Centre, University Hospital of Wales, Cardiff, UK
| | | | | | | | - Daniel R McGowan
- Oxford University Hospitals NHS FT, Oxford, UK. .,Department of Oncology, University of Oxford, Oxford, UK.
| |
Collapse
|
23
|
Skwarski M, McGowan DR, Belcher E, Di Chiara F, Stavroulias D, McCole M, Derham JL, Chu KY, Teoh E, Chauhan J, O'Reilly D, Harris BHL, Macklin PS, Bull JA, Green M, Rodriguez-Berriguete G, Prevo R, Folkes LK, Campo L, Ferencz P, Croal PL, Flight H, Qi C, Holmes J, O'Connor JPB, Gleeson FV, McKenna WG, Harris AL, Bulte D, Buffa FM, Macpherson RE, Higgins GS. Mitochondrial Inhibitor Atovaquone Increases Tumor Oxygenation and Inhibits Hypoxic Gene Expression in Patients with Non-Small Cell Lung Cancer. Clin Cancer Res 2021; 27:2459-2469. [PMID: 33597271 PMCID: PMC7611473 DOI: 10.1158/1078-0432.ccr-20-4128] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 01/17/2021] [Accepted: 02/11/2021] [Indexed: 01/11/2023]
Abstract
PURPOSE Tumor hypoxia fuels an aggressive tumor phenotype and confers resistance to anticancer treatments. We conducted a clinical trial to determine whether the antimalarial drug atovaquone, a known mitochondrial inhibitor, reduces hypoxia in non-small cell lung cancer (NSCLC). PATIENTS AND METHODS Patients with NSCLC scheduled for surgery were recruited sequentially into two cohorts: cohort 1 received oral atovaquone at the standard clinical dose of 750 mg twice daily, while cohort 2 did not. Primary imaging endpoint was change in tumor hypoxic volume (HV) measured by hypoxia PET-CT. Intercohort comparison of hypoxia gene expression signatures using RNA sequencing from resected tumors was performed. RESULTS Thirty patients were evaluable for hypoxia PET-CT analysis, 15 per cohort. Median treatment duration was 12 days. Eleven (73.3%) atovaquone-treated patients had meaningful HV reduction, with median change -28% [95% confidence interval (CI), -58.2 to -4.4]. In contrast, median change in untreated patients was +15.5% (95% CI, -6.5 to 35.5). Linear regression estimated the expected mean HV was 55% (95% CI, 24%-74%) lower in cohort 1 compared with cohort 2 (P = 0.004), adjusting for cohort, tumor volume, and baseline HV. A key pharmacodynamics endpoint was reduction in hypoxia-regulated genes, which were significantly downregulated in atovaquone-treated tumors. Data from multiple additional measures of tumor hypoxia and perfusion are presented. No atovaquone-related adverse events were reported. CONCLUSIONS This is the first clinical evidence that targeting tumor mitochondrial metabolism can reduce hypoxia and produce relevant antitumor effects at the mRNA level. Repurposing atovaquone for this purpose may improve treatment outcomes for NSCLC.
Collapse
Affiliation(s)
- Michael Skwarski
- Department of Oncology, University of Oxford, Oxford, England, United Kingdom
- Department of Oncology, Oxford University Hospitals National Health Service Foundation Trust, Oxford, England, United Kingdom
| | - Daniel R McGowan
- Department of Oncology, University of Oxford, Oxford, England, United Kingdom
- Radiation Physics and Protection, Oxford University Hospitals National Health Service Foundation Trust, Oxford, England, United Kingdom
| | - Elizabeth Belcher
- Department of Cardiothoracic Surgery, Oxford University Hospitals National Health Service Foundation Trust, Oxford, England, United Kingdom
| | - Francesco Di Chiara
- Department of Cardiothoracic Surgery, Oxford University Hospitals National Health Service Foundation Trust, Oxford, England, United Kingdom
| | - Dionisios Stavroulias
- Department of Cardiothoracic Surgery, Oxford University Hospitals National Health Service Foundation Trust, Oxford, England, United Kingdom
| | - Mark McCole
- Department of Cellular Pathology, Oxford University Hospitals National Health Service Foundation Trust, Oxford, England, United Kingdom
| | - Jennifer L Derham
- Department of Oncology, Oxford University Hospitals National Health Service Foundation Trust, Oxford, England, United Kingdom
| | - Kwun-Ye Chu
- Department of Oncology, University of Oxford, Oxford, England, United Kingdom
- Department of Oncology, Oxford University Hospitals National Health Service Foundation Trust, Oxford, England, United Kingdom
| | - Eugene Teoh
- Department of Oncology, Oxford University Hospitals National Health Service Foundation Trust, Oxford, England, United Kingdom
| | - Jagat Chauhan
- Ludwig Institute for Cancer Research Oxford, University of Oxford, Oxford, England, United Kingdom
| | - Dawn O'Reilly
- Department of Oncology, University of Oxford, Oxford, England, United Kingdom
| | - Benjamin H L Harris
- Department of Oncology, University of Oxford, Oxford, England, United Kingdom
| | - Philip S Macklin
- Nuffield Department of Medicine, University of Oxford, Oxford, England, United Kingdom
| | - Joshua A Bull
- Wolfson Centre for Mathematical Biology, University of Oxford, Oxford, England, United Kingdom
| | - Marcus Green
- Department of Oncology, University of Oxford, Oxford, England, United Kingdom
| | | | - Remko Prevo
- Department of Oncology, University of Oxford, Oxford, England, United Kingdom
| | - Lisa K Folkes
- Department of Oncology, University of Oxford, Oxford, England, United Kingdom
| | - Leticia Campo
- Department of Oncology, University of Oxford, Oxford, England, United Kingdom
| | - Petra Ferencz
- Institute of Biomedical Engineering, University of Oxford, Oxford, England, United Kingdom
| | - Paula L Croal
- Institute of Biomedical Engineering, University of Oxford, Oxford, England, United Kingdom
| | - Helen Flight
- Oncology Clinical Trials Office, Department of Oncology, University of Oxford, Oxford, England, United Kingdom
| | - Cathy Qi
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, England, United Kingdom
| | - Jane Holmes
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, England, United Kingdom
| | - James P B O'Connor
- Division of Cancer Sciences, University of Manchester, Manchester, England, United Kingdom
| | - Fergus V Gleeson
- Department of Radiology, Oxford University Hospitals National Health Service Foundation Trust, Oxford, England, United Kingdom
| | - W Gillies McKenna
- Department of Oncology, University of Oxford, Oxford, England, United Kingdom
| | - Adrian L Harris
- Department of Oncology, University of Oxford, Oxford, England, United Kingdom
| | - Daniel Bulte
- Institute of Biomedical Engineering, University of Oxford, Oxford, England, United Kingdom
| | - Francesca M Buffa
- Department of Oncology, University of Oxford, Oxford, England, United Kingdom
| | - Ruth E Macpherson
- Department of Radiology, Oxford University Hospitals National Health Service Foundation Trust, Oxford, England, United Kingdom
| | - Geoff S Higgins
- Department of Oncology, University of Oxford, Oxford, England, United Kingdom.
- Department of Oncology, Oxford University Hospitals National Health Service Foundation Trust, Oxford, England, United Kingdom
| |
Collapse
|
24
|
Arnaldi D, Chincarini A, Hu MT, Sonka K, Boeve B, Miyamoto T, Puligheddu M, De Cock VC, Terzaghi M, Plazzi G, Tachibana N, Morbelli S, Rolinski M, Dusek P, Lowe V, Miyamoto M, Figorilli M, de Verbizier D, Bossert I, Antelmi E, Meli R, Barber TR, Trnka J, Miyagawa T, Serra A, Pizza F, Bauckneht M, Bradley KM, Zogala D, McGowan DR, Jordan L, Manni R, Nobili F. Dopaminergic imaging and clinical predictors for phenoconversion of REM sleep behaviour disorder. Brain 2021; 144:278-287. [PMID: 33348363 PMCID: PMC8599912 DOI: 10.1093/brain/awaa365] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 08/01/2020] [Accepted: 08/13/2020] [Indexed: 11/15/2022] Open
Abstract
This is an international multicentre study aimed at evaluating the combined value of dopaminergic neuroimaging and clinical features in predicting future phenoconversion of idiopathic REM sleep behaviour (iRBD) subjects to overt synucleinopathy. Nine centres sent 123I-FP-CIT-SPECT data of 344 iRBD patients and 256 controls for centralized analysis. 123I-FP-CIT-SPECT images were semiquantified using DaTQUANTTM, obtaining putamen and caudate specific to non-displaceable binding ratios (SBRs). The following clinical variables were also analysed: (i) Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale, motor section score; (ii) Mini-Mental State Examination score; (iii) constipation; and (iv) hyposmia. Kaplan-Meier survival analysis was performed to estimate conversion risk. Hazard ratios for each variable were calculated with Cox regression. A generalized logistic regression model was applied to identify the best combination of risk factors. Bayesian classifier was used to identify the baseline features predicting phenoconversion to parkinsonism or dementia. After quality check of the data, 263 iRBD patients (67.6 ± 7.3 years, 229 males) and 243 control subjects (67.2 ± 10.1 years, 110 males) were analysed. Fifty-two (20%) patients developed a synucleinopathy after average follow-up of 2 years. The best combination of risk factors was putamen dopaminergic dysfunction of the most affected hemisphere on imaging, defined as the lower value between either putamina (P < 0.000001), constipation, (P < 0.000001) and age over 70 years (P = 0.0002). Combined features obtained from the generalized logistic regression achieved a hazard ratio of 5.71 (95% confidence interval 2.85-11.43). Bayesian classifier suggested that patients with higher Mini-Mental State Examination score and lower caudate SBR asymmetry were more likely to develop parkinsonism, while patients with the opposite pattern were more likely to develop dementia. This study shows that iRBD patients older than 70 with constipation and reduced nigro-putaminal dopaminergic function are at high risk of short-term phenoconversion to an overt synucleinopathy, providing an effective stratification approach for future neuroprotective trials. Moreover, we provide cut-off values for the significant predictors of phenoconversion to be used in single subjects.
Collapse
Affiliation(s)
- Dario Arnaldi
- Clinical Neurology, Department of Neuroscience (DINOGMI), University of Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Andrea Chincarini
- National Institute of Nuclear Physics (INFN), Genoa section, Genoa, Italy
| | - Michele T Hu
- Oxford Parkinson’s Disease Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Karel Sonka
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Bradley Boeve
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Tomoyuki Miyamoto
- Department of Neurology, Dokkyo Medical University Saitama Medical Centre, Saitama, Japan
| | - Monica Puligheddu
- Sleep Disorder Centre, Department of Medical Sciences and Public Health, University of Cagliari, Italy
| | - Valérie Cochen De Cock
- Department of Sleep and Neurology, Beau Soleil Clinic, and EuroMov Digital Health in Motion, University of Montpellier, Montpellier, France
| | - Michele Terzaghi
- Unit of Sleep Medicine and Epilepsy, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Giuseppe Plazzi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Naoko Tachibana
- Division of Sleep Medicine, Kansai Electric Power Medical Research Institute, Osaka, Japan
| | - Silvia Morbelli
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Nuclear Medicine, Department of Health Sciences (DISSAL), University of Genoa, Italy
| | - Michal Rolinski
- Oxford Parkinson’s Disease Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
- Institute of Clinical Neurosciences, University of Bristol, Bristol, UK
| | - Petr Dusek
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Val Lowe
- Department of Nuclear Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Masayuki Miyamoto
- Centre of Sleep Medicine, Dokkyo Medical University Hospital, Tochigi, Japan
| | - Michela Figorilli
- Sleep Disorder Centre, Department of Medical Sciences and Public Health, University of Cagliari, Italy
| | | | - Irene Bossert
- Nuclear Medicine Unit, ICS Maugeri SpA SB IRCCS, Pavia, Italy
| | - Elena Antelmi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- Neurology Unit, Movement Disorders Division, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Riccardo Meli
- Clinical Neurology, Department of Neuroscience (DINOGMI), University of Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Thomas R Barber
- Oxford Parkinson’s Disease Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Jiří Trnka
- Institute of Nuclear Medicine, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Toji Miyagawa
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Alessandra Serra
- Nuclear Medicine Unit, Department of Medical Science and Public Health, University of Cagliari, Cagliari, Italy
| | - Fabio Pizza
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Matteo Bauckneht
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Nuclear Medicine, Department of Health Sciences (DISSAL), University of Genoa, Italy
| | | | - David Zogala
- Institute of Nuclear Medicine, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Daniel R McGowan
- Radiation Physics and Protection Department, Churchill Hospital, Oxford, UK
| | - Lennon Jordan
- Department of Nuclear Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Raffaele Manni
- Unit of Sleep Medicine and Epilepsy, IRCCS Mondino Foundation, Pavia, Italy
| | - Flavio Nobili
- Clinical Neurology, Department of Neuroscience (DINOGMI), University of Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| |
Collapse
|
25
|
Walker MD, Bradley KM, McGowan DR. Data-Driven Respiratory Motion Correction in Clinical PET - A Turning Point. J Nucl Med 2020; 62:jnumed.120.257022. [PMID: 33443091 DOI: 10.2967/jnumed.120.257022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 12/17/2020] [Indexed: 11/16/2022] Open
Affiliation(s)
- Matthew D Walker
- Oxford University Hospitals NHS Foundation Trust, United Kingdom
| | | | - Daniel R McGowan
- Oxford University Hospitals NHS Foundation Trust, United Kingdom
| |
Collapse
|
26
|
Abbott EM, Falzone N, Lee BQ, Kartsonaki C, Winter H, Greenhalgh TA, McGowan DR, Syed N, Denis-Bacelar AM, Boardman P, Sharma RA, Vallis KA. The Impact of Radiobiologically Informed Dose Prescription on the Clinical Benefit of 90Y SIRT in Colorectal Cancer Patients. J Nucl Med 2020; 61:1658-1664. [PMID: 32358093 DOI: 10.2967/jnumed.119.233650] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Accepted: 03/16/2020] [Indexed: 12/31/2022] Open
Abstract
The purpose of this study was to establish the dose-response relationship of selective internal radiation therapy (SIRT) in patients with metastatic colorectal cancer (mCRC), when informed by radiobiological sensitivity parameters derived from mCRC cell lines exposed to 90Y. Methods: Twenty-three mCRC patients with liver metastases refractory to chemotherapy were included. 90Y bremsstrahlung SPECT images were transformed into dose maps assuming the local dose deposition method. Baseline and follow-up CT scans were segmented to derive liver and tumor volumes. Mean, median, and D70 (minimum dose to 70% of tumor volume) values determined from dose maps were correlated with change in tumor volume and volumetric RECIST response using linear and logistic regression, respectively. Radiosensitivity parameters determined by clonogenic assays of mCRC cell lines HT-29 and DLD-1 after exposure to 90Y or external beam radiotherapy (EBRT; 6 MV photons) were used in biologically effective dose (BED) calculations. Results: Mean administered radioactivity was 1,469 ± 428 MBq (range, 847-2,185 MBq), achieving a mean absorbed radiation dose to tumor of 35.5 ± 9.4 Gy and mean normal liver dose of 26.4 ± 6.8 Gy. A 1.0 Gy increase in mean, median, and D70 absorbed dose was associated with a reduction in tumor volume of 1.8%, 1.8%, and 1.5%, respectively, and an increased probability of a volumetric RECIST response (odds ratio, 1.09, 1.09, and 1.10, respectively). Threshold mean, median and D70 doses for response were 48.3, 48.8, and 41.8 Gy, respectively. EBRT-equivalent BEDs for 90Y are up to 50% smaller than those calculated by applying protraction-corrected radiobiological parameters derived from EBRT alone. Conclusion: Dosimetric studies have assumed equivalence between 90Y SIRT and EBRT, leading to inflation of BED for SIRT and possible undertreatment. Radiobiological parameters for 90Y were applied to a BED model, providing a calculation method that has the potential to improve assessment of tumor control.
Collapse
Affiliation(s)
- Elliot M Abbott
- Oxford Institute for Radiation Oncology, Department of Oncology, Oxford University, Oxford, United Kingdom
| | - Nadia Falzone
- Oxford Institute for Radiation Oncology, Department of Oncology, Oxford University, Oxford, United Kingdom
| | - Boon Q Lee
- Oxford Institute for Radiation Oncology, Department of Oncology, Oxford University, Oxford, United Kingdom
| | | | - Helen Winter
- Department of Oncology, Oxford University, Oxford, United Kingdom
| | | | - Daniel R McGowan
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Nigar Syed
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | | | - Philip Boardman
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Ricky A Sharma
- Radiation Oncology, University College London, London, United Kingdom
| | - Katherine A Vallis
- Oxford Institute for Radiation Oncology, Department of Oncology, Oxford University, Oxford, United Kingdom
| |
Collapse
|
27
|
Walker MD, Morgan AJ, Bradley KM, McGowan DR. Data-Driven Respiratory Gating Outperforms Device-Based Gating for Clinical 18F-FDG PET/CT. J Nucl Med 2020; 61:1678-1683. [PMID: 32245898 DOI: 10.2967/jnumed.120.242248] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 03/16/2020] [Indexed: 11/16/2022] Open
Abstract
A data-driven method for respiratory gating in PET has recently been commercially developed. We sought to compare the performance of the algorithm with an external, device-based system for oncologic 18F-FDG PET/CT imaging. Methods: In total, 144 whole-body 18F-FDG PET/CT examinations were acquired, with a respiratory gating waveform recorded by an external, device-based respiratory gating system. In each examination, 2 of the bed positions covering the liver and lung bases were acquired with a duration of 6 min. Quiescent-period gating retaining approximately 50% of coincidences was then able to produce images with an effective duration of 3 min for these 2 bed positions, matching the other bed positions. For each examination, 4 reconstructions were performed and compared: data-driven gating (DDG) (we use the term DDG-retro to distinguish that we did not use the real-time R-threshold-based application of DDG that is available within the manufacturer's product), external device-based gating (real-time position management (RPM)-gated), no gating but using only the first 3 min of data (ungated-matched), and no gating retaining all coincidences (ungated-full). Lesions in the images were quantified and image quality scored by a radiologist who was masked to the method of data processing. Results: Compared with the other reconstruction options, DDG-retro increased the SUVmax and decreased the threshold-defined lesion volume. Compared with RPM-gated, DDG-retro gave an average increase in SUVmax of 0.66 ± 0.1 g/mL (n = 87, P < 0.0005). Although the results from the masked image evaluation were most commonly equivalent, DDG-retro was preferred over RPM-gated in 13% of examinations, whereas the opposite occurred in just 2% of examinations. This was a significant preference for DDG-retro (P = 0.008, n = 121). Liver lesions were identified in 23 examinations. Considering this subset of data, DDG-retro was ranked superior to ungated-full in 6 of 23 (26%) cases. Gated reconstruction using the external device failed in 16% of examinations, whereas DDG-retro always provided a clinically acceptable image. Conclusion: In this clinical evaluation, DDG-retro provided performance superior to that of the external device-based system. For most examinations the performance was equivalent, but DDG-retro had superior performance in 13% of examinations, leading to a significant preference overall.
Collapse
Affiliation(s)
- Matthew D Walker
- Radiation Physics and Protection, Oxford University Hospitals NHS FT, Oxford, United Kingdom
| | - Andrew J Morgan
- Radiation Physics and Protection, Oxford University Hospitals NHS FT, Oxford, United Kingdom
| | - Kevin M Bradley
- Department of Radiology, Churchill Hospital, Oxford, United Kingdom.,Wales Research and Diagnostic PET Imaging Centre, Cardiff University, Cardiff, United Kingdom; and
| | - Daniel R McGowan
- Radiation Physics and Protection, Oxford University Hospitals NHS FT, Oxford, United Kingdom.,Department of Oncology, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
28
|
Barber TR, Griffanti L, Bradley KM, McGowan DR, Lo C, Mackay CE, Hu MT, Klein JC. Nigrosome 1 imaging in REM sleep behavior disorder and its association with dopaminergic decline. Ann Clin Transl Neurol 2019; 7:26-35. [PMID: 31820587 PMCID: PMC6952317 DOI: 10.1002/acn3.50962] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 10/18/2019] [Accepted: 11/08/2019] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVES Rapid eye movement sleep behavior disorder (RBD) patients have a high risk of developing a Parkinsonian disorder, offering an opportunity for neuroprotective intervention. Predicting near-term conversion, however, remains a challenge. Dopamine transporter imaging, while informative, is expensive and not widely available. Here, we investigate the utility of susceptibility-weighted MRI (SWI) to detect abnormalities of the substantia nigra in RBD, and explore their association with striatal dopaminergic deficits. METHODS SWI of the substantia nigra was performed in 46 RBD patients, 27 Parkinson's patients, and 32 control subjects. Dorsal nigral hyperintensity (DNH) was scored by two blinded raters, and separately quantified using a semiautomated process. Forty-two RBD patients were also imaged with 123 I-ioflupane single-photon emission computed tomography (DaT SPECT/CT). RESULTS Consensus visual DNH classification was possible in 87% of participants. 27.5% of RBD patients had lost DNH, compared with 7.7% of control subjects and 96% of Parkinson's patients. RBD patients lacking DNH had significantly lower putamen dopaminergic SPECT/CT activity compared to RBD patients with DNH present (specific uptake ratios 1.89 vs. 2.33, P = 0.002). The mean quantified DNH signal intensity declined in a stepwise pattern, with RBD patients having lower intensity than controls (0.837 vs. 0.877, P = 0.01) but higher than PD patients (0.837 vs. 0.765, P < 0.001). INTERPRETATION Over one quarter of RBD patients have abnormal substantia nigra SWI reminiscent of Parkinson's, which is associated with a greater dopaminergic deficit. This modality may help enrich neuroprotective trials with early converters.
Collapse
Affiliation(s)
- Thomas R. Barber
- Oxford Parkinson’s Disease CentreOxfordUnited Kingdom
- Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
- Oxford Centre for Human Brain ActivityWellcome Centre for Integrative NeuroimagingDepartment of PsychiatryUniversity of OxfordOxfordUnited Kingdom
| | - Ludovica Griffanti
- Oxford Parkinson’s Disease CentreOxfordUnited Kingdom
- Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
- Oxford Centre for Functional MRI of the BrainWellcome Centre for Integrative NeuroimagingNuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| | | | - Daniel R. McGowan
- Radiation Physics & Protection DepartmentChurchill HospitalOxfordUnited Kingdom
| | - Christine Lo
- Oxford Parkinson’s Disease CentreOxfordUnited Kingdom
- Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| | - Clare E. Mackay
- Oxford Parkinson’s Disease CentreOxfordUnited Kingdom
- Oxford Centre for Human Brain ActivityWellcome Centre for Integrative NeuroimagingDepartment of PsychiatryUniversity of OxfordOxfordUnited Kingdom
| | - Michele T. Hu
- Oxford Parkinson’s Disease CentreOxfordUnited Kingdom
- Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| | - Johannes C. Klein
- Oxford Parkinson’s Disease CentreOxfordUnited Kingdom
- Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
- Oxford Centre for Human Brain ActivityWellcome Centre for Integrative NeuroimagingDepartment of PsychiatryUniversity of OxfordOxfordUnited Kingdom
- Oxford Centre for Functional MRI of the BrainWellcome Centre for Integrative NeuroimagingNuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| |
Collapse
|
29
|
McGowan DR, Skwarski M, Higgins GS. Reply to 'The use of buparlisib as a radiosensitiser: What about toxicity?'. Eur J Cancer 2019; 119:196-197. [PMID: 31427118 DOI: 10.1016/j.ejca.2019.07.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 07/11/2019] [Indexed: 11/26/2022]
Affiliation(s)
- Daniel R McGowan
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
| | - Michael Skwarski
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
| | - Geoff S Higgins
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK.
| |
Collapse
|
30
|
Barber TR, Griffanti L, Muhammed K, Drew DS, Bradley KM, McGowan DR, Crabbe M, Lo C, Mackay CE, Husain M, Hu MT, Klein JC. Apathy in rapid eye movement sleep behaviour disorder is associated with serotonin depletion in the dorsal raphe nucleus. Brain 2019; 141:2848-2854. [PMID: 30212839 PMCID: PMC6158712 DOI: 10.1093/brain/awy240] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 08/01/2018] [Indexed: 11/13/2022] Open
Abstract
Apathy is a common and under-recognized disorder that often emerges in the prodromal phase of Parkinsonian diseases. The mechanism by which this occurs is not known, but recent evidence from patients with established Parkinson's disease suggests that serotonergic dysfunction may play a role. The integrity of the raphe serotonergic system can be assessed alongside dopaminergic basal ganglia imaging using the radioligand 123I-ioflupane, which binds both serotonin and dopamine transporters. To investigate the relative roles of these neurotransmitters in prodromal parkinsonism, we imaged patients with idiopathic rapid eye movement sleep behaviour disorder, the majority of whom will develop a parkinsonian disorder in future. Forty-three patients underwent brain imaging with 123I-ioflupane single photon emission computed tomography and structural MRI. Apathy was quantified using the Lille Apathy Rating Scale. Other clinical parkinsonian features were assessed using standard measures. A negative correlation was observed between apathy severity and serotonergic 123I-ioflupane signal in the dorsal raphe nucleus (r = -0.55, P < 0.001). There was no significant correlation between apathy severity and basal ganglia dopaminergic signal, nor between dorsal raphe signal and other neuropsychiatric scores. This specific association between apathy and raphe 123I-ioflupane signal suggests that the serotonergic system might represent a target for the treatment of apathy.
Collapse
Affiliation(s)
- Thomas R Barber
- Oxford Parkinson's Disease Centre, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Ludovica Griffanti
- Oxford Parkinson's Disease Centre, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,Oxford Centre for Functional MRI of the Brain, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Kinan Muhammed
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Daniel S Drew
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,Department of Experimental Psychology, University of Oxford, Oxford, UK
| | | | - Daniel R McGowan
- Radiation Physics and Protection Department, Churchill Hospital, Oxford, UK
| | - Marie Crabbe
- Oxford Parkinson's Disease Centre, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Christine Lo
- Oxford Parkinson's Disease Centre, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Clare E Mackay
- Oxford Parkinson's Disease Centre, Oxford, UK.,Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Masud Husain
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Michele T Hu
- Oxford Parkinson's Disease Centre, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Johannes C Klein
- Oxford Parkinson's Disease Centre, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK.,Oxford Centre for Functional MRI of the Brain, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| |
Collapse
|
31
|
Scott NP, McGowan DR. Optimising quantitative 90Y PET imaging: an investigation into the effects of scan length and Bayesian penalised likelihood reconstruction. EJNMMI Res 2019; 9:40. [PMID: 31076913 PMCID: PMC6510762 DOI: 10.1186/s13550-019-0512-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 04/23/2019] [Indexed: 01/03/2023] Open
Abstract
Background Positron emission tomography (PET) imaging of 90Y following selective internal radiation therapy (SIRT) is possible, but image quality is poor, and therefore, accurate quantification and dosimetry are challenging. This study aimed to quantitatively optimise 90Y PET imaging using a new Bayesian penalised likelihood (BPL) reconstruction algorithm (Q.Clear, GE Healthcare). The length of time per bed was also investigated to study its impact on quantification accuracy. Methods A NEMA IQ phantom with an 8:1 sphere-to-background ratio was scanned overnight on a GE Discovery 710 PET/CT scanner. Datasets were rebinned into varying lengths of time (5–60 min); the 15-min rebins were reconstructed using BPL reconstruction with a range of noise penalisation weighting factors (beta values). The metrics of contrast recovery (CR), background variability (BV), and recovered activity percentage (RAP) were calculated in order to identify the optimum beta value. Reconstructions were then carried out on the rest of the timing datasets using the optimised beta value; the same metrics were used to assess the quantification accuracy of the reconstructed images. Results A beta value of 1000 produced the highest CR and RAP (76% and 73%, 37 mm sphere) without overly accentuating the noise (BV) in the image. There was no statistically significant increase (p < 0.05) in either the CR or RAP for scan times of > 15 min. For the 5-min acquisitions, there was a statistically significant decrease in RAP (28 mm sphere, p < 0.01) when compared to the 15-min acquisition. Conclusion Our results indicate that an acquisition length of 15 min and beta value of 1000 (when using Q.Clear reconstruction) are optimum for quantitative 90Y PET imaging. Increasing the acquisition time to more than 15 min reduces the image noise but has no significant impact on image quantification. Electronic supplementary material The online version of this article (10.1186/s13550-019-0512-y) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Nathaniel P Scott
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Old Road, Oxford, OX37LE, UK. .,Department of Oncology, University of Oxford, Old Road Campus Research Building, Oxford, UK.
| | - Daniel R McGowan
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Old Road, Oxford, OX37LE, UK.,Department of Oncology, University of Oxford, Old Road Campus Research Building, Oxford, UK
| |
Collapse
|
32
|
McGowan DR, Skwarski M, Bradley KM, Campo L, Fenwick JD, Gleeson FV, Green M, Horne A, Maughan TS, McCole MG, Mohammed S, Muschel RJ, Ng SM, Panakis N, Prevo R, Strauss VY, Stuart R, Tacconi EMC, Vallis KA, McKenna WG, Macpherson RE, Higgins GS. Buparlisib with thoracic radiotherapy and its effect on tumour hypoxia: A phase I study in patients with advanced non-small cell lung carcinoma. Eur J Cancer 2019; 113:87-95. [PMID: 30991262 PMCID: PMC6522060 DOI: 10.1016/j.ejca.2019.03.015] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Accepted: 03/11/2019] [Indexed: 01/08/2023]
Abstract
BACKGROUND Pre-clinically, phosphoinositide 3-kinase (PI3K) inhibition radiosensitises tumours by increasing intrinsic radiosensitivity and by reducing tumour hypoxia. We assessed whether buparlisib, a class 1 PI3K inhibitor, can be safely combined with radiotherapy in patients with non-small cell lung carcinoma (NSCLC) and investigated its effect on tumour hypoxia. METHODS This was a 3 + 3 dose escalation and dose expansion phase I trial in patients with advanced NSCLC. Buparlisib dose levels were 50 mg, 80 mg and 100 mg once daily orally for 2 weeks, with palliative thoracic radiotherapy (20 Gy in 5 fractions) delivered during week 2. Tumour hypoxic volume (HV) was measured using 18F-fluoromisonidazole positron-emission tomography-computed tomography at baseline and following 1 week of buparlisib. RESULTS Twenty-one patients were recruited with 9 patients evaluable for maximum tolerated dose (MTD) analysis. No dose-limiting toxicity was reported; therefore, 100 mg was declared the MTD, and 10 patients received this dose in the expansion phase. Ninety-four percent of treatment-related adverse events were ≤grade 2 with fatigue (67%), nausea (24%) and decreased appetite (19%) most common per patient. One serious adverse event (grade 3 hypoalbuminaemia) was possibly related to buparlisib. No unexpected radiotherapy toxicity was reported. Ten (67%) of 15 patients evaluable for imaging analysis were responders with 20% median reduction in HV at the MTD. CONCLUSION This is the first clinical trial to combine a PI3K inhibitor with radiotherapy in NSCLC and investigate the effects of PI3K inhibition on tumour hypoxia. This combination was well tolerated and PI3K inhibition reduced hypoxia, warranting investigation into whether this novel class of radiosensitisers can improve radiotherapy outcomes.
Collapse
Affiliation(s)
- Daniel R McGowan
- Department of Oncology, University of Oxford, Oxford, United Kingdom; Radiation Physics and Protection, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Michael Skwarski
- Department of Oncology, University of Oxford, Oxford, United Kingdom; Department of Oncology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Kevin M Bradley
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Leticia Campo
- Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - John D Fenwick
- Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Fergus V Gleeson
- Department of Oncology, University of Oxford, Oxford, United Kingdom; Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Marcus Green
- Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Amanda Horne
- Department of Oncology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Timothy S Maughan
- Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Mark G McCole
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Seid Mohammed
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom
| | - Ruth J Muschel
- Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Stasya M Ng
- Oncology Clinical Trials Office, Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Niki Panakis
- Department of Oncology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Remko Prevo
- Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Victoria Y Strauss
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom
| | - Robert Stuart
- Department of Oncology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | | | - Katherine A Vallis
- Department of Oncology, University of Oxford, Oxford, United Kingdom; Department of Oncology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - W Gillies McKenna
- Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Ruth E Macpherson
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Geoff S Higgins
- Department of Oncology, University of Oxford, Oxford, United Kingdom; Department of Oncology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
| |
Collapse
|
33
|
Walker MD, Morgan AJ, Bradley KM, McGowan DR. Evaluation of data-driven respiratory gating waveforms for clinical PET imaging. EJNMMI Res 2019; 9:1. [PMID: 30607651 PMCID: PMC6318161 DOI: 10.1186/s13550-018-0470-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Accepted: 12/18/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND We aimed to evaluate the clinical robustness of a commercially developed data-driven respiratory gating algorithm based on principal component analysis, for use in routine PET imaging. METHODS One hundred fifty-seven adult FDG PET examinations comprising a total of 1149 acquired bed positions were used for the assessment. These data are representative of FDG scans currently performed at our institution. Data were acquired for 4 min/bed position (3 min/bed for legs). The data-driven gating (DDG) algorithm was applied to each bed position, including those where minimal respiratory motion was expected. The algorithm provided a signal-to-noise measure of respiratory-like frequencies within the data, denoted as R. Qualitative evaluation was performed by visual examination of the waveforms, with each waveform scored on a 3-point scale by two readers and then averaged (score S of 0 = no respiratory signal, 1 = some respiratory-like signal but indeterminate, 2 = acceptable signal considered to be respiratory). Images were reconstructed using quiescent period gating and compared with non-gated images reconstructed with a matched number of coincidences. If present, the SUVmax of a well-defined lesion in the thorax or abdomen was measured and compared between the two reconstructions. RESULTS There was a strong (r = 0.86) and significant correlation between R and scores S. Eighty-six percent of waveforms with R ≥ 15 were scored as acceptable for respiratory gating. On average, there were 1.2 bed positions per patient examination with R ≥ 15. Waveforms with high R and S were found to originate from bed positions corresponding to the thorax and abdomen: 90% of waveforms with R ≥ 15 had bed centres in the range 5.6 cm superior to 27 cm inferior from the dome of the liver. For regions where respiratory motion was expected to be minimal, R tended to be < 6 and S tended to be 0. The use of DDG significantly increased the SUVmax of focal lesions, by an average of 11% when considering lesions in bed positions with R ≥ 15. CONCLUSIONS The majority of waveforms with high R corresponded to the part of the patient where respiratory motion was expected. The waveforms were deemed suitable for respiratory gating when assessed visually, and when used were found to increase SUVmax in focal lesions.
Collapse
Affiliation(s)
- Matthew D Walker
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK.
| | - Andrew J Morgan
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK
| | - Kevin M Bradley
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Daniel R McGowan
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK.,Department of Oncology, Old Road Campus Research Building, University of Oxford, Oxford, UK
| |
Collapse
|
34
|
Doganay O, Matin T, Chen M, Kim M, McIntyre A, McGowan DR, Bradley KM, Povey T, Gleeson FV. Time-series hyperpolarized xenon-129 MRI of lobar lung ventilation of COPD in comparison to V/Q-SPECT/CT and CT. Eur Radiol 2018; 29:4058-4067. [PMID: 30552482 PMCID: PMC6610266 DOI: 10.1007/s00330-018-5888-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Revised: 10/08/2018] [Accepted: 11/13/2018] [Indexed: 12/23/2022]
Abstract
Purpose To derive lobar ventilation in patients with chronic obstructive pulmonary disease (COPD) using a rapid time-series hyperpolarized xenon-129 (HPX) magnetic resonance imaging (MRI) technique and compare this to ventilation/perfusion single-photon emission computed tomography (V/Q-SPECT), correlating the results with high-resolution computed tomography (CT) and pulmonary function tests (PFTs). Materials and methods Twelve COPD subjects (GOLD stages I–IV) participated in this study and underwent HPX-MRI, V/Q-SPECT/CT, high-resolution CT, and PFTs. HPX-MRI was performed using a novel time-series spiral k-space sampling approach. Relative percentage ventilations were calculated for individual lobe for comparison to the relative SPECT lobar ventilation and perfusion. The absolute HPX-MRI percentage ventilation in each lobe was compared to the absolute CT percentage emphysema score calculated using a signal threshold method. Pearson’s correlation and linear regression tests were performed to compare each imaging modality. Results Strong correlations were found between the relative lobar percentage ventilation with HPX-MRI and percentage ventilation SPECT (r = 0.644; p < 0.001) and percentage perfusion SPECT (r = 0.767; p < 0.001). The absolute CT percentage emphysema and HPX percentage ventilation correlation was also statistically significant (r = 0.695, p < 0.001). The whole lung HPX percentage ventilation correlated with the PFT measurements (FEV1 with r = − 0.886, p < 0.001*, and FEV1/FVC with r = − 0.861, p < 0.001*) better than the whole lung CT percentage emphysema score (FEV1 with r = − 0.635, p = 0.027; and FEV1/FVC with r = − 0.652, p = 0.021). Conclusion Lobar ventilation with HPX-MRI showed a strong correlation with lobar ventilation and perfusion measurements derived from SPECT/CT, and is better than the emphysema score obtained with high-resolution CT. Key Points • The ventilation hyperpolarized xenon-129 MRI correlates well with ventilation and perfusion with SPECT/CT with the advantage of higher temporal and spatial resolution. • The hyperpolarized xenon-129 MRI correlates with the PFT measurements better than the high-resolution CT with the advantage of avoiding the use of ionizing radiation. Electronic supplementary material The online version of this article (10.1007/s00330-018-5888-y) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Ozkan Doganay
- Department of Oncology, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, OX3 7DQ, Oxford, UK.
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, OX3 7LE, Oxford, UK.
| | - Tahreema Matin
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, OX3 7LE, Oxford, UK
| | - Mitchell Chen
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, OX3 7LE, Oxford, UK
| | - Minsuok Kim
- Department of Engineering Science, University of Oxford, OX1 3PJ, Oxford, UK
| | - Anthony McIntyre
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, OX3 7LE, Oxford, UK
| | - Daniel R McGowan
- Department of Oncology, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, OX3 7DQ, Oxford, UK
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, OX3 7LE, Oxford, UK
| | - Kevin M Bradley
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, OX3 7LE, Oxford, UK
| | - Thomas Povey
- Department of Engineering Science, University of Oxford, OX1 3PJ, Oxford, UK
| | - Fergus V Gleeson
- Department of Oncology, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, OX3 7DQ, Oxford, UK
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Old Rd, OX3 7LE, Oxford, UK
| |
Collapse
|
35
|
Lord SR, Cheng WC, Liu D, Gaude E, Haider S, Metcalf T, Patel N, Teoh EJ, Gleeson F, Bradley K, Wigfield S, Zois C, McGowan DR, Ah-See ML, Thompson AM, Sharma A, Bidaut L, Pollak M, Roy PG, Karpe F, James T, English R, Adams RF, Campo L, Ayers L, Snell C, Roxanis I, Frezza C, Fenwick JD, Buffa FM, Harris AL. Integrated Pharmacodynamic Analysis Identifies Two Metabolic Adaption Pathways to Metformin in Breast Cancer. Cell Metab 2018; 28:679-688.e4. [PMID: 30244975 PMCID: PMC6224605 DOI: 10.1016/j.cmet.2018.08.021] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2017] [Revised: 04/21/2018] [Accepted: 08/24/2018] [Indexed: 12/13/2022]
Abstract
Late-phase clinical trials investigating metformin as a cancer therapy are underway. However, there remains controversy as to the mode of action of metformin in tumors at clinical doses. We conducted a clinical study integrating measurement of markers of systemic metabolism, dynamic FDG-PET-CT, transcriptomics, and metabolomics at paired time points to profile the bioactivity of metformin in primary breast cancer. We show metformin reduces the levels of mitochondrial metabolites, activates multiple mitochondrial metabolic pathways, and increases 18-FDG flux in tumors. Two tumor groups are identified with distinct metabolic responses, an OXPHOS transcriptional response (OTR) group for which there is an increase in OXPHOS gene transcription and an FDG response group with increased 18-FDG uptake. Increase in proliferation, as measured by a validated proliferation signature, suggested that patients in the OTR group were resistant to metformin treatment. We conclude that mitochondrial response to metformin in primary breast cancer may define anti-tumor effect.
Collapse
Affiliation(s)
- Simon R Lord
- Department of Oncology, University of Oxford, Churchill Hospital, Oxford OX3 7LE, UK; Molecular Oncology Laboratories, Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Oxford OX3 7LE, UK.
| | - Wei-Chen Cheng
- Department of Oncology, University of Oxford, Churchill Hospital, Oxford OX3 7LE, UK
| | - Dan Liu
- Department of Oncology, University of Oxford, Churchill Hospital, Oxford OX3 7LE, UK
| | - Edoardo Gaude
- MRC Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Cambridge Biomedical Campus, Cambridge CB2 0XZ, UK
| | - Syed Haider
- Breast Cancer Now Research Centre, The Institute of Cancer Research, London SW3 6JB, UK
| | - Tom Metcalf
- Institute of Translational Medicine, University of Liverpool, Royal Liverpool University Hospital, Liverpool L69 3GA, UK
| | - Neel Patel
- Department of Nuclear Medicine, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Oxford OX3 7LE, UK
| | - Eugene J Teoh
- Department of Oncology, University of Oxford, Churchill Hospital, Oxford OX3 7LE, UK; Molecular Oncology Laboratories, Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK; Department of Nuclear Medicine, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Oxford OX3 7LE, UK
| | - Fergus Gleeson
- Department of Oncology, University of Oxford, Churchill Hospital, Oxford OX3 7LE, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Oxford OX3 7LE, UK; Department of Nuclear Medicine, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Oxford OX3 7LE, UK
| | - Kevin Bradley
- Department of Nuclear Medicine, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Oxford OX3 7LE, UK
| | - Simon Wigfield
- Molecular Oncology Laboratories, Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK
| | - Christos Zois
- Molecular Oncology Laboratories, Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK
| | - Daniel R McGowan
- Department of Oncology, University of Oxford, Churchill Hospital, Oxford OX3 7LE, UK
| | - Mei-Lin Ah-See
- Department of Oncology, Luton and Dunstable Hospital, Luton, UK
| | - Alastair M Thompson
- Department of Breast Surgical Oncology, MD Anderson Cancer Centre, Houston, TX 77030, USA
| | - Anand Sharma
- Department of Oncology, University of Oxford, Churchill Hospital, Oxford OX3 7LE, UK; Molecular Oncology Laboratories, Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK
| | - Luc Bidaut
- College of Science, University of Lincoln, Lincoln LN6 7TS, UK; Clinical Research Imaging Facility, University of Dundee, Ninewells Hospital, Dundee DD2 1SY, UK
| | - Michael Pollak
- Department of Oncology, McGill University, Montreal, QC H3T 1E2, Canada
| | - Pankaj G Roy
- Breast Surgery Unit, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Oxford OX3 7LE, UK
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Oxford OX3 7LE, UK
| | - Tim James
- Department of Clinical Biochemistry, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Ruth English
- Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 7LE, UK
| | - Rosie F Adams
- Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 7LE, UK
| | - Leticia Campo
- Department of Oncology, University of Oxford, Churchill Hospital, Oxford OX3 7LE, UK
| | - Lisa Ayers
- Department of Clinical and Laboratory Immunology, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Oxford OX3 7LE, UK
| | - Cameron Snell
- Department of Anatomical Pathology, Mater Research Institute, Brisbane 4101, Australia
| | - Ioannis Roxanis
- Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Christian Frezza
- MRC Cancer Unit, University of Cambridge, Hutchison/MRC Research Centre, Cambridge Biomedical Campus, Cambridge CB2 0XZ, UK
| | - John D Fenwick
- Institute of Translational Medicine, University of Liverpool, Royal Liverpool University Hospital, Liverpool L69 3GA, UK
| | - Francesca M Buffa
- Department of Oncology, University of Oxford, Churchill Hospital, Oxford OX3 7LE, UK
| | - Adrian L Harris
- Department of Oncology, University of Oxford, Churchill Hospital, Oxford OX3 7LE, UK; Molecular Oncology Laboratories, Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Oxford OX3 7LE, UK
| |
Collapse
|
36
|
McGowan DR, Skwarski M, Papiez BW, Macpherson RE, Gleeson FV, Schnabel JA, Higgins GS, Fenwick JD. Whole tumor kinetics analysis of 18F-fluoromisonidazole dynamic PET scans of non-small cell lung cancer patients, and correlations with perfusion CT blood flow. EJNMMI Res 2018; 8:73. [PMID: 30069753 PMCID: PMC6070455 DOI: 10.1186/s13550-018-0430-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 07/23/2018] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND To determine the relative abilities of compartment models to describe time-courses of 18F-fluoromisonidazole (FMISO) tumor uptake in patients with advanced stage non-small cell lung cancer (NSCLC) imaged using dynamic positron emission tomography (dPET), and study correlations between values of the blood flow-related parameter K1 obtained from fits of the models and an independent blood flow measure obtained from perfusion CT (pCT). NSCLC patients had a 45-min dynamic FMISO PET/CT scan followed by two static PET/CT acquisitions at 2 and 4-h post-injection. Perfusion CT scanning was then performed consisting of a 45-s cine CT. Reversible and irreversible two-, three- and four-tissue compartment models were fitted to 30 time-activity-curves (TACs) obtained for 15 whole tumor structures in 9 patients, each imaged twice. Descriptions of the TACs provided by the models were compared using the Akaike and Bayesian information criteria (AIC and BIC) and leave-one-out cross-validation. The precision with which fitted model parameters estimated ground-truth uptake kinetics was determined using statistical simulation techniques. Blood flow from pCT was correlated with K1 from PET kinetic models in addition to FMISO uptake levels. RESULTS An irreversible three-tissue compartment model provided the best description of whole tumor FMISO uptake time-courses according to AIC, BIC, and cross-validation scores totaled across the TACs. The simulation study indicated that this model also provided more precise estimates of FMISO uptake kinetics than other two- and three-tissue models. The K1 values obtained from fits of the irreversible three-tissue model correlated strongly with independent blood flow measurements obtained from pCT (Pearson r coefficient = 0.81). The correlation from the irreversible three-tissue model (r = 0.81) was stronger than that from than K1 values obtained from fits of a two-tissue compartment model (r = 0.68), or FMISO uptake levels in static images taken at time-points from tracer injection through to 4 h later (maximum at 2 min, r = 0.70). CONCLUSIONS Time-courses of whole tumor FMISO uptake by advanced stage NSCLC are described best by an irreversible three-tissue compartment model. The K1 values obtained from fits of the irreversible three-tissue model correlated strongly with independent blood flow measurements obtained from perfusion CT (r = 0.81).
Collapse
Affiliation(s)
- Daniel R. McGowan
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ UK
- Radiation Physics and Protection, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Michael Skwarski
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ UK
| | - Bartlomiej W. Papiez
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Ruth E. Macpherson
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Fergus V. Gleeson
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ UK
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Julia A. Schnabel
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Geoff S. Higgins
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ UK
- Department of Oncology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - John D. Fenwick
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ UK
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| |
Collapse
|
37
|
Abstract
4D reconstruction of dynamic positron emission tomography (dPET) data can improve the signal-to-noise ratio in reconstructed image sequences by fitting smooth temporal functions to the voxel time-activity-curves (TACs) during the reconstruction, though the optimal choice of function remains an open question. We propose a spline-residue model, which describes TACs as weighted sums of convolutions of the arterial input function with cubic B-spline basis functions. Convolution with the input function constrains the spline-residue model at early time-points, potentially enhancing noise suppression in early time-frames, while still allowing a wide range of TAC descriptions over the entire imaged time-course, thus limiting bias. Spline-residue based 4D-reconstruction is compared to that of a conventional (non-4D) maximum a posteriori (MAP) algorithm, and to 4D-reconstructions based on adaptive-knot cubic B-splines, the spectral model and an irreversible two-tissue compartment ('2C3K') model. 4D reconstructions were carried out using a nested-MAP algorithm including spatial and temporal roughness penalties. The algorithms were tested using Monte-Carlo simulated scanner data, generated for a digital thoracic phantom with uptake kinetics based on a dynamic [18F]-Fluromisonidazole scan of a non-small cell lung cancer patient. For every algorithm, parametric maps were calculated by fitting each voxel TAC within a sub-region of the reconstructed images with the 2C3K model. Compared to conventional MAP reconstruction, spline-residue-based 4D reconstruction achieved >50% improvements for five of the eight combinations of the four kinetics parameters for which parametric maps were created with the bias and noise measures used to analyse them, and produced better results for 5/8 combinations than any of the other reconstruction algorithms studied, while spectral model-based 4D reconstruction produced the best results for 2/8. 2C3K model-based 4D reconstruction generated the most biased parametric maps. Inclusion of a temporal roughness penalty function improved the performance of 4D reconstruction based on the cubic B-spline, spectral and spline-residue models.
Collapse
Affiliation(s)
- George P Ralli
- Department of Oncology, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, United Kingdom
| | - Michael A Chappell
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, United Kingdom
| | - Daniel R McGowan
- Department of Oncology, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, United Kingdom
- Radiation Physics and Protection, Oxford University Hospitals NHS Foundation Trust, Churchill Hospital, Oxford OX3 7LE, United Kingdom
| | - Ricky A Sharma
- NIHR University College London Hospitals Biomedical Research Centre, UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6DD, United Kingdom
| | - Geoff S Higgins
- Department of Oncology, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, United Kingdom
| | - John D Fenwick
- Institute of Translational Medicine, University of Liverpool, UCD Block, Royal Liverpool University Hospital, Daulby Street, Liverpool L69 3GA, United Kingdom
| |
Collapse
|
38
|
Affiliation(s)
- Kevin M Bradley
- Oxford University Hospitals NHS Foundation Trust Oxford, OX3 7LE, U.K. E-mail:
| | | | | | | | | | | | | | | |
Collapse
|
39
|
Walker MD, Bradley KM, McGowan DR. Evaluation of principal component analysis-based data-driven respiratory gating for positron emission tomography. Br J Radiol 2018; 91:20170793. [PMID: 29419327 PMCID: PMC5911393 DOI: 10.1259/bjr.20170793] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Objective: Respiratory motion can degrade PET image quality and lead to inaccurate quantification of lesion uptake. Such motion can be mitigated via respiratory gating. Our objective was to evaluate a data-driven gating (DDG) technique that is being developed commercially for clinical PET/CT. Methods: A data-driven respiratory gating algorithm based on principal component analysis (PCA) was applied to phantom and FDG patient data. An anthropomorphic phantom and a NEMA IEC Body phantom were filled with 18F, placed on a respiratory motion platform, and imaged using a PET/CT scanner. Motion waveforms were measured using an infrared camera [the Real-time Position Management™ system (RPM)] and also extracted from the PET data using the DDG algorithm. The waveforms were compared via calculation of Pearson’s correlation coefficients. PET data were reconstructed using quiescent period gating (QPG) and compared via measurement of recovery percentage and background variability. Results: Data-driven gating had similar performance to the external gating system, with correlation coefficients in excess of 0.97. Phantom and patient images were visually clearer with improved contrast when QPG was applied as compared to no motion compensation. Recovery coefficients in the phantoms were not significantly different between DDG- and RPM-based QPG, but were significantly higher than those found for no motion compensation (p < 0.05). Conclusion: A PCA-based DDG algorithm was evaluated and found to provide a reliable respiratory gating signal in anthropomorphic phantom studies and in example patients. Advances in knowledge: The prototype commercial DDG algorithm may enable reliable respiratory gating in routine clinical PET-CT.
Collapse
Affiliation(s)
- Matthew D Walker
- 1 Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust , Oxford , UK
| | - Kevin M Bradley
- 2 Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust , Oxford , UK
| | - Daniel R McGowan
- 1 Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust , Oxford , UK.,3 Department of Oncology, University of Oxford , Oxford , UK
| |
Collapse
|
40
|
Teoh EJ, McGowan DR, Schuster DM, Tsakok MT, Gleeson FV, Bradley KM. Bayesian penalised likelihood reconstruction (Q.Clear) of 18F-fluciclovine PET for imaging of recurrent prostate cancer: semi-quantitative and clinical evaluation. Br J Radiol 2018; 91:20170727. [PMID: 29303359 PMCID: PMC6190769 DOI: 10.1259/bjr.20170727] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Objective: 18F-Fluciclovine (FACBC) is an amino acid PET radiotracer approved for recurrent prostate cancer imaging. We investigate the use of Bayesian penalised likelihood (BPL) reconstruction for 18F-fluciclovine PET. Methods: 15 18F-fluciclovine scans were reconstructed using ordered subset expectation maximisation (OSEM), OSEM + point spread function (PSF) modelling and BPL using β-values 100–600. Lesion maximum standardised uptake value (SUVmax), organ SUVmean and standard deviation were measured. Deidentified reconstructions (OSEM, PSF, BPL using β200–600) from 10 cases were visually analysed by two readers who indicated their most and least preferred reconstructions, and scored overall image quality, noise level, background marrow image quality and lesion conspicuity. Results: Comparing BPL to OSEM, there were significant increments in lesion SUVmax and signal-to-background up to β400, with highest gain in β100 reconstructions (mean ΔSUVmax 3.9, p < 0.0001). Organ noise levels increased on PSF, β100 and β200 reconstructions. Across BPL reconstructions, there was incremental reduction in organ noise with increasing β, statistically significant beyond β300–500 (organ-dependent). Comparing with OSEM and PSF, lesion signal-to-noise was significantly increased in BPL reconstructions where β ≥ 300 and ≥ 200 respectively. On visual analysis, β 300 had the first and second highest scores for image quality, β500 and β600 equal highest scores for marrow image quality and least noise, PSF and β 200 had first and second highest scores for lesion conspicuity. For overall preference, one reader preferred β 300 in 9/10 cases and the other preferred β 200 in all cases. Conclusion: BPL reconstruction of 18F-fluciclovine PET images improves signal-to-noise ratio, affirmed by overall reader preferences. On balance, β300 is suggested for 18F-fluciclovine whole body PET image reconstruction using BPL. Advances in knowledge: The optimum β is different to that previously published for 18F-fluorodeoxyglucose, and has practical implications for a relatively new tracer in an environment with modern reconstruction technologies.
Collapse
Affiliation(s)
- Eugene J Teoh
- 1 Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust , Oxford , UK.,2 Department of Oncology, University of Oxford , Oxford , UK
| | - Daniel R McGowan
- 2 Department of Oncology, University of Oxford , Oxford , UK.,3 Department of Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Trust , Oxford , UK
| | - David M Schuster
- 4 Department of Radiology and Imaging Sciences, Emory University , Atlanta, GA , USA
| | - Maria T Tsakok
- 1 Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust , Oxford , UK
| | - Fergus V Gleeson
- 1 Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust , Oxford , UK.,2 Department of Oncology, University of Oxford , Oxford , UK
| | - Kevin M Bradley
- 1 Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust , Oxford , UK
| |
Collapse
|
41
|
Porter CA, Bradley KM, Hippeläinen ET, Walker MD, McGowan DR. Phantom and clinical evaluation of the effect of full Monte Carlo collimator modelling in post-SIRT yttrium-90 Bremsstrahlung SPECT imaging. EJNMMI Res 2018; 8:7. [PMID: 29356993 PMCID: PMC5778088 DOI: 10.1186/s13550-018-0361-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Accepted: 01/10/2018] [Indexed: 01/06/2023] Open
Abstract
Background Post-therapy SPECT/CT imaging of 90Y microspheres delivered to hepatic malignancies is difficult, owing to the continuous, high-energy Bremsstrahlung spectrum emitted by 90Y. This study aimed to evaluate the utility of a commercially available software package (HybridRecon, Hermes Medical Solutions AB) which incorporates full Monte Carlo collimator modelling. Analysis of image quality was performed on both phantom and clinical images in order to ultimately provide a recommendation of an optimum reconstruction for post-therapy 90Y microsphere SPECT/CT imaging. A 3D-printed anthropomorphic liver phantom was filled with 90Y with a sphere-to-background ratio of 4:1 and imaged on a GE Discovery 670 SPECT/CT camera. Datasets were reconstructed using ordered-subsets expectation maximization (OSEM) 1–7 iterations in order to identify the optimal OSEM reconstruction (5 iterations, 15 subsets). Quantitative analysis was subsequently carried out on phantom datasets obtained using four reconstruction algorithms: the default OSEM protocol (2 iterations, 10 subsets) and the optimised OSEM protocol, both with and without full Monte Carlo collimator modelling. The quantitative metrics contrast recovery (CR) and background variability (BV) were calculated. The four algorithms were then used to retrospectively reconstruct 10 selective internal radiation therapy (SIRT) patient datasets which were subsequently blind scored for image quality by a consultant radiologist. Results The optimised OSEM reconstruction (5 iterations, 15 subsets with full MC collimator modelling) increased the CR by 42% (p < 0.001) compared to the default OSEM protocol (2 iterations, 10 subsets). The use of full Monte Carlo collimator modelling was shown to further improve CR by 14% (30 mm sphere, CR = 90%, p < 0.05). The consultant radiologist had a significant preference for the optimised OSEM over the default OSEM protocol (p < 0.001), with the optimised OSEM being the favoured reconstruction in every one of the 10 clinical cases presented. Conclusions OSEM (5 iterations, 15 subsets) with full Monte Carlo collimator modelling is quantitatively the optimal image reconstruction for post-SIRT 90Y Bremsstrahlung SPECT/CT imaging. The use of full Monte Carlo collimator modelling for correction of image-degrading effects significantly increases contrast recovery without degrading clinical image quality.
Collapse
Affiliation(s)
- Charlotte A Porter
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK.
| | - Kevin M Bradley
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Eero T Hippeläinen
- HUS Medical Imaging Centre, Clinical Physiology and Nuclear Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Matthew D Walker
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK
| | - Daniel R McGowan
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK.,Department of Oncology, University of Oxford, Old Road Campus Research Building, Oxford, UK
| |
Collapse
|
42
|
McGowan DR, Macpherson RE, Hackett SL, Liu D, Gleeson FV, McKenna WG, Higgins GS, Fenwick JD. 18 F-fluoromisonidazole uptake in advanced stage non-small cell lung cancer: A voxel-by-voxel PET kinetics study. Med Phys 2017; 44:4665-4676. [PMID: 28644546 PMCID: PMC5600259 DOI: 10.1002/mp.12416] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Revised: 06/05/2017] [Accepted: 06/08/2017] [Indexed: 11/05/2022] Open
Abstract
PURPOSE The aim of this study was to determine the relative abilities of compartment models to describe time-courses of 18 F-fluoromisonidazole (FMISO) uptake in tumor voxels of patients with non-small cell lung cancer (NSCLC) imaged using dynamic positron emission tomography. Also to use fits of the best-performing model to investigate changes in fitted rate-constants with distance from the tumor edge. METHODS Reversible and irreversible two- and three-tissue compartment models were fitted to 24 662 individual voxel time activity curves (TACs) obtained from tumors in nine patients, each imaged twice. Descriptions of the TACs provided by the models were compared using the Akaike and Bayesian information criteria (AIC and BIC). Two different models (two- and three-tissue) were fitted to 30 measured voxel TACs to provide ground-truth TACs for a statistical simulation study. Appropriately scaled noise was added to each of the resulting ground-truth TACs, generating 1000 simulated noisy TACs for each ground-truth TAC. The simulation study was carried out to provide estimates of the accuracy and precision with which parameter values are determined, the estimates being obtained for both assumptions about the ground-truth kinetics. A BIC clustering technique was used to group the fitted rate-constants, taking into consideration the underlying uncertainties on the fitted rate-constants. Voxels were also categorized according to their distance from the tumor edge. RESULTS For uptake time-courses of individual voxels an irreversible two-tissue compartment model was found to be most precise. The simulation study indicated that this model had a one standard deviation precision of 39% for tumor fractional blood volumes and 37% for the FMISO binding rate-constant. Weighted means of fitted FMISO binding rate-constants of voxels in all tumors rose significantly with increasing distance from the tumor edge, whereas fitted fractional blood volumes fell significantly. When grouped using the BIC clustering, many centrally located voxels had high-fitted FMISO binding rate-constants and low rate-constants for tracer flow between the vasculature and tumor, both indicative of hypoxia. Nevertheless, many of these voxels had tumor-to-blood (TBR) values lower than the 1.4 level commonly expected for hypoxic tissues, possibly due to the low rate-constants for tracer flow between the vasculature and tumor cells in these voxels. CONCLUSIONS Time-courses of FMISO uptake in NSCLC tumor voxels are best analyzed using an irreversible two-tissue compartment model, fits of which provide more precise parameter values than those of a three-tissue model. Changes in fitted model parameter values indicate that levels of hypoxia rise with increasing distance from tumor edges. The average FMISO binding rate-constant is higher for voxels in tumor centers than in the next tumor layer out, but the average value of the more simplistic TBR metric is lower in tumor centers. For both metrics, higher values might be considered indicative of hypoxia, and the mismatch in this case is likely to be due to poor perfusion at the tumor center. Kinetics analysis of dynamic PET images may therefore provide more accurate measures of the hypoxic status of such regions than the simpler TBR metric, a hypothesis we are presently exploring in a study of tumor imaging versus histopathology.
Collapse
Affiliation(s)
- Daniel R. McGowan
- Cancer Research UK/MRC Oxford Institute for Radiation OncologyGray LaboratoriesDepartment of OncologyUniversity of OxfordOxfordUK
- Radiation Physics and ProtectionOxford University Hospitals NHS Foundation TrustOxfordUK
| | - Ruth E. Macpherson
- Department of RadiologyOxford University Hospitals NHS Foundation TrustOxfordUK
| | - Sara L. Hackett
- Cancer Research UK/MRC Oxford Institute for Radiation OncologyGray LaboratoriesDepartment of OncologyUniversity of OxfordOxfordUK
| | - Dan Liu
- Cancer Research UK/MRC Oxford Institute for Radiation OncologyGray LaboratoriesDepartment of OncologyUniversity of OxfordOxfordUK
| | - Fergus V. Gleeson
- Cancer Research UK/MRC Oxford Institute for Radiation OncologyGray LaboratoriesDepartment of OncologyUniversity of OxfordOxfordUK
- Department of RadiologyOxford University Hospitals NHS Foundation TrustOxfordUK
| | - W. Gillies McKenna
- Cancer Research UK/MRC Oxford Institute for Radiation OncologyGray LaboratoriesDepartment of OncologyUniversity of OxfordOxfordUK
- Department of OncologyOxford University Hospitals NHS Foundation TrustOxfordUK
| | - Geoff S. Higgins
- Cancer Research UK/MRC Oxford Institute for Radiation OncologyGray LaboratoriesDepartment of OncologyUniversity of OxfordOxfordUK
- Department of OncologyOxford University Hospitals NHS Foundation TrustOxfordUK
| | - John D. Fenwick
- Cancer Research UK/MRC Oxford Institute for Radiation OncologyGray LaboratoriesDepartment of OncologyUniversity of OxfordOxfordUK
| |
Collapse
|
43
|
O' Doherty J, McGowan DR, Abreu C, Barrington S. Erratum to: Effect of Bayesian-penalized likelihood reconstruction on [13N]-NH3 rest perfusion quantification. J Nucl Cardiol 2017; 24:1457. [PMID: 28466373 PMCID: PMC6728287 DOI: 10.1007/s12350-017-0892-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Jim O' Doherty
- PET Imaging Centre, Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St. Thomas' Hospital, 1st Floor, Lambeth Wing, London, SE1 7EH, United Kingdom.
| | - Daniel R McGowan
- Department of Oncology, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, United Kingdom
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, United Kingdom
| | - Carla Abreu
- PET Imaging Centre, Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St. Thomas' Hospital, 1st Floor, Lambeth Wing, London, SE1 7EH, United Kingdom
| | - Sally Barrington
- PET Imaging Centre, Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St. Thomas' Hospital, 1st Floor, Lambeth Wing, London, SE1 7EH, United Kingdom
| |
Collapse
|
44
|
O' Doherty J, McGowan DR, Abreu C, Barrington S. Effect of Bayesian-penalized likelihood reconstruction on [13N]-NH3 rest perfusion quantification. J Nucl Cardiol 2017; 24:282-290. [PMID: 27435278 PMCID: PMC5084874 DOI: 10.1007/s12350-016-0554-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Accepted: 05/05/2016] [Indexed: 01/07/2023]
Abstract
ABSTACT OBJECTIVES: Myocardial blood flow (MBF) imaging is used in patients with suspected cardiac sarcoidosis, and also in stress/rest studies. The accuracy of MBF is dependent on imaging parameters such as new reconstruction methodologies. In this work, we aim to assess the impact of a novel PET reconstruction algorithm (Bayesian-penalized likelihood-BPL) on the values determined from the calculation of [13N]-NH3 MBF values. METHODS Data from 21 patients undergoing rest MBF evaluation [13N]-NH3 as part of sarcoidosis imaging were retrospectively analyzed. Each scan was reconstructed with a range of BPL coefficients (1-500), and standard clinical FBP and OSEM reconstructions. MBF values were calculated via an automated software routine for all datasets. RESULTS Reconstruction of [13N]-NH3 dynamic data using the BPL, OSEM, or FBP reconstruction showed no quantitative differences for the calculation of territorial or global MBF (P = .97). Image noise was lower using OSEM or BPL reconstructions than FBP and noise from BPL reached levels seen in OSEM images between B = 300 and B = 400. Intrasubject differences between all reconstructions over all patients in respect of all cardiac territories showed a maximum coefficient of variation of 9.74%. CONCLUSION Quantitation of MBF via kinetic modeling of cardiac rest MBF by [13N]-NH3 is minimally affected by the use of a BPL reconstruction technique, with BPL images presenting with less noise.
Collapse
Affiliation(s)
- Jim O' Doherty
- PET Imaging Centre, Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St. Thomas' Hospital, 1st Floor, Lambeth Wing, London, SE1 7EH, United Kingdom.
| | - Daniel R McGowan
- Department of Oncology, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, United Kingdom
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, United Kingdom
| | - Carla Abreu
- PET Imaging Centre, Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St. Thomas' Hospital, 1st Floor, Lambeth Wing, London, SE1 7EH, United Kingdom
| | - Sally Barrington
- PET Imaging Centre, Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St. Thomas' Hospital, 1st Floor, Lambeth Wing, London, SE1 7EH, United Kingdom
| |
Collapse
|
45
|
Morley NCD, McGowan DR, Gleeson FV, Bradley KM. Software Respiratory Gating of Positron Emission Tomography-Computed Tomography Improves Pulmonary Nodule Detection. Am J Respir Crit Care Med 2017; 195:261-262. [PMID: 27755923 PMCID: PMC5394788 DOI: 10.1164/rccm.201607-1371im] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
| | - Daniel R McGowan
- 3 Radiation Physics and Protection, Oxford University Hospitals, Oxford, United Kingdom; and
- 2 Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Fergus V Gleeson
- 1 Department of Radiology and
- 2 Department of Oncology, University of Oxford, Oxford, United Kingdom
| | | |
Collapse
|
46
|
Teoh EJ, McGowan DR, Bradley KM, Belcher E, Black E, Moore A, Sykes A, Gleeson FV. 18F-FDG PET/CT assessment of histopathologically confirmed mediastinal lymph nodes in non-small cell lung cancer using a penalised likelihood reconstruction. Eur Radiol 2016; 26:4098-4106. [PMID: 26914696 PMCID: PMC4898597 DOI: 10.1007/s00330-016-4253-2] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Revised: 12/23/2015] [Accepted: 01/26/2016] [Indexed: 12/26/2022]
Abstract
PURPOSE To investigate whether using a Bayesian penalised likelihood reconstruction (BPL) improves signal-to-background (SBR), signal-to-noise (SNR) and SUVmax when evaluating mediastinal nodal disease in non-small cell lung cancer (NSCLC) compared to ordered subset expectation maximum (OSEM) reconstruction. MATERIALS AND METHODS 18F-FDG PET/CT scans for NSCLC staging in 47 patients (112 nodal stations with histopathological confirmation) were reconstructed using BPL and compared to OSEM. Node and multiple background SUV parameters were analysed semi-quantitatively and visually. RESULTS Comparing BPL to OSEM, there were significant increases in SUVmax (mean 3.2-4.0, p<0.0001), SBR (mean 2.2-2.6, p<0.0001) and SNR (mean 27.7-40.9, p<0.0001). Mean background SNR on OSEM was 10.4 (range 7.6-14.0), increasing to 12.4 (range 8.2-16.7, p<0.0001). Changes in background SUVs were minimal (largest mean difference 0.17 for liver SUVmean, p<0.001). There was no significant difference between either algorithm on receiver operating characteristic analysis (p=0.26), although on visual analysis, there was an increase in sensitivity and small decrease in specificity and accuracy on BPL. CONCLUSION BPL increases SBR, SNR and SUVmax of mediastinal nodes in NSCLC compared to OSEM, but did not improve the accuracy for determining nodal involvement. KEY POINTS • Penalised likelihood PET reconstruction was applied for assessing mediastinal nodes in NSCLC. • The new reconstruction generated significant increases in signal-to-background, signal-to-noise and SUVmax. • This led to an improvement in visual sensitivity using the new algorithm. • Higher SUV max thresholds may be appropriate for semi-quantitative analyses with penalised likelihood.
Collapse
Affiliation(s)
- Eugene J Teoh
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
| | - Daniel R McGowan
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK.
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK.
| | - Kevin M Bradley
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK
| | - Elizabeth Belcher
- Department of Thoracic Surgery, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Edward Black
- Department of Thoracic Surgery, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, UK
| | - Alastair Moore
- Department of Respiratory Medicine, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK
| | - Annemarie Sykes
- Department of Respiratory Medicine, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK
| | - Fergus V Gleeson
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
| |
Collapse
|
47
|
Rowley LM, Bradley KM, Boardman P, Hallam A, McGowan DR. Optimization of Image Reconstruction for 90Y Selective Internal Radiotherapy on a Lutetium Yttrium Orthosilicate PET/CT System Using a Bayesian Penalized Likelihood Reconstruction Algorithm. J Nucl Med 2016; 58:658-664. [PMID: 27688476 DOI: 10.2967/jnumed.116.176552] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 09/08/2016] [Indexed: 02/07/2023] Open
Abstract
Imaging on a γ-camera with 90Y after selective internal radiotherapy (SIRT) may allow for verification of treatment delivery but suffers relatively poor spatial resolution and imprecise dosimetry calculation. 90Y PET/CT imaging is possible on 3-dimensional, time-of-flight machines; however, images are usually poor because of low count statistics and noise. A new PET reconstruction software using a Bayesian penalized likelihood (BPL) reconstruction algorithm (termed Q.Clear) was investigated using phantom and patient scans to optimize the reconstruction for post-SIRT imaging and clarify whether BPL leads to an improvement in clinical image quality using 90Y. Methods: Phantom studies over an activity range of 0.5-4.2 GBq were performed to assess the contrast recovery, background variability, and contrast-to-noise ratio for a range of BPL and ordered-subset expectation maximization (OSEM) reconstructions on a PET/CT scanner. Patient images after SIRT were reconstructed using the same parameters and were scored and ranked on the basis of image quality, as assessed by visual evaluation, with the corresponding SPECT/CT Bremsstrahlung images by 2 experienced radiologists. Results: Contrast-to-noise ratio was significantly better in BPL reconstructions when compared with OSEM in phantom studies. The patient-derived BPL and matching Bremsstrahlung images scored higher than OSEM reconstructions when scored by radiologists. BPL with a β value of 4,000 was ranked the highest of all images. Deadtime was apparent in the system above a total phantom activity of 3.3 GBq. Conclusion: BPL with a β value of 4,000 is the optimal image reconstruction in PET/CT for confident radiologic reading when compared with other reconstruction parameters for 90Y imaging after SIRT imaging. Activity in the field of view should be below 3.3 GBq at the time of PET imaging to avoid deadtime losses for this scanner.
Collapse
Affiliation(s)
- Lisa M Rowley
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Kevin M Bradley
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom; and
| | - Philip Boardman
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom; and
| | - Aida Hallam
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Daniel R McGowan
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.,Department of Oncology, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
48
|
Teoh EJ, McGowan DR, Bradley KM, Belcher E, Black E, Gleeson FV. Novel penalised likelihood reconstruction of PET in the assessment of histologically verified small pulmonary nodules. Eur Radiol 2016; 26:576-84. [PMID: 25991490 PMCID: PMC4551414 DOI: 10.1007/s00330-015-3832-y] [Citation(s) in RCA: 75] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2014] [Revised: 04/20/2015] [Accepted: 04/28/2015] [Indexed: 11/23/2022]
Abstract
OBJECTIVES Investigate the effect of a novel Bayesian penalised likelihood (BPL) reconstruction algorithm on analysis of pulmonary nodules examined with 18F-FDG PET/CT, and to determine its effect on small, sub-10-mm nodules. METHODS 18F-FDG PET/CTs performed for nodule evaluation in 104 patients (121 nodules) were retrospectively reconstructed using the new algorithm, and compared to time-of-flight ordered subset expectation maximisation (OSEM) reconstruction. Nodule and background parameters were analysed semi-quantitatively and visually. RESULTS BPL compared to OSEM resulted in statistically significant increases in nodule SUVmax (mean 5.3 to 8.1, p < 0.00001), signal-to-background (mean 3.6 to 5.3, p < 0.00001) and signal-to-noise (mean 24 to 41, p < 0.00001). Mean percentage increase in SUVmax (%ΔSUVmax) was significantly higher in nodules ≤10 mm (n = 31, mean 73%) compared to >10 mm (n = 90, mean 42 %) (p = 0.025). Increase in signal-to-noise was higher in nodules ≤10 mm (224%, mean 12 to 27) compared to >10 mm (165%, mean 28 to 46). When applying optimum SUVmax thresholds for detecting malignancy, the sensitivity and accuracy increased using BPL, with the greatest improvements in nodules ≤10 mm. CONCLUSION BPL results in a significant increase in signal-to-background and signal-to-noise compared to OSEM. When semi-quantitative analyses to diagnose malignancy are applied, higher SUVmax thresholds may be warranted owing to the SUVmax increase compared to OSEM. KEY POINTS • Novel Bayesian penalised likelihood PET reconstruction was applied for lung nodule evaluation. • This was compared to current standard of care OSEM reconstruction. • The novel reconstruction generated significant increases in lung nodule signal-to-background and signal-to-noise. • These increases were highest in small, sub-10-mm pulmonary nodules. • Higher SUV max thresholds may be warranted when using semi-quantitative analyses to diagnose malignancy.
Collapse
Affiliation(s)
- Eugene J Teoh
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Old Road, Headington, Oxford, OX3 7LE, UK.
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK.
| | - Daniel R McGowan
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
- Radiation Physics and Protection, Churchill Hospital, Oxford University Hospitals NHS Trust, Old Road, Oxford, OX3 7LE, UK
| | - Kevin M Bradley
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Old Road, Headington, Oxford, OX3 7LE, UK
| | - Elizabeth Belcher
- Department of Thoracic Surgery, John Radcliffe Hospital, Oxford University Hospitals NHS Trust, Headley Way, Oxford, OX3 7DU, UK
| | - Edward Black
- Department of Thoracic Surgery, John Radcliffe Hospital, Oxford University Hospitals NHS Trust, Headley Way, Oxford, OX3 7DU, UK
| | - Fergus V Gleeson
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Trust, Old Road, Headington, Oxford, OX3 7LE, UK
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
| |
Collapse
|
49
|
McGowan DR, Macpherson RE, Bradley KM, Fenwick JD, Gleeson FV, Higgins GS. 18F-Misonidazole PET-CT scan detection of occult bone metastasis. Thorax 2016; 71:97. [PMID: 26349764 PMCID: PMC4678574 DOI: 10.1136/thoraxjnl-2015-207400] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2015] [Accepted: 08/14/2015] [Indexed: 11/03/2022]
Affiliation(s)
- Daniel R McGowan
- Department of Oncology, University of Oxford, Oxford, UK Radiation Physics and Protection, Oxford University Hospitals NHS Trust, Oxford, UK
| | - Ruth E Macpherson
- Department of Radiology, Oxford University Hospitals NHS Trust, Oxford, UK
| | - Kevin M Bradley
- Department of Radiology, Oxford University Hospitals NHS Trust, Oxford, UK
| | - John D Fenwick
- Department of Oncology, University of Oxford, Oxford, UK
| | - Fergus V Gleeson
- Department of Oncology, University of Oxford, Oxford, UK Department of Radiology, Oxford University Hospitals NHS Trust, Oxford, UK
| | | |
Collapse
|
50
|
Parvizi N, Franklin JM, McGowan DR, Teoh EJ, Bradley KM, Gleeson FV. Does a novel penalized likelihood reconstruction of 18F-FDG PET-CT improve signal-to-background in colorectal liver metastases? Eur J Radiol 2015; 84:1873-8. [PMID: 26163992 DOI: 10.1016/j.ejrad.2015.06.025] [Citation(s) in RCA: 68] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Accepted: 06/22/2015] [Indexed: 12/28/2022]
Abstract
PURPOSE Iterative reconstruction algorithms are widely used to reconstruct positron emission tomography computerised tomography (PET/CT) data. Lesion detection in the liver by 18F-fluorodeoxyglucose PET/CT (18F-FDG-PET/CT) is hindered by 18F-FDG uptake in background liver parenchyma. The aim of this study was to compare semi-quantitative parameters of histologically-proven colorectal liver metastases detected by 18F-FDG-PET/CT using data based on a Bayesian penalised likelihood (BPL) reconstruction, with data based on a conventional time-of-flight (ToF) ordered subsets expectation maximisation (OSEM) reconstruction. METHODS A BPL reconstruction algorithm was used to retrospectively reconstruct sinogram PET data. This data was compared with OSEM reconstructions. A volume of interest was placed within normal background liver parenchyma. Lesions were segmented using automated thresholding. Lesion maximum standardised uptake value (SUVmax), standard deviation of background liver parenchyma SUV, signal-to-background ratio (SBR), and signal-to-noise ratio (SNR) were collated. Data was analysed using paired Student's t-tests and the Pearson correlation. RESULTS Forty-two liver metastases from twenty-four patients were included in the analysis. The average lesion SUVmax increased from 8.8 to 11.6 (p<0.001) after application of the BPL algorithm, with no significant difference in background noise. SBR increased from 4.0 to 4.9 (p<0.001) and SNR increased from 10.6 to 13.1 (p<0.001) using BPL. There was a statistically significant negative correlation between lesion size and the percentage increase in lesion SUVmax (p=0.03). CONCLUSIONS This BPL reconstruction algorithm improved SNR and SBR for colorectal liver metastases detected by 18F-FDG-PET/CT, increasing the lesion SUVmax without increasing background liver SUV or image noise. This may improve the detection of FDG-avid focal liver lesions and the diagnostic performance of clinical 18F-FDG-PET/CT in this setting, with the largest impact for small foci.
Collapse
Affiliation(s)
- Nassim Parvizi
- Department of Clinical Radiology, Oxford University Hospitals NHS Trust, Churchill Hospital, Old Road, Headington, Oxford, Oxfordshire OX3 7LE, UK.
| | - James M Franklin
- Department of Clinical Radiology, Oxford University Hospitals NHS Trust, Churchill Hospital, Old Road, Headington, Oxford, Oxfordshire OX3 7LE, UK.
| | - Daniel R McGowan
- Department of Oncology, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford, Oxfordshire OX3 7DQ,UK; Radiation Physics and Protection, Oxford University Hospitals NHS Trust, Churchill Hospital, Old Road, Headington, Oxford, Oxfordshire OX3 7LE, UK.
| | - Eugene J Teoh
- Department of Clinical Radiology, Oxford University Hospitals NHS Trust, Churchill Hospital, Old Road, Headington, Oxford, Oxfordshire OX3 7LE, UK.
| | - Kevin M Bradley
- Department of Clinical Radiology, Oxford University Hospitals NHS Trust, Churchill Hospital, Old Road, Headington, Oxford, Oxfordshire OX3 7LE, UK.
| | - Fergus V Gleeson
- Department of Clinical Radiology, Oxford University Hospitals NHS Trust, Churchill Hospital, Old Road, Headington, Oxford, Oxfordshire OX3 7LE, UK.
| |
Collapse
|