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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] [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.
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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
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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: 85] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [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.
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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
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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] [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).
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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
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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] [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.
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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
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Taylor E, Yeung I, Keller H, Wouters BG, Milosevic M, Hedley DW, Jaffray DA. Quantifying hypoxia in human cancers using static PET imaging. Phys Med Biol 2016; 61:7957-7974. [PMID: 27779123 DOI: 10.1088/0031-9155/61/22/7957] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Compared to FDG, the signal of 18F-labelled hypoxia-sensitive tracers in tumours is low. This means that in addition to the presence of hypoxic cells, transport properties contribute significantly to the uptake signal in static PET images. This sensitivity to transport must be minimized in order for static PET to provide a reliable standard for hypoxia quantification. A dynamic compartmental model based on a reaction-diffusion formalism was developed to interpret tracer pharmacokinetics and applied to static images of FAZA in twenty patients with pancreatic cancer. We use our model to identify tumour properties-well-perfused without substantial necrosis or partitioning-for which static PET images can reliably quantify hypoxia. Normalizing the measured activity in a tumour voxel by the value in blood leads to a reduction in the sensitivity to variations in 'inter-corporal' transport properties-blood volume and clearance rate-as well as imaging study protocols. Normalization thus enhances the correlation between static PET images and the FAZA binding rate K 3, a quantity which quantifies hypoxia in a biologically significant way. The ratio of FAZA uptake in spinal muscle and blood can vary substantially across patients due to long muscle equilibration times. Normalized static PET images of hypoxia-sensitive tracers can reliably quantify hypoxia for homogeneously well-perfused tumours with minimal tissue partitioning. The ideal normalizing reference tissue is blood, either drawn from the patient before PET scanning or imaged using PET. If blood is not available, uniform, homogeneously well-perfused muscle can be used. For tumours that are not homogeneously well-perfused or for which partitioning is significant, only an analysis of dynamic PET scans can reliably quantify hypoxia.
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Affiliation(s)
- Edward Taylor
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada. Techna Institute, University Health Network, Toronto, Canada
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Quartuccio N, Caobelli F, Di Mauro F, Cammaroto G. Non-18F-FDG PET/CT in the management of patients affected by HNC: state-of-the-art. Nucl Med Commun 2016; 37:891-8. [PMID: 27139114 DOI: 10.1097/mnm.0000000000000530] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PET/computed tomography with F-fluorodeoxyglucose is considered a powerful molecular imaging technique that can provide useful information in the management of patients affected by head and neck cancer. However, misleading findings have been reported because of nonspecific uptake caused by peritumoural inflammation and physiologic changes in nonmalignant tissues in the head and neck region. More specific β-emitting tracers have been introduced that can track other pathological processes. We aimed to review the existing literature performing the search until June 2015 on non-F-fluorodeoxyglucose PET tracers in head and neck cancer to highlight their role in clinical practice.
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Affiliation(s)
- Natale Quartuccio
- aWolfson Molecular Imaging Centre, University of Manchester, Manchester, UK bDepartment of Nuclear Medicine, Hannover Medical School, Hanover, Germany cDepartment of Nuclear Medicine, Universitätsspital Basel, Basel, Switzerland dNuclear Medicine Unit, Department of Biomedical Sciences and Morphologic and Functional Images eDepartment of Otorhinolaryngology, University of Messina, Messina fYoung Executive Committee of the Italian Association of Nuclear Medicine (AIMN), Milan, Italy
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Alonzi R. Functional Radiotherapy Targeting using Focused Dose Escalation. Clin Oncol (R Coll Radiol) 2015; 27:601-17. [PMID: 26456478 DOI: 10.1016/j.clon.2015.06.015] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2015] [Accepted: 06/17/2015] [Indexed: 12/12/2022]
Abstract
Various quantitative and semi-quantitative imaging biomarkers have been identified that may serve as valid surrogates for the risk of recurrence after radiotherapy. Tumour characteristics, such as hypoxia, vascularity, cellular proliferation and clonogen density, can be geographically mapped using biological imaging techniques. The potential gains in therapeutic ratio from the precision targeting of areas of intrinsic resistance makes focused dose escalation an exciting field of study. This overview will explore the issues surrounding biologically optimised radiotherapy, including its requirements, feasibility, technical considerations and potential applicability.
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Affiliation(s)
- R Alonzi
- Mount Vernon Cancer Centre, Northwood, UK.
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Liu D, Chalkidou A, Landau DB, Marsden PK, Fenwick JD. Interstitial diffusion and the relationship between compartment modelling and multi-scale spatial-temporal modelling of (18)F-FLT tumour uptake dynamics. Phys Med Biol 2014; 59:5175-202. [PMID: 25138724 DOI: 10.1088/0031-9155/59/17/5175] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Tumour cell proliferation can be imaged via positron emission tomography of the radiotracer 3'-deoxy-3'-18F-fluorothymidine (18F-FLT). Conceptually, the number of proliferating cells might be expected to correlate more closely with the kinetics of 18F-FLT uptake than with uptake at a fixed time. Radiotracer uptake kinetics are standardly visualized using parametric maps of compartment model fits to time-activity-curves (TACs) of individual voxels. However the relationship between the underlying spatiotemporal accumulation of FLT and the kinetics described by compartment models has not yet been explored. In this work tumour tracer uptake is simulated using a mechanistic spatial-temporal model based on a convection-diffusion-reaction equation solved via the finite difference method. The model describes a chain of processes: the flow of FLT between the spatially heterogeneous tumour vasculature and interstitium; diffusion and convection of FLT within the interstitium; transport of FLT into cells; and intracellular phosphorylation. Using values of model parameters estimated from the biological literature, simulated FLT TACs are generated with shapes and magnitudes similar to those seen clinically. Results show that the kinetics of the spatial-temporal model can be recovered accurately by fitting a 3-tissue compartment model to FLT TACs simulated for those tumours or tumour sub-volumes that can be viewed as approximately closed, for which tracer diffusion throughout the interstitium makes only a small fractional change to the quantity of FLT they contain. For a single PET voxel of width 2.5-5 mm we show that this condition is roughly equivalent to requiring that the relative difference in tracer uptake between the voxel and its neighbours is much less than one.
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Affiliation(s)
- Dan Liu
- Department of Oncology, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, UK
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Hypoxia in head and neck cancer in theory and practice: a PET-based imaging approach. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2014; 2014:624642. [PMID: 25214887 PMCID: PMC4158154 DOI: 10.1155/2014/624642] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/13/2014] [Accepted: 08/06/2014] [Indexed: 11/24/2022]
Abstract
Hypoxia plays an important role in tumour recurrence among head and neck cancer patients. The identification and quantification of hypoxic regions are therefore an essential aspect of disease management. Several predictive assays for tumour oxygenation status have been developed in the past with varying degrees of success. To date, functional imaging techniques employing positron emission tomography (PET) have been shown to be an important tool for both pretreatment assessment and tumour response evaluation during therapy. Hypoxia-specific PET markers have been implemented in several clinics to quantify hypoxic tumour subvolumes for dose painting and personalized treatment planning and delivery. Several new radiotracers are under investigation. PET-derived functional parameters and tracer pharmacokinetics serve as valuable input data for computational models aiming at simulating or interpreting PET acquired data, for the purposes of input into treatment planning or radio/chemotherapy response prediction programs. The present paper aims to cover the current status of hypoxia imaging in head and neck cancer together with the justification for the need and the role of computer models based on PET parameters in understanding patient-specific tumour behaviour.
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Hackett SL, Liu D, Chalkidou A, Marsden P, Landau D, Fenwick JD. Estimation of input functions from dynamic [18F]FLT PET studies of the head and neck with correction for partial volume effects. EJNMMI Res 2013; 3:84. [PMID: 24369816 PMCID: PMC4109699 DOI: 10.1186/2191-219x-3-84] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2013] [Accepted: 12/16/2013] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND We present a method for extracting arterial input functions from dynamic [18F]FLT PET images of the head and neck, directly accounting for the partial volume effect. The method uses two blood samples, for which the optimum collection times are assessed. METHODS Six datasets comprising dynamic PET images, co-registered computed tomography (CT) scans and blood-sampled input functions were collected from four patients with head and neck tumours. In each PET image set, a region was identified that comprised the carotid artery (outlined on CT images) and surrounding tissue within the voxels containing the artery. The time course of activity in the region was modelled as the sum of the blood-sampled input function and a compartmental model of tracer uptake in the surrounding tissue.The time course of arterial activity was described by a mathematical function with seven parameters. The parameters of the function and the compartmental model were simultaneously estimated, aiming to achieve the best match between the modelled and imaged time course of regional activity and the best match of the estimated blood activity to between 0 and 3 samples. The normalised root-mean-square (RMSnorm) differences and errors in areas under the curves (AUCs) between the measured and estimated input functions were assessed. RESULTS A one-compartment model of tracer movement to and from the artery best described uptake in the tissue surrounding the artery, so the final model of the input function and tissue kinetics has nine parameters to be estimated. The estimated and blood-sampled input functions agreed well when two blood samples, obtained at times between 2 and 8 min and between 8 and 60 min, were used in the estimation process (RMSnorm values of 1.1 ± 0.5 and AUC errors for the peak and tail region of the curves of 15% ± 9% and 10% ± 8%, respectively). A third blood sample did not significantly improve the accuracy of the estimated input functions. CONCLUSIONS Input functions for FLT-PET studies of the head and neck can be estimated well using a one-compartment model of tracer movement and TWO blood samples obtained after the peak in arterial activity.
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Affiliation(s)
- Sara L Hackett
- Gray Institute for Radiation Oncology and Biology, Department of Oncology,
University of Oxford, Oxford OX3 7DQ, UK
| | - Dan Liu
- Gray Institute for Radiation Oncology and Biology, Department of Oncology,
University of Oxford, Oxford OX3 7DQ, UK
| | - Anastasia Chalkidou
- PET Imaging Centre, Guys and St Thomas’ Hospital, King’s College
London, London SE1 7EH, UK
| | - Paul Marsden
- PET Imaging Centre, Guys and St Thomas’ Hospital, King’s College
London, London SE1 7EH, UK
| | - David Landau
- Department of Oncology, Guys and St Thomas’ Hospital, King’s College
London, London SE1 7EH, UK
| | - John D Fenwick
- Gray Institute for Radiation Oncology and Biology, Department of Oncology,
University of Oxford, Oxford OX3 7DQ, UK
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