1
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Smith CLC, Zwezerijnen GJC, den Hollander ME, Weijland J, Yaqub M, Boellaard R. Mitigating SUV uncertainties using total body PET imaging. Eur J Nucl Med Mol Imaging 2024; 51:1070-1078. [PMID: 37953391 PMCID: PMC10881693 DOI: 10.1007/s00259-023-06503-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 10/30/2023] [Indexed: 11/14/2023]
Abstract
PURPOSE Standardised uptake values (SUV) are commonly used to quantify 18F-FDG lesion uptake. However, SUVs may suffer from several uncertainties and errors. Long-axial field-of-view (LAFOV) PET/CT systems might enable image-based quality control (QC) by deriving 18F-FDG activity and weight from total body (TB) 18F-FDG PET images. In this study, we aimed to develop these image-based QC to reduce errors and mitigate SUV uncertainties. METHODS Twenty-five out of 81 patient scans from a LAFOV PET/CT system were used to determine regression fits for deriving of image-derived activity and weight. Thereafter, the regression fits were applied to 56 independent 18F-FDG PET scans from the same scanner to determine if injected activity and weight could be obtained accurately from TB and half-body (HB) scans. Additionally, we studied the impact of image-based values on the precision of liver SUVmean and lesion SUVpeak. Finally, 20 scans were acquired from a short-axial field-of-view (SAFOV) PET/CT system to determine if the regression fits also applied to HB scans from a SAFOV system. RESULTS Both TB and HB 18F-FDG activity and weight significantly predicted reported injected activity (r = 0.999; r = 0.984) and weight (r = 0.999; r = 0.987), respectively. After applying the regression fits, 18F-FDG activity and weight were accurately derived within 4.8% and 3.2% from TB scans and within 4.9% and 3.1% from HB, respectively. Image-derived values also mitigated liver and lesion SUV variability compared with reported values. Moreover, 18F-FDG activity and weight obtained from a SAFOV scanner were derived within 6.7% and 4.5%, respectively. CONCLUSION 18F-FDG activity and weight can be derived accurately from TB and HB scans, and image-derived values improved SUV precision and corrected for lesion SUV errors. Therefore, image-derived values should be included as QC to generate a more reliable and reproducible quantitative uptake measurement.
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Affiliation(s)
- Charlotte L C Smith
- Department of Radiology and Nuclear Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117 1081 HV, Amsterdam, The Netherlands.
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.
| | - Gerben J C Zwezerijnen
- Department of Radiology and Nuclear Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Marijke E den Hollander
- Department of Radiology and Nuclear Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117 1081 HV, Amsterdam, The Netherlands
| | - Jolijn Weijland
- Department of Radiology and Nuclear Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117 1081 HV, Amsterdam, The Netherlands
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, De Boelelaan 1117 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
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2
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Alig SK, Shahrokh Esfahani M, Garofalo A, Li MY, Rossi C, Flerlage T, Flerlage JE, Adams R, Binkley MS, Shukla N, Jin MC, Olsen M, Telenius A, Mutter JA, Schroers-Martin JG, Sworder BJ, Rai S, King DA, Schultz A, Bögeholz J, Su S, Kathuria KR, Liu CL, Kang X, Strohband MJ, Langfitt D, Pobre-Piza KF, Surman S, Tian F, Spina V, Tousseyn T, Buedts L, Hoppe R, Natkunam Y, Fornecker LM, Castellino SM, Advani R, Rossi D, Lynch R, Ghesquières H, Casasnovas O, Kurtz DM, Marks LJ, Link MP, André M, Vandenberghe P, Steidl C, Diehn M, Alizadeh AA. Distinct Hodgkin lymphoma subtypes defined by noninvasive genomic profiling. Nature 2024; 625:778-787. [PMID: 38081297 PMCID: PMC11293530 DOI: 10.1038/s41586-023-06903-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 11/28/2023] [Indexed: 01/06/2024]
Abstract
The scarcity of malignant Hodgkin and Reed-Sternberg cells hampers tissue-based comprehensive genomic profiling of classic Hodgkin lymphoma (cHL). By contrast, liquid biopsies show promise for molecular profiling of cHL due to relatively high circulating tumour DNA (ctDNA) levels1-4. Here we show that the plasma representation of mutations exceeds the bulk tumour representation in most cases, making cHL particularly amenable to noninvasive profiling. Leveraging single-cell transcriptional profiles of cHL tumours, we demonstrate Hodgkin and Reed-Sternberg ctDNA shedding to be shaped by DNASE1L3, whose increased tumour microenvironment-derived expression drives high ctDNA concentrations. Using this insight, we comprehensively profile 366 patients, revealing two distinct cHL genomic subtypes with characteristic clinical and prognostic correlates, as well as distinct transcriptional and immunological profiles. Furthermore, we identify a novel class of truncating IL4R mutations that are dependent on IL-13 signalling and therapeutically targetable with IL-4Rα-blocking antibodies. Finally, using PhasED-seq5, we demonstrate the clinical value of pretreatment and on-treatment ctDNA levels for longitudinally refining cHL risk prediction and for detection of radiographically occult minimal residual disease. Collectively, these results support the utility of noninvasive strategies for genotyping and dynamic monitoring of cHL, as well as capturing molecularly distinct subtypes with diagnostic, prognostic and therapeutic potential.
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Affiliation(s)
- Stefan K Alig
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA
| | | | - Andrea Garofalo
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA
| | - Michael Yu Li
- Centre for Lymphoid Cancer, British Columbia Cancer, Vancouver, British Columbia, Canada
| | - Cédric Rossi
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA
- Hematology Department, University Hospital F. Mitterrand and Inserm UMR 1231, Dijon, France
| | - Tim Flerlage
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Jamie E Flerlage
- Department of Oncology, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Ragini Adams
- Department of Pediatrics, Division of Pediatric Hematology and Oncology, Stanford University, Stanford, CA, USA
| | - Michael S Binkley
- Department of Radiation Oncology, Stanford University Medical Center, Stanford, CA, USA
| | - Navika Shukla
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA
| | - Michael C Jin
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA
| | - Mari Olsen
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA
| | - Adèle Telenius
- Centre for Lymphoid Cancer, British Columbia Cancer, Vancouver, British Columbia, Canada
| | - Jurik A Mutter
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA
| | - Joseph G Schroers-Martin
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA
| | - Brian J Sworder
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA
| | - Shinya Rai
- Centre for Lymphoid Cancer, British Columbia Cancer, Vancouver, British Columbia, Canada
| | - Daniel A King
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA
| | - Andre Schultz
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA
| | - Jan Bögeholz
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA
| | - Shengqin Su
- Department of Radiation Oncology, Stanford University Medical Center, Stanford, CA, USA
| | - Karan R Kathuria
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA
| | - Chih Long Liu
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA
| | - Xiaoman Kang
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA
| | - Maya J Strohband
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA
| | - Deanna Langfitt
- Department of Bone Marrow Transplant and Cellular Therapy, St Jude Children's Research Hospital, Memphis, TN, USA
| | | | - Sherri Surman
- Department of Infectious Diseases, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Feng Tian
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA
| | - Valeria Spina
- Laboratory of Molecular Diagnostics, Department of Medical Genetics EOLAB, Bellinzona, Switzerland
| | - Thomas Tousseyn
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | | | - Richard Hoppe
- Department of Radiation Oncology, Stanford University Medical Center, Stanford, CA, USA
| | | | - Luc-Matthieu Fornecker
- Institut de Cancérologie Strasbourg Europe (ICANS) and University of Strasbourg, Strasbourg, France
| | - Sharon M Castellino
- Department of Pediatrics, Emory University, Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Ranjana Advani
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA
| | - Davide Rossi
- Clinic of Hematology, Oncology Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
- Laboratory of Experimental Hematology, Institute of Oncology Research, Bellinzona, Switzerland
- Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland
| | - Ryan Lynch
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Hervé Ghesquières
- Department of Hematology, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, Pierre Benite, France
| | - Olivier Casasnovas
- Hematology Department, University Hospital F. Mitterrand and Inserm UMR 1231, Dijon, France
| | - David M Kurtz
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA
| | - Lianna J Marks
- Department of Pediatrics, Division of Pediatric Hematology and Oncology, Stanford University, Stanford, CA, USA
| | - Michael P Link
- Department of Pediatrics, Division of Pediatric Hematology and Oncology, Stanford University, Stanford, CA, USA
| | - Marc André
- Department of Haematology, Université Catholique de Louvain, CHU UCL Namur, Yvoir, Belgium
| | - Peter Vandenberghe
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Department of Hematology, University Hospitals Leuven, Leuven, Belgium
| | - Christian Steidl
- Centre for Lymphoid Cancer, British Columbia Cancer, Vancouver, British Columbia, Canada
| | - Maximilian Diehn
- Department of Radiation Oncology, Stanford University Medical Center, Stanford, CA, USA.
| | - Ash A Alizadeh
- Department of Medicine, Divisions of Oncology and Hematology, Stanford University, Stanford, CA, USA.
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3
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Lewis KL, Trotman J. Integration of PET in DLBCL. Semin Hematol 2023; 60:291-304. [PMID: 38326144 DOI: 10.1053/j.seminhematol.2023.12.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 11/24/2023] [Accepted: 12/04/2023] [Indexed: 02/09/2024]
Abstract
F-fluorodeoxyglucose positron emission tomography-computerized tomography (18FDG-PET/CT) is the gold-standard imaging modality for staging and response assessment for most lymphomas. This review focuses on the utility of 18FDG-PET/CT, and its role in staging, prognostication and response assessment in diffuse large B-cell lymphoma (DLBCL), including emerging possibilities for future use.
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Affiliation(s)
| | - Judith Trotman
- Concord Repatriation General Hospital, Concord, NSW, Australia
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4
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Alberts I, Seibel S, Xue S, Viscione M, Mingels C, Sari H, Afshar-Oromieh A, Limacher A, Rominger A. Investigating the influence of long-axial versus short-axial field of view PET/CT on stage migration in lymphoma and non-small cell lung cancer. Nucl Med Commun 2023; 44:988-996. [PMID: 37578376 PMCID: PMC10566597 DOI: 10.1097/mnm.0000000000001745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/27/2023] [Indexed: 08/15/2023]
Abstract
OBJECTIVES The objective of this study was to evaluate the influence of a long-axial field-of-view (LAFOV) on stage migration using a large single-centre retrospective cohort in lymphoma and non-small cell lung cancer (NSCLC). METHODS A retrospective study is performed for patients undergoing PET/computed tomography (CT) on either a short-axial field-of-view (SAFOV) or LAFOV PET/CT system for the staging of known or suspected NSCLC or for therapeutic response in lymphoma. The primary endpoint was the Deauville therapy response score for patients with lymphoma for the two systems. Secondary endpoints were the American Joint Committee on Cancer stage for NSCLC, the frequency of cN3 and cM1 findings, the probability for a positive nodal staging (cN1-3) for NSCLC and the diagnostic accuracy for nodal staging in NSCLC. RESULTS One thousand two hundred eighteen records were screened and 597 patients were included for analysis ( N = 367 for lymphoma and N = 291 for NSCLC). For lymphoma, no significant differences were found in the proportion of patients with complete metabolic response versus non-complete metabolic response Deauville response scores ( P = 0.66). For NSCLC no significant differences were observed between the two scanners for the frequency of cN3 and cM1 findings, for positive nodal staging, neither the sensitivity nor the specificity. CONCLUSIONS In this study use of a LAFOV system was neither associated with upstaging in lymphoma nor NSCLC compared to a digital SAFOV system. Diagnostic accuracy was comparable between the two systems in NSCLC despite shorter acquisition times for LAFOV.
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Affiliation(s)
- Ian Alberts
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern
| | - Sigrid Seibel
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern
| | - Song Xue
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern
| | - Marco Viscione
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern
| | - Clemens Mingels
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern
| | - Hasan Sari
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne
| | - Ali Afshar-Oromieh
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern
| | | | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern
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5
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Ferrández MC, Golla SSV, Eertink JJ, de Vries BM, Wiegers SE, Zwezerijnen GJC, Pieplenbosch S, Schilder L, Heymans MW, Zijlstra JM, Boellaard R. Sensitivity of an AI method for [ 18F]FDG PET/CT outcome prediction of diffuse large B-cell lymphoma patients to image reconstruction protocols. EJNMMI Res 2023; 13:88. [PMID: 37758869 PMCID: PMC10533444 DOI: 10.1186/s13550-023-01036-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/19/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Convolutional neural networks (CNNs), applied to baseline [18F]-FDG PET/CT maximum intensity projections (MIPs), show potential for treatment outcome prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study is to investigate the robustness of CNN predictions to different image reconstruction protocols. Baseline [18F]FDG PET/CT scans were collected from 20 DLBCL patients. EARL1, EARL2 and high-resolution (HR) protocols were applied per scan, generating three images with different image qualities. Image-based transformation was applied by blurring EARL2 and HR images to generate EARL1 compliant images using a Gaussian filter of 5 and 7 mm, respectively. MIPs were generated for each of the reconstructions, before and after image transformation. An in-house developed CNN predicted the probability of tumor progression within 2 years for each MIP. The difference in probabilities per patient was then calculated between both EARL2 and HR with respect to EARL1 (delta probabilities or ΔP). We compared these to the probabilities obtained after aligning the data with ComBat using the difference in median and interquartile range (IQR). RESULTS CNN probabilities were found to be sensitive to different reconstruction protocols (EARL2 ΔP: median = 0.09, interquartile range (IQR) = [0.06, 0.10] and HR ΔP: median = 0.1, IQR = [0.08, 0.16]). Moreover, higher resolution images (EARL2 and HR) led to higher probability values. After image-based and ComBat transformation, an improved agreement of CNN probabilities among reconstructions was found for all patients. This agreement was slightly better after image-based transformation (transformed EARL2 ΔP: median = 0.022, IQR = [0.01, 0.02] and transformed HR ΔP: median = 0.029, IQR = [0.01, 0.03]). CONCLUSION Our CNN-based outcome predictions are affected by the applied reconstruction protocols, yet in a predictable manner. Image-based harmonization is a suitable approach to harmonize CNN predictions across image reconstruction protocols.
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Affiliation(s)
- Maria C Ferrández
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.
| | - Sandeep S V Golla
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Jakoba J Eertink
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bart M de Vries
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Sanne E Wiegers
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Gerben J C Zwezerijnen
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Simone Pieplenbosch
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Louise Schilder
- Department of Internal Medicine, Amstelland Hospital, Amstelveen, The Netherlands
| | - Martijn W Heymans
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Methodology, Amsterdam, The Netherlands
| | - Josée M Zijlstra
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
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6
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Ferrández MC, Golla SSV, Eertink JJ, de Vries BM, Lugtenburg PJ, Wiegers SE, Zwezerijnen GJC, Pieplenbosch S, Kurch L, Hüttmann A, Hanoun C, Dührsen U, de Vet HCW, Zijlstra JM, Boellaard R. An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients. Sci Rep 2023; 13:13111. [PMID: 37573446 PMCID: PMC10423266 DOI: 10.1038/s41598-023-40218-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 08/07/2023] [Indexed: 08/14/2023] Open
Abstract
Convolutional neural networks (CNNs) may improve response prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to investigate the feasibility of a CNN using maximum intensity projection (MIP) images from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) baseline scans to predict the probability of time-to-progression (TTP) within 2 years and compare it with the International Prognostic Index (IPI), i.e. a clinically used score. 296 DLBCL 18F-FDG PET/CT baseline scans collected from a prospective clinical trial (HOVON-84) were analysed. Cross-validation was performed using coronal and sagittal MIPs. An external dataset (340 DLBCL patients) was used to validate the model. Association between the probabilities, metabolic tumour volume and Dmaxbulk was assessed. Probabilities for PET scans with synthetically removed tumors were also assessed. The CNN provided a 2-year TTP prediction with an area under the curve (AUC) of 0.74, outperforming the IPI-based model (AUC = 0.68). Furthermore, high probabilities (> 0.6) of the original MIPs were considerably decreased after removing the tumours (< 0.4, generally). These findings suggest that MIP-based CNNs are able to predict treatment outcome in DLBCL.
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Affiliation(s)
- Maria C Ferrández
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.
| | - Sandeep S V Golla
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Jakoba J Eertink
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Bart M de Vries
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Pieternella J Lugtenburg
- Department of Hematology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Sanne E Wiegers
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Gerben J C Zwezerijnen
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Simone Pieplenbosch
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lars Kurch
- Department of Nuclear Medicine, Clinic and Polyclinic for Nuclear Medicine, University of Leipzig, Leipzig, Germany
| | - Andreas Hüttmann
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Christine Hanoun
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Ulrich Dührsen
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Henrica C W de Vet
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Methodology, Amsterdam Public Health Research Institute, Methodology, Amsterdam, The Netherlands
| | - Josée M Zijlstra
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
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7
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Voorwerk L, Isaeva OI, Horlings HM, Balduzzi S, Chelushkin M, Bakker NAM, Champanhet E, Garner H, Sikorska K, Loo CE, Kemper I, Mandjes IAM, de Maaker M, van Geel JJL, Boers J, de Boer M, Salgado R, van Dongen MGJ, Sonke GS, de Visser KE, Schumacher TN, Blank CU, Wessels LFA, Jager A, Tjan-Heijnen VCG, Schröder CP, Linn SC, Kok M. PD-L1 blockade in combination with carboplatin as immune induction in metastatic lobular breast cancer: the GELATO trial. NATURE CANCER 2023; 4:535-549. [PMID: 37038006 PMCID: PMC10132987 DOI: 10.1038/s43018-023-00542-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 03/08/2023] [Indexed: 04/12/2023]
Abstract
Invasive lobular breast cancer (ILC) is the second most common histological breast cancer subtype, but ILC-specific trials are lacking. Translational research revealed an immune-related ILC subset, and in mouse ILC models, synergy between immune checkpoint blockade and platinum was observed. In the phase II GELATO trial ( NCT03147040 ), patients with metastatic ILC were treated with weekly carboplatin (area under the curve 1.5 mg ml-1 min-1) as immune induction for 12 weeks and atezolizumab (PD-L1 blockade; triweekly) from the third week until progression. Four of 23 evaluable patients had a partial response (17%), and 2 had stable disease, resulting in a clinical benefit rate of 26%. From these six patients, four had triple-negative ILC (TN-ILC). We observed higher CD8+ T cell infiltration, immune checkpoint expression and exhausted T cells after treatment. With this GELATO trial, we show that ILC-specific clinical trials are feasible and demonstrate promising antitumor activity of atezolizumab with carboplatin, particularly for TN-ILC, and provide insights for the design of highly needed ILC-specific trials.
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Affiliation(s)
- Leonie Voorwerk
- Division of Tumor Biology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Olga I Isaeva
- Division of Tumor Biology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Hugo M Horlings
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Sara Balduzzi
- Department of Biometrics, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Maksim Chelushkin
- Division of Tumor Biology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Noor A M Bakker
- Division of Tumor Biology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Elisa Champanhet
- Division of Tumor Biology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Hannah Garner
- Division of Tumor Biology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Karolina Sikorska
- Department of Biometrics, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Claudette E Loo
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Inge Kemper
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Ingrid A M Mandjes
- Department of Biometrics, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Michiel de Maaker
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Jasper J L van Geel
- Department of Medical Oncology, University Medical Center Groningen, Groningen, the Netherlands
| | - Jorianne Boers
- Department of Medical Oncology, University Medical Center Groningen, Groningen, the Netherlands
| | - Maaike de Boer
- Department of Medical Oncology, GROW, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Roberto Salgado
- Department of Pathology, GZA-ZNA hospitals, Antwerp, Belgium
- Division of Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Marloes G J van Dongen
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Gabe S Sonke
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Karin E de Visser
- Division of Tumor Biology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
- Department of Immunology, Leiden University Medical Center, Leiden, the Netherlands
| | - Ton N Schumacher
- Oncode Institute, Utrecht, the Netherlands
- Division of Molecular Oncology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Hematology, Leiden University Medical Center, Leiden, the Netherlands
| | - Christian U Blank
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Division of Molecular Oncology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Lodewyk F A Wessels
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Oncode Institute, Utrecht, the Netherlands
| | - Agnes Jager
- Department of Medical Oncology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Vivianne C G Tjan-Heijnen
- Department of Medical Oncology, GROW, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Carolien P Schröder
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Oncology, University Medical Center Groningen, Groningen, the Netherlands
| | - Sabine C Linn
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Marleen Kok
- Division of Tumor Biology and Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands.
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands.
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8
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Fragoso Costa P, Jentzen W, SÜßELBECK F, Fendler WP, Rischpler C, Herrmann K, Conti M, Kersting D, Weber M. Reduction of emission time for [68Ga]Ga-PSMA PET/CT using the digital biograph vision: a phantom study. THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF... 2023; 67:57-68. [PMID: 34309334 DOI: 10.23736/s1824-4785.21.03300-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
BACKGROUND The aim of this phantom study was to optimize the [68Ga]Ga-PSMA PET/CT examination in terms of scan time duration and image reconstruction parameters, in combination with PSF and TOF modelling, in a digital Biograph Vision PET/CT scanner. METHODS Three types of phantoms were used: 1) soft-tissue tumor phantom consisting of six spheres mounted in a torso phantom; 2) bone-lung tumor phantom; 3) resolution phantom. Phantom inserts were filled with activity concentrations (ACs) that were derived from clinical data. Phantom data were acquired in list-mode at one bed position. Images with emission data ranging from 30 to 210 s in 30-s increments were reconstructed from a reference image acquired with 3.5-min emission. Iterative image reconstruction (OSEM), point-spread-function (PSF) and time-of-flight (TOF) options were applied using different iterations, Gaussian filters, and voxel sizes. The criteria for image quality was lesion detectability and lesion quantification, evaluated as contrast-to-noise ratio (CNR) and maximum AC (peak AC), respectively. A threshold value of CNR above 6 and percentage maximum AC (peak AC) deviation range of ±20% of the reference image were considered acceptable. The proposed single-bed scan time reduction was projected to a whole-body examination (patient validation scan) using the continuous-bed-motion mode. RESULTS Sphere and background ACs of 20 kBq/mL and 1 kBq/mL were selected, respectively. The optimized single-bed scan time was approximately 60 s using OSEM-TOF or OSEM-TOF+PSF (four iterations, 4.0-mm Gaussian filter and almost isotropic voxel size of 3.0-mm side length), resulting in a PET spatial resolution of 6.3 mm for OSEM-TOF and 5.5 mm for OSEM-TOF+PSF. In the patient validation, the maximum percentage difference in lesion quantification between standard and optimized protocol (whole-body scan time of 15 vs. 5 min) was below 19%. CONCLUSIONS A reduction of single-bed and whole-body scan time for [68Ga]Ga-PSMA PET/CT compared to current recommended clinical acquisition protocols is postulated. Clinical studies are warranted to validate the applicability of this protocol.
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Affiliation(s)
- Pedro Fragoso Costa
- Department of Nuclear Medicine, Essen University Hospital, Duisburg-Essen University, Essen, Germany -
| | - Walter Jentzen
- Department of Nuclear Medicine, Essen University Hospital, Duisburg-Essen University, Essen, Germany
| | - Finja SÜßELBECK
- Department of Nuclear Medicine, Essen University Hospital, Duisburg-Essen University, Essen, Germany
| | - Wolfgang P Fendler
- Department of Nuclear Medicine, Essen University Hospital, Duisburg-Essen University, Essen, Germany
| | - Christoph Rischpler
- Department of Nuclear Medicine, Essen University Hospital, Duisburg-Essen University, Essen, Germany
| | - Ken Herrmann
- Department of Nuclear Medicine, Essen University Hospital, Duisburg-Essen University, Essen, Germany
| | | | - David Kersting
- Department of Nuclear Medicine, Essen University Hospital, Duisburg-Essen University, Essen, Germany
| | - Manuel Weber
- Department of Nuclear Medicine, Essen University Hospital, Duisburg-Essen University, Essen, Germany
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9
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Performance of Deauville Criteria in [18F]FDG-PET/CT Diagnostics of Giant Cell Arteritis. Diagnostics (Basel) 2023; 13:diagnostics13010157. [PMID: 36611449 PMCID: PMC9818714 DOI: 10.3390/diagnostics13010157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 12/28/2022] [Accepted: 12/29/2022] [Indexed: 01/04/2023] Open
Abstract
In this retrospective study, PET/CT data from 59 patients with suspected giant cell arteritis (GCA) were reviewed using the Deauville criteria to determine an optimal cut-off between PET positivity and negativity. Seventeen standardised vascular regions were analysed per patient by three investigators blinded to clinical information. Statistical analysis included ROC curves with areas under the curve (AUC), Cohen's and Fleiss' kappa (κ) to calculate sensitivity, specificity, accuracy, and agreement. According to final clinician's diagnosis and the revised 2017 ACR criteria GCA was confirmed in 29 of 59 (49.2 %) patients. With a diagnostic cut-off ≥ 4 (highest tracer uptake of a vessel wall exceeds liver uptake) for PET positivity, all investigators achieved high accuracy (range, 89.8-93.2%) and AUC (range, 0.94-0.97). Sensitivity and specificity ranged from 89.7-96.6% and 83.3-96.7%, respectively. Agreement between the three investigators suggested 'almost perfect agreement' (Fleiss' κ = 0.84) A Deauville score of ≥4 as threshold for PET positivity yielded excellent results with high accuracy and almost perfect inter-rater agreement, suggesting a standardized, reproducible, and reliable score in diagnosing GCA. However, the small sample size and reference standard could lead to biases. Therefore, verification in a multicentre study with a larger patient cohort and prospective setting is needed.
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10
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Eertink JJ, Zwezerijnen GJC, Cysouw MCF, Wiegers SE, Pfaehler EAG, Lugtenburg PJ, van der Holt B, Hoekstra OS, de Vet HCW, Zijlstra JM, Boellaard R. Comparing lesion and feature selections to predict progression in newly diagnosed DLBCL patients with FDG PET/CT radiomics features. Eur J Nucl Med Mol Imaging 2022; 49:4642-4651. [PMID: 35925442 PMCID: PMC9606052 DOI: 10.1007/s00259-022-05916-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 07/14/2022] [Indexed: 01/06/2023]
Abstract
PURPOSE Biomarkers that can accurately predict outcome in DLBCL patients are urgently needed. Radiomics features extracted from baseline [18F]-FDG PET/CT scans have shown promising results. This study aims to investigate which lesion- and feature-selection approaches/methods resulted in the best prediction of progression after 2 years. METHODS A total of 296 patients were included. 485 radiomics features (n = 5 conventional PET, n = 22 morphology, n = 50 intensity, n = 408 texture) were extracted for all individual lesions and at patient level, where all lesions were aggregated into one VOI. 18 features quantifying dissemination were extracted at patient level. Several lesion selection approaches were tested (largest or hottest lesion, patient level [all with/without dissemination], maximum or median of all lesions) and compared to the predictive value of our previously published model. Several data reduction methods were applied (principal component analysis, recursive feature elimination (RFE), factor analysis, and univariate selection). The predictive value of all models was tested using a fivefold cross-validation approach with 50 repeats with and without oversampling, yielding the mean cross-validated AUC (CV-AUC). Additionally, the relative importance of individual radiomics features was determined. RESULTS Models with conventional PET and dissemination features showed the highest predictive value (CV-AUC: 0.72-0.75). Dissemination features had the highest relative importance in these models. No lesion selection approach showed significantly higher predictive value compared to our previous model. Oversampling combined with RFE resulted in highest CV-AUCs. CONCLUSION Regardless of the applied lesion selection or feature selection approach and feature reduction methods, patient level conventional PET features and dissemination features have the highest predictive value. Trial registration number and date: EudraCT: 2006-005174-42, 01-08-2008.
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Affiliation(s)
- Jakoba J Eertink
- Department of Hematology, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands. .,Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.
| | - Gerben J C Zwezerijnen
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.,Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Matthijs C F Cysouw
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.,Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sanne E Wiegers
- Department of Hematology, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | | | - Pieternella J Lugtenburg
- Department of Hematology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Wytemaweg 80, 3015 CN, Rotterdam, the Netherlands
| | - Bronno van der Holt
- Department of Hematology, HOVON Data Center, Erasmus MC Cancer Institute, Dr. Molewaterplein 40, 3015 GD, Rotterdam, the Netherlands
| | - Otto S Hoekstra
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.,Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Henrica C W de Vet
- Epidemiology and Data Science, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Amsterdam Public Health Research Institute, Methodology, Amsterdam, The Netherlands
| | - Josée M Zijlstra
- Department of Hematology, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands.,Radiology and Nuclear Medicine, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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11
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Ferrández MC, Eertink JJ, Golla SSV, Wiegers SE, Zwezerijnen GJC, Pieplenbosch S, Zijlstra JM, Boellaard R. Combatting the effect of image reconstruction settings on lymphoma [ 18F]FDG PET metabolic tumor volume assessment using various segmentation methods. EJNMMI Res 2022; 12:44. [PMID: 35904645 PMCID: PMC9338209 DOI: 10.1186/s13550-022-00916-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/18/2022] [Indexed: 11/15/2022] Open
Abstract
Background [18F]FDG PET-based metabolic tumor volume (MTV) is a promising prognostic marker for lymphoma patients. The aim of this study is to assess the sensitivity of several MTV segmentation methods to variations in image reconstruction methods and the ability of ComBat to improve MTV reproducibility. Methods Fifty-six lesions were segmented from baseline [18F]FDG PET scans of 19 lymphoma patients. For each scan, EARL1 and EARL2 standards and locally clinically preferred reconstruction protocols were applied. Lesions were delineated using 9 semiautomatic segmentation methods: fixed threshold based on standardized uptake value (SUV), (SUV = 4, SUV = 2.5), relative threshold (41% of SUVmax [41M], 50% of SUVpeak [A50P]), majority vote-based methods that select voxels detected by at least 2 (MV2) and 3 (MV3) out of the latter 4 methods, Nestle thresholding, and methods that identify the optimal method based on SUVmax (L2A, L2B). MTVs from EARL2 and locally clinically preferred reconstructions were compared to those from EARL1. Finally, different versions of ComBat were explored to harmonize the data.
Results MTVs from the SUV4.0 method were least sensitive to the use of different reconstructions (MTV ratio: median = 1.01, interquartile range = [0.96–1.10]). After ComBat harmonization, an improved agreement of MTVs among different reconstructions was found for most segmentation methods. The regular implementation of ComBat (‘Regular ComBat’) using non-transformed distributions resulted in less accurate and precise MTV alignments than a version using log-transformed datasets (‘Log-transformed ComBat’). Conclusion MTV depends on both segmentation method and reconstruction methods. ComBat reduces reconstruction dependent MTV variability, especially when log-transformation is used to account for the non-normal distribution of MTVs. Supplementary Information The online version contains supplementary material available at 10.1186/s13550-022-00916-9.
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Affiliation(s)
- Maria C Ferrández
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands.
| | - Jakoba J Eertink
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Sandeep S V Golla
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Sanne E Wiegers
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Gerben J C Zwezerijnen
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Simone Pieplenbosch
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Josée M Zijlstra
- Cancer Center Amsterdam, Department of Hematology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Cancer Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
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12
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Mikhaeel NG, Heymans MW, Eertink JJ, de Vet HC, Boellaard R, Dührsen U, Ceriani L, Schmitz C, Wiegers SE, Hüttmann A, Lugtenburg PJ, Zucca E, Zwezerijnen GJ, Hoekstra OS, Zijlstra JM, Barrington SF. Proposed New Dynamic Prognostic Index for Diffuse Large B-Cell Lymphoma: International Metabolic Prognostic Index. J Clin Oncol 2022; 40:2352-2360. [PMID: 35357901 PMCID: PMC9287279 DOI: 10.1200/jco.21.02063] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 01/23/2022] [Accepted: 02/09/2022] [Indexed: 02/06/2023] Open
Abstract
PURPOSE Baseline metabolic tumor volume (MTV) is a promising biomarker in diffuse large B-cell lymphoma (DLBCL). Our aims were to determine the best statistical relationship between MTV and survival and to compare MTV with the International Prognostic Index (IPI) and its individual components to derive the best prognostic model. METHODS PET scans and clinical data were included from five published studies in newly diagnosed diffuse large B-cell lymphoma. Transformations of MTV were compared with the primary end points of 3-year progression-free survival (PFS) and overall survival (OS) to derive the best relationship for further analyses. MTV was compared with IPI categories and individual components to derive the best model. Patients were grouped into three groups for survival analysis using Kaplan-Meier analysis; 10% at highest risk, 30% intermediate risk, and 60% lowest risk, corresponding with expected clinical outcome. Validation of the best model was performed using four studies as a test set and the fifth study for validation and repeated five times. RESULTS The best relationship for MTV and survival was a linear spline model with one knot located at the median MTV value of 307.9 cm3. MTV was a better predictor than IPI for PFS and OS. The best model combined MTV with age as continuous variables and individual stage as I-IV. The MTV-age-stage model performed better than IPI and was also better at defining a high-risk group (3-year PFS 46.3% v 58.0% and 3-year OS 51.5% v 66.4% for the new model and IPI, respectively). A regression formula was derived to estimate individual patient survival probabilities. CONCLUSION A new prognostic index is proposed using MTV, age, and stage, which outperforms IPI and enables individualized estimates of patient outcome.
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Affiliation(s)
- N. George Mikhaeel
- Department of Clinical Oncology, Guy's Cancer Centre and School of Cancer and Pharmaceutical Sciences, King's College London University, London, United Kingdom
| | - Martijn W. Heymans
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Jakoba J. Eertink
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Hematology, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Henrica C.W. de Vet
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Ronald Boellaard
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Ulrich Dührsen
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Luca Ceriani
- Department of Oncology, IOSI—Oncology Institute of Southern Switzerland, Bellinzona; Università della Svizzera Italiana, Bellinzona, Switzerland
- SAKK—Swiss Group for Clinical Cancer Research, Bern, Switzerland
| | - Christine Schmitz
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Sanne E. Wiegers
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Hematology, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Andreas Hüttmann
- Department of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Pieternella J. Lugtenburg
- Department of Hematology, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, the Netherlands
| | - Emanuele Zucca
- Department of Oncology, IOSI—Oncology Institute of Southern Switzerland, Bellinzona; Università della Svizzera Italiana, Bellinzona, Switzerland
- SAKK—Swiss Group for Clinical Cancer Research, Bern, Switzerland
| | - Gerben J.C. Zwezerijnen
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Otto S. Hoekstra
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Josée M. Zijlstra
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Hematology, Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Sally F. Barrington
- King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's Health Partners, Kings College London, London, United Kingdom
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13
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Miedema IHC, Zwezerijnen GJC, Huisman MC, Doeleman E, Mathijssen RHJ, Lammers T, Hu Q, van Dongen GAMS, Rijcken CJF, Vugts DJ, Menke-van der Houven van Oordt CW. PET-CT Imaging of Polymeric Nanoparticle Tumor Accumulation in Patients. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2201043. [PMID: 35427430 DOI: 10.1002/adma.202201043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 04/08/2022] [Indexed: 06/14/2023]
Abstract
Several FDA/EMA-approved nanomedicines have demonstrated improved pharmacokinetics and toxicity profiles compared to their conventional chemotherapeutic counterparts. The next step to increase therapeutic efficacy depends on tumor accumulation, which can be highly heterogeneous. A clinical tool for patient stratification is urgently awaited. Therefore, a docetaxel-entrapping polymeric nanoparticle (89 Zr-CPC634) is radiolabeled, and positron emission tomography/computed tomography (PET/CT) imaging is performed in seven patients with solid tumors with two different doses of CPC634: an on-treatment (containing 60 mg m-2 docetaxel) and a diagnostic (1-2 mg docetaxel) dose (NCT03712423). Pharmacokinetic half-life for 89 Zr-CPC634 is mean 97.0 ± 14.4 h on-treatment, and 62.4 ± 12.9 h for the diagnostic dose (p = 0.003). At these doses accumulation is observed in 46% and 41% of tumor lesions with a median accumulation in positive lesions 96 h post-injection of 4.94 and 4.45%IA kg-1 (p = 0.91), respectively. In conclusion, PET/CT imaging with a diagnostic dose of 89 Zr-CPC634 accurately reflects on-treatment tumor accumulation and thus opens the possibility for patient stratification in cancer nanomedicine with polymeric nanoparticles.
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Affiliation(s)
- Iris H C Miedema
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Medical Oncology, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
| | - Gerben J C Zwezerijnen
- Cancer Center Amsterdam, Imaging and Biomarkers, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
| | - Marc C Huisman
- Cancer Center Amsterdam, Imaging and Biomarkers, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
| | - Ellen Doeleman
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Medical Oncology, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
| | - Ron H J Mathijssen
- Erasmus University Medical Center, Erasmus University, Erasmus MC Cancer Institute, Department of Medical Oncology, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - Twan Lammers
- Institute for Experimental Molecular Imaging, Helmholtz Institute for Biomedical Engineering, RWTH - Aachen University, Templergraben 55, 52062, Aachen, Germany
| | - Qizhi Hu
- Cristal Therapeutics, Oxfordlaan 55, Maastricht, 6229 EV, The Netherlands
| | - Guus A M S van Dongen
- Cancer Center Amsterdam, Imaging and Biomarkers, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
| | | | - Danielle J Vugts
- Cancer Center Amsterdam, Imaging and Biomarkers, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
| | - C Willemien Menke-van der Houven van Oordt
- Amsterdam UMC location Vrije Universiteit Amsterdam, Department of Medical Oncology, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
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14
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Husby T, Johansen H, Bogsrud T, Hustad KV, Evensen BV, Boellard R, Giskeødegård GF, Fagerli UM, Eikenes L. A comparison of FDG PET/MR and PET/CT for staging, response assessment, and prognostic imaging biomarkers in lymphoma. Ann Hematol 2022; 101:1077-1088. [PMID: 35174405 PMCID: PMC8993743 DOI: 10.1007/s00277-022-04789-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 02/08/2022] [Indexed: 12/16/2022]
Abstract
The aim of the current study was to investigate the diagnostic performance of FDG PET/MR compared to PET/CT in a patient cohort including Hodgkins lymphoma, diffuse large B-cell lymphoma, and high-grade B-cell lymphoma at baseline and response assessment. Sixty-one patients were examined with FDG PET/CT directly followed by PET/MR. Images were read by two pairs of nuclear medicine physicians and radiologists. Concordance for lymphoma involvement between PET/MR and the reference standard PET/CT was assessed at baseline and response assessment. Correlation of prognostic biomarkers Deauville score, criteria of response, SUVmax, SUVpeak, and MTV was performed between PET/MR and PET/CT. Baseline FDG PET/MR showed a sensitivity of 92.5% and a specificity 97.9% compared to the reference standard PET/CT (κ 0.91) for nodal sites. For extranodal sites, a sensitivity of 80.4% and a specificity of 99.5% were found (κ 0.84). Concordance in Ann Arbor was found in 57 of 61 patients (κ 0.92). Discrepancies were due to misclassification of region and not lesion detection. In response assessment, a sensitivity of 100% and a specificity 99.9% for all sites combined were found (κ 0.92). There was a perfect agreement on Deauville scores 4 and 5 and criteria of response between the two modalities. Intraclass correlation coefficient (ICC) for SUVmax, SUVpeak, and MTV values showed excellent reliability (ICC > 0.9). FDG PET/MR is a reliable alternative to PET/CT in this patient population, both in terms of lesion detection at baseline staging and response assessment, and for quantitative prognostic imaging biomarkers.
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Affiliation(s)
- Trine Husby
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Postboks 8905, Trondheim, Norway.,Department of Oncology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Håkon Johansen
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Trond Bogsrud
- PET-Centre, University Hospital of North Norway, Tromsø, Norway.,Aarhus University Hosipital, Aarhus, Denmark
| | - Kari Vekseth Hustad
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Birte Veslemøy Evensen
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Ronald Boellard
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, The Netherlands.,Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, University Medical Centers Amsterdam, VUMC, Amsterdam, The Netherlands
| | - Guro F Giskeødegård
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Unn-Merete Fagerli
- Department of Oncology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.,Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Live Eikenes
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Postboks 8905, Trondheim, Norway.
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Whole-body CD8 + T cell visualization before and during cancer immunotherapy: a phase 1/2 trial. Nat Med 2022; 28:2601-2610. [PMID: 36471036 PMCID: PMC9800278 DOI: 10.1038/s41591-022-02084-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 10/12/2022] [Indexed: 12/12/2022]
Abstract
Immune checkpoint inhibitors (ICIs), by reinvigorating CD8+ T cell mediated immunity, have revolutionized cancer therapy. Yet, the systemic CD8+ T cell distribution, a potential biomarker of ICI response, remains poorly characterized. We assessed safety, imaging dose and timing, pharmacokinetics and immunogenicity of zirconium-89-labeled, CD8-specific, one-armed antibody positron emission tomography tracer 89ZED88082A in patients with solid tumors before and ~30 days after starting ICI therapy (NCT04029181). No tracer-related side effects occurred. Positron emission tomography imaging with 10 mg antibody revealed 89ZED88082A uptake in normal lymphoid tissues, and tumor lesions across the body varying within and between patients two days after tracer injection (n = 38, median patient maximum standard uptake value (SUVmax) 5.2, IQI 4.0-7.4). Higher SUVmax was associated with mismatch repair deficiency and longer overall survival. Uptake was higher in lesions with stromal/inflamed than desert immunophenotype. Tissue radioactivity was localized to areas with immunohistochemically confirmed CD8 expression. Re-imaging patients on treatment showed no change in average (geometric mean) tumor tracer uptake compared to baseline, but individual lesions showed diverse changes independent of tumor response. The imaging data suggest enormous heterogeneity in CD8+ T cell distribution and pharmacodynamics within and between patients. In conclusion, 89ZED88082A can characterize the complex dynamics of CD8+ T cells in the context of ICIs, and may inform immunotherapeutic treatments.
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Eertink JJ, Arens AIJ, Huijbregts JE, Celik F, de Keizer B, Stroobants S, de Jong D, Wiegers SE, Zwezerijnen GJC, Burggraaff CN, Boellaard R, de Vet HCW, Hoekstra OS, Lugtenburg PJ, Chamuleau MED, Zijlstra JM. Aberrant patterns of PET response during treatment for DLBCL patients with MYC gene rearrangements. Eur J Nucl Med Mol Imaging 2021; 49:943-952. [PMID: 34476551 PMCID: PMC8803795 DOI: 10.1007/s00259-021-05498-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 07/13/2021] [Indexed: 11/29/2022]
Abstract
Purpose MYC gene rearrangements in diffuse large B-cell lymphoma (DLBCL) patients are associated with poor prognosis. Our aim was to compare patterns of 2[18F]fluoro-2-deoxy-D-glucose positron emission tomography computed tomography (PET/CT) response in MYC + and MYC- DLBCL patients. Methods Interim PET/CT (I-PET) and end of treatment PET/CT (EoT-PET) scans of 81 MYC + and 129 MYC- DLBCL patients from 2 HOVON trials were reviewed using the Deauville 5-point scale (DS). DS1-3 was regarded as negative and DS4-5 as positive. Standardized uptake values (SUV) and metabolic tumor volume (MTV) were quantified at baseline, I-PET, and EoT-PET. Negative (NPV) and positive predictive values (PPV) were calculated using 2-year overall survival. Results MYC + DLBCL patients had significantly more positive EoT-PET scans than MYC- patients (32.5 vs 15.7%, p = 0.004). I-PET positivity rates were comparable (28.8 vs 23.8%). In MYC + patients 23.2% of the I-PET negative patients converted to positive at EoT-PET, vs only 2% for the MYC- patients (p = 0.002). Nine (34.6%) MYC + DLBCL showed initially uninvolved localizations at EoT-PET, compared to one (5.3%) MYC- patient. A total of 80.8% of EoT-PET positive MYC + patients showed both increased lesional SUV and MTV compared to I-PET. In MYC- patients, 31.6% showed increased SUV and 42.1% showed increased MTV. NPV of I-PET and EoT-PET was high for both MYC subgroups (81.8–94.1%). PPV was highest at EoT-PET for MYC + patients (61.5%). Conclusion MYC + DLBCL patients demonstrate aberrant PET response patterns compared to MYC- patients with more frequent progression during treatment after I-PET negative assessment and new lesions at sites that were not initially involved. Trial registration number and date of registration HOVON-84: EudraCT: 2006–005,174-42, retrospectively registered 01–08-2008. HOVON-130: EudraCT: 2014–002,654-39, registered 26–01-2015 Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05498-7.
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Affiliation(s)
- J J Eertink
- Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - A I J Arens
- Department of Radiology, Nuclear Medicine and Anatomy, Radboud University Medical Center, Geert Grooteplein Zuid 10, Nijmegen, The Netherlands
| | - J E Huijbregts
- Department of Radiology and Nuclear Medicine, Gelre Ziekenhuizen, Albert Schweitzerlaan 31, Apeldoorn, The Netherlands
| | - F Celik
- Department of Radiology and Nuclear Medicine, Deventer Ziekenhuis, Nico Bolkesteinlaan 75, Deventer, The Netherlands
| | - B de Keizer
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, The Netherlands
| | - S Stroobants
- Department of Nuclear Medicine, Antwerp University Hospital (UZA), Antwerp, Belgium
| | - D de Jong
- Department of Pathology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, de Boelelaan 1117, Amsterdam, The Netherlands
| | - S E Wiegers
- Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - G J C Zwezerijnen
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, de Boelelaan 1117, Amsterdam, The Netherlands
| | - C N Burggraaff
- Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - R Boellaard
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, de Boelelaan 1117, Amsterdam, The Netherlands
| | - H C W de Vet
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, de Boelelaan 1117, Amsterdam, The Netherlands
| | - O S Hoekstra
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, de Boelelaan 1117, Amsterdam, The Netherlands
| | - P J Lugtenburg
- Department of Hematology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Wytemaweg 80, Rotterdam, The Netherlands
| | - M E D Chamuleau
- Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - J M Zijlstra
- Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
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18F-FDG PET baseline radiomics features improve the prediction of treatment outcome in diffuse large B-cell lymphoma. Eur J Nucl Med Mol Imaging 2021; 49:932-942. [PMID: 34405277 PMCID: PMC8803694 DOI: 10.1007/s00259-021-05480-3] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 06/17/2021] [Indexed: 12/21/2022]
Abstract
Purpose Accurate prognostic markers are urgently needed to identify diffuse large B-Cell lymphoma (DLBCL) patients at high risk of progression or relapse. Our purpose was to investigate the potential added value of baseline radiomics features to the international prognostic index (IPI) in predicting outcome after first-line treatment. Methods Three hundred seventeen newly diagnosed DLBCL patients were included. Lesions were delineated using a semi-automated segmentation method (standardized uptake value ≥ 4.0), and 490 radiomics features were extracted. We used logistic regression with backward feature selection to predict 2-year time to progression (TTP). The area under the curve (AUC) of the receiver operator characteristic curve was calculated to assess model performance. High-risk groups were defined based on prevalence of events; diagnostic performance was assessed using positive and negative predictive values. Results The IPI model yielded an AUC of 0.68. The optimal radiomics model comprised the natural logarithms of metabolic tumor volume (MTV) and of SUVpeak and the maximal distance between the largest lesion and any other lesion (Dmaxbulk, AUC 0.76). Combining radiomics and clinical features showed that a combination of tumor- (MTV, SUVpeak and Dmaxbulk) and patient-related parameters (WHO performance status and age > 60 years) performed best (AUC 0.79). Adding radiomics features to clinical predictors increased PPV with 15%, with more accurate selection of high-risk patients compared to the IPI model (progression at 2-year TTP, 44% vs 28%, respectively). Conclusion Prediction models using baseline radiomics combined with currently used clinical predictors identify patients at risk of relapse at baseline and significantly improved model performance. Trial registration number and date EudraCT: 2006–005,174-42, 01–08-2008. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05480-3.
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Rogasch JMM, Boellaard R, Pike L, Borchmann P, Johnson P, Wolf J, Barrington SF, Kobe C. Moving the goalposts while scoring-the dilemma posed by new PET technologies. Eur J Nucl Med Mol Imaging 2021; 48:2696-2710. [PMID: 33990846 PMCID: PMC8263433 DOI: 10.1007/s00259-021-05403-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 05/06/2021] [Indexed: 02/07/2023]
Affiliation(s)
- Julian M M Rogasch
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
| | - Ronald Boellaard
- Radiology and Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam UMC, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Lucy Pike
- King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, UK
| | - Peter Borchmann
- German Hodgkin Study Group, Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, University of Cologne, Cologne, Germany
| | - Peter Johnson
- Cancer Research UK Centre, University of Southampton, Southampton, UK
| | - Jürgen Wolf
- Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf, University Hospital Cologne and University of Cologne, Cologne, Germany
| | - Sally F Barrington
- King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, UK
| | - Carsten Kobe
- Department of Nuclear Medicine, University Hospital of Cologne, Kerpener Str. 62, 50937, Cologne, Germany.
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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] [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.
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20
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Jansen BHE, Bodar YJL, Zwezerijnen GJC, Meijer D, van der Voorn JP, Nieuwenhuijzen JA, Wondergem M, Roeleveld TA, Boellaard R, Hoekstra OS, van Moorselaar RJA, Oprea-Lager DE, Vis AN. Pelvic lymph-node staging with 18F-DCFPyL PET/CT prior to extended pelvic lymph-node dissection in primary prostate cancer - the SALT trial. Eur J Nucl Med Mol Imaging 2021; 48:509-520. [PMID: 32789599 PMCID: PMC7835187 DOI: 10.1007/s00259-020-04974-w] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 07/23/2020] [Indexed: 01/01/2023]
Abstract
PURPOSE The detection of lymph-node metastases (N1) with conventional imaging such as magnetic resonance imaging (MRI) and computed tomography (CT) is inadequate for primarily diagnosed prostate cancer (PCa). Prostate-specific membrane antigen (PSMA) PET/CT is successfully introduced for the staging of (biochemically) recurrent PCa. Besides the frequently used 68gallium-labelled PSMA tracers, 18fluorine-labelled PSMA tracers are available. This study examined the diagnostic accuracy of 18F-DCFPyL (PSMA) PET/CT for lymph-node staging in primary PCa. METHODS This was a prospective, multicentre cohort study. Patients with primary PCa underwent 18F-DCFPyL PET/CT prior to robot-assisted radical prostatectomy (RARP) with extended pelvic lymph-node dissection (ePLND). Patients were included between October 2017 and January 2020. A Memorial Sloan Kettering Cancer Centre (MSKCC) nomogram risk probability of ≥ 8% of lymph-node metastases was set to perform ePLND. All images were reviewed by two experienced nuclear physicians, and were compared with post-operative histopathologic results. RESULTS A total of 117 patients was analysed. Lymph-node metastases (N1) were histologically diagnosed in 17/117 patients (14.5%). The sensitivity, specificity, positive predictive value and negative predictive value for the 18F-DCFPyL PET/CT detection of pelvic lymph-node metastases on a patient level were 41.2% (confidence interval (CI): 19.4-66.5%), 94.0% (CI 86.9-97.5%), 53.8% (CI 26.1-79.6%) and 90.4% (CI 82.6-95.0%), respectively. CONCLUSION 18F-DCFPyL PET/CT showed a high specificity (94.4%), yet a limited sensitivity (41.2%) for the detection of pelvic lymph-node metastases in primary PCa. This implies that current PSMA PET/CT imaging cannot replace diagnostic ePLND. Further research is necessary to define the exact place of PSMA PET/CT imaging in the primary staging of PCa.
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Affiliation(s)
- B H E Jansen
- Department of Urology, Amsterdam University Medical Centres (VU University), De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
- Department of Radiology & Nuclear medicine, Amsterdam University Medical Centres (VU University), De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
- Prostate Cancer Network, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
| | - Y J L Bodar
- Department of Urology, Amsterdam University Medical Centres (VU University), De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
- Department of Radiology & Nuclear medicine, Amsterdam University Medical Centres (VU University), De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
- Prostate Cancer Network, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
| | - G J C Zwezerijnen
- Department of Radiology & Nuclear medicine, Amsterdam University Medical Centres (VU University), De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - D Meijer
- Department of Urology, Amsterdam University Medical Centres (VU University), De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Department of Radiology & Nuclear medicine, Amsterdam University Medical Centres (VU University), De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Prostate Cancer Network, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - J P van der Voorn
- Department of Pathology, Amsterdam University Medical Centres (VU University), Amsterdam, The Netherlands
| | - J A Nieuwenhuijzen
- Department of Urology, Amsterdam University Medical Centres (VU University), De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Prostate Cancer Network, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - M Wondergem
- Department of Nuclear medicine, Noordwest Ziekenhuisgroep, Alkmaar, The Netherlands
| | - T A Roeleveld
- Prostate Cancer Network, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Department of Urology, Noordwest Ziekenhuisgroep, Alkmaar, The Netherlands
| | - R Boellaard
- Department of Radiology & Nuclear medicine, Amsterdam University Medical Centres (VU University), De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - O S Hoekstra
- Department of Radiology & Nuclear medicine, Amsterdam University Medical Centres (VU University), De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - R J A van Moorselaar
- Department of Urology, Amsterdam University Medical Centres (VU University), De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Prostate Cancer Network, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - D E Oprea-Lager
- Department of Radiology & Nuclear medicine, Amsterdam University Medical Centres (VU University), De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - A N Vis
- Department of Urology, Amsterdam University Medical Centres (VU University), De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Prostate Cancer Network, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
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Schalbroeck R, van Velden FHP, de Geus-Oei LF, Yaqub M, van Amelsvoort T, Booij J, Selten JP. Striatal dopamine synthesis capacity in autism spectrum disorder and its relation with social defeat: an [ 18F]-FDOPA PET/CT study. Transl Psychiatry 2021; 11:47. [PMID: 33441546 PMCID: PMC7806928 DOI: 10.1038/s41398-020-01174-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 12/14/2020] [Accepted: 12/15/2020] [Indexed: 01/29/2023] Open
Abstract
Alterations in dopamine signalling have been implied in autism spectrum disorder (ASD), and these could be associated with the risk of developing a psychotic disorder in ASD adults. Negative social experiences and feelings of social defeat might result in an increase in dopamine functioning. However, few studies examined dopamine functioning in vivo in ASD. Here we examine whether striatal dopamine synthesis capacity is increased in ASD and associated with social defeat. Forty-four unmedicated, non-psychotic adults diagnosed with ASD and 22 matched controls, aged 18-30 years, completed a dynamic 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine positron emission tomography/computed tomography ([18F]-FDOPA PET/CT) scan to measure presynaptic dopamine synthesis capacity in the striatum. We considered unwanted loneliness, ascertained using the UCLA Loneliness Scale, as primary measure of social defeat. We found no statistically significant difference in striatal dopamine synthesis capacity between ASD and controls (F1,60 = 0.026, p = 0.87). In ASD, striatal dopamine synthesis capacity was not significantly associated with loneliness (β = 0.01, p = 0.96). Secondary analyses showed comparable results when examining the associative, limbic, and sensorimotor sub-regions of the striatum (all p-values > 0.05). Results were similar before and after adjusting for age, sex, smoking-status, and PET/CT-scanner-type. In conclusion, in unmedicated, non-psychotic adults with ASD, striatal dopamine synthesis capacity is not increased and not associated with social defeat.
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Affiliation(s)
- Rik Schalbroeck
- Rivierduinen Institute for Mental Healthcare, Leiden, The Netherlands. .,School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands. .,Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
| | - Floris H. P. van Velden
- grid.10419.3d0000000089452978Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Lioe-Fee de Geus-Oei
- grid.10419.3d0000000089452978Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands ,grid.6214.10000 0004 0399 8953Biomedical Imaging Group, University of Twente, Enschede, The Netherlands
| | - Maqsood Yaqub
- grid.16872.3a0000 0004 0435 165XDepartment of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, location Vrije Universiteit Medical Center, Amsterdam, The Netherlands
| | - Therese van Amelsvoort
- grid.5012.60000 0001 0481 6099School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Jan Booij
- grid.5650.60000000404654431Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, location Academic Medical Center, Amsterdam, The Netherlands
| | - Jean-Paul Selten
- Rivierduinen Institute for Mental Healthcare, Leiden, The Netherlands ,grid.5012.60000 0001 0481 6099School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
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Barrington SF, Trotman J. The role of PET in the first-line treatment of the most common subtypes of non-Hodgkin lymphoma. LANCET HAEMATOLOGY 2021; 8:e80-e93. [DOI: 10.1016/s2352-3026(20)30365-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 10/12/2020] [Accepted: 11/02/2020] [Indexed: 01/24/2023]
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Wyrzykowski M, Siminiak N, Kaźmierczak M, Ruchała M, Czepczyński R. Impact of the Q.Clear reconstruction algorithm on the interpretation of PET/CT images in patients with lymphoma. EJNMMI Res 2020; 10:99. [PMID: 32845406 PMCID: PMC7450027 DOI: 10.1186/s13550-020-00690-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 08/19/2020] [Indexed: 12/24/2022] Open
Abstract
Background Q.Clear is a new Bayesian penalized-likelihood PET reconstruction algorithm. It has been documented that Q.Clear increases the SUVmax values of different malignant lesions. Purpose SUVmax values are crucial for the interpretation of PET/CT images in patients with lymphoma, particularly when the early and final responses to treatment are evaluated. The aim of the study was to systematically analyse the impact of the use of Q.Clear on the interpretation of PET/CT in patients with lymphoma. Methods A total of 280 18F-FDG PET/CT scans in patients with lymphoma were performed for staging (sPET), for early treatment response (iPET), after the end of treatment (ePET) and when a relapse of lymphoma was suspected (rPET). Scans were separately reconstructed with two algorithms, Q.Clear and OSEM, and further compared. Results The stage of lymphoma was concordantly diagnosed in 69/70 patients with both algorithms on sPET. Discordant assessment of the Deauville score (p < 0.001) was found in 11 cases (15.7%) of 70 iPET scans and in 11 cases of 70 ePET scans. An upgrade from a negative to a positive scan by Q.Clear occurred in 3 cases (4.3%) of iPET scans and 7 cases (10.0%) of ePET scans. The results of all 70 rPET scans were concordant. The SUVmax values of the target lymphoma lesions measured with Q.Clear were higher than those measured with OSEM in 88.8% of scans. Conclusion Although the Q.Clear algorithm may alter the interpretations of PET/CT in only a small proportion of patients, we recommend using standard OSEM reconstruction for the assessment of treatment response.
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Affiliation(s)
| | - Natalia Siminiak
- Department of Endocrinology and Metabolism, Poznan University of Medical Sciences, Poznań, Poland
| | - Maciej Kaźmierczak
- Department of Hematology and Bone Marrow Transplantation, Poznan University of Medical Sciences, Poznań, Poland
| | - Marek Ruchała
- Department of Endocrinology and Metabolism, Poznan University of Medical Sciences, Poznań, Poland
| | - Rafał Czepczyński
- Department of Nuclear Medicine, Affidea Poznań, Poznań, Poland.,Department of Endocrinology and Metabolism, Poznan University of Medical Sciences, Poznań, Poland
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Lowe G, Spottiswoode B, Declerck J, Sullivan K, Sharif MS, Wong WL, Sanghera B. Positron emission tomography PET/CT harmonisation study of different clinical PET/CT scanners using commercially available software. BJR Open 2020; 2:20190035. [PMID: 33178963 PMCID: PMC7594895 DOI: 10.1259/bjro.20190035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 04/30/2020] [Accepted: 05/04/2020] [Indexed: 11/06/2022] Open
Abstract
Objectives: Harmonisation is the process whereby standardised uptake values from different scanners can be made comparable. This PET/CT pilot study aimed to evaluate the effectiveness of harmonisation of a modern scanner with image reconstruction incorporating resolution recovery (RR) with another vendor older scanner operated in two-dimensional (2D) mode, and for both against a European standard (EARL). The vendor-proprietary software EQ•PET was used, which achieves harmonisation with a Gaussian smoothing. A substudy investigated effect of RR on harmonisation. Methods: Phantom studies on each scanner were performed to optimise the smoothing parameters required to achieve successful harmonisation. 80 patients were retrospectively selected; half were imaged on each scanner. As proof of principle, a cohort of 10 patients was selected from the modern scanner subjects to study the effects of RR on harmonisation. Results: Before harmonisation, the modern scanner without RR adhered to EARL specification. Using the phantom data, filters were derived for optimal harmonisation between scanners and with and without RR as applicable, to the EARL standard. The 80-patient cohort did not reveal any statistically significant differences. In the 10-patient cohort SUVmax for RR > no RR irrespective of harmonisation but differences lacked statistical significance (one-way ANOVA F(3.36) = 0.37, p = 0.78). Bland-Altman analysis showed that harmonisation reduced the SUVmax ratio between RR and no RR to 1.07 (95% CI 0.96–1.18) with no outliers. Conclusions: EQ•PET successfully enabled harmonisation between modern and older scanners and against the EARL standard. Harmonisation reduces SUVmax and dependence on the use of RR in the modern scanner. Advances in knowledge: EQ•PET is feasible to harmonise different PET/CT scanners and reduces the effect of RR on SUVmax.
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Affiliation(s)
- Gerry Lowe
- Cancer Centre, Mount Vernon Hospital, London, UK
| | | | | | - Keith Sullivan
- Health Research Methods Unit, University of Hertfordshire, Hertfordshire, UK
| | - Mhd Saeed Sharif
- School of Arch., Comp. and Eng., University of East London, London, UK
| | - Wai-Lup Wong
- Paul Strickland Scanner Centre, Mount Vernon Hospital, London, UK
| | - Bal Sanghera
- Paul Strickland Scanner Centre, Mount Vernon Hospital, London, UK
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25
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Interobserver agreement of the visual Herder scale for the assessment of solitary pulmonary nodules on 18F Fluorodeoxyglucose PET/computed tomography. Nucl Med Commun 2020; 41:235-240. [DOI: 10.1097/mnm.0000000000001146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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26
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Evaluation of a Bayesian penalized likelihood reconstruction algorithm for low-count clinical 18F-FDG PET/CT. EJNMMI Phys 2019; 6:32. [PMID: 31889228 PMCID: PMC6937357 DOI: 10.1186/s40658-019-0262-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 11/14/2019] [Indexed: 02/07/2023] Open
Abstract
Background Recently, a Bayesian penalized likelihood (BPL) reconstruction algorithm was introduced for a commercial PET/CT with the potential to improve image quality. We compared the performance of this BPL algorithm with conventional reconstruction algorithms under realistic clinical conditions such as daily practiced at many European sites, i.e. low 18F-FDG dose and short acquisition times. Results To study the performance of the BPL algorithm, regular clinical 18F-FDG whole body PET scans were made. In addition, two types of phantoms were scanned with 4-37 mm sized spheres filled with 18F-FDG at sphere-to-background ratios of 10-to-1, 4-to-1, and 2-to-1. Images were reconstructed using standard ordered-subset expectation maximization (OSEM), OSEM with point spread function (PSF), and the BPL algorithm using β-values of 450, 550 and 700. To quantify the image quality, the lesion detectability, activity recovery, and the coefficient of variation (COV) within a single bed position (BP) were determined. We found that when applying the BPL algorithm both smaller lesions in clinical studies as well as spheres in phantom studies can be detected more easily due to a higher SUV recovery, especially for higher contrast ratios. Under standard clinical scanning conditions, i.e. low number of counts, the COV is higher for the BPL (β=450) than the OSEM+PSF algorithm. Increase of the β-value to 550 or 700 results in a COV comparable to OSEM+PSF, however, at the cost of contrast, though still better than OSEM+PSF. At the edges of the axial field of view (FOV) where BPs overlap, COV can increase to levels at which bands become visible in clinical images, related to the lower local axial sensitivity of the PET/CT, which is due to the limited bed overlap of 23% such as advised by the manufacturer. Conclusions The BPL algorithm performs better than the standard OSEM+PSF algorithm on small lesion detectability, SUV recovery, and noise suppression. Increase of the percentage of bed overlap, time per BP, administered activity, or the β-value, all have a direct positive impact on image quality, though the latter with some loss of small lesion detectability. Thus, BPL algorithms are very interesting for improving image quality, especially in small lesion detectability.
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Koopman D, Jager PL, Slump CH, Knollema S, van Dalen JA. SUV variability in EARL-accredited conventional and digital PET. EJNMMI Res 2019; 9:106. [PMID: 31823097 PMCID: PMC6904705 DOI: 10.1186/s13550-019-0569-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Accepted: 10/16/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND A high SUV-reproducibility is crucial when different PET scanners are in use. We evaluated the SUV variability in whole-body FDG-PET scans of patients with suspected or proven cancer using an EARL-accredited conventional and digital PET scanner. In a head-to-head comparison we studied images of 50 patients acquired on a conventional scanner (cPET, Ingenuity TF PET/CT, Philips) and compared them with images acquired on a digital scanner (dPET, Vereos PET/CT, Philips). The PET scanning order was randomised and EARL-compatible reconstructions were applied. We measured SUVmean, SUVpeak, SUVmax and lesion diameter in up to 5 FDG-positive lesions per patient. The relative difference ΔSUV between cPET and dPET was calculated for each SUV-parameter. Furthermore, we calculated repeatability coefficients, reflecting the 95% confidence interval of ΔSUV. RESULTS We included 128 lesions with an average size of 19 ± 14 mm. Average ΔSUVs were 6-8% with dPET values being higher for all three SUV-parameters (p < 0.001). ΔSUVmax was significantly higher than ΔSUVmean (8% vs. 6%, p = 0.002) and than ΔSUVpeak (8% vs. 7%, p = 0.03). Repeatability coefficients across individual lesions were 27% (ΔSUVmean and ΔSUVpeak) and 33% (ΔSUVmax) (p < 0.001). CONCLUSIONS With EARL-accredited conventional and digital PET, we found a limited SUV variability with average differences up to 8%. Furthermore, only a limited number of lesions showed a SUV difference of more than 30%. These findings indicate that EARL standardisation works. TRIAL REGISTRATION This prospective study was registered on the 31th of October 2017 at ClinicalTrials.cov. URL: https://clinicaltrials.gov/ct2/show/NCT03457506?id=03457506&rank=1.
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Affiliation(s)
- Daniëlle Koopman
- Department of Nuclear Medicine, Isala, Dokter van Heesweg 2, 8025, AB, Zwolle, the Netherlands. .,Technical Medicine Center, University of Twente, Enschede, the Netherlands.
| | - Pieter L Jager
- Department of Nuclear Medicine, Isala, Dokter van Heesweg 2, 8025, AB, Zwolle, the Netherlands
| | - Cornelis H Slump
- Technical Medicine Center, University of Twente, Enschede, the Netherlands
| | - Siert Knollema
- Department of Nuclear Medicine, Isala, Dokter van Heesweg 2, 8025, AB, Zwolle, the Netherlands
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28
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Boellaard R, Sera T, Kaalep A, Hoekstra OS, Barrington SF, Zijlstra JM. Updating PET/CT performance standards and PET/CT interpretation criteria should go hand in hand. EJNMMI Res 2019; 9:95. [PMID: 31664529 PMCID: PMC6820631 DOI: 10.1186/s13550-019-0565-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 09/25/2019] [Indexed: 11/10/2022] Open
Abstract
This letter aims at explaining that adjusting the performance of PET/CT systems to a new standard also requires updating of interpretation criteria. Simply changing one aspect of the imaging procedure, i.e., PET/CT performance and image quality, and not adapting interpretation criteria will result in an increase of false positive (or negative) reads.
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Affiliation(s)
- Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, VUMC, de Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands. .,Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. .,EANM Research Limited (EARL), Vienna, Austria.
| | - Terez Sera
- EANM Research Limited (EARL), Vienna, Austria.,Department of Nuclear Medicine, University of Szeged, Korányi fasor 6, Szeged, Hungary
| | - Andres Kaalep
- Department of Medical Technology, North Estonia Medical Centre Foundation, 19 J. Sütiste Street, Tallinn, Estonia
| | - Otto S Hoekstra
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, VUMC, de Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Sally F Barrington
- King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, Amsterdam, UK
| | - Josée M Zijlstra
- Department of Hematology, Cancer Centre Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, de Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
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Vanhoutte M, Semah F, Lopes R, Jaillard A, Petyt G, Aziz AL, Lahousse H, Declerck J, Pasquier F, Spottiswoode B, Fahmi R. Using EQ·PET to reduce reconstruction-dependent variations in [ 18F]FDG-PET brain imaging. Phys Med Biol 2019; 64:175002. [PMID: 31344691 DOI: 10.1088/1361-6560/ab35b4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
This study aims at assessing whether EANM harmonisation strategy combined with EQ·PET methodology could be successfully applied to harmonize brain 2-deoxy-2[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography (PET) images. The NEMA NU 2 body phantom was prepared according to the EANM guidelines with an [18F]FDG solution. Raw PET phantom data were reconstructed with three different reconstruction protocols frequently used in clinical PET brain imaging: ([Formula: see text]) Ordered subset expectation maximization (OSEM) 3D with time of flight (TOF), 2 iterations and 21 subsets; ([Formula: see text]) OSEM 3D with TOF, 6 iterations and 21 subsets; and ([Formula: see text]) OSEM 3D with TOF, point spread function (PSF), and 8 iterations and 21 subsets. EQ·PET filters were computed as the Gaussian smoothing that best independently aligned the recovery coefficients (RCs) of reconstructions [Formula: see text] and [Formula: see text] with the RCs of the reference reconstruction, [Formula: see text]. The performance of the EQ·PET filter to reduce variations in quantification due to differences in reconstruction was investigated using clinical PET brain images of 35 early-onset Alzheimer's disease (EOAD) patients. Qualitative assessments and multiple quantitative metrics on the cortical surface at different scale levels with or without partial volume effect correction were evaluated on the [18F]FDG brain data before and after application of the EQ·PET filter. The EQ·PET methodology succeeded in finding the optimal smoothing that minimised root-mean-square error (RMSE) calculated using human brain [18F]FDG-PET datasets of EOAD patients, providing harmonized comparisons in the neurological context. Performance was superior for TOF than for TOF + PSF reconstructions. Results showed the capability of the EQ·PET methodology to minimize reconstruction-induced variabilities between brain [18F]FDG-PET images. However, moderate variabilities remained after harmonizing PSF reconstructions with standard non-PSF OSEM reconstructions, suggesting that precautions should be taken when using PSF modelling.
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Affiliation(s)
- Matthieu Vanhoutte
- University of Lille, Inserm U1171, CHU Lille, F-59000 Lille, France. Department of Nuclear Medicine, CHU Lille, F-59000 Lille, France. Department of Neuroradiology, CHU Lille, F-59000 Lille, France. Author to whom any correspondence should be addressed
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Barrington SF, Phillips EH, Counsell N, Hancock B, Pettengell R, Johnson P, Townsend W, Culligan D, Popova B, Clifton-Hadley L, McMillan A, Hoskin P, O’Doherty MJ, Illidge T, Radford J. Positron Emission Tomography Score Has Greater Prognostic Significance Than Pretreatment Risk Stratification in Early-Stage Hodgkin Lymphoma in the UK RAPID Study. J Clin Oncol 2019; 37:1732-1741. [PMID: 31112475 PMCID: PMC6638600 DOI: 10.1200/jco.18.01799] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/05/2019] [Indexed: 02/03/2023] Open
Abstract
PURPOSE Accurate stratification of patients is an important goal in Hodgkin lymphoma (HL), but the role of pretreatment clinical risk stratification in the context of positron emission tomography (PET) -adapted treatment is unclear. We performed a subsidiary analysis of the RAPID trial to assess the prognostic value of pretreatment risk factors and PET score in determining outcomes. PATIENTS AND METHODS Patients with stage IA to IIA HL and no mediastinal bulk underwent PET assessment after three cycles of doxorubicin, bleomycin, vinblastine, and dacarbazine; 143 PET-positive patients (PET score, 3 to 5) received a fourth doxorubicin, bleomycin, vinblastine, and dacarbazine cycle and involved-field radiotherapy, and 419 patients in complete metabolic remission were randomly assigned to receive involved-field radiotherapy (n = 208) or no additional treatment (n = 211). Cox regression was used to investigate the association between PET score and pretreatment risk factors with HL-specific event-free survival (EFS). RESULTS High PET score was associated with inferior EFS, before (P < .001) and after adjustment (P = .01) for baseline risk stratification. Only patients with a postchemotherapy PET score of 5 (uptake ≥ three times maximum liver uptake) had an increased risk of progression or HL-related death (hazard ratio, 9.4 v score of 3; 95% CI, 2.8 to 31.3 and hazard ratio, 6.7 v score of 4; 95% CI, 1.4 to 31.7). Patients with a PET score of 5 also had inferior progression-free and overall survival. There was no association between European Organisation for Research and Treatment of Cancer or German Hodgkin Study Group risk group and EFS, before or after adjusting for PET score (all P > .4). CONCLUSION In RAPID, a positive PET scan did not carry uniform prognostic weight; only a PET score of 5 was associated with inferior outcomes. This suggests that in future trials involving patients without B symptoms or mediastinal bulk, a score of 5 rather than a positive PET result should be used to guide treatment escalation in early-stage HL.
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Affiliation(s)
- Sally F. Barrington
- King’s College London and Guy’s and St Thomas’ PET Centre, Kings College London, King’s Health Partners, London, United Kingdom
| | - Elizabeth H. Phillips
- Cancer Research UK and University College London Cancer Trials Centre, University College London, London, United Kingdom
| | - Nicholas Counsell
- Cancer Research UK and University College London Cancer Trials Centre, University College London, London, United Kingdom
| | - Barry Hancock
- University of Sheffield and Weston Park Hospital, Sheffield, United Kingdom
| | | | - Peter Johnson
- Cancer Research UK Centre, University of Southampton, Southampton, United Kingdom
| | | | | | - Bilyana Popova
- Cancer Research UK and University College London Cancer Trials Centre, University College London, London, United Kingdom
| | - Laura Clifton-Hadley
- Cancer Research UK and University College London Cancer Trials Centre, University College London, London, United Kingdom
| | - Andrew McMillan
- Nottingham City Hospitals National Health Service (NHS) Trust, Nottingham, United Kingdom
| | - Peter Hoskin
- Mount Vernon Hospital, Middlesex, United Kingdom
- Institute of Cancer Sciences and the Christie NHS Foundation Trust, University of Manchester, Manchester, United Kingdom
| | - Michael J. O’Doherty
- King’s College London and Guy’s and St Thomas’ PET Centre, Kings College London, King’s Health Partners, London, United Kingdom
| | - Tim Illidge
- Institute of Cancer Sciences and the Christie NHS Foundation Trust, University of Manchester, Manchester, United Kingdom
| | - John Radford
- Institute of Cancer Sciences and the Christie NHS Foundation Trust, University of Manchester, Manchester, United Kingdom
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31
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Jauw YWS, Bensch F, Brouwers AH, Hoekstra OS, Zijlstra JM, Pieplenbosch S, Schröder CP, Zweegman S, van Dongen GAMS, Menke-van der Houven van Oordt CW, de Vries EGE, de Vet HCW, Boellaard R, Huisman MC. Interobserver reproducibility of tumor uptake quantification with 89Zr-immuno-PET: a multicenter analysis. Eur J Nucl Med Mol Imaging 2019; 46:1840-1849. [PMID: 31209514 PMCID: PMC6647131 DOI: 10.1007/s00259-019-04377-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 05/27/2019] [Indexed: 10/31/2022]
Abstract
PURPOSE In-vivo quantification of tumor uptake of 89-zirconium (89Zr)-labelled monoclonal antibodies (mAbs) with PET provides a potential tool in strategies to optimize tumor targeting and therapeutic efficacy. A specific challenge for 89Zr-immuno-PET is low tumor contrast. This is expected to result in interobserver variation in tumor delineation. Therefore, the aim of this study was to determine interobserver reproducibility of tumor uptake measures by tumor delineation on 89Zr-immuno-PET scans. METHODS Data were obtained from previously published clinical studies performed with 89Zr-rituximab, 89Zr-cetuximab and 89Zr-trastuzumab. Tumor lesions on 89Zr-immuno-PET were identified as focal uptake exceeding local background by a nuclear medicine physician. Three observers independently manually delineated volumes of interest (VOI). Maximum, peak and mean standardized uptake values (SUVmax, SUVpeak and SUVmean) were used to quantify tumor uptake. Interobserver variability was expressed as the coefficient of variation (CoV). The performance of semi-automatic VOI delineation using 50% of background-corrected ACpeak was described. RESULTS In total, 103 VOI were delineated (3-6 days post injection (D3-D6)). Tumor uptake (median, interquartile range) was 9.2 (5.2-12.6), 6.9 (4.0-9.6) and 5.5 (3.3-7.8) for SUVmax, SUVpeak and SUVmean. Interobserver variability was 0% (0-12), 0% (0-2) and 7% (5-14), respectively (n = 103). The success rate of the semi-automatic method was 45%. Inclusion of background was the main reason for failure of semi-automatic VOI. CONCLUSIONS This study shows that interobserver reproducibility of tumor uptake quantification on 89Zr-immuno-PET was excellent for SUVmax and SUVpeak using a standardized manual procedure for tumor segmentation. Semi-automatic delineation was not robust due to limited tumor contrast.
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Affiliation(s)
- Yvonne W S Jauw
- Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands. .,Department of Radiology & Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands.
| | - Frederike Bensch
- Department of Medical Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Adrienne H Brouwers
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Otto S Hoekstra
- Department of Radiology & Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Josée M Zijlstra
- Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Simone Pieplenbosch
- Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.,Department of Radiology & Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Carolien P Schröder
- Department of Medical Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Sonja Zweegman
- Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Guus A M S van Dongen
- Department of Radiology & Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | | | - Elisabeth G E de Vries
- Department of Medical Oncology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Henrica C W de Vet
- Department of Epidemiology and Biostatistics, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Department of Radiology & Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands.,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Marc C Huisman
- Department of Radiology & Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands
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