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Weir-McCall JR, Debruyn E, Harris S, Qureshi NR, Rintoul RC, Gleeson FV, Gilbert FJ. Diagnostic Accuracy of a Convolutional Neural Network Assessment of Solitary Pulmonary Nodules Compared With PET With CT Imaging and Dynamic Contrast-Enhanced CT Imaging Using Unenhanced and Contrast-Enhanced CT Imaging. Chest 2023; 163:444-454. [PMID: 36087795 PMCID: PMC9899635 DOI: 10.1016/j.chest.2022.08.2227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 08/19/2022] [Accepted: 08/24/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Solitary pulmonary nodules (SPNs) measuring 8 to 30 mm in diameter require further workup to determine the likelihood of malignancy. RESEARCH QUESTION What is the diagnostic performance of a lung cancer prediction convolutional neural network (LCP-CNN) in SPNs using unenhanced and contrast-enhanced CT imaging compared with the current clinical workup? STUDY DESIGN AND METHODS This was a post hoc analysis of the Single Pulmonary Nodule Investigation: Accuracy and Cost-Effectiveness of Dynamic Contrast Enhanced Computed Tomography in the Characterisation of Solitary Pulmonary Nodules trial, a prospective multicenter study comparing the diagnostic accuracy of dynamic contrast-enhanced (DCE) CT imaging with PET imaging in SPNs. The LCP-CNN was designed and validated in an external cohort. LCP-CNN-generated risk scores were created from the noncontrast and contrast-enhanced CT scan images from the DCE CT imaging. The gold standard was histologic analysis or 2 years of follow-up. The area under the receiver operating characteristic curves (AUC) were calculated using LCP-CNN score, maximum standardized uptake value, and DCE CT scan maximum enhancement and were compared using the DeLong test. RESULTS Two hundred seventy participants (mean ± SD age, 68.3 ± 8.8 years; 49% women) underwent PET with CT scan imaging and DCE CT imaging with CT scan data available centrally for LCP-CNN analysis. The accuracy of the LCP-CNN on the noncontrast images (AUC, 0.83; 95% CI, 0.79-0.88) was superior to that of DCE CT imaging (AUC, 0.76; 95% CI, 0.69-0.82; P = .03) and equal to that of PET with CT scan imaging (AUC, 0.86; 95% CI, 0.81-0.90; P = .35). The presence of contrast resulted in a small reduction in diagnostic accuracy, with the AUC falling from 0.83 (95% CI, 0.79-0.88) on the noncontrast images to 0.80 to 0.83 after contrast (P < .05 for 240 s after contrast only). INTERPRETATION An LCP-CNN algorithm provides an AUC equivalent to PET with CT scan imaging in the diagnosis of solitary pulmonary nodules. TRIAL REGISTRATION ClinicalTrials.gov Identifier; No.: NCT02013063.
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Affiliation(s)
- Jonathan R Weir-McCall
- Department of Radiology, University of Cambridge School of Clinical Medicine, Biomedical Research Centre, University of Cambridge; Department of Radiology, Royal Papworth Hospital, Cambridge
| | - Elise Debruyn
- College of Medicine, University of Illinois at Chicago, Chicago, IL
| | - Scott Harris
- Faculty of Public Health Sciences and Medical Statistics, University of Southampton, Southampton
| | | | - Robert C Rintoul
- Department of Oncology, University of Cambridge; Department of Thoracic Oncology, Royal Papworth Hospital
| | - Fergus V Gleeson
- Department of Radiology, Churchill Hospital and University of Oxford, Oxford, England
| | - Fiona J Gilbert
- Department of Radiology, University of Cambridge School of Clinical Medicine, Biomedical Research Centre, University of Cambridge.
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Gilbert FJ, Harris S, Miles KA, Weir-McCall JR, Qureshi NR, Rintoul RC, Dizdarevic S, Pike L, Sinclair D, Shah A, Eaton R, Clegg A, Benedetto V, Hill JE, Cook A, Tzelis D, Vale L, Brindle L, Madden J, Cozens K, Little LA, Eichhorst K, Moate P, McClement C, Peebles C, Banerjee A, Han S, Poon FW, Groves AM, Kurban L, Frew AJ, Callister ME, Crosbie P, Gleeson FV, Karunasaagarar K, Kankam O, George S. Dynamic contrast-enhanced CT compared with positron emission tomography CT to characterise solitary pulmonary nodules: the SPUtNIk diagnostic accuracy study and economic modelling. Health Technol Assess 2022; 26:1-180. [PMID: 35289267 DOI: 10.3310/wcei8321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Current pathways recommend positron emission tomography-computerised tomography for the characterisation of solitary pulmonary nodules. Dynamic contrast-enhanced computerised tomography may be a more cost-effective approach. OBJECTIVES To determine the diagnostic performances of dynamic contrast-enhanced computerised tomography and positron emission tomography-computerised tomography in the NHS for solitary pulmonary nodules. Systematic reviews and a health economic evaluation contributed to the decision-analytic modelling to assess the likely costs and health outcomes resulting from incorporation of dynamic contrast-enhanced computerised tomography into management strategies. DESIGN Multicentre comparative accuracy trial. SETTING Secondary or tertiary outpatient settings at 16 hospitals in the UK. PARTICIPANTS Participants with solitary pulmonary nodules of ≥ 8 mm and of ≤ 30 mm in size with no malignancy in the previous 2 years were included. INTERVENTIONS Baseline positron emission tomography-computerised tomography and dynamic contrast-enhanced computer tomography with 2 years' follow-up. MAIN OUTCOME MEASURES Primary outcome measures were sensitivity, specificity and diagnostic accuracy for positron emission tomography-computerised tomography and dynamic contrast-enhanced computerised tomography. Incremental cost-effectiveness ratios compared management strategies that used dynamic contrast-enhanced computerised tomography with management strategies that did not use dynamic contrast-enhanced computerised tomography. RESULTS A total of 380 patients were recruited (median age 69 years). Of 312 patients with matched dynamic contrast-enhanced computer tomography and positron emission tomography-computerised tomography examinations, 191 (61%) were cancer patients. The sensitivity, specificity and diagnostic accuracy for positron emission tomography-computerised tomography and dynamic contrast-enhanced computer tomography were 72.8% (95% confidence interval 66.1% to 78.6%), 81.8% (95% confidence interval 74.0% to 87.7%), 76.3% (95% confidence interval 71.3% to 80.7%) and 95.3% (95% confidence interval 91.3% to 97.5%), 29.8% (95% confidence interval 22.3% to 38.4%) and 69.9% (95% confidence interval 64.6% to 74.7%), respectively. Exploratory modelling showed that maximum standardised uptake values had the best diagnostic accuracy, with an area under the curve of 0.87, which increased to 0.90 if combined with dynamic contrast-enhanced computerised tomography peak enhancement. The economic analysis showed that, over 24 months, dynamic contrast-enhanced computerised tomography was less costly (£3305, 95% confidence interval £2952 to £3746) than positron emission tomography-computerised tomography (£4013, 95% confidence interval £3673 to £4498) or a strategy combining the two tests (£4058, 95% confidence interval £3702 to £4547). Positron emission tomography-computerised tomography led to more patients with malignant nodules being correctly managed, 0.44 on average (95% confidence interval 0.39 to 0.49), compared with 0.40 (95% confidence interval 0.35 to 0.45); using both tests further increased this (0.47, 95% confidence interval 0.42 to 0.51). LIMITATIONS The high prevalence of malignancy in nodules observed in this trial, compared with that observed in nodules identified within screening programmes, limits the generalisation of the current results to nodules identified by screening. CONCLUSIONS Findings from this research indicate that positron emission tomography-computerised tomography is more accurate than dynamic contrast-enhanced computerised tomography for the characterisation of solitary pulmonary nodules. A combination of maximum standardised uptake value and peak enhancement had the highest accuracy with a small increase in costs. Findings from this research also indicate that a combined positron emission tomography-dynamic contrast-enhanced computerised tomography approach with a slightly higher willingness to pay to avoid missing small cancers or to avoid a 'watch and wait' policy may be an approach to consider. FUTURE WORK Integration of the dynamic contrast-enhanced component into the positron emission tomography-computerised tomography examination and the feasibility of dynamic contrast-enhanced computerised tomography at lung screening for the characterisation of solitary pulmonary nodules should be explored, together with a lower radiation dose protocol. STUDY REGISTRATION This study is registered as PROSPERO CRD42018112215 and CRD42019124299, and the trial is registered as ISRCTN30784948 and ClinicalTrials.gov NCT02013063. FUNDING This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 26, No. 17. See the NIHR Journals Library website for further project information.
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Affiliation(s)
- Fiona J Gilbert
- Department of Radiology, University of Cambridge School of Clinical Medicine, Biomedical Research Centre, University of Cambridge, Cambridge, UK
| | - Scott Harris
- Public Health Sciences and Medical Statistics, University of Southampton, Southampton, UK
| | - Kenneth A Miles
- Department of Radiology, University of Cambridge School of Clinical Medicine, Biomedical Research Centre, University of Cambridge, Cambridge, UK
- Department of Radiology, Royal Papworth Hospital, Cambridge, UK
| | - Jonathan R Weir-McCall
- Department of Radiology, University of Cambridge School of Clinical Medicine, Biomedical Research Centre, University of Cambridge, Cambridge, UK
| | - Nagmi R Qureshi
- Department of Radiology, Royal Papworth Hospital, Cambridge, UK
| | - Robert C Rintoul
- Department of Thoracic Oncology, Royal Papworth Hospital, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Sabina Dizdarevic
- Departments of Imaging and Nuclear Medicine and Respiratory Medicine, Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
- Brighton and Sussex Medical School, Brighton, UK
| | - 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, London, UK
| | - Donald Sinclair
- King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Andrew Shah
- Radiation Protection Department, East and North Hertfordshire NHS Trust, Stevenage, UK
| | - Rosemary Eaton
- Radiation Protection Department, East and North Hertfordshire NHS Trust, Stevenage, UK
| | - Andrew Clegg
- Faculty of Health and Wellbeing, University of Central Lancashire, Preston, UK
| | - Valerio Benedetto
- Faculty of Health and Wellbeing, University of Central Lancashire, Preston, UK
| | - James E Hill
- Faculty of Health and Wellbeing, University of Central Lancashire, Preston, UK
| | - Andrew Cook
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Dimitrios Tzelis
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Luke Vale
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Lucy Brindle
- School of Health Sciences, University of Southampton, Southampton, UK
| | - Jackie Madden
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Kelly Cozens
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Louisa A Little
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Kathrin Eichhorst
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Patricia Moate
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Chris McClement
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Charles Peebles
- Department of Radiology and Respiratory Medicine, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Anindo Banerjee
- Department of Radiology and Respiratory Medicine, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Sai Han
- West of Scotland PET Centre, Gartnavel Hospital, Glasgow, UK
| | - Fat Wui Poon
- West of Scotland PET Centre, Gartnavel Hospital, Glasgow, UK
| | - Ashley M Groves
- Institute of Nuclear Medicine, University College London, London, UK
| | - Lutfi Kurban
- Department of Radiology, Aberdeen Royal Hospitals NHS Trust, Aberdeen, UK
| | - Anthony J Frew
- Departments of Imaging and Nuclear Medicine and Respiratory Medicine, Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
- Brighton and Sussex Medical School, Brighton, UK
| | - Matthew E Callister
- Department of Respiratory Medicine, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Philip Crosbie
- North West Lung Centre, University Hospital of South Manchester, Manchester, UK
| | - Fergus V Gleeson
- Department of Radiology, Churchill Hospital, Oxford, UK
- University of Oxford, Oxford, UK
| | | | - Osei Kankam
- Department of Thoracic Medicine, East Sussex Healthcare NHS Trust, Saint Leonards-on-Sea, UK
| | - Steve George
- University Hospital Southampton NHS Foundation Trust, Southampton, UK
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
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Gilbert FJ, Harris S, Miles KA, Weir-McCall JR, Qureshi NR, Rintoul RC, Dizdarevic S, Pike L, Sinclair D, Shah A, Eaton R, Jones J, Clegg A, Benedetto V, Hill J, Cook A, Tzelis D, Vale L, Brindle L, Madden J, Cozens K, Little L, Eichhorst K, Moate P, McClement C, Peebles C, Banerjee A, Han S, Poon FW, Groves AM, Kurban L, Frew A, Callister MEJ, Crosbie PA, Gleeson FV, Karunasaagarar K, Kankam O, George S. Comparative accuracy and cost-effectiveness of dynamic contrast-enhanced CT and positron emission tomography in the characterisation of solitary pulmonary nodules. Thorax 2021; 77:988-996. [PMID: 34887348 DOI: 10.1136/thoraxjnl-2021-216948] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 10/24/2021] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Dynamic contrast-enhanced CT (DCE-CT) and positron emission tomography/CT (PET/CT) have a high reported accuracy for the diagnosis of malignancy in solitary pulmonary nodules (SPNs). The aim of this study was to compare the accuracy and cost-effectiveness of these. METHODS In this prospective multicentre trial, 380 participants with an SPN (8-30 mm) and no recent history of malignancy underwent DCE-CT and PET/CT. All patients underwent either biopsy with histological diagnosis or completed CT follow-up. Primary outcome measures were sensitivity, specificity and overall diagnostic accuracy for PET/CT and DCE-CT. Costs and cost-effectiveness were estimated from a healthcare provider perspective using a decision-model. RESULTS 312 participants (47% female, 68.1±9.0 years) completed the study, with 61% rate of malignancy at 2 years. The sensitivity, specificity, positive predictive value and negative predictive values for DCE-CT were 95.3% (95% CI 91.3 to 97.5), 29.8% (95% CI 22.3 to 38.4), 68.2% (95% CI 62.4% to 73.5%) and 80.0% (95% CI 66.2 to 89.1), respectively, and for PET/CT were 79.1% (95% CI 72.7 to 84.2), 81.8% (95% CI 74.0 to 87.7), 87.3% (95% CI 81.5 to 91.5) and 71.2% (95% CI 63.2 to 78.1). The area under the receiver operator characteristic curve (AUROC) for DCE-CT and PET/CT was 0.62 (95% CI 0.58 to 0.67) and 0.80 (95% CI 0.76 to 0.85), respectively (p<0.001). Combined results significantly increased diagnostic accuracy over PET/CT alone (AUROC=0.90 (95% CI 0.86 to 0.93), p<0.001). DCE-CT was preferred when the willingness to pay per incremental cost per correctly treated malignancy was below £9000. Above £15 500 a combined approach was preferred. CONCLUSIONS PET/CT has a superior diagnostic accuracy to DCE-CT for the diagnosis of SPNs. Combining both techniques improves the diagnostic accuracy over either test alone and could be cost-effective. TRIAL REGISTRATION NUMBER NCT02013063.
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Affiliation(s)
- Fiona J Gilbert
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Scott Harris
- Public Health Sciences and Medical Statistics, University of Southampton, Southampton, Southampton, UK
| | - Kenneth A Miles
- Institute of Nuclear Medicine, University College London, London, UK
| | - Jonathan R Weir-McCall
- Department of Radiology, University of Cambridge, Cambridge, UK.,Department of Radiology, Royal Papworth Hospital NHS Foundation Trust, Cambridge, UK
| | - Nagmi R Qureshi
- Department of Radiology, Royal Papworth Hospital NHS Foundation Trust, Cambridge, UK
| | - Robert Campbell Rintoul
- Department of Thoracic Oncology, Royal Papworth Hospital NHS Foundation Trust, Cambridge, UK.,Department of Oncology, University of Cambridge, Cambridge, UK
| | - Sabina Dizdarevic
- Imaging and Nuclear Medicine, Brighton and Sussex University Hospitals NHS Trust, Brighton, UK.,Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Lucy Pike
- King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, Kings College London, London, UK
| | - Donald Sinclair
- King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, Kings College London, London, UK
| | - Andrew Shah
- Radiation Protection, East and North Hertfordshire NHS Trust, Stevenage, UK
| | - Rosemary Eaton
- Radiation Protection, East and North Hertfordshire NHS Trust, Stevenage, UK
| | - Jeremy Jones
- Centre for Innovation and Leadership in Health Sciences, University of Southampton, Southampton, UK
| | - Andrew Clegg
- Synthesis, Economic Evaluation and Decision Science (SEEDS) Group, Applied Health Research Hub, University of Central Lancashire, Preston, UK
| | - Valerio Benedetto
- Synthesis, Economic Evaluation and Decision Science (SEEDS) Group, Applied Health Research Hub, University of Central Lancashire, Preston, UK
| | - James Hill
- Synthesis, Economic Evaluation and Decision Science (SEEDS) Group, Applied Health Research Hub, University of Central Lancashire, Preston, UK
| | - Andrew Cook
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Dimitrios Tzelis
- Population Health Science Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Luke Vale
- Population Health Science Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Lucy Brindle
- School of Health Sciences, University of Southampton, Southampton, UK
| | - Jackie Madden
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Kelly Cozens
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Louisa Little
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Kathrin Eichhorst
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Patricia Moate
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Chris McClement
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Charles Peebles
- Department of Radiology and Respiratory Medicine, Southampton University Hospitals NHS Foundation Trust, Southampton, UK
| | - Anindo Banerjee
- Department of Radiology and Respiratory Medicine, Southampton University Hospitals NHS Foundation Trust, Southampton, UK
| | - Sai Han
- West of Scotland PET Centre, Gartnavel General Hospital, Glasgow, UK
| | - Fat-Wui Poon
- West of Scotland PET Centre, Gartnavel General Hospital, Glasgow, UK
| | - Ashley M Groves
- Institute of Nuclear Medicine, University College London, London, UK
| | - Lutfi Kurban
- Department of Radiology, Aberdeen Royal Hospitals NHS Trust, Aberdeen, UK
| | - Anthony Frew
- Imaging and Nuclear Medicine, Brighton and Sussex University Hospitals NHS Trust, Brighton, UK
| | | | - Phil A Crosbie
- Division of Infection, Immunity and Respiratory Medicine, University Hospital of South Manchester, Manchester, UK
| | - Fergus Vincent Gleeson
- Department of Radiology, Churchill Hospital, Oxford, UK.,Department of Radiology, University of Oxford, Oxford, UK
| | | | - Osei Kankam
- Department of Thoracic Medicine, East Sussex Healthcare NHS Trust, Saint Leonards-on-Sea, UK
| | - Steve George
- Public Health Sciences and Medical Statistics, University of Southampton, Southampton, Southampton, UK
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Can dynamic imaging, using 18F-FDG PET/CT and CT perfusion differentiate between benign and malignant pulmonary nodules? Radiol Oncol 2021; 55:259-267. [PMID: 34051709 PMCID: PMC8366734 DOI: 10.2478/raon-2021-0024] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 04/24/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The aim of the study was to derive and compare metabolic parameters relating to benign and malignant pulmonary nodules using dynamic 2-deoxy-2-[fluorine-18]fluoro-D-glucose (18F-FDG) PET/CT, and nodule perfusion parameters derived through perfusion computed tomography (CT). PATIENTS AND METHODS Twenty patients with 21 pulmonary nodules incidentally detected on CT underwent a dynamic 18F-FDG PET/CT and a perfusion CT. The maximum standardized uptake value (SUVmax) was measured on conventional 18F-FDG PET/CT images. The influx constant (Ki ) was calculated from the dynamic 18F-FDG PET/CT data using Patlak model. Arterial flow (AF) using the maximum slope model and blood volume (BV) using the Patlak plot method for each nodule were calculated from the perfusion CT data. All nodules were characterized as malignant or benign based on histopathology or 2 year follow up CT. All parameters were statistically compared between the two groups using the nonparametric Mann-Whitney test. RESULTS Twelve malignant and 9 benign lung nodules were analysed (median size 20.1 mm, 9-29 mm) in 21 patients (male/female = 11/9; mean age ± SD: 65.3 ± 7.4; age range: 50-76 years). The average SUVmax values ± SD of the benign and malignant nodules were 2.2 ± 1.7 vs. 7.0 ± 4.5, respectively (p = 0.0148). Average Ki values in benign and malignant nodules were 0.0057 ± 0.0071 and 0.0230 ± 0.0155 min-1, respectively (p = 0.0311). Average BV for the benign and malignant nodules were 11.6857 ± 6.7347 and 28.3400 ± 15.9672 ml/100 ml, respectively (p = 0.0250). Average AF for the benign and malignant nodules were 74.4571 ± 89.0321 and 89.200 ± 49.8883 ml/100g/min, respectively (p = 0.1613). CONCLUSIONS Dynamic 18F-FDG PET/CT and perfusion CT derived blood volume had similar capability to differentiate benign from malignant lung nodules.
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Weir-McCall JR, Harris S, Miles KA, Qureshi NR, Rintoul RC, Dizdarevic S, Pike L, Cheow HK, Gilbert FJ. Impact of solitary pulmonary nodule size on qualitative and quantitative assessment using 18F-fluorodeoxyglucose PET/CT: the SPUTNIK trial. Eur J Nucl Med Mol Imaging 2021; 48:1560-1569. [PMID: 33130961 PMCID: PMC8113131 DOI: 10.1007/s00259-020-05089-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Accepted: 10/20/2020] [Indexed: 12/19/2022]
Abstract
PURPOSE To compare qualitative and semi-quantitative PET/CT criteria, and the impact of nodule size on the diagnosis of solitary pulmonary nodules in a prospective multicentre trial. METHODS Patients with an SPN on CT ≥ 8 and ≤ 30 mm were recruited to the SPUTNIK trial at 16 sites accredited by the UK PET Core Lab. Qualitative assessment used a five-point ordinal PET-grade compared to the mediastinal blood pool, and a combined PET/CT grade using the CT features. Semi-quantitative measures included SUVmax of the nodule, and as an uptake ratio to the mediastinal blood pool (SURBLOOD) or liver (SURLIVER). The endpoints were diagnosis of lung cancer via biopsy/histology or completion of 2-year follow-up. Impact of nodule size was analysed by comparison between nodule size tertiles. RESULTS Three hundred fifty-five participants completed PET/CT and 2-year follow-up, with 59% (209/355) malignant nodules. The AUCs of the three techniques were SUVmax 0.87 (95% CI 0.83;0.91); SURBLOOD 0.87 (95% CI 0.83; 0.91, p = 0.30 versus SUVmax); and SURLIVER 0.87 (95% CI 0.83; 0.91, p = 0.09 vs. SUVmax). The AUCs for all techniques remained stable across size tertiles (p > 0.1 for difference), although the optimal diagnostic threshold varied by size. For nodules < 12 mm, an SUVmax of 1.75 or visual uptake equal to the mediastinum yielded the highest accuracy. For nodules > 16 mm, an SUVmax ≥ 3.6 or visual PET uptake greater than the mediastinum was the most accurate. CONCLUSION In this multicentre trial, SUVmax was the most accurate technique for the diagnosis of solitary pulmonary nodules. Diagnostic thresholds should be altered according to nodule size. TRIAL REGISTRATION ISRCTN - ISRCTN30784948. ClinicalTrials.gov - NCT02013063.
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Affiliation(s)
- J R Weir-McCall
- Department of Radiology, Biomedical Research Centre, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Radiology, Royal Papworth Hospital, Cambridge, UK
| | - S Harris
- Public Health Sciences and Medical Statistics, University of Southampton, Southampton, UK
| | - K A Miles
- Institute of Nuclear Medicine, University College London, London, UK
| | - N R Qureshi
- Department of Radiology, Royal Papworth Hospital, Cambridge, UK
| | - R C Rintoul
- Department of Thoracic Oncology, Royal Papworth Hospital / Department of Oncology, University of Cambridge, Cambridge, UK
| | - S Dizdarevic
- Departments of Imaging and Nuclear Medicine and Respiratory Medicine, Brighton and Sussex University Hospitals NHS Trust, Brighton and Sussex Medical School, Brighton, UK
| | - L Pike
- King's College London and Guy's & St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Heok K Cheow
- Addenbrookes Hospital, Cambridge University Hospitals NHS Trust, Cambridge, UK
| | - Fiona J Gilbert
- Department of Radiology, Biomedical Research Centre, University of Cambridge School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK.
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