BSREM for Brain Metastasis Detection with 18F-FDG-PET/CT in Lung Cancer Patients.
18F-FDG
BSREM
Brain metastases
Lung cancer
OSEM
PET/CT
Journal
Journal of digital imaging
ISSN: 1618-727X
Titre abrégé: J Digit Imaging
Pays: United States
ID NLM: 9100529
Informations de publication
Date de publication:
06 2022
06 2022
Historique:
received:
10
07
2021
accepted:
13
12
2021
revised:
10
07
2021
pubmed:
26
2
2022
medline:
3
6
2022
entrez:
25
2
2022
Statut:
ppublish
Résumé
The aim of the study was to analyze the use of block sequential regularized expectation maximization (BSREM) with different β-values for the detection of brain metastases in digital fluorine-18 labeled 2-deoxy-2-fluoro-D-glucose (18F-FDG) PET/CT in lung cancer patients. We retrospectively analyzed staging/restaging 18F-FDG PET/CT scans of 40 consecutive lung cancer patients with new brain metastases, confirmed by MRI. PET images were reconstructed using BSREM (β-values of 100, 200, 300, 400, 500, 600, 700) and OSEM. Two independent blinded readers (R1 and R2) evaluated each reconstruction using a 4-point scale for general image quality, noise, and lesion detectability. SUVmax of metastases, brain background, target-to-background ratio (TBR), and contrast recovery (CR) ratio were recorded for each reconstruction. Among all reconstruction techniques, differences in qualitative parameters were analyzed using non-parametric Friedman test, while differences in quantitative parameters were compared using analysis of variances for repeated measures. Cohen's kappa (k) was used to measure inter-reader agreement. The overall detectability of brain metastases was highest for BSREM200 (R1: 2.83 ± 1.17; R2: 2.68 ± 1.32) and BSREM300 (R1: 2.78 ± 1.23; R2: 2.68 ± 1.36), followed by BSREM100, which had lower accuracy owing to noise. The highest median TBR was found for BSREM100 (R1: 2.19 ± 1.05; R2: 2.42 ± 1.08), followed by BSREM200 and BSREM300. Image quality ratings were significantly different among reconstructions (p < 0.001). The median quality score was higher for BSREM100-300, and both noise and metastases' SUVmax decreased with increasing β-value. Inter-reader agreement was particularly high for the detectability of photopenic metastases and blurring (all k > 0.65). BSREM200 and BSREM300 yielded the best results for the detection of brain metastases, surpassing both BSREM400 and OSEM, typically used in clinical practice.
Identifiants
pubmed: 35212859
doi: 10.1007/s10278-021-00570-y
pii: 10.1007/s10278-021-00570-y
pmc: PMC9156589
doi:
Substances chimiques
Fluorine Radioisotopes
0
Radiopharmaceuticals
0
Fluorodeoxyglucose F18
0Z5B2CJX4D
Fluorine-18
GZ5I74KB8G
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
581-593Informations de copyright
© 2022. The Author(s).
Références
Achrol AS, Rennert RC, Anders C, et al. Brain metastases [Internet]. Nat. Rev. Dis. Prim. Nature Publishing Group; 2019 [cited 2020]. Available from: https://pubmed.ncbi.nlm.nih.gov/30655533/
Lee H, Jeong SH, Jeong BH, et al. Incidence of brain metastasis at the initial diagnosis of lung squamous cell carcinoma on the basis of stage, excluding brain metastasis. J Thorac Oncol [Internet]. Lippincott Williams and Wilkins; 2016 [cited 2020];11:426–431. Available from: https://pubmed.ncbi.nlm.nih.gov/26746367/
Mamon HJ, Yeap BY, Jänne PA, et al. High risk of brain metastases in surgically staged IIIA non-small-cell lung cancer patients treated with surgery, chemotherapy, and radiation [Internet]. J. Clin. Oncol. J Clin Oncol; 2005 [cited 2020]. page 1530–1537. Available from: https://pubmed.ncbi.nlm.nih.gov/15735128/
N Duma R Santana-Davila Molina JR. Non-Small Cell Lung Cancer: Epidemiology, Screening, Diagnosis, and Treatment [Internet]. Mayo Clin. Proc. Elsevier Ltd; 2019 [cited, 2020 page 1623–1640 Available from: https://doi.org/10.1016/j.mayocp.2019.01.013
Hochstenbag M, Twijnstra A, Wilmink J, et al. Asymptomatic brain metastases (BM) in small cell lung cancer (SCLC): MR-imaging is useful at initial diagnosis. J Neurooncol [Internet]. J Neurooncol; 2000 [cited 2020];48:243–248. Available from: https://pubmed.ncbi.nlm.nih.gov/11100822/
Patchell RA. Metastatic brain tumors. Neurol. Clin. W.B. Saunders; 1995. page 915–925.
Carolan H, Sun AY, Bezjak A, et al. Does the incidence and outcome of brain metastases in locally advanced non-small cell lung cancer justify prophylactic cranial irradiation or early detection? Lung Cancer [Internet]. Lung Cancer; 2005 [cited 2020]. page 109–115. Available from: https://pubmed.ncbi.nlm.nih.gov/15949596/
Andre F, Grunenwald D, Pujol JL, et al. Patterns of relapse of N2 nonsmall-cell lung carcinoma patients treated with preoperative chemotherapy: Should prophylactic cranial irradiation be reconsidered? Cancer [Internet]. John Wiley & Sons, Ltd; 2001 [cited 2020];91:2394–2400. Available from: https://onlinelibrary.wiley.com/doi/ https://doi.org/10.1002/1097-0142(20010615)91:12%3C2394::AID-CNCR1273%3E3.0.CO;2-6
Gaspar LE, Chansky K, Albain KS, et al. Time from treatment to subsequent diagnosis of brain metastases in stage III non-small-cell lung cancer: A retrospective review by the Southwest Oncology Group [Internet]. J. Clin. Oncol. J Clin Oncol; 2005 [cited 2020]. page 2955–2961. Available from: https://pubmed.ncbi.nlm.nih.gov/15860851/
Schoenmaekers J, Hofman P, Bootsma G, et al. Screening for brain metastases in patients with stage III non–small-cell lung cancer, magnetic resonance imaging or computed tomography? A prospective study. Eur J Cancer [Internet]. Elsevier Ltd; 2019 [cited 2020];115:88–96. Available from: https://pubmed.ncbi.nlm.nih.gov/31129385/
Li Y, Jin G, Su D. Comparison of Gadolinium-enhanced MRI and 18FDG PET/PET-CT for the diagnosis of brain metastases in lung cancer patients: A meta-analysis of 5 prospective studies. Oncotarget [Internet]. Impact Journals, LLC; 2017 [cited 2020];8:35743–35749. Available from: https://pubmed.ncbi.nlm.nih.gov/28415747/
Planchard D, Popat S, Kerr K, et al. by the ESMO Guidelines Committee Metastatic non-small cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up † 29 (suppl 4): iv192-iv237. Ann Oncol. 2018.
Ettinger DS, Wood DE, Chair V, et al. Continue NCCN Guidelines Panel Disclosures NCCN Guidelines Version 1.2020 Non-Small Cell Lung Cancer. 2019.
Barrón-Barrón F, Guzmán-De Alba E, Alatorre-Alexander J, et al. Guía de Práctica Clínica Nacional para el manejo del cáncer de pulmón de células no pequeñas en estadios tempranos, localmente avanzados y metastásicos. Salud Publica Mex [Internet]. NLM (Medline); 2019 [cited 2020];61:359–414. Available from: https://pubmed.ncbi.nlm.nih.gov/31276353/
S Wang S Zimmermann K Parikh Current Diagnosis Management of Small-Cell Lung Cancer [Internet]. Mayo Clin. Proc. Elsevier Ltd; 2019 [cited, et al 2020 page 1599–1622 Available from: https://doi.org/10.1016/j.mayocp.2019.01.034
Kandathil A, Kay FU, Butt YM, et al. Role of FDG PET/CT in the eighth edition of TNM staging of non– Small cell lung cancer. Radiographics [Internet]. Radiological Society of North America Inc.; 2018 [cited 2020];38:2134–2149. Available from: www.ajronline.org
Grootjans W, De Geus-Oei LF, Troost EGC, et al. PET in the management of locally advanced and metastatic NSCLC [Internet]. Nat. Rev. Clin. Oncol. Nature Publishing Group; 2015 [cited 2020]. page 395–407. Available from: https://pubmed.ncbi.nlm.nih.gov/25917254/
PY Salaün R Abgral O Malard Good clinical practice recommendations for the use of PET, CT in oncology. Eur J Nucl Med Mol Imaging [Internet]. Springer, et al 2020 [cited 2020];47:28–50 Available from: https://doi.org/10.1007/s00259-019-04553-8
S Fuchs N Grössmann M Ferch Evidence-based indications for the planning of PET or PET, CT capacities are needed [Internet]. Clin. Transl. Imaging. Springer-Verlag Italia s.r.l., 2019 [cited, et al 2020 page 65–81 Available from: https://doi.org/10.1007/s40336-019-00314-7
Bochev P, Klisarova A, Kaprelyan A, et al. Brain metastases detectability of routine whole body 18F-FDG PET and low dose CT scanning in 2502 asymptomatic patients with solid extracranial tumors. Hell J Nucl Med [Internet]. Hell J Nucl Med; 2012 [cited 2020];15. Available from: https://pubmed.ncbi.nlm.nih.gov/22741148/
Hjorthaug K, Højbjerg JA, Knap MM, et al. Accuracy of 18F-FDG PET-CT in triaging lung cancer patients with suspected brain metastases for MRI. Nucl Med Commun [Internet]. Lippincott Williams and Wilkins; 2015 [cited 2020];36:1084–1090. Available from: https://pubmed.ncbi.nlm.nih.gov/26302460/
Nia ES, Garland LL, Eshghi N, et al. Incidence of brain metastases on follow-up 18F-FDG PET/CT scans of non-small cell lung cancer patients: Should we include the brain? J Nucl Med Technol [Internet]. Society of Nuclear Medicine Inc.; 2017 [cited 2020];45:193–197. Available from: https://pubmed.ncbi.nlm.nih.gov/28705927/
Kung B, Auyong T, Tong C. Prevalence of Detecting Unknown Cerebral Metastases in Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography and its Potential Clinical Impact. World J Nucl Med [Internet]. Medknow; 2014 [cited 2020];13:108. Available from: https://pubmed.ncbi.nlm.nih.gov/25191125/
Tasdemir B, Urakci Z, Dostbil Z, et al. Effectiveness of the addition of the brain region to the FDG-PET/CT imaging area in patients with suspected or diagnosed lung cancer. Radiol Medica [Internet]. Springer-Verlag Italia s.r.l.; 2016 [cited 2020];121:218–224. Available from: https://pubmed.ncbi.nlm.nih.gov/26541882/
Cheng JC, Matthews J, Sossi V, et al. Incorporating HYPR de-noising within iterative PET reconstruction (HYPR-OSEM). Phys Med Biol [Internet]. Institute of Physics Publishing; 2017 [cited 2020];62:6666–6687. Available from: https://pubmed.ncbi.nlm.nih.gov/28644152/
Wolpert F, Weller M, Berghoff AS, et al. Diagnostic value of 18F-fluordesoxyglucose positron emission tomography for patients with brain metastasis from unknown primary site. Eur J Cancer [Internet]. Elsevier Ltd; 2018 [cited 2020];96:64–72. Available from: https://pubmed.ncbi.nlm.nih.gov/29677642/
Messerli M, Kotasidis F, Burger IA, et al. Impact of different image reconstructions on PET quantification in non-small cell lung cancer: A comparison of adenocarcinoma and squamous cell carcinoma. Br J Radiol [Internet]. British Institute of Radiology; 2019 [cited 2020];92. Available from: /pmc/articles/PMC6540860/?report=abstract
Lois C, Jakoby BW, Long MJ, et al. An assessment of the impact of incorporating time-of-flight information into clinical PET/CT imaging. J Nucl Med [Internet]. J Nucl Med; 2010 [cited 2020];51:237–245. Available from: https://pubmed.ncbi.nlm.nih.gov/20080882/
Salvadori J, Perrin M, Marie PY, et al. High-Resolution Brain 18F-FDG Images Provided by Fully Digital PET. Clin Nucl Med [Internet]. Lippincott Williams and Wilkins; 2019 [cited 2021];44:301–302. Available from: https://pubmed.ncbi.nlm.nih.gov/30789394/
Lantos J, Mittra ES, Levin CS, et al. Standard OSEM vs. regularized PET image reconstruction: qualitative and quantitative comparison using phantom data and various clinical radiopharmaceuticals. Am J Nucl Med Mol Imaging [Internet]. 2018 [cited 2020];8:110–118. Available from: http://www.ncbi.nlm.nih.gov/pubmed/29755844
ter Voert EEGW, Muehlematter UJ, Delso G, et al. Quantitative performance and optimal regularization parameter in block sequential regularized expectation maximization reconstructions in clinical 68Ga-PSMA PET/MR. EJNMMI Res [Internet]. Springer Verlag; 2018 [cited 2020];8:70. Available from: http://www.ncbi.nlm.nih.gov/pubmed/30054750
Messerli M, Stolzmann P, Egger-Sigg M, et al. Impact of a Bayesian penalized likelihood reconstruction algorithm on image quality in novel digital PET/CT: clinical implications for the assessment of lung tumors. EJNMMI Phys [Internet]. Springer International Publishing; 2018 [cited 2020];5:27. Available from: http://www.ncbi.nlm.nih.gov/pubmed/30255439
Bjöersdorff M, Oddstig J, Karindotter-Borgendahl N, et al. Impact of penalizing factor in a block-sequential regularized expectation maximization reconstruction algorithm for 18F-fluorocholine PET-CT regarding image quality and interpretation. EJNMMI Phys [Internet]. Springer International Publishing; 2019 [cited 2020];6. Available from: https://pubmed.ncbi.nlm.nih.gov/30900064/
Trägårdh E, Minarik D, Almquist H, et al. Impact of acquisition time and penalizing factor in a block-sequential regularized expectation maximization reconstruction algorithm on a Si-photomultiplier-based PET-CT system for 18F-FDG. EJNMMI Res [Internet]. Springer Verlag; 2019 [cited 2020];9:64. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31342214
Lindström E, Sundin A, Trampal C, et al. Evaluation of penalized-likelihood estimation reconstruction on a digital time-of-flight PET/CT scanner for 18 F-FDG whole-body examinations. J Nucl Med. Society of Nuclear Medicine Inc.; 2018;59:1152–1158.
Sah BR, Stolzmann P, Delso G, et al. Clinical evaluation of a block sequential regularized expectation maximization reconstruction algorithm in 18F-FDG PET/CT studies. Nucl Med Commun. Lippincott Williams and Wilkins; 2017;38:57–66.
Shkumat NA, Vali R, Shammas A. Clinical evaluation of reconstruction and acquisition time for pediatric 18F-FDG brain PET using digital PET/CT. Pediatr Radiol. Springer; 2020;50:966–972.
doi: 10.1007/s00247-020-04640-1
Lindström E, Oddstig J, Danfors T, et al. Image reconstruction methods affect software-aided assessment of pathologies of [18F]flutemetamol and [18F]FDG brain-PET examinations in patients with neurodegenerative diseases. NeuroImage Clin [Internet]. Elsevier Inc.; 2020 [cited 2020];28. Available from: https://pubmed.ncbi.nlm.nih.gov/32882645/
Vennart NJ, Bird N, Buscombe J, et al. Optimization of PET/CT image quality using the GE “Sharp IR” point-spread function reconstruction algorithm. Nucl Med Commun [Internet]. Lippincott Williams and Wilkins; 2017 [cited 2020];38:471–479. Available from: https://pubmed.ncbi.nlm.nih.gov/28394818/
Rogasch JMM, Albers J, Steinkrüger FL, et al. Point Spread Function Reconstruction for Integrated 18F-FET PET/MRI in Patients With Glioma: Does It Affect SUVs and Respective Tumor-to-Background Ratios? Clin Nucl Med [Internet]. Lippincott Williams and Wilkins; 2019 [cited 2021];44:e280–e285. Available from: https://pubmed.ncbi.nlm.nih.gov/30562198/
Hudson HM, Larkin RS. Accelerated Image Reconstruction Using Ordered Subsets of Projection Data. IEEE Trans Med Imaging [Internet]. IEEE Trans Med Imaging; 1994 [cited 2020];13:601–609. Available from: https://pubmed.ncbi.nlm.nih.gov/18218538/
Jeih L, Strother SC. The convergence of object dependent resolution in maximum likelihood based tomographic image reconstruction. Phys Med Biol [Internet]. Phys Med Biol; 1993 [cited 2020];38:55–70. Available from: https://pubmed.ncbi.nlm.nih.gov/8426869/
Qi J, Leahy RM. Iterative reconstruction techniques in emission computed tomography [Internet]. Phys. Med. Biol. Phys Med Biol; 2006 [cited 2020]. Available from: https://pubmed.ncbi.nlm.nih.gov/16861768/
Vandenberghe S, Moskal P, Karp JS. State of the art in total body PET. EJNMMI Phys. Springer; 2020.
doi: 10.1186/s40658-020-00290-2
Sekine T, Delso G, Zeimpekis KG, et al. Reduction of 18F-FDG dose in clinical PET/MR imaging by using silicon photomultiplier detectors. Radiology. Radiological Society of North America Inc.; 2018;286:249–259.
Queiroz MA, Delso G, Wollenweber S, et al. Dose optimization in TOF-PET/MR compared to TOF-PET/CT. PLoS One. Public Library of Science; 2015;10.
Hatami S, Frye SA, McMunn A, et al. Added Value of Digital Over Analog PET/CT: More Significant as Image Field of View (FOV) and Body Mass Index (BMI) Increases. J Nucl Med Technol [Internet]. Society of Nuclear Medicine; 2020 [cited 2020];jnmt.120.244160. Available from: https://pubmed.ncbi.nlm.nih.gov/32887763/
Ahn S, Fessler JA. Globally convergent image reconstruction for emission tomography using relaxed ordered subsets algorithms. IEEE Trans Med Imaging. 2003;22:613–626.
doi: 10.1109/TMI.2003.812251
Schwyzer M, Ferraro DA, Muehlematter UJ, et al. Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks – Initial results. Lung Cancer. Elsevier Ireland Ltd; 2018;126:170–173.
doi: 10.1016/j.lungcan.2018.11.001
Aljared A, Alharbi AA, Huellner MW. BSREM Reconstruction for Improved Detection of In-Transit Metastases with Digital FDG-PET/CT in Patients with Malignant Melanoma. Clin Nucl Med. Lippincott Williams and Wilkins; 2018;43:370–371.
Nguyen NC, Vercher-Conejero JL, Sattar A, et al. Image quality and diagnostic performance of a digital pet prototype in patients with oncologic diseases: Initial experience and comparison with analog PET. J Nucl Med. Society of Nuclear Medicine Inc.; 2015;56:1378–1385.
Gnesin S, Kieffer C, Zeimpekis K, et al. Phantom-based image quality assessment of clinical 18F-FDG protocols in digital PET/CT and comparison to conventional PMT-based PET/CT. EJNMMI Phys [Internet]. Springer; 2020 [cited 2020];7. Available from: https://pubmed.ncbi.nlm.nih.gov/31907664/
Liberini V, Kotasidis F, Treyer V, et al. Impact of PET data driven respiratory motion correction and BSREM reconstruction of 68Ga-DOTATATE PET/CT for differentiating neuroendocrine tumors (NET) and intrapancreatic accessory spleens (IPAS). Sci Rep [Internet]. Nature Publishing Group; 2021 [cited 2021];11:2273. Available from: http://www.nature.com/articles/s41598-020-80855-4
Baratto L, Duan H, Ferri V, et al. The Effect of Various β Values on Image Quality and Semiquantitative Measurements in 68Ga-RM2 and 68Ga-PSMA-11 PET/MRI Images Reconstructed with a Block Sequential Regularized Expectation Maximization Algorithm. Clin Nucl Med [Internet]. Lippincott Williams and Wilkins; 2020 [cited 2021];45:506–513. Available from: https://pubmed.ncbi.nlm.nih.gov/32433170/
McHugh ML. Interrater reliability: The kappa statistic. Biochem Medica [Internet]. Biochemia Medica, Editorial Office; 2012 [cited 2020];22:276–282. Available from: /pmc/articles/PMC3900052/?report=abstract
IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp.
58. Teoh EJ, McGowan DR, Bradley KM, et al. Novel penalised likelihood reconstruction of PET in the assessment of histologically verified small pulmonary nodules. Eur Radiol. Springer Verlag; 2016;26:576–584.
doi: 10.1007/s00330-015-3832-y
59. Lindström E, Velikyan I, Regula N, et al. Regularized reconstruction of digital time-of-flight 68Ga-PSMA-11 PET/CT for the detection of recurrent disease in prostate cancer patients. Theranostics. 2019;9:3476–3484.
doi: 10.7150/thno.31970
Lindström E, Lindsjö L, Sundin A, et al. Evaluation of block-sequential regularized expectation maximization reconstruction of 68Ga-DOTATOC, 18F-fluoride, and 11C-acetate whole-body examinations acquired on a digital time-of-flight PET/CT scanner. EJNMMI Phys [Internet]. Springer; 2020 [cited 2020];7. Available from: https://pubmed.ncbi.nlm.nih.gov/32542512/
Shkumat NA, Vali R, Shammas A. Clinical evaluation of reconstruction and acquisition time for pediatric 18F-FDG brain PET using digital PET/CT. Pediatr Radiol [Internet]. Springer; 2020 [cited 2020];50:966–972. Available from: https://pubmed.ncbi.nlm.nih.gov/32125447/
Caribé PRRV, Koole M, D’Asseler Y, et al. Noise reduction using a Bayesian penalized-likelihood reconstruction algorithm on a time-of-flight PET-CT scanner. EJNMMI Phys [Internet]. Springer; 2019 [cited 2020];6:22. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31823084
Diaz ME, Debowski M, Hukins C, et al. Non-small cell lung cancer brain metastasis screening in the era of positron emission tomography-CT staging: Current practice and outcomes. J Med Imaging Radiat Oncol [Internet]. Blackwell Publishing; 2018 [cited 2020];62:383–388. Available from: http://doi.wiley.com/ https://doi.org/10.1111/1754-9485.12732
Silvestri GA, Gonzalez A V., Jantz MA, et al. Methods for staging non-small cell lung cancer: Diagnosis and management of lung cancer, 3rd ed: American college of chest physicians evidence-based clinical practice guidelines. Chest [Internet]. Chest; 2013 [cited 2020];143. Available from: https://pubmed.ncbi.nlm.nih.gov/23649440/
Milano MT, Bates JE, Budnik J, et al. Risk of brain metastases in T1–3N0 NSCLC: a population-based analysis. Lung Cancer Manag [Internet]. Future Medicine Ltd; 2020 [cited 2020];9:LMT25. Available from: /pmc/articles/PMC7110582/?report=abstract
Waqar SN, Samson PP, Robinson CG, et al. Non–small-cell Lung Cancer With Brain Metastasis at Presentation. Clin Lung Cancer [Internet]. Elsevier Inc.; 2018 [cited 2020];19:e373–e379. Available from: https://pubmed.ncbi.nlm.nih.gov/29526531/