dc.contributor.author | Meijer, TWH | |
dc.contributor.author | de Geus-Oei, L-F | |
dc.contributor.author | Visser, EP | |
dc.contributor.author | Oyen, WJG | |
dc.contributor.author | Looijen-Salamon, MG | |
dc.contributor.author | Visvikis, D | |
dc.contributor.author | Verhagen, AFTM | |
dc.contributor.author | Bussink, J | |
dc.contributor.author | Vriens, D | |
dc.date.accessioned | 2016-11-23T13:38:12Z | |
dc.date.issued | 2017-05 | |
dc.identifier.citation | Radiology, 2017, 283 (2), pp. 547 - 559 | |
dc.identifier.issn | 0033-8419 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/251 | |
dc.identifier.eissn | 1527-1315 | |
dc.identifier.doi | 10.1148/radiol.2016160329 | |
dc.description.abstract | Purpose To assess whether dynamic fluorine 18 ( 18 F) fluorodeoxyglucose (FDG) positron emission tomography (PET) has added value over static 18 F-FDG PET for tumor delineation in non-small cell lung cancer (NSCLC) radiation therapy planning by using pathology volumes as the reference standard and to compare pharmacokinetic rate constants of 18 F-FDG metabolism, including regional variation, between NSCLC histologic subtypes. Materials and Methods The study was approved by the institutional review board. Patients gave written informed consent. In this prospective observational study, 1-hour dynamic 18 F-FDG PET/computed tomographic examinations were performed in 35 patients (36 resectable NSCLCs) between 2009 and 2014. Static and parametric images of glucose metabolic rate were obtained to determine lesion volumes by using three delineation strategies. Pathology volume was calculated from three orthogonal dimensions (n = 32). Whole tumor and regional rate constants and blood volume fraction (V B ) were computed by using compartment modeling. Results Pathology volumes were larger than PET volumes (median difference, 8.7-25.2 cm 3 ; Wilcoxon signed rank test, P < .001). Static fuzzy locally adaptive Bayesian (FLAB) volumes corresponded best with pathology volumes (intraclass correlation coefficient, 0.72; P < .001). Bland-Altman analyses showed the highest precision and accuracy for static FLAB volumes. Glucose metabolic rate and 18 F-FDG phosphorylation rate were higher in squamous cell carcinoma (SCC) than in adenocarcinoma (AC), whereas V B was lower (Mann-Whitney U test or t test, P = .003, P = .036, and P = .019, respectively). Glucose metabolic rate, 18 F-FDG phosphorylation rate, and V B were less heterogeneous in AC than in SCC (Friedman analysis of variance). Conclusion Parametric images are not superior to static images for NSCLC delineation. FLAB-based segmentation on static 18 F-FDG PET images is in best agreement with pathology volume and could be useful for NSCLC autocontouring. Differences in glycolytic rate and V B between SCC and AC are relevant for research in targeting agents and radiation therapy dose escalation. © RSNA, 2016 Online supplemental material is available for this article. | |
dc.format | Print-Electronic | |
dc.format.extent | 547 - 559 | |
dc.language | eng | |
dc.language.iso | eng | |
dc.subject | Humans | |
dc.subject | Carcinoma, Non-Small-Cell Lung | |
dc.subject | Lung Neoplasms | |
dc.subject | Fluorodeoxyglucose F18 | |
dc.subject | Glucose | |
dc.subject | Radiopharmaceuticals | |
dc.subject | Image Interpretation, Computer-Assisted | |
dc.subject | Positron-Emission Tomography | |
dc.subject | Neoplasm Staging | |
dc.subject | Metabolic Clearance Rate | |
dc.subject | Sensitivity and Specificity | |
dc.subject | Reproducibility of Results | |
dc.subject | Aged | |
dc.subject | Aged, 80 and over | |
dc.subject | Middle Aged | |
dc.subject | Male | |
dc.subject | Molecular Imaging | |
dc.subject | Biomarkers, Tumor | |
dc.title | Tumor Delineation and Quantitative Assessment of Glucose Metabolic Rate within Histologic Subtypes of Non-Small Cell Lung Cancer by Using Dynamic <sup>18</sup>F Fluorodeoxyglucose PET. | |
dc.type | Journal Article | |
dcterms.dateAccepted | 2016-08-15 | |
rioxxterms.versionofrecord | 10.1148/radiol.2016160329 | |
rioxxterms.licenseref.startdate | 2017-05 | |
rioxxterms.type | Journal Article/Review | |
dc.relation.isPartOf | Radiology | |
pubs.issue | 2 | |
pubs.notes | Not known | |
pubs.organisational-group | /ICR | |
pubs.organisational-group | /ICR/Primary Group | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Translational Molecular Imaging | |
pubs.organisational-group | /ICR | |
pubs.organisational-group | /ICR/Primary Group | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Translational Molecular Imaging | |
pubs.publication-status | Published | |
pubs.volume | 283 | |
pubs.embargo.terms | Not known | |
icr.researchteam | Translational Molecular Imaging | en_US |
dc.contributor.icrauthor | Oyen, Willem | |