Optimal respiratory-gated [18F]FDG PET/CT significantly impacts the quantification of metabolic parameters and their correlation with overall survival in patients with ... View Full Text


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Article Info

DATE

2019-12

AUTHORS

Esther M. M. Smeets, Dominique S. Withaar, Willem Grootjans, John J. Hermans, Kees van Laarhoven, Lioe-Fee de Geus-Oei, Martin Gotthardt, Erik H. J. G. Aarntzen

ABSTRACT

PURPOSE: Metabolic parameters are increasingly being used to characterize tumors. Motion artifacts due to patient respiration introduce uncertainties in quantification of metabolic parameters during positron emission tomography (PET) image acquisition. The present study investigates the impact of amplitude-based optimal respiratory gating (ORG) on quantification of PET-derived image features in patients with pancreatic ductal adenocarcinoma (PDAC), in correlation with overall survival (OS). METHODS: Sixty-nine patients with histologically proven primary PDAC underwent 2'-deoxy-2'-[18F]fluoroglucose ([18F]FDG) PET/CT imaging during diagnostic work-up. Standard image acquisition and reconstruction was performed in accordance with the EANM guidelines and ORG images were reconstructed with a duty cycle of 35%. PET-derived image features, including standard parameters, first- and second-order texture features, were calculated from the standard and corresponding ORG images, and correlation with OS was assessed. RESULTS: ORG significantly impacts the quantification of nearly all features; values of single-voxel parameters (e.g., SUVmax) showed a wider range, volume-based parameters (e.g., SUVmean) were reduced, and texture features were significantly changed. After correction for motion artifacts using ORG, some features that describe intra-tumoral heterogeneity were more strongly correlated to OS. CONCLUSIONS: Correction for respiratory motion artifacts using ORG impacts the quantification of metabolic parameters in PDAC lesions. The correlation of metabolic parameters with OS was significantly affected, in particular parameters that describe intra-tumor heterogeneity. Therefore, interpretation of single-voxel or average metabolic parameters in relation to clinical outcome should be done cautiously. Furthermore, ORG is a valuable tool to improve quantification of intra-tumoral heterogeneity in PDAC. More... »

PAGES

24

References to SciGraph publications

  • 2014-01. Prognostic value of FDG uptake in primary inoperable non-small cell lung cancer in MEDICAL ONCOLOGY
  • 2014-12. Chemotherapy regimens for advanced pancreatic cancer: a systematic review and network meta-analysis in BMC CANCER
  • 2011-12. Predictive value of metabolic 18FDG-PET response on outcomes in patients with locally advanced pancreatic carcinoma treated with definitive concurrent chemoradiotherapy in BMC GASTROENTEROLOGY
  • 2015-02. Whole genomes redefine the mutational landscape of pancreatic cancer in NATURE
  • 2018-07. Multiparametric PET/MR imaging biomarkers are associated with overall survival in patients with pancreatic cancer in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2013-09. Influence of tumour micro-environment heterogeneity on therapeutic response in NATURE
  • 2014-03. High FDG uptake predicts poorer survival in locally advanced nonsmall cell lung cancer patients undergoing curative radiotherapy, independently of tumor size in JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY
  • 2015-09. The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis in SCIENTIFIC REPORTS
  • 2011-12. Motion effects on SUV and lesion volume in 3D and 4D PET scanning in AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE
  • 2016-03. Genomic analyses identify molecular subtypes of pancreatic cancer in NATURE
  • 2003-12. 18-fluorodeoxyglucose positron emission tomography in predicting survival of patients with pancreatic carcinoma in JOURNAL OF GASTROINTESTINAL SURGERY
  • 2014-12. Amplitude-based optimal respiratory gating in positron emission tomography in patients with primary lung cancer in EUROPEAN RADIOLOGY
  • 2016-07. Intratumoral heterogeneity of 18F-FDG uptake predicts survival in patients with pancreatic ductal adenocarcinoma in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2014-12. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach in NATURE COMMUNICATIONS
  • 2015-02. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0 in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • 2010-01. FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0 in EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13550-019-0492-y

    DOI

    http://dx.doi.org/10.1186/s13550-019-0492-y

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112739259

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30868318


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    N-Triples is a line-based linked data format ideal for batch operations.

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    Turtle is a human-readable linked data format.

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    RDF/XML is a standard XML format for linked data.

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