Literature DB >> 24315703

Feasibility study of FDG PET/CT-derived primary tumour glycolysis as a prognostic indicator of survival in patients with non-small-cell lung cancer.

G Mehta1, A Chander1, C Huang2, M Kelly2, P Fielding3.   

Abstract

AIM: To assess the feasibility and prognostic value of measuring total lesion glycolysis of the primary tumour (TLG(primary)) using combined 2-[18F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography/computed tomography (PET/CT) in patients with proven or suspected non-small-cell lung cancer (NSCLC) in the routine diagnostic setting.
MATERIALS AND METHODS: At the All wales Research and Diagnostic Positron Emission Tomography Centre in Cardiff (PETIC), in the calendar year 2011, 288 consecutive patients were identified with a single pulmonary mass in whom NSCLC was confirmed or clinically diagnosed following multidisciplinary team review. In a retrospective analysis, for each patient the PET-derived volume of the primary tumour and SUVMEAN was calculated using adaptive thresholds of 40% and 50% of the SUVMAX of the primary tumour. The TLG(primary) (calculated by volume x SUVMEAN) was calculated at these two thresholds and was used to predict survival in a multivariate analysis with TNM (tumour, node, metastasis) stage, age, sex, and SUV(MAX). The primary endpoint was overall survival over a minimum follow-up of at least 7 months.
RESULTS: In virtually every case, the primary tumour could be measured using the automated software with minimal use of manual adjustments. In multivariate analysis, TNM clinical stage, log(TLG(primary)) and sex were independent predictors of overall survival.
CONCLUSION: Measurements of primary tumour total lesion glycolysis are simple to perform and provide additional prognostic information over and above that provided by TNM staging.
Copyright © 2013 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

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Year:  2013        PMID: 24315703     DOI: 10.1016/j.crad.2013.10.010

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  4 in total

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Journal:  BMC Cancer       Date:  2014-12-01       Impact factor: 4.430

4.  Deep segmentation networks predict survival of non-small cell lung cancer.

Authors:  Stephen Baek; Yusen He; Bryan G Allen; John M Buatti; Brian J Smith; Ling Tong; Zhiyu Sun; Jia Wu; Maximilian Diehn; Billy W Loo; Kristin A Plichta; Steven N Seyedin; Maggie Gannon; Katherine R Cabel; Yusung Kim; Xiaodong Wu
Journal:  Sci Rep       Date:  2019-11-21       Impact factor: 4.379

  4 in total

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