Literature DB >> 20545013

Influence of ROI definition, partial volume correction and SUV normalization on SUV-survival correlation in oesophageal cancer.

Mark van Heijl1, Jikke M Omloo, Mark I van Berge Henegouwen, Jan J van Lanschot, Gerrit W Sloof, Ronald Boellaard.   

Abstract

OBJECTIVE: An explanation for the discrepancies in the reported correlations between standardized uptake value (SUV) and survival might be the application of different SUV methodologies. The primary aim of this study was to examine the influence of using different methodologies on SUV-survival correlation.
METHODS: Data were used from a prospective cohort study consisting of oesophageal cancer patients in whom preoperative fluorodeoxyglucose positron emission tomography was performed. Various methodologies of SUV calculation/correction were correlated with the default (SUV A41% corrected for body surface area): different volume of interest definitions, different SUV normalization, with and without serum glucose correction, and with (PVC+ ) and without partial volume correction (PVC- ). Receiver operating characteristic (ROC) curves using any type of SUV for the identification of potential correlation with disease-free survival were also compared.
RESULTS: Fifty-two patients were included for this study. Significant correlations were found between SUV A41% and all the other described SUVs: SUV 50% (r2=0.99; P< 0.001), SUV A50% (r2= 0.98; P< 0.001), SUVmax (r2= 0.98; P < 0.001), SUV A41% PVC+ (r2= 0.97; P < 0.001) and SUV A41% glucose (r2= 0.93; P <0.001). No correlation was found between volume of interest 41% and SUV A41%, with or without, PVC (P = 0.85 and P = 0.41). Significant correlations were found between SUVmax corrected for body surface area, SUVmax corrected for body weight (r2=0.96; P < 0.001) and SUV corrected for lean body mass (r2= 0.98; P < 0.001). ROC curves for various SUV methodologies showed an almost identical area under the curve for any type of SUV.
CONCLUSION: A strong correlation was found between all the investigated SUV methodologies. Moreover, when looking for correlations between SUV and disease-free survival, the areas under the ROC curves were almost identical for any type of SUV methodology. 2010 Wolters Kluwer Health / Lippincott Williams & Wilkins.

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Year:  2010        PMID: 20545013

Source DB:  PubMed          Journal:  Nucl Med Commun        ISSN: 0143-3636            Impact factor:   1.690


  9 in total

1.  Impact of partial-volume effect correction on the predictive and prognostic value of baseline 18F-FDG PET images in esophageal cancer.

Authors:  Mathieu Hatt; Adrien Le Pogam; Dimitris Visvikis; Olivier Pradier; Catherine Cheze Le Rest
Journal:  J Nucl Med       Date:  2012-01       Impact factor: 10.057

2.  Predictive value of pre-therapy (18)F-FDG PET/CT for the outcome of (18)F-FDG PET-guided radiotherapy in patients with head and neck cancer.

Authors:  M Picchio; M Kirienko; P Mapelli; I Dell'Oca; E Villa; F Gallivanone; L Gianolli; C Messa; I Castiglioni
Journal:  Eur J Nucl Med Mol Imaging       Date:  2013-08-29       Impact factor: 9.236

3.  Glucose-corrected standardized uptake value (SUVgluc) is the most accurate SUV parameter for evaluation of pulmonary nodules.

Authors:  Amin Haghighat Jahromi; Farshad Moradi; Carl K Hoh
Journal:  Am J Nucl Med Mol Imaging       Date:  2019-10-15

4.  Prognostic value of 18F-FDG PET image-based parameters in oesophageal cancer and impact of tumour delineation methodology.

Authors:  Mathieu Hatt; Dimitris Visvikis; Nidal M Albarghach; Florent Tixier; Olivier Pradier; Catherine Cheze-le Rest
Journal:  Eur J Nucl Med Mol Imaging       Date:  2011-03-02       Impact factor: 9.236

5.  Predictive value of early and late residual 18F-fluorodeoxyglucose and 18F-fluorothymidine uptake using different SUV measurements in patients with non-small-cell lung cancer treated with erlotinib.

Authors:  Carsten Kobe; Matthias Scheffler; Arne Holstein; Thomas Zander; Lucia Nogova; Adriaan A Lammertsma; Ronald Boellaard; Bernd Neumaier; Roland T Ullrich; Markus Dietlein; Jürgen Wolf; Deniz Kahraman
Journal:  Eur J Nucl Med Mol Imaging       Date:  2012-04-14       Impact factor: 9.236

6.  Ferret thoracic anatomy by 2-deoxy-2-(18F)fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (18F-FDG PET/CT) imaging.

Authors:  Albert Wu; Huaiyu Zheng; Jennifer Kraenzle; Ashley Biller; Carol D Vanover; Mary Proctor; Leslie Sherwood; Marlene Steffen; Chin Ng; Daniel J Mollura; Colleen B Jonsson
Journal:  ILAR J       Date:  2012

7.  A novel metric for quantification of homogeneous and heterogeneous tumors in PET for enhanced clinical outcome prediction.

Authors:  Arman Rahmim; C Ross Schmidtlein; Andrew Jackson; Sara Sheikhbahaei; Charles Marcus; Saeed Ashrafinia; Madjid Soltani; Rathan M Subramaniam
Journal:  Phys Med Biol       Date:  2015-12-07       Impact factor: 3.609

8.  Radiomics Features Differentiate Between Normal and Tumoral High-Fdg Uptake.

Authors:  Chih-Yang Hsu; Mike Doubrovin; Chia-Ho Hua; Omar Mohammed; Barry L Shulkin; Sue Kaste; Sara Federico; Monica Metzger; Matthew Krasin; Christopher Tinkle; Thomas E Merchant; John T Lucas
Journal:  Sci Rep       Date:  2018-03-02       Impact factor: 4.379

Review 9.  Impact of partial-volume correction in oncological PET studies: a systematic review and meta-analysis.

Authors:  Matthijs C F Cysouw; Gerbrand M Kramer; Linda J Schoonmade; Ronald Boellaard; Henrica C W de Vet; Otto S Hoekstra
Journal:  Eur J Nucl Med Mol Imaging       Date:  2017-08-04       Impact factor: 9.236

  9 in total

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