Skip to main content

Main menu

  • Home
  • Content
    • Current
    • Ahead of print
    • Past Issues
    • JNM Supplement
    • SNMMI Annual Meeting Abstracts
    • Continuing Education
    • JNM Podcasts
  • Subscriptions
    • Subscribers
    • Institutional and Non-member
    • Rates
    • Journal Claims
    • Corporate & Special Sales
  • Authors
    • Submit to JNM
    • Information for Authors
    • Assignment of Copyright
    • AQARA requirements
  • Info
    • Reviewers
    • Permissions
    • Advertisers
  • About
    • About Us
    • Editorial Board
    • Contact Information
  • More
    • Alerts
    • Feedback
    • Help
    • SNMMI Journals
  • SNMMI
    • JNM
    • JNMT
    • SNMMI Journals
    • SNMMI

User menu

  • Subscribe
  • My alerts
  • Log in
  • My Cart

Search

  • Advanced search
Journal of Nuclear Medicine
  • SNMMI
    • JNM
    • JNMT
    • SNMMI Journals
    • SNMMI
  • Subscribe
  • My alerts
  • Log in
  • My Cart
Journal of Nuclear Medicine

Advanced Search

  • Home
  • Content
    • Current
    • Ahead of print
    • Past Issues
    • JNM Supplement
    • SNMMI Annual Meeting Abstracts
    • Continuing Education
    • JNM Podcasts
  • Subscriptions
    • Subscribers
    • Institutional and Non-member
    • Rates
    • Journal Claims
    • Corporate & Special Sales
  • Authors
    • Submit to JNM
    • Information for Authors
    • Assignment of Copyright
    • AQARA requirements
  • Info
    • Reviewers
    • Permissions
    • Advertisers
  • About
    • About Us
    • Editorial Board
    • Contact Information
  • More
    • Alerts
    • Feedback
    • Help
    • SNMMI Journals
  • View or Listen to JNM Podcast
  • Visit JNM on Facebook
  • Join JNM on LinkedIn
  • Follow JNM on Twitter
  • Subscribe to our RSS feeds
Research ArticleOncology

Test–Retest Reproducibility of 18F-FDG PET/CT Uptake in Cancer Patients Within a Qualified and Calibrated Local Network

Brenda F. Kurland, Lanell M. Peterson, Andrew T. Shields, Jean H. Lee, Darrin W. Byrd, Alena Novakova-Jiresova, Mark Muzi, Jennifer M. Specht, David A. Mankoff, Hannah M. Linden and Paul E. Kinahan
Journal of Nuclear Medicine May 2019, 60 (5) 608-614; DOI: https://doi.org/10.2967/jnumed.118.209544
Brenda F. Kurland
1Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Lanell M. Peterson
2Division of Medical Oncology, University of Washington/Seattle Cancer Care Alliance, Seattle, Washington
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Andrew T. Shields
3Department of Radiology, University of Washington, Seattle, Washington; and
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jean H. Lee
3Department of Radiology, University of Washington, Seattle, Washington; and
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Darrin W. Byrd
3Department of Radiology, University of Washington, Seattle, Washington; and
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Alena Novakova-Jiresova
2Division of Medical Oncology, University of Washington/Seattle Cancer Care Alliance, Seattle, Washington
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mark Muzi
3Department of Radiology, University of Washington, Seattle, Washington; and
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jennifer M. Specht
2Division of Medical Oncology, University of Washington/Seattle Cancer Care Alliance, Seattle, Washington
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
David A. Mankoff
4Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hannah M. Linden
2Division of Medical Oncology, University of Washington/Seattle Cancer Care Alliance, Seattle, Washington
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Paul E. Kinahan
3Department of Radiology, University of Washington, Seattle, Washington; and
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • Supplemental
  • Info & Metrics
  • PDF
Loading

Article Figures & Data

Figures

  • Tables
  • Additional Files
  • FIGURE 1.
    • Download figure
    • Open in new tab
    • Download powerpoint
    FIGURE 1.

    Bland–Altman plots of difference in SUVmax vs. average SUVmax: 10 patients (51 lesions) with repeat scans using same scanner (A); 2 patients (34 lesions) using different scanners from same academic institution (B); and 11 patients (77 lesions) using different scanners from different sites (C). Within each panel, plotting character/color is same for multiple lesions in single patient. Dashed lines = average difference and 95% limits of agreement. The 2 lesions from melanoma patient (SUVmax, 38.3 and 25.0 on first scan and 19.2 and 16.4 on second scan) are not shown in C but contribute to limits-of-agreement calculations.

  • FIGURE 2.
    • Download figure
    • Open in new tab
    • Download powerpoint
    FIGURE 2.

    (A) Coronal images from 60-y-old woman with stage IV ductal breast carcinoma (blue circles in Fig. 1A, same scanner). SUVmax for 9 evaluable lesions ranged from 3.4 to 5.1 (average, 4.0) for first scan and from 3.1 to 4.9 (average, 4.2) for second scan. Percentage difference was −16% to +16% (average, 3.9%); SUV unit difference was −0.62 to +0.64 (average, 0.15). (B) A 73-y-old woman with stage IV mixed ductal/lobular breast carcinoma (yellow circles in Fig. 1C, different institutions). SUVmax for 17 evaluable lesions was 2.0–12.2 (average, 4.8) for first scan and 1.9–12.0 (average, 4.4) for second scan. Percentage difference was −24% to +25% (average, −7.1%); SUV unit difference was −1.4 to +1.0 (average, −0.39). Normal liver SUVmean was 2.5 (A) and 2.6 (B) in both scans.

  • FIGURE 3.
    • Download figure
    • Open in new tab
    • Download powerpoint
    FIGURE 3.

    Percentage difference in SUVmax vs. average SUVmax: 12 patients (85 lesions) with repeat scans using same scanner or different scanners from same unit (combined data from Figs. 1A and 1B) (A); 11 patients (77 lesions) using different scanners from different sites (B). Plotting character/color identifies multiple lesions in single patient, as for Figure 1. Dashed lines = average percentage difference and 95% limits of agreement.

  • FIGURE 4.
    • Download figure
    • Open in new tab
    • Download powerpoint
    FIGURE 4.

    Bland–Altman plot of liver SUVmean (n = 23). Light-green circles = same scanner; dark-green circles = different scanners from same site; gray circles = different scanner models from different sites; dashed lines = average difference and 95% limits of agreement.

Tables

  • Figures
  • Additional Files
    • View popup
    TABLE 1

    Scanning Protocol Characteristics

    Characteristic (PET emission images)Discovery STE (both)Gemini TF 64Biograph 20 mCTBiograph 6
    N17 3D, 18 2D623
    Slice thickness (mm)3.27455
    Pixel size (mm)5.4744.074.06
    Pixel volume (cm3)0.0980.0640.0830.083
    Reconstruction diameter (mm)700576815683
    Array size (pixels)128 × 128144 × 144200 × 200168 × 168
    Bed-position duration (min)5, 742, 3.53
    Average coverage (total cm/total min)2.1, 2.42.32.8, 4.94.0, 4.1
    Reconstruction methodOSEM 3D/2DBLOB-OS-TOFPSF+TOF 2i21sPSF, 3i24s
    Scatter correction methodModel-basedSS-SIMULModel-basedModel-based
    • 3D = 3-dimensional; 2D = 2-dimensional; OSEM = ordered-subset expectation maximization; TOF = time-of-flight; PSF = point-spread function; 2i21s = 2 iterations, 21 subsets; 3i24s = 3 iterations, 24 subsets; SS-SIMUL = single-scatter simulation.

    • All scans were in inferior-to-superior direction.

    • View popup
    TABLE 2

    Patient Characteristics

    CharacteristicSame site/scanner (n = 10)Same institution, different scanner (n = 2)Different site/scanner (n = 11)All patients (n = 23)
    Age (y)53.5 (32–67)58 (45–71)66 (43–76)60 (32–76)
    Body mass index at scan 1 (kg/m2)28.3 (18.3–37.6)38.6 (31.4–45.7)28.4 (22.5–43.2)28.4 (18.3–45.7)
    Time between scans (d)8 (2–15)7 (1–13)10 (7–14)9 (1–15)
    Lesions* (n)5 (1–9)17 (9–25)5 (1–17)5 (1–25)
    Sex (n)
     Male––33 (13%)
     Female102820 (87%)
    Diagnosis (n)
     Breast cancer102618 (78%)
     Other†––55 (22%)
    Ongoing treatment between scans (n)
     None2–57 (30%)
     Bisphosphonates or biologic only1113 (13%)
     Endocrine therapy‡4–37 (30%)
     Chemotherapy§3126 (26%)
    PET/CT scanner (n)
     Discovery STE (both)102–12 (52%)
     Ingenuity TF––66 (26%)
     Biograph 6––33 (13%)
     Biograph 20 mCT––22 (9%)
    • ↵* All identified, but ≤25 lesions/patient used in analysis.

    • ↵† 1 each: colorectal, head/neck, stage IV lung, stage III melanoma, neuroendocrine/Merkel cell cancer.

    • ↵‡ 2 also bisphosphonates; 3 also biologic.

    • ↵§ 4 also biologic; 1 also endocrine. Biologics: erlotinib, trastuzumab, everolimus, pertuzumab, denosumab, ado-trastuzumab emtansine. Cytotoxic agents: capecitabine, cyclophosphamide, doxorubicin.

    • Continuous data are expressed as median and range.

    • View popup
    TABLE 3

    Linear Mixed-Effects Models (Linear Regression for Liver)

    ModelFitted % difference in repeat scans95% confidence interval
    Model 1 (SUVmax)*
     A. Same scanner (n = 10)8%6%–11%
     B. Same institution,  different scanner (n = 2)6%3%–11%
     C. Different institution  and scanner (n = 11)18%13%–24%
    Model 2 (SUVmax)†
     Same scanner or  institution8%6%–10%
     Different institution and  scanner18%13%–24%
    Model 3 (liver SUVmean)‡
     Same scanner or  institution5%3%–10%
     Different scanner and  institution6%3%–11%
    • ↵* C > A (P = 0.0015), C > B (P = 0.003), A and B not different on average (P = 0.66) (Tukey–Kramer adjustment for pairwise comparisons).

    • ↵† P < 0.001, Wald test.

    • ↵‡ P = 0.85, Wald test (n = 23).

    • Group differences are back-transformed from log(absolute percentage difference + 1); n = 162 tumors in 23 patients.

Additional Files

  • Figures
  • Tables
  • Supplemental Data

    Files in this Data Supplement:

    • Supplemental Data
PreviousNext
Back to top

In this issue

Journal of Nuclear Medicine: 60 (5)
Journal of Nuclear Medicine
Vol. 60, Issue 5
May 1, 2019
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Index by author
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on Journal of Nuclear Medicine.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Test–Retest Reproducibility of 18F-FDG PET/CT Uptake in Cancer Patients Within a Qualified and Calibrated Local Network
(Your Name) has sent you a message from Journal of Nuclear Medicine
(Your Name) thought you would like to see the Journal of Nuclear Medicine web site.
Citation Tools
Test–Retest Reproducibility of 18F-FDG PET/CT Uptake in Cancer Patients Within a Qualified and Calibrated Local Network
Brenda F. Kurland, Lanell M. Peterson, Andrew T. Shields, Jean H. Lee, Darrin W. Byrd, Alena Novakova-Jiresova, Mark Muzi, Jennifer M. Specht, David A. Mankoff, Hannah M. Linden, Paul E. Kinahan
Journal of Nuclear Medicine May 2019, 60 (5) 608-614; DOI: 10.2967/jnumed.118.209544

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Test–Retest Reproducibility of 18F-FDG PET/CT Uptake in Cancer Patients Within a Qualified and Calibrated Local Network
Brenda F. Kurland, Lanell M. Peterson, Andrew T. Shields, Jean H. Lee, Darrin W. Byrd, Alena Novakova-Jiresova, Mark Muzi, Jennifer M. Specht, David A. Mankoff, Hannah M. Linden, Paul E. Kinahan
Journal of Nuclear Medicine May 2019, 60 (5) 608-614; DOI: 10.2967/jnumed.118.209544
Twitter logo Facebook logo LinkedIn logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One
Bookmark this article

Jump to section

  • Article
    • Abstract
    • MATERIALS AND METHODS
    • RESULTS
    • DISCUSSION
    • CONCLUSION
    • DISCLOSURE
    • Acknowledgments
    • Footnotes
    • REFERENCES
  • Figures & Data
  • Supplemental
  • Info & Metrics
  • PDF

Related Articles

  • This Month in JNM
  • PubMed
  • Google Scholar

Cited By...

  • 18F-Fluoroestradiol PET Imaging in a Phase II Trial of Vorinostat to Restore Endocrine Sensitivity in ER+/HER2- Metastatic Breast Cancer
  • Tumor Subregion Evolution-Based Imaging Features to Assess Early Response and Predict Prognosis in Oropharyngeal Cancer
  • Google Scholar

More in this TOC Section

Oncology

  • Comparative PSMA expression at early (1hour) vs late (2hour) in primary and secondary sites of involvement in prostate cancer.
  • Imaging spectrum of peritoneal carcinomatosis associated with various etiologies on 18F-FDG PET/CT
  • Hybrid Imaging Features of Musculoskeletal Tumors
Show more Oncology

Clinical

  • Comparative PSMA expression at early (1hour) vs late (2hour) in primary and secondary sites of involvement in prostate cancer.
  • Imaging spectrum of peritoneal carcinomatosis associated with various etiologies on 18F-FDG PET/CT
  • Hybrid Imaging Features of Musculoskeletal Tumors
Show more Clinical

Similar Articles

Keywords

  • 18F-FDG PET/CT
  • test–retest
  • SUV
  • reproducibility
  • quantitative imaging
SNMMI

© 2025 SNMMI

Powered by HighWire