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
  • Log out
  • My Cart

Search

  • Advanced search
Journal of Nuclear Medicine
  • SNMMI
    • JNM
    • JNMT
    • SNMMI Journals
    • SNMMI
  • Subscribe
  • My alerts
  • Log in
  • Log out
  • 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 ArticleIs It Really Happening?

Is 18F-FDG Metabolic Tumor Volume in Lymphoma Really Happening?

Sally F. Barrington, Anne-Ségolène Cottereau and Josée M. Zijlstra
Journal of Nuclear Medicine April 2024, 65 (4) 510-511; DOI: https://doi.org/10.2967/jnumed.123.267022
Sally F. Barrington
1King’s College London and Guy’s and St. Thomas’ PET Centre, School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Anne-Ségolène Cottereau
2Department of Nuclear Medicine, Cochin Hospital, APHP, Paris Cité University, Paris, France; and
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Josée M. Zijlstra
3Department of Hematology and Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, The Netherlands
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Info & Metrics
  • PDF
Loading

Tumor burden influences prognosis in lymphoma, with unidimensional bulk used for risk assessment and decisions about radiotherapy consolidation (1). Total metabolic tumor volume (TMTV) using 18F-FDG PET emerged 10 y ago as a promising biomarker (2) that was superior to bulk (3). However, until now, metabolic tumor volume (MTV) has not been used in clinical practice or trial design. We attribute this to lack of common methodology, the perception that measurement is difficult, and the unavailability of software tools. Consensus is also required about which tumor areas to include (4). Furthermore, MTV has been evaluated in datasets providing binary cutoffs to divide patients into prognostic groups, which are data-driven and population-dependent (5).

WHAT IS CHANGING?

A New Benchmark Method Has Been Established

Because prognostication and interobserver agreement are equally good irrespective of the measurement method (6), choice should reflect ease of use. One method has emerged as simple, quick to perform using academic and commercial software, and closely matching the visual perception of nuclear medicine reads from 6 published methods in diffuse large B-cell lymphoma (DLBCL) (7) and Hodgkin lymphoma (8). The delineation method uses an SUV of at least 4.0 and a minimum individual lesion volume of 3 cm3. The SUV of 4.0 limits physiologic uptake that requires editing compared with lower thresholds and reduces underestimation of heterogeneous lesions compared with percentage SUV thresholds. The 41% SUVmax threshold, although frequently studied, has increased variability across software depending on whether SUVmax is defined as the maximum in the TMTV or as lesional. If lesional, the clustering algorithm can influence how lesions are outlined. The minimum volume reduces measurement complexity without significantly influencing TMTV. The method using an SUV of 4.0 is insensitive to uptake time and to the presence or absence of later progression in patients (7) and is the least sensitive to reconstruction method, including ultra-high-sensitivity reconstructions (9) used in advanced technologies such as total-body PET/CT. In approximately 80% of DLBCL cases, minimal reader interaction was required—for example, removing physiologic uptake with single clicks, achievable in 2–3 min (7) with additional manual editing in 20% of cases.

The vision for standardization of MTV measurement was outlined in this journal (4) with a proposal for a benchmark dataset using a common method with consensus MTV values and segmentations as outputs. Data from the MTV road map have been presented involving 12 readers from 9 countries using 3 academic software programs. Readers analyzed 60 cases from 3 lymphoma subtypes (10). TMTV measurement was unaffected by the software used, with close reader agreement in 52 of 60 cases. Disagreement was mainly due to interpretation of diffuse splenic uptake with smaller, less clinically relevant differences due to manual editing of physiologic uptake.

The benchmark will soon be made publicly available so readers can check the reliability of MTV measurements using local software and their clinical interpretation. New measurement methods, including artificial intelligence approaches, can be evaluated against the benchmark, with both being tested in the same dataset provided patient outcomes are known, to determine whether newer methods improve prognostication, reduce reader time, or improve agreement. The benchmark can also be used to explore questions such as the prognostic relevance of the spleen.

A concern about the transition to one method could be whether research using other methods might be wasted and that the method using an SUV of 4.0 has not been widely tested in indolent, albeit less common, subtypes. A statistical method for combining batches (ComBat) of data using different methods (11) has been successfully applied in retrospective trial datasets (12). Nonetheless, having a standardized approach for prospective study in aggressive lymphoma is a critical step for universal adoption of MTV as a biomarker.

New Prognostic Indices Have Been Incorporated

Other major developments are testing of TMTV in large datasets, expression as a continuous variable, and incorporation with established risk factors.

The method using an SUV of 4.0 was explored in 1,214 patients with newly diagnosed DLBCL (13) by the Positron Emission Tomography Reanalysis (PETRA) consortium in 5 international trials. First, the best statistical relationship was derived to associate MTV with progression-free and overall survival. The relationship was a linear spline with 2 coefficients, such that the same incremental change had different impacts on survival above and below the median. MTV performed better than the International Prognostic Index (comprising binary cutoffs for age, lactate dehydrogenase, stage, performance status, and more than one extranodal site) (14). Most International Prognostic Index factors proved redundant when combined with MTV. The optimal “International Metabolic Prognostic Index” included 3 factors: MTV and age (as continuous variables) and stage (I–IV). Its continuous nature means progression-free survival can be predicted for individual patients by entering MTV, age, and stage in a simple Excel (Microsoft) spreadsheet (https://petralymphoma.org/impi). The International Metabolic Prognostic Index allows for intelligent trial design, selecting a progression-free survival cutoff at which the benefit of a novel treatment will likely outweigh the risk of standard treatment in high-risk patients. The integration of MTV with patient factors has also been explored in 2,174 patients from trial and real-world datasets, with performance status being identified as an independent risk factor and MTV plus performance status outperforming the International Prognostic Index (12). Optimal selection of high-risk patients is relevant because new treatments such as CAR T-cell therapy and bispecific monoclonal antibodies are being tested in phase III trials in first- and second-line DLBCL.

HOW SHOULD WE BUILD ON THE SUCCESS OF MTV?

The success of MTV has generated interest in other radiomic features, which can be measured once TMTV is delineated. The independent prognostic value of disease dissemination—for example, the maximum distance between lesions—was first reported by Cottereau et al. (15). Biologic explanations for this phenomenon were recently explored in Hodgkin lymphoma (16). The PETRA consortium suggested the potential to replace “stage” in the International Metabolic Prognostic Index by “Dmaxbulk,” the maximum distance between the bulkiest and the furthest lesion, with a small incremental benefit for a radiomics score that also included performance status and SUVpeak (17). Preliminary reports that integrate PET with emerging molecular markers in circulating tumor DNA (18) may further improve baseline and dynamic risk. Methods to establish reliable dynamic MTV measurement are also being explored (19).

Confidence in TMTV is growing with agreement about standardization. A similar pragmatic approach of a simple, albeit not perfect, method led to widespread adoption of the Deauville score for lymphoma (20).

Now MTV needs to feature in trial design, either alone or within prognostic indices such as the International Metabolic Prognostic Index for risk stratification. To develop clinical decision tools, MTV (with or without other radiomic features) should be prospectively evaluated at baseline and interim with liquid biomarkers for minimal residual disease.

The first trial using MTV, Deauville score, and circulating tumor DNA to risk-adapt treatment is already under way in Hodgkin lymphoma (www.clinicaltrials.gov/study/NCT04866654).

In conclusion, MTV for risk stratification in DLBCL is feasible now in the clinic and being evaluated in a clinical trial on Hodgkin lymphoma. A benchmark dataset will be available soon for standardization of measurement by PET centers, software developers, and vendors. “The time to prepare for risk adaptation in lymphoma by standardizing measurement of metabolic tumor burden is over” (4): it is time to get on board.

DISCLOSURE

Sally Barrington acknowledges support from the National Institute for Health and Care Research (NIHR) (RP-2016-07-001). This work was also supported by core funding from the Wellcome/EPSRC Centre for Medical Engineering at King’s College London (WT203148/Z/16/Z). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. Josée Zijlstra acknowledges support from KWF Dutch Cancer Society. No other potential conflict of interest relevant to this article was reported.

ACKNOWLEDGMENT

We acknowledge the vision of our late colleague, Prof. Michel Meignan, founder of the international workshops on PET in lymphoma and myeloma (https://www.lymphomapet.com/), whose leadership inspired the development of MTV as a biomarker.

Footnotes

  • Published online Feb. 22, 2024.

  • © 2024 by the Society of Nuclear Medicine and Molecular Imaging.

REFERENCES

  1. 1.↵
    1. Specht L,
    2. Nordentoft AM,
    3. Cold S,
    4. Clausen NT,
    5. Nissen NI
    . Tumor burden as the most important prognostic factor in early stage Hodgkin’s disease. Relations to other prognostic factors and implications for choice of treatment. Cancer. 1988;61:1719–1727.
    OpenUrlCrossRefPubMed
  2. 2.↵
    1. Meignan M,
    2. Sasanelli M,
    3. Casasnovas RO,
    4. et al
    . Metabolic tumour volumes measured at staging in lymphoma: methodological evaluation on phantom experiments and patients. Eur J Nucl Med Mol Imaging. 2014;41:1113–1122.
    OpenUrlCrossRefPubMed
  3. 3.↵
    1. Mikhaeel NG,
    2. Smith D,
    3. Dunn JT,
    4. et al
    . Combination of baseline metabolic tumour volume and early response on PET/CT improves progression-free survival prediction in DLBCL. Eur J Nucl Med Mol Imaging. 2016;43:1209–1219.
    OpenUrlCrossRefPubMed
  4. 4.↵
    1. Barrington SF,
    2. Meignan M
    . Time to prepare for risk adaptation in lymphoma by standardizing measurement of metabolic tumor burden. J Nucl Med. 2019;60:1096–1102.
    OpenUrl
  5. 5.↵
    1. El-Galaly TC,
    2. Villa D,
    3. Cheah CY,
    4. Gormsen LC
    . Pre-treatment total metabolic tumour volumes in lymphoma: does quantity matter? Br J Haematol. 2022;197:139–155.
    OpenUrl
  6. 6.↵
    1. Ilyas H,
    2. Mikhaeel NG,
    3. Dunn JT,
    4. et al
    . Defining the optimal method for measuring baseline metabolic tumour volume in diffuse large B cell lymphoma. Eur J Nucl Med Mol Imaging. 2018;45:1142–1154.
    OpenUrl
  7. 7.↵
    1. Barrington SF,
    2. Zwezerijnen BGJC,
    3. de Vet HCW,
    4. et al
    . Automated segmentation of baseline metabolic total tumor burden in diffuse large B-cell lymphoma: which method is most successful? A study on behalf of the PETRA consortium; 32680929. J Nucl Med. 2021;62:332–337.
    OpenUrlAbstract/FREE Full Text
  8. 8.↵
    1. Driessen J,
    2. Zwezerijnen GJC,
    3. Schöder H,
    4. et al
    . The impact of semiautomatic segmentation methods on metabolic tumor volume, intensity, and dissemination radiomics in 18F-FDG PET scans of patients with classical Hodgkin lymphoma. J Nucl Med. 2022;63:1424–1430.
    OpenUrlAbstract/FREE Full Text
  9. 9.↵
    1. Ferrández MC,
    2. Eertink JJ,
    3. Golla SSV,
    4. et al
    . Combatting the effect of image reconstruction settings on lymphoma [18F]FDG PET metabolic tumor volume assessment using various segmentation methods. EJNMMI Res. 2022;12:44–49.
    OpenUrl
  10. 10.↵
    1. Boellaard R,
    2. Buvat I,
    3. Ceriani L,
    4. et al
    . International benchmark for total metabolic tumor volume assessment in baseline FDG PET/CT of lymphoma patients [abstract]. J Nucl Med. 2023;64(suppl 1):83.
    OpenUrl
  11. 11.↵
    1. Orlhac F,
    2. Eertink JJ,
    3. Cottereau A,
    4. et al
    . A guide to ComBat harmonization of imaging biomarkers in multicenter studies. J Nucl Med. 2022;63:172–179.
    OpenUrlAbstract/FREE Full Text
  12. 12.↵
    1. Thieblemont C,
    2. Chartier L,
    3. Dührsen U,
    4. et al
    . A tumor volume and performance status model to predict outcome before treatment in diffuse large B-cell lymphoma. Blood Adv. 2022;6:5995–6004.
    OpenUrl
  13. 13.↵
    1. Mikhaeel NG,
    2. Heymans MW,
    3. Eertink JJ,
    4. et al
    . Proposed new dynamic prognostic index for diffuse large B-cell lymphoma: international metabolic prognostic index. J Clin Oncol. 2022;40:2352–2360.
    OpenUrl
  14. 14.↵
    International Non-Hodgkin’s Lymphoma Prognostic Factors Project. A predictive model for aggressive non-Hodgkin’s lymphoma. N Engl J Med. 1993;329:987–994.
    OpenUrlCrossRefPubMed
  15. 15.↵
    1. Cottereau AS,
    2. Meignan M,
    3. Nioche C,
    4. et al
    . Risk stratification in diffuse large B-cell lymphoma using lesion dissemination and metabolic tumor burden calculated from baseline PET/CT. Ann Oncol. 2021;32:404–411.
    OpenUrl
  16. 16.↵
    1. Durmo R,
    2. Donati B,
    3. Rebaud L,
    4. et al
    . Prognostic value of lesion dissemination in doxorubicin, bleomycin, vinblastine, and dacarbazine-treated, interimPET-negative classical Hodgkin lymphoma patients: a radio-genomic study. Hematol Oncol. 2022;40:645–657.
    OpenUrl
  17. 17.↵
    1. Eertink JJ,
    2. Zwezerijnen GJC,
    3. Heymans MW,
    4. et al
    . Baseline PET radiomics outperforms the IPI risk score for prediction of outcome in diffuse large B-cell lymphoma. Blood. 2023;141:3055–3064.
    OpenUrl
  18. 18.↵
    1. Kurtz DM,
    2. Scherer F,
    3. Jin MC,
    4. et al
    . Circulating tumor DNA measurements as early outcome predictors in diffuse large B-cell lymphoma. J Clin Oncol. 2018;36:2845–2853.
    OpenUrlCrossRef
  19. 19.↵
    1. Zwezerijnen GJC,
    2. Eertink JJ,
    3. Burggraaff CN,
    4. et al
    . Interobserver agreement on automated metabolic tumor volume measurements of Deauville score 4 and 5 lesions at interim 18F-FDG PET in diffuse large B-cell lymphoma. J Nucl Med. 2021;62:1531–1536.
    OpenUrl
  20. 20.↵
    1. Barrington SF,
    2. Mikhaeel NG,
    3. Kostakoglu L,
    4. et al
    . Role of imaging in the staging and response assessment of lymphoma: consensus of the International Conference on Malignant Lymphomas Imaging Working Group. J Clin Oncol. 2014;32:3048–3058.
    OpenUrlAbstract/FREE Full Text
  • Received for publication December 15, 2023.
  • Revision received January 29, 2024.
PreviousNext
Back to top

In this issue

Journal of Nuclear Medicine: 65 (4)
Journal of Nuclear Medicine
Vol. 65, Issue 4
April 1, 2024
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Index by author
  • Complete Issue (PDF)
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.
Is 18F-FDG Metabolic Tumor Volume in Lymphoma Really Happening?
(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
Is 18F-FDG Metabolic Tumor Volume in Lymphoma Really Happening?
Sally F. Barrington, Anne-Ségolène Cottereau, Josée M. Zijlstra
Journal of Nuclear Medicine Apr 2024, 65 (4) 510-511; DOI: 10.2967/jnumed.123.267022

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Is 18F-FDG Metabolic Tumor Volume in Lymphoma Really Happening?
Sally F. Barrington, Anne-Ségolène Cottereau, Josée M. Zijlstra
Journal of Nuclear Medicine Apr 2024, 65 (4) 510-511; DOI: 10.2967/jnumed.123.267022
Twitter logo Facebook logo LinkedIn logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One
Bookmark this article

Jump to section

  • Article
    • WHAT IS CHANGING?
    • HOW SHOULD WE BUILD ON THE SUCCESS OF MTV?
    • DISCLOSURE
    • ACKNOWLEDGMENT
    • Footnotes
    • REFERENCES
  • Info & Metrics
  • PDF

Related Articles

  • No related articles found.
  • PubMed
  • Google Scholar

Cited By...

  • Measuring Total Metabolic Tumor Volume from 18F-FDG PET: A Reality Check
  • PET-Based Risk Stratification in Primary Mediastinal B-Cell Lymphoma: A Comparative Analysis of Different Segmentation Methods in the IELSG37 Trial Patient Cohort
  • Is Automatic Tumor Segmentation on Whole-Body 18F-FDG PET Images a Clinical Reality?
  • Google Scholar

More in this TOC Section

  • Is 211At Really Happening?
  • Are FAP Theranostics Really Happening? Will Radiochemistry or Biology Win?
  • SPECT Deserves RESPECT: The Potential of SPECT/CT to Optimize Patient Outcomes with Theranostics Therapy
Show more Is It Really Happening?

Similar Articles

SNMMI

© 2025 SNMMI

Powered by HighWire