Developing and validating a novel metabolic tumor volume risk stratification system for supplementing non-small cell lung cancer staging

  • Yonglin PuEmail author
  • James X. Zhang
  • Haiyan Liu
  • Daniel Appelbaum
  • Jianfeng Meng
  • Bill C. Penney
Original Article



We hypothesized that whole-body metabolic tumor volume (MTVwb) could be used to supplement non-small cell lung cancer (NSCLC) staging due to its independent prognostic value. The goal of this study was to develop and validate a novel MTVwb risk stratification system to supplement NSCLC staging.


We performed an IRB-approved retrospective review of 935 patients with NSCLC and FDG-avid tumor divided into modeling and validation cohorts based on the type of PET/CT scanner used for imaging. In addition, sensitivity analysis was conducted by dividing the patient population into two randomized cohorts. Cox regression and Kaplan-Meier survival analyses were performed to determine the prognostic value of the MTVwb risk stratification system.


The cut-off values (10.0, 53.4 and 155.0 mL) between the MTVwb quartiles of the modeling cohort were applied to both the modeling and validation cohorts to determine each patient’s MTVwb risk stratum. The survival analyses showed that a lower MTVwb risk stratum was associated with better overall survival (all p < 0.01), independent of TNM stage together with other clinical prognostic factors, and the discriminatory power of the MTVwb risk stratification system, as measured by Gönen and Heller’s concordance index, was not significantly different from that of TNM stage in both cohorts. Also, the prognostic value of the MTVwb risk stratum was robust in the two randomized cohorts. The discordance rate between the MTVwb risk stratum and TNM stage or substage was 45.1% in the modeling cohort and 50.3% in the validation cohort.


This study developed and validated a novel MTVwb risk stratification system, which has prognostic value independent of the TNM stage and other clinical prognostic factors in NSCLC, suggesting that it could be used for further NSCLC pretreatment assessment and for refining treatment decisions in individual patients.


Non-small cell lung cancer Whole-body metabolic tumor volume Risk stratification TNM staging 18F-FDG PET/CT Tumor burden 



We acknowledge the contributions of Kristen Wroblewski, MS, Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA, for her statistical guidance; and Mark K. Ferguson, MD, Thoracic Surgery Service, The University of Chicago, Chicago, IL, USA, for constructive comments. This work was supported in part by a grant (R21 CA181885) from the National Cancer Institute of the National Institutes of Health. We particularly thank our chest oncological team at the University of Chicago for taking care of our study patients. The authors have not used writing assistance.

Authors’ contributions

Guarantors of the integrity of the entire study: Yonglin Pu, James X. Zhang and Bill C. Penney.

Study concepts/study design, data acquisition and data analysis/interpretation: all authors.

Manuscript drafting and revision for important intellectual content: all authors.

Approval of final version of submitted manuscript: all authors.

Agreement to appropriately resolve any questions related to the work: all authors.

Literature research: Yonglin Pu and James X. Zhang.

Clinical studies: Daniel Appelbaum, Haiyan Liu and Yonglin Pu.

Statistical analysis: Yonglin Pu, Jianfeng Meng, and James X. Zhang.

Manuscript editing: all authors.


This work was supported in part by a grant (R21 CA181885) from the National Cancer Institute of the National Institutes of Health.

Compliance with ethical standards

Conflicts interest


Ethical approval

This study was approved by our Institutional Review Board of the University of Chicago, which waived the requirement for informed consent, and all procedures were carried out in accordance with relevant guidelines and regulations.

Informed consent

The requirement for informed consent was waived.

Supplementary material

259_2018_4059_MOESM1_ESM.docx (9.1 mb)
ESM 1 (DOCX 9352 kb)


  1. 1.
    Goldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WE, et al. The IASLC Lung Cancer Staging Project: proposals for revision of the TNM stage groupings in the forthcoming (eighth) edition of the TNM Classification for Lung Cancer. J Thorac Oncol. 2016;11:39–51. Scholar
  2. 2.
    Liao S, Penney BC, Wroblewski K, Zhang H, Simon CA, Kampalath R, et al. Prognostic value of metabolic tumor burden on 18F-FDG PET in nonsurgical patients with non-small cell lung cancer. Eur J Nucl Med Mol Imaging. 2012;39:27–38. Scholar
  3. 3.
    Liao S, Penney BC, Zhang H, Suzuki K, Pu Y. Prognostic value of the quantitative metabolic volumetric measurement on 18F-FDG PET/CT in stage IV nonsurgical small-cell lung cancer. Acad Radiol. 2012;19:69–77. Scholar
  4. 4.
    Zhang H, Wroblewski K, Appelbaum D, Pu Y. Independent prognostic value of whole-body metabolic tumor burden from FDG-PET in non-small cell lung cancer. Int J Comput Assist Radiol Surg. 2013;8:181–91. Scholar
  5. 5.
    Zhang H, Wroblewski K, Liao S, Kampalath R, Penney BC, Zhang Y, et al. Prognostic value of metabolic tumor burden from (18)F-FDG PET in surgical patients with non-small-cell lung cancer. Acad Radiol. 2013;20:32–40. Scholar
  6. 6.
    Hyun SH, Ahn HK, Ahn MJ, Ahn YC, Kim J, Shim YM, et al. Volume-based assessment with 18F-FDG PET/CT improves outcome prediction for patients with stage IIIa-N2 non-small cell lung cancer. Am J Roentgenol. 2015;205:623–8. Scholar
  7. 7.
    Im HJ, Pak K, Cheon GJ, Kang KW, Kim SJ, Kim IJ, et al. Prognostic value of volumetric parameters of 18F-FDG PET in non-small-cell lung cancer: a meta-analysis. Eur J Nucl Med Mol Imaging. 2014;42:241–51. Scholar
  8. 8.
    Winther-Larsen A, Fledelius J, Sorensen BS, Meldgaard P. Metabolic tumor burden as marker of outcome in advanced EGFR wild-type NSCLC patients treated with erlotinib. Lung Cancer. 2016;94:81–7. Scholar
  9. 9.
    Chung HW, Lee KY, Kim HJ, Kim WS, So Y. FDG PET/CT metabolic tumor volume and total lesion glycolysis predict prognosis in patients with advanced lung adenocarcinoma. J Cancer Res Clin Oncol. 2014;140:89–98. Scholar
  10. 10.
    Ohri N, Duan F, MacHtay M, Gorelick JJ, Snyder BS, Alavi A, et al. Pretreatment FDG-PET metrics in stage III non-small cell lung cancer: ACRIN 6668/RTOG 0235. J Natl Cancer Inst. 2015;107(4):djv004. Scholar
  11. 11.
    Satoh Y, Onishi H, Nambu A, Araki T. Volume-based parameters measured by using FDG PET/CT in patients with stage I NSCLC treated with stereotactic body radiation therapy: prognostic value. Radiology. 2014;270:275–81. Scholar
  12. 12.
    Dashevsky BZ, Zhang  C, Yan L, Yuan C, Xiong L, Liu Y, Whole body metabolic tumor volume is a prognostic marker in patients with newly diagnosed stage 3B non-small cell lung cancer, confirmed with external validation. European Journal of Hybrid Imaging EJNMMI Multimodality Journal. 2017;1:8.
  13. 13.
    Abelson JA, Murphy JD, Trakul N, Bazan JG, Maxim PG, Graves EE, et al. Metabolic imaging metrics correlate with survival in early stage lung cancer treated with stereotactic ablative radiotherapy. Lung Cancer. 2012;78:219–24. Scholar
  14. 14.
    Yoo SW, Kim J, Chong A, Kwon SY, Min JJ, Song HC, et al. Metabolic tumor volume measured by F-18 FDG PET/CT can further stratify the prognosis of patients with stage IV non-small cell lung cancer. Nucl Med Mol Imaging. 2012;46:286–93. Scholar
  15. 15.
    Zhang H, Wroblewski K, Jiang Y, Penney BC, Appelbaum D, Simon CA, et al. A new PET/CT volumetric prognostic index for non-small cell lung cancer. Lung Cancer. 2015;89:43–9. Scholar
  16. 16.
    Finkle JH, Jo SY, Ferguson MK, Liu HY, Zhang C, Zhu X, et al. Risk-stratifying capacity of PET/CT metabolic tumor volume in stage IIIA non-small cell lung cancer. Eur J Nucl Med Mol Imaging. 2017;44:1275–84. Scholar
  17. 17.
    Zhu X, Liao C, Penney BC, Li F, Ferguson MK, Simon CA, et al. Prognostic value of quantitative PET/CT in patients with a nonsmall cell lung cancer and another primary cancer. Nucl Med Commun. 2017;38:185–92. Scholar
  18. 18. U.S., Social Security Death Index, 1935-2014. Provo, UT: Operations Inc; 2014.
  19. 19.
    Shankar LK, Hoffman JM, Bacharach S, Graham MM, Karp J, Lammertsma AA, et al. Consensus recommendations for the use of 18F-FDG PET as an indicator of therapeutic response in patients in National Cancer Institute trials. J Nucl Med. 2006;47:1059–66.PubMedPubMedCentralGoogle Scholar
  20. 20.
    Zhang C, Liao C, Penney BC, Appelbaum DE, Simon CA, Pu Y. Relationship between overall survival of patients with non-small cell lung cancer and whole-body metabolic tumor burden seen on postsurgical fluorodeoxyglucose PET images. Radiology. 2015;275:862–9. Scholar
  21. 21.
    Werner-Wasik M, Nelson AD, Choi W, Arai Y, Faulhaber PF, Kang P, et al. What is the best way to contour lung tumors on PET scans? Multiobserver validation of a gradient-based method using a NSCLC digital PET phantom. Int J Radiat Oncol Biol Phys. 2012;82:1164–71. Scholar
  22. 22.
    Gönen M, Heller G. Concordance probability and discriminatory power in proportional hazards regression. Biometrika. 2005;92:965–70. Scholar
  23. 23.
    Mooney CZ. Bootstrap statistical inference: examples and evaluations for political science. Am J Polit Sci. 1996;40:570–602.CrossRefGoogle Scholar
  24. 24.
    Ramnath N, Dilling TJ, Harris LJ, Kim AW, Michaud GC, Balekian AA, et al. Treatment of stage III non-small cell lung cancer: diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143:e314S–40S. Scholar
  25. 25.
    Obara PL, Wroblewski K, Zhang CP, Hou P, Jiang Y, Chen P, et al. Quantification of metabolic tumor activity and burden in patients with NSCLC: is manual adjustment of semi-automatic gradient based measurements necessary? Nucl Med Commun. 2015;36:782–9. Scholar
  26. 26.
    Lee P, Weerasuriya DK, Lavori PW, Quon A, Hara W, Maxim PG, et al. Metabolic tumor burden predicts for disease progression and death in lung cancer. Int J Radiat Oncol Biol Phys. 2007;69:328–33. Scholar
  27. 27.
    Lee P, Bazan JG, Lavori PW, Weerasuriya DK, Quon A, Le QT, et al. Metabolic tumor volume is an independent prognostic factor in patients treated definitively for nonsmall-cell lung cancer. Clin Lung Cancer. 2012;13:52–8. Scholar
  28. 28.
    Kim K, Kim SJ, Kim IJ, Kim YS, Pak K, Kim H. Prognostic value of volumetric parameters measured by F-18 FDG PET/CT in surgically resected non-small-cell lung cancer. Nucl Med Commun. 2012;33:613–20. Scholar
  29. 29.
    Hyun SH, Choi JY, Kim K, Kim J, Shim YM, Um SW, et al. Volume-based parameters of 18F-fluorodeoxyglucose positron emission tomography/computed tomography improve outcome prediction in early-stage non-small cell lung cancer after surgical resection. Ann Surg. 2013;257:364–70. Scholar
  30. 30.
    Carvalho S, Leijenaar RTH, Velazquez ER, Oberije C, Parmar C, Van Elmpt W, et al. Prognostic value of metabolic metrics extracted from baseline positron emission tomography images in non-small cell lung cancer. Acta Oncol. 2013;52:1398–404. Scholar
  31. 31.
    Liu H, Chen P, Wroblewski K, Hou P, Zhang C, Jiang Y, et al. Consistency of metabolic tumor volume of non-small-cell lung cancer primary tumor measured using 18F-FDG PET/CT at two different tracer uptake times. Nucl Med Commun. 2016;37:50–6. Scholar
  32. 32.
    Cancer Trends Progress Report. National Cancer Institute, NIH, DHHS, Bethesda, MD, February 2018.
  33. 33.
    Morgensztern D, Ng SH, Gao F, Govindan R. Trends in stage distribution for patients with non-small cell lung cancer: a National Cancer Database survey. J Thorac Oncol. 2010;5:29–33. Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of RadiologyThe University of ChicagoChicagoUSA
  2. 2.Department of MedicineThe University of ChicagoChicagoUSA
  3. 3.Department of Nuclear Medicine, First Hospital and Molecular Imaging Precision Medical Collaborative Innovation CenterShanxi Medical UniversityTaiyuanChina
  4. 4.Department of Respiratory MedicineNanxishan Hospital of Guangxi Zhuang Autonomous RegionGuilinChina

Personalised recommendations