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Risk-stratifying capacity of PET/CT metabolic tumor volume in stage IIIA non-small cell lung cancer

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Abstract

Objectives

Stage IIIA non-small cell lung cancer (NSCLC) is heterogeneous in tumor burden, and its treatment is variable. Whole-body metabolic tumor volume (MTVWB) has been shown to be an independent prognostic index for overall survival (OS). However, the potential of MTVWB to risk-stratify stage IIIA NSCLC has previously been unknown. If we can identify subgroups within the stage exhibiting significant OS differences using MTVWB, MTVWB may lead to adjustments in patients’ risk profile evaluations and may, therefore, influence clinical decision making regarding treatment. We estimated the risk-stratifying capacity of MTVWB in stage IIIA by comparing OS of stratified stage IIIA with stage IIB and IIIB NSCLC.

Methods

We performed a retrospective review of 330 patients with clinical stage IIB, IIIA, and IIIB NSCLC diagnosed between 2004 and 2014. The patients’ clinical TNM stage, initial MTVWB, and long-term survival data were collected. Patients with TNM stage IIIA disease were stratified by MTVWB. The optimal MTVWB cutoff value for stage IIIA patients was calculated using sequential log-rank tests. Univariate and multivariate cox regression analyses and Kaplan-Meier OS analysis with log-rank tests were performed.

Results

The optimal MTVWB cut-point was 29.2 mL for the risk-stratification of stage IIIA. We identified statistically significant differences in OS between stage IIB and IIIA patients (p < 0.01), between IIIA and IIIB patients (p < 0.01), and between the stage IIIA patients with low MTVWB (below 29.2 mL) and the stage IIIA patients with high MTVWB (above 29.2 mL) (p < 0.01). There was no OS difference between the low MTVWB stage IIIA and the cohort of stage IIB patients (p = 0.485), or between the high MTVWB stage IIIA patients and the cohort of stage IIIB patients (p = 0.459). Similar risk-stratification capacity of MTVWB was observed in a large range of cutoff values from 15 to 55 mL in stage IIIA patients.

Conclusions

Using MTVWB cutoff points ranging from 15 to 55 mL with an optimal value of 29.2 mL, stage IIIA NSCLC may be effectively stratified into subgroups with no significant survival difference from stages IIB or IIIB NSCLC. This may result in more accurate survival estimation and more appropriate risk adapted treatment selection in stage IIIA NSCLC.

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References

  1. Edge SB, Compton CC. The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol. 2010;17:1471–4. doi:10.1245/s10434-010-0985-4.

    Article  PubMed  Google Scholar 

  2. Gadgeel SM, Ramalingam SS, Kalemkerian GP. Treatment of lung cancer. Radiol Clin N Am. 2012;50:961–74. doi:10.1016/j.rcl.2012.06.003.

    Article  PubMed  Google Scholar 

  3. Goldstraw P, Crowley J, Chansky K, Giroux DJ, Groome PA, Rami-Porta R, et al. The IASLC Lung Cancer Staging Project: proposals for the revision of the TNM stage groupings in the forthcoming (seventh) edition of the TNM Classification of malignant tumours. J Thorac Oncol. 2007;2:706–14. doi:10.1097/JTO.0b013e31812f3c1a.

    Article  PubMed  Google Scholar 

  4. Groome PA, Bolejack V, Crowley JJ, Kennedy C, Krasnik M, Sobin LH, et al. The IASLC Lung Cancer Staging Project: validation of the proposals for revision of the T, N, and M descriptors and consequent stage groupings in the forthcoming (seventh) edition of the TNM classification of malignant tumours. J Thorac Oncol. 2007;2:694–705. doi:10.1097/JTO.0b013e31812d05d5.

    Article  PubMed  Google Scholar 

  5. Mountain CF. Revisions in the international system for staging lung cancer. Chest. 1997;111:1710–7.

    Article  CAS  PubMed  Google Scholar 

  6. Spira A, Ettinger DS. Multidisciplinary management of lung cancer. N Engl J Med. 2004;350:379–92. doi:10.1056/NEJMra035536.

    Article  CAS  PubMed  Google Scholar 

  7. Uybico SJ, Wu CC, Suh RD, Le NH, Brown K, Krishnam MS. Lung cancer staging essentials: the new TNM staging system and potential imaging pitfalls. Radiographics. 2010;30:1163–81. doi:10.1148/rg.305095166.

    Article  PubMed  Google Scholar 

  8. 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. doi:10.1016/j.jtho.2015.09.009.

    Article  PubMed  Google Scholar 

  9. Albain KS, Rusch VW, Crowley JJ, Rice TW, Turrisi Iii AT, Weick JK, et al. Concurrent cisplatin/etoposide plus chest radiotherapy followed by surgery for stages IIIA(N2) and IIIB non-small-cell lung cancer: mature results of Southwest Oncology Group Phase II Study 8805. J Clin Oncol. 1995;13:1880–92.

    Article  CAS  PubMed  Google Scholar 

  10. Albain KS, Swann RS, Rusch VW, Turrisi Iii AT, Shepherd FA, Smith C, et al. Radiotherapy plus chemotherapy with or without surgical resection for stage III non-small-cell lung cancer: a phase III randomised controlled trial. Lancet. 2009;374:379–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Choi NC, Carey RW, Daly W, Mathisen D, Wain J, Wright C, et al. Potential impact on survival of improved tumor downstaging and resection rate by preoperative twice-daily radiation and concurrent chemotherapy in stage IIIA non-small-cell lung cancer. J Clin Oncol. 1997;15:712–22.

    Article  CAS  PubMed  Google Scholar 

  12. Curran Jr WJ, Paulus R, Langer CJ, Komaki R, Lee JS, Hauser S, et al. Sequential vs concurrent chemoradiation for stage iii non-small cell lung cancer: randomized phase III trial RTOG 9410. J Natl Cancer Inst. 2011;103:1452–60. doi:10.1093/jnci/djr325.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Furuse K, Fukuoka M, Kawahara M, Nishikawa H, Takada Y, Kudoh S, et al. Phase III study of concurrent versus sequential thoracic radiotherapy in combination with mitomycin, vindesine, and cisplatin in unresectable stage III non-small-cell lung cancer. J Clin Oncol. 1999;17:2692–9.

    Article  CAS  PubMed  Google Scholar 

  14. Roth JA, Fossella F, Komaki R, Ryan MB, Putnam Jr JB, Lee JS, et al. A randomized trial comparing perioperative chemotherapy and surgery with surgery alone in resectable stage III non-small-cell lung cancer. J Natl Cancer Inst. 1994;86:673–80.

    Article  CAS  PubMed  Google Scholar 

  15. van Meerbeeck JP, Kramer GWPM, Van Schil PEY, Legrand C, Smit EF, Schramel F, et al. Randomized controlled trial of resection versus radiotherapy after induction chemotherapy in stage IIIA-N2 non-small-cell lung cancer. J Natl Cancer Inst. 2007;99:442–50. doi:10.1093/jnci/djk093.

    Article  PubMed  Google Scholar 

  16. Vokes EE, Herndon Ii JE, Kelley MJ, Cicchetti MG, Ramnath N, Neill H, et al. Induction chemotherapy followed by chemoradiotherapy compared with chemoradiotherapy alone for regionally advanced unresectable stage III non-small-cell lung cancer: cancer and leukemia group B. J Clin Oncol. 2007;25:1698–704. doi:10.1200/JCO.2006.07.3569.

    Article  CAS  PubMed  Google Scholar 

  17. Scott WJ, Howington J, Feigenberg S, Movsas B, Pisters K. Treatment of non-small cell lung cancer stage I and stage II: ACCP evidence-based clinical practice guidelines (2nd edition). Chest. 2007;132:234S–42S. doi:10.1378/chest.07-1378.

    Article  PubMed  Google Scholar 

  18. Jett JR, Schild SE, Keith RL, Kesler KA. Treatment of non-small cell lung cancer, stage IIIB: ACCP evidence-based clinical practice guidelines (2nd edition). Chest. 2007;132:266S–76S. doi:10.1378/chest.07-1380.

    Article  PubMed  Google Scholar 

  19. 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. doi:10.1016/j.lungcan.2012.08.016.

    Article  PubMed  Google Scholar 

  20. 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. doi:10.1007/s00432-013-1545-7.

    Article  CAS  PubMed  Google Scholar 

  21. Im HJ, Pak K, Cheon GJ, Kang KW, Kim SJ, Kim IJ, et al. Prognostic value of volumetric parameters of F-FDG PET in non-small-cell lung cancer: a meta-analysis. Eur J Nucl Med Mol Imaging. 2014. doi:10.1007/s00259-014-2903-7.

    Google Scholar 

  22. 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. doi:10.1007/s00259-011-1934-6.

    Article  CAS  PubMed  Google Scholar 

  23. 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. doi:10.1093/jnci/djv004.

  24. 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. doi:10.1148/radiol.13130652.

    Article  PubMed  Google Scholar 

  25. 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. doi:10.1016/j.lungcan.2016.01.024.

    Article  PubMed  Google Scholar 

  26. 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. doi:10.1007/s13139-012-0165-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. 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.

    Article  PubMed  Google Scholar 

  28. 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. doi:10.1016/j.acra.2012.07.002.

    Article  PubMed  Google Scholar 

  29. 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. AJR Am J Roentgenol. 2015;205:623–8. doi:10.2214/AJR.14.13847.

    Article  PubMed  Google Scholar 

  30. Rami-Porta R. Revised (8th) edition of TNM staging system for lung cancer. J Thorac Oncol. 2015;10:S69.

    Article  Google Scholar 

  31. Rami-Porta RBV, Crowley J, Ball D, Kim J, Lyons G, Rice T, et al. The IASLC Lung Cancer Staging Project: proposals for the revisions of the T descriptors in the forthcoming eighth edition of the TNM classification for lung cancer. J Thorac Oncol. 2015;10:990–1003.

    Article  PubMed  Google Scholar 

  32. Mandrekar JN, Mandrekar SJ. Cutpoint determination methods in survival analysis using SAS. The 28th SAS Users Group International Conference (SUGI). Seattle; 2003.

  33. Tunes-da-Silva G, Klein JP. Cutpoint selection for discretizing a continuous covariate for generalized estimating equations. Comput Stat Data Anal. 2011;55:226–35.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Pérez Hoyos S. Cutpoint determination in continuous predictive variables in survival analysis. 2014 Spanish Stata Users Group meeting. Barcelona; 2014.

  35. Bazan JG, Duan F, Snyder BS, Horng D, Graves EE, Siegel BA, et al. Metabolic tumor volume predicts overall survival and local control in patients with stage III non-small cell lung cancer treated in ACRIN 6668/RTOG 0235. Eur J Nucl Med Mol Imaging. 2017;44:17–24. doi:10.1007/s00259-016-3520-4.

    Article  CAS  PubMed  Google Scholar 

  36. Antoch G, Saoudi N, Kuehl H, Dahmen G, Mueller SP, Beyer T, et al. Accuracy of whole-body dual-modality fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography and computed tomography (FDG-PET/CT) for tumor staging in solid tumors: comparison with CT and PET. J Clin Oncol. 2004;22:4357–68. doi:10.1200/JCO.2004.08.120.

    Article  PubMed  Google Scholar 

  37. Beggs AD, Hain SF, Curran KM, O’Doherty MJ. FDG-PET as a “metabolic biopsy” tool in non-lung lesions with indeterminate biopsy. Eur J Nucl Med Mol Imaging. 2002;29:542–6. doi:10.1007/s00259-001-0736-7.

    Article  CAS  PubMed  Google Scholar 

  38. Fischer BM, Mortensen J, Langer SW, Loft A, Berthelsen AK, Petersen BI, et al. A prospective study of PET/CT in initial staging of small-cell lung cancer: comparison with CT, bone scintigraphy and bone marrow analysis. Ann Oncol. 2007;18:338–45. doi:10.1093/annonc/mdl374.

    Article  CAS  PubMed  Google Scholar 

  39. Ohno Y, Koyama H, Onishi Y, Takenaka D, Nogami M, Yoshikawa T, et al. Non-small cell lung cancer: whole-body MR examination for M-stage assessment—utility for whole-body diffusion-weighted imaging compared with integrated FDG PET/CT. Radiology. 2008;248:643–54. doi:10.1148/radiol.2482072039.

    Article  PubMed  Google Scholar 

  40. Plathow C, Aschoff P, Lichy MP, Eschmann S, Hehr T, Brink I, et al. Positron emission tomography/computed tomography and whole-body magnetic resonance imaging in staging of advanced nonsmall cell lung cancer—initial results. Investig Radiol. 2008;43:290–7. doi:10.1097/RLI.0b013e318163273a.

    Article  Google Scholar 

  41. Xu G, Zhao L, He Z. Performance of whole-body PET/CT for the detection of distant malignancies in various cancers: a systematic review and meta-analysis. J Nucl Med. 2012;53:1847–54. doi:10.2967/jnumed.112.105049.

    Article  PubMed  Google Scholar 

  42. Yi CA, Shin KM, Lee KS, Kim BT, Kim H, Kwon OJ, et al. Non-small cell lung cancer staging: efficacy comparison of integrated PET/CT versus 3.0-T whole-body MR imaging. Radiology. 2008;248:632–42. doi:10.1148/radiol.2482071822.

    Article  PubMed  Google Scholar 

  43. 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.

    CAS  PubMed  Google Scholar 

  44. Daisne JF, Duprez T, Weynand B, Lonneux M, Hamoir M, Reychler H, et al. Tumor volume in pharyngolaryngeal squamous cell carcinoma: comparison at CT, MR imaging, and FDG PET and validation with surgical specimen. Radiology. 2004;233:93–100. doi:10.1148/radiol.2331030660.

    Article  PubMed  Google Scholar 

  45. Obara P, Liu H, Wroblewski K, Zhang CP, Hou P, Jiang Y, et al. Quantification of metabolic tumor activity and burden in patients with non-small-cell lung cancer: Is manual adjustment of semiautomatic gradient-based measurements necessary? Nucl Med Commun. 2015;36:782–9. doi:10.1097/MNM.0000000000000317.

    Article  CAS  PubMed  Google Scholar 

  46. Chen HH, Chiu NT, Su WC, Guo HR, Lee BF. Prognostic value of whole-body total lesion glycolysis at pretreatment FDG PET/CT in non-small cell lung cancer. Radiology. 2012;264:559–66. doi:10.1148/radiol.12111148.

    Article  PubMed  Google Scholar 

  47. Liu H, Chen P, Wroblewski K, Hou P, Zhang CP, 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. doi:10.1097/MNM.0000000000000396.

    PubMed  PubMed Central  Google Scholar 

  48. Geets X, Lee JA, Bol A, Lonneux M, Gregoire V. A gradient-based method for segmenting FDG-PET images: methodology and validation. Eur J Nucl Med Mol Imaging. 2007;34:1427–38. doi:10.1007/s00259-006-0363-4.

    Article  PubMed  Google Scholar 

  49. 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. doi:10.1016/j.ijrobp.2010.12.055.

    Article  PubMed  Google Scholar 

  50. Graves EE, Quon A, Loo Jr BW. RT_Image: an open-source tool for investigating PET in radiation oncology. Technol Cancer Res Treat. 2007;6:111–21.

    Article  PubMed  Google Scholar 

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Authors’ contributions

Guarantors of integrity of entire study: Joshua H. Finkle and Yonglin Pu.

Study concepts/study design or data acquisition or data analysis/interpretation: all authors;

Manuscript drafting or manuscript revision for important intellectual content: all authors

Approval of final version of submitted manuscript: all authors;

Agrees to ensure any questions related to the work are appropriately resolved by all authors;

Literature research: Joshua H. Finkle, Stephanie Y. Jo, Yonglin Pu.

Clinical studies: Haiyan Liu, Chenpeng Zhang, Xuee Zhu, Yonglin Pu.

Statistical analysis: Joshua H. Finkle

Manuscript editing: all authors

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Correspondence to Yonglin Pu.

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

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The data supporting the findings can be found in the corresponding author’s institution.

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The authors have no competing interests.

Funding

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

Additional information

Joshua H. Finkle and Stephanie Y. Jo contributed equally to this work.

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Finkle, J.H., Jo, S.Y., Ferguson, M.K. 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 44, 1275–1284 (2017). https://doi.org/10.1007/s00259-017-3659-7

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