Molecular Imaging and Biology

, Volume 20, Issue 6, pp 1061–1067 | Cite as

To Explore a Representative Hypoxic Parameter to Predict the Treatment Response and Prognosis Obtained by [18F]FMISO-PET in Patients with Non-small Cell Lung Cancer

  • Li Li
  • Yuchun Wei
  • Yong Huang
  • Qingxi Yu
  • Wenju Liu
  • Shuqiang Zhao
  • Jinsong Zheng
  • Hong Lu
  • Jinming Yu
  • Shuanghu YuanEmail author
Research Article



To explore a representative hypoxic parameter to predict the treatment response and prognosis for [18F]fluoromisonidazole ([18F]FMISO) positron emission tomography (PET)/X-ray computed tomography (CT) in patients with non-small cell lung cancer (NSCLC).


Twenty-nine patients with NSCLC underwent FMISO-PET scans before chemoradiotherapy (CRT). The maximum standard uptake values (SUVmax) in the tumor, normal lung, aortic arch, and vertical ridge muscle were measured, and the tumor-to-lung (T/L) ratios, tumor-to-blood (T/B) ratios, ands tumor-to-muscle (T/M) ratios were calculated and analyzed. Fractional hypoxic volume (FHV) was expressed as percentage of hypoxic volume.


SUVmax, T/L ratio, T/B ratio, and FHV were all significantly different between the responders and the non-responders (SUVmax, 2.07 ± 0.53 vs. 2.61 ± 0.69, P = 0.026; T/L ratio, 3.16 ± 0.85 vs. 4.09 ± 1.46, P = 0.047; T/B ratio, 1.27 ± 0.20 vs. 1.48 ± 0.32, P = 0.042; 38.92 ± 18.47 vs. 52.91 ± 11.29 %, P = 0.020). However, the T/M ratio was not significantly different between the two populations (1.46 ± 0.31 vs. 1.67 ± 0.33, P = 0.098). The correlation ratio between hypoxic parameters and treatment responses ranged from high to low as FHV (r = 0.412); SUVmax (r = 0.400); T/L ratio (r = 0.379), P < 0.05; and T/B ratio (r = 0.355), P = 0.059. According to the area under curve (AUC) to predict response, the hypoxic parameters were arranged as FHV (AUC = 0.748), SUVmax (AUC = 0.731), T/L ratio (AUC = 0.719), and T/B ratio (AUC = 0.705). Binary logistic regression analyses showed that FHV was the only independent predictor for treatment response with the P value of 0.038. In the progression-free survival (PFS) prediction, both FHV and SUVmax reached statistical significance by Kaplan–Meier plots (FHV, 46.99 %, P = 0.010; SUVmax, 1.99, P = 0.046) while only FHV was the independent prognostic factor in multivariate analysis by Cox proportional hazard model (P = 0.037).


FHV may be a representative hypoxic parameter to predict the CRT response and PFS in patients with NSCLC.

Key words

[18F]FMISO PET/CT Hypoxia Representative Hypoxic parameter Predict FHV Response Prognosis CRT Non-small cell lung cancer 


Funding Information

This study was partially funded by the Special Fund for Scientific Research in the Public Interest (201402011), the National Key Research and Development Plan(2016YFC0904700), the Natural Science Foundation of China (NSFC81172133, NSFC81372413), the Shandong Key Research and Development Plan(2017CXGC1209, 2017GSF18164), and the Outstanding Youth Natural Science Foundation of Shandong Province (JQ201423).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

Our investigation of 29 patients was approved by the Shandong Cancer Hospital affiliated to Shandong University Ethical Committee and has, therefore, been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki. All persons gave their informed consent prior to their inclusion in the study.


  1. 1.
    Brown JM, Giaccia AJ (1998) The unique physiology of solid tumors: opportunities (and problems) for cancer therapy. Cancer Res 58:1408–1416PubMedGoogle Scholar
  2. 2.
    Chapman JD, Baer K, Lee J (1983) Characteristics of the metabolism-induced binding of misonidazole to hypoxic mammalian cells. Cancer Res 43:1523–1528PubMedGoogle Scholar
  3. 3.
    Carlin S, Zhang H, Reese M, Ramos NN, Chen Q, Ricketts SA (2014) A comparison of the imaging characteristics and microregional distribution of 4 hypoxia PET tracers. J Nucl Med 55:515–521CrossRefGoogle Scholar
  4. 4.
    Bollineni VR, Wiegman EM, Pruim J, Groen HJM, Langendijk JA (2012) Hypoxia imaging using positron emission tomography in non-small cell lung cancer: implications for radiotherapy. Cancer Treat Rev 38:1027–1032CrossRefGoogle Scholar
  5. 5.
    Meng X, Kong FM, Yu J (2012) Implementation of hypoxia measurement into lung cancer therapy. Lung Cancer 75:146–150CrossRefGoogle Scholar
  6. 6.
    Eschmann SM, Paulsen F, Reimold M, Dittmann H, Welz S, Reischl G, Machulla HJ, Bares R (2005) Prognostic impact of hypoxia imaging with 18F-misonidazole PET in non-small cell lung cancer and head and neck cancer before radiotherapy. J Nucl Med 46:253–260PubMedGoogle Scholar
  7. 7.
    Li L, Yu J, Xing L, Ma K, Zhu H, Guo H, Sun X, Li J, Yang G, Li W, Yue J, Li B (2006) Serial hypoxia imaging with 99mTc-HL91 SPECT to predict radiotherapy response in nonsmall cell lung cancer. Am J Clin Oncol 29:628–633CrossRefGoogle Scholar
  8. 8.
    Lehtiö K, Eskola O, Viljanen T, Oikonen V, Grönroos T, Sillanmäki L, Grénman R, Minn H (2004) Imaging perfusion and hypoxia with PET to predict radiotherapy response in head-and-neck cancer. Int J Radiat Oncol Biol Phys 59:971–982CrossRefGoogle Scholar
  9. 9.
    Rajendran JG, Schwartz DL, O'Sullivan J, Peterson LM, Ng P, Scharnhorst J, Grierson JR, Krohn KA (2006) Tumor hypoxia imaging with [F-18] fluoromisonidazole positron emission tomography in head and neck cancer. Clin Cancer Res 12:5435–5441CrossRefGoogle Scholar
  10. 10.
    Tang G, Wang M, Tang X, Gan M, Luo L (2005) Fully automated one-pot synthesis of [18F]fluoromisonidazole. Nucl Med Biol 32:553–558CrossRefGoogle Scholar
  11. 11.
    Grönroos T, Bentzen L, Marjamäki P et al (2004) Comparison of the biodistribution of two hypoxia markers [18F]FETNIM and [18F]FMISO in an experimental mammary carcinoma. Eur J Nucl Med Mol Imaging 31:513–520CrossRefGoogle Scholar
  12. 12.
    Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M, Rubinstein L, Shankar L, Dodd L, Kaplan R, Lacombe D, Verweij J (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45:228–247CrossRefGoogle Scholar
  13. 13.
    Imamura Y, Azuma K, Kurata S, Hattori S, Sasada T, Kinoshita T, Okamoto M, Kawayama T, Kaida H, Ishibashi M, Aizawa H (2011) Prognostic value of SUVmax measurements obtained by FDG-PET in patients with non-small cell lung cancer receiving chemotherapy. Lung Cancer 71:49–54CrossRefGoogle Scholar
  14. 14.
    Koh WJ, Bergman KS, Rasey JS, Peterson LM, Evans ML, Graham MM, Grierson JR, Lindsley KL, Lewellen TK, Krohn KA, Griffin TW (1995) Evaluation of oxygenation status during fractionated radiotherapy in human nonsmall cell lung cancers using [F-18]fluoromisonidazole positron emission tomography. Int J Radiat Oncol Biol Phys 33:391–398CrossRefGoogle Scholar
  15. 15.
    Rasey JS, Koh WJ, Evans ML, Peterson LM, Lewellen TK, Graham MM, Krohn KA (1996) Quantifying regional hypoxia in human tumors with positron emission tomography of [18F]fluoromisonidazole: a pretherapy study of 37 patients. Int J Radiat Oncol Biol Phys 36:417–428CrossRefGoogle Scholar
  16. 16.
    Rajendran JG, Wilson DC, Conrad EU, Peterson LM, Bruckner JD, Rasey JS, Chin LK, Hofstrand PD, Grierson JR, Eary JF, Krohn KA (2003) [18F]FMISO and [18F]FDG PET imaging in soft tissue sarcomas: correlation of hypoxia, metabolism and VEGF expression. Eur J Nucl Med Mol Imaging 30:695–704CrossRefGoogle Scholar
  17. 17.
    Bollineni VR, Kerner GS, Pruim J et al (2013) PET imaging of tumor hypoxia using 18F-fluoroazomycin arabinoside in stage III-IV non-small cell lung cancer patients. J Nucl Med 54:1175–1180CrossRefGoogle Scholar
  18. 18.
    Hugonnet F, Fournier L, Medioni J, Smadja C, Hindie E, Huchet V, Itti E, Cuenod CA, Chatellier G, Oudard S, Faraggi M, for the Hypoxia in Renal Cancer (HYRC) Multicenter Group (2011) Metastatic renal cell carcinoma: relationship between initial metastasis hypoxia, change after 1 month's sunitinib, and therapeutic response: an 18F-fluoromisonidazole PET/CT study. J Nucl Med 52:1048–1055CrossRefGoogle Scholar
  19. 19.
    Toyonaga T, Hirata K, Yamaguchi S, Hatanaka KC, Yuzawa S, Manabe O, Kobayashi K, Watanabe S, Shiga T, Terasaka S, Kobayashi H, Kuge Y, Tamaki N (2016) 18F-fluoromisonidazole positron emission tomography can predict pathological necrosis of brain tumors. Eur J Nucl Med Mol Imaging 43:1469–1476CrossRefGoogle Scholar
  20. 20.
    Cheng J, Lei L, Xu J, Sun Y, Zhang Y, Wang X, Pan L, Shao Z, Zhang Y, Liu G (2013) 18F-fluoromisonidazole PET/CT: a potential tool for predicting primary endocrine therapy resistance in breast cancer. J Nucl Med 54:333–340CrossRefGoogle Scholar
  21. 21.
    Szeto MD, Chakraborty G, Hadley J, Rockne R, Muzi M, Alvord EC, Krohn KA, Spence AM, Swanson KR (2009) Quantitative metrics of net proliferation and invasion link biological aggressiveness assessed by MRI with hypoxia assessed by FMISO-PET in newly diagnosed glioblastomas. Cancer Res 69:4502–4509CrossRefGoogle Scholar
  22. 22.
    Rajendran JG, Mankoff DA, O’Sullivan F, Peterson LM, Schwartz DL, Conrad EU, Spence AM, Muzi M, Farwell DG, Krohn KA (2004) Hypoxia and glucose metabolism in malignant tumors: evaluation by [18F]fluoromisonidazole and [18F]fluorodeoxyglucose positron emission tomography imaging. Clin Cancer Res 10:2245–2252CrossRefGoogle Scholar
  23. 23.
    Nehmeh SA, Lee NY, Schröder H, Squire O, Zanzonico PB, Erdi YE, Greco C, Mageras G, Pham HS, Larson SM, Ling CC, Humm JL (2008) Reproducibility of intratumor distribution of 18F-fluoromisonidazole in head and neck cancer. Int J Radiat Oncol Biol Phys 70:235–242CrossRefGoogle Scholar

Copyright information

© World Molecular Imaging Society 2018

Authors and Affiliations

  • Li Li
    • 1
    • 2
  • Yuchun Wei
    • 2
  • Yong Huang
    • 2
  • Qingxi Yu
    • 2
  • Wenju Liu
    • 3
  • Shuqiang Zhao
    • 2
  • Jinsong Zheng
    • 2
  • Hong Lu
    • 2
  • Jinming Yu
    • 2
  • Shuanghu Yuan
    • 2
    • 4
    • 5
    Email author
  1. 1.School of Medicine and Life SciencesUniversity of Jinan-Shandong Academy of Medical SciencesJinanChina
  2. 2.Shandong Cancer Hospital and Institute-Shandong Cancer Hospital Affiliated to Shandong UniversityJinanChina
  3. 3.Department of Radiation OncologyLiaocheng People’s HospitalLiaochengChina
  4. 4.Shandong Academy of Medical SciencesJinanChina
  5. 5.Department of Radiation OncologyShandong Cancer Hospital and Institute-Shandong Cancer Hospital Affiliated to Shandong UniversityJinanChina

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