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Multiparametric PET/MR imaging biomarkers are associated with overall survival in patients with pancreatic cancer

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

To correlate the overall survival (OS) with the imaging biomarkers of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), diffusion-weighted imaging (DWI), magnetic resonance spectroscopy, and glucose metabolic activity derived from integrated fluorine 18 fluorodeoxyglucose positron emission tomography (18F–FDG PET)/MRI in patients with pancreatic cancer.

Methods

This prospective study was approved by the institutional review board and informed consent was obtained from all participants. Sixty-three consecutive patients (mean age, 62.7 ± 12 y; men/women, 40/23) with pancreatic cancer underwent PET/MRI before treatment. The imaging biomarkers were comprised of DCE-MRI parameters (peak, IAUC 60 , Ktrans, k ep , v e ), the minimum apparent diffusion coefficient (ADCmin), choline level, standardized uptake values, metabolic tumor volume, and total lesion glycolysis (TLG) of the tumors. The relationships between these imaging biomarkers with OS were evaluated with the Kaplan-Meier and Cox proportional hazard models.

Results

Seventeen (27%) patients received curative surgery, with the median follow-up duration being 638 days. Univariate analysis showed that patients at a low TNM stage (≦3, P = 0.041), high peak (P = 0.006), high ADCmin (P = 0.002) and low TLG (P = 0.01) had better OS. Moreover, high TLG/peak ratio was associated with poor OS (P = 0.016). Multivariate analysis indicated that ADCmin (P = 0.011) and TLG/peak ratio (P = 0.006) were independent predictors of OS after adjustment for age, gender, tumor size, and TNM stage. The TLG/peak ratio was an independent predictor of OS in a subgroup of patients who did not receive curative surgery (P = 0.013).

Conclusion

The flow-metabolism mismatch reflected by the TLG/peak ratio may better predict OS than other imaging biomarkers from PET/MRI in pancreatic cancer patients.

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References

  1. Ryan DP, Hong TS, Bardeesy N. Pancreatic adenocarcinoma. N Engl J Med. 2014;371:2140–1. https://doi.org/10.1056/NEJMc1412266.

    Article  PubMed  Google Scholar 

  2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin. 2017;67:7–30. https://doi.org/10.3322/caac.21387.

    Article  PubMed  Google Scholar 

  3. O'Connor JP, Aboagye EO, Adams JE, Aerts HJ, Barrington SF, Beer AJ, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol. 2017;14:169–86. https://doi.org/10.1038/nrclinonc.2016.162.

    Article  PubMed  CAS  Google Scholar 

  4. Rosenkrantz AB, Friedman K, Chandarana H, Melsaether A, Moy L, Ding YS, et al. Current status of hybrid PET/MRI in oncologic imaging. AJR Am J Roentgenol. 2016;206:162–72. https://doi.org/10.2214/AJR.15.14968.

    Article  PubMed  Google Scholar 

  5. Torigian DA, Zaidi H, Kwee TC, Saboury B, Udupa JK, Cho ZH, et al. PET/MR imaging: technical aspects and potential clinical applications. Radiology. 2013;267:26–44. https://doi.org/10.1148/radiol.13121038.

    Article  PubMed  Google Scholar 

  6. Wang J, Shih TT, Yen RF. Multiparametric evaluation of treatment response to Neoadjuvant chemotherapy in breast cancer using integrated PET/MR. Clin Nucl Med. 2017;42:506–13. https://doi.org/10.1097/RLU.0000000000001684.

    Article  PubMed  Google Scholar 

  7. Joo I, Lee JM, Lee DH, Lee ES, Paeng JC, Lee SJ, et al. Preoperative assessment of pancreatic cancer with FDG PET/MR imaging versus FDG PET/CT plus contrast-enhanced multidetector CT: a prospective preliminary study. Radiology. 2017;282:149–59. https://doi.org/10.1148/radiol.2016152798.

    Article  PubMed  Google Scholar 

  8. Miles KA, Williams RE. Warburg revisited: imaging tumour blood flow and metabolism. Cancer Imaging : Off Publ Int Cancer Imaging Soc. 2008;8:81–6. https://doi.org/10.1102/1470-7330.2008.0011.

    Article  CAS  Google Scholar 

  9. Komar G, Kauhanen S, Liukko K, Seppanen M, Kajander S, Ovaska J, et al. Decreased blood flow with increased metabolic activity: a novel sign of pancreatic tumor aggressiveness. Clin Canc Res : Off J Am Assoc Canc Res. 2009;15:5511–7. https://doi.org/10.1158/1078-0432.CCR-09-0414.

    Article  CAS  Google Scholar 

  10. Michalski CW, Erkan M, Friess H, Kleeff J. Tumor metabolism to blood flow ratio in pancreatic cancer: helpful in patient stratification? Future Oncol. 2010;6:13–5. https://doi.org/10.2217/fon.09.151.

    Article  PubMed  CAS  Google Scholar 

  11. Mankoff DA, Dunnwald LK, Partridge SC, Specht JM. Blood flow-metabolism mismatch: good for the tumor, bad for the patient. Clin Canc Res : Off J Am Assoc Canc Res. 2009;15:5294–6. https://doi.org/10.1158/1078-0432.CCR-09-1448.

    Article  Google Scholar 

  12. Padhani AR, Miles KA. Multiparametric imaging of tumor response to therapy. Radiology. 2010;256:348–64. https://doi.org/10.1148/radiol.10091760.

    Article  PubMed  Google Scholar 

  13. Shen G, Ma H, Liu B, Ren P, Kuang A. Correlation of the apparent diffusion coefficient and the standardized uptake value in neoplastic lesions: a meta-analysis. Nucl Med Commun. 20176; https://doi.org/10.1097/MNM.0000000000000746.

  14. Rakheja R, Chandarana H, DeMello L, Jackson K, Geppert C, Faul D, et al. Correlation between standardized uptake value and apparent diffusion coefficient of neoplastic lesions evaluated with whole-body simultaneous hybrid PET/MRI. AJR Am J Roentgenol. 2013;201:1115–9. https://doi.org/10.2214/AJR.13.11304.

    Article  PubMed  Google Scholar 

  15. Sakane M, Tatsumi M, Kim T, Hori M, Onishi H, Nakamoto A, et al. Correlation between apparent diffusion coefficients on diffusion-weighted MRI and standardized uptake value on FDG-PET/CT in pancreatic adenocarcinoma. Acta Radiol. 2015;56:1034–41. https://doi.org/10.1177/0284185114549825.

    Article  PubMed  Google Scholar 

  16. Chen BB, Tien YW, Chang MC, Cheng MF, Chang YT, Wu CH, et al. PET/MRI in pancreatic and periampullary cancer: correlating diffusion-weighted imaging, MR spectroscopy and glucose metabolic activity with clinical stage and prognosis. Eur J Nucl Med Mol Imaging. 2016;43:1753–64. https://doi.org/10.1007/s00259-016-3356-y.

    Article  PubMed  Google Scholar 

  17. Chen BB, Hsu CY, Yu CW, Liang PC, Hsu C, Hsu CH, et al. Dynamic contrast-enhanced MR imaging of advanced Hepatocellular carcinoma: comparison with the liver parenchyma and correlation with the survival of patients receiving systemic therapy. Radiology. 2016;281:454–64. https://doi.org/10.1148/radiol.2016152659.

    Article  PubMed  Google Scholar 

  18. Tofts PS, Brix G, Buckley DL, Evelhoch JL, Henderson E, Knopp MV, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magnet Res Imaging : JMRI. 1999;10:223–32.

    Article  CAS  Google Scholar 

  19. Liu K, Xie P, Peng W, Zhou Z. Dynamic contrast-enhanced magnetic resonance imaging for pancreatic ductal adenocarcinoma at 3.0-T magnetic resonance: correlation with histopathology. J Comput Assist Tomography. 2015;39:13–8. https://doi.org/10.1097/RCT.0000000000000171.

    Article  Google Scholar 

  20. Wu L, Lv P, Zhang H, Fu C, Yao X, Wang C, et al. Dynamic contrast-enhanced (DCE) MRI assessment of microvascular characteristics in the murine orthotopic pancreatic cancer model. Magn Reson Imaging. 2015;33:737–60. https://doi.org/10.1016/j.mri.2014.08.014.

    Article  PubMed  Google Scholar 

  21. Kim JH, Lee JM, Park JH, Kim SC, Joo I, Han JK, et al. Solid pancreatic lesions: characterization by using timing bolus dynamic contrast-enhanced MR imaging assessment--a preliminary study. Radiology. 2013;266:185–96. https://doi.org/10.1148/radiol.12120111.

    Article  PubMed  Google Scholar 

  22. Yao X, Zeng M, Wang H, Sun F, Rao S, Ji Y. Evaluation of pancreatic cancer by multiple breath-hold dynamic contrast-enhanced magnetic resonance imaging at 3.0T. Europe J Radiol. 2012;81:e917–22. https://doi.org/10.1016/j.ejrad.2012.05.011.

    Article  Google Scholar 

  23. Akisik MF, Sandrasegaran K, Bu G, Lin C, Hutchins GD, Chiorean EG. Pancreatic cancer: utility of dynamic contrast-enhanced MR imaging in assessment of antiangiogenic therapy. Radiology. 2010;256:441–9. https://doi.org/10.1148/radiol.10091733.

    Article  PubMed  Google Scholar 

  24. Barral M, Taouli B, Guiu B, Koh DM, Luciani A, Manfredi R, et al. Diffusion-weighted MR imaging of the pancreas: current status and recommendations. Radiology. 2015;274:45–63. https://doi.org/10.1148/radiol.14130778.

    Article  PubMed  Google Scholar 

  25. De Robertis R, Tinazzi Martini P, Demozzi E, Dal Corso F, Bassi C, Pederzoli P, et al. Diffusion-weighted imaging of pancreatic cancer. World J Radiol. 2015;7:319–28. https://doi.org/10.4329/wjr.v7.i10.319.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Hecht EM, Liu MZ, Prince MR, Jambawalikar S, Remotti HE, Weisberg SW, et al. Can diffusion-weighted imaging serve as a biomarker of fibrosis in pancreatic adenocarcinoma? J Magnet Res Imaging : JMRI. 2017;46:393–402. https://doi.org/10.1002/jmri.25581.

    Article  Google Scholar 

  27. Ma W, Li N, Zhao W, Ren J, Wei M, Yang Y, et al. Apparent diffusion coefficient and dynamic contrast-enhanced magnetic resonance imaging in pancreatic cancer: characteristics and correlation with Histopathologic parameters. J Comput Assist Tomogr. 2016;40:709–16. https://doi.org/10.1097/RCT.0000000000000434.

    Article  PubMed  Google Scholar 

  28. Niwa T, Ueno M, Ohkawa S, Yoshida T, Doiuchi T, Ito K, et al. Advanced pancreatic cancer: the use of the apparent diffusion coefficient to predict response to chemotherapy. Br J Radiol. 2009;82:28–34. https://doi.org/10.1259/bjr/43911400.

    Article  PubMed  CAS  Google Scholar 

  29. Kurosawa J, Tawada K, Mikata R, Ishihara T, Tsuyuguchi T, Saito M, et al. Prognostic relevance of apparent diffusion coefficient obtained by diffusion-weighted MRI in pancreatic cancer. J Magnet Res Imaging : JMRI. 2015; https://doi.org/10.1002/jmri.24939.

  30. Penet MF, Shah T, Bharti S, Krishnamachary B, Artemov D, Mironchik Y, et al. Metabolic imaging of pancreatic ductal adenocarcinoma detects altered choline metabolism. Clin Cancer Res : Off J Am Assoc Canc Res. 2015;21:386–95. https://doi.org/10.1158/1078-0432.CCR-14-0964.

    Article  CAS  Google Scholar 

  31. Battini S, Faitot F, Imperiale A, Cicek AE, Heimburger C, Averous G, et al. Metabolomics approaches in pancreatic adenocarcinoma: tumor metabolism profiling predicts clinical outcome of patients. BMC Med. 2017;15:56. https://doi.org/10.1186/s12916-017-0810-z.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Nunna P, Sheikhbahaei S, Ahn S, Young B, Subramaniam RM. The role of positron emission tomography/computed tomography in management and prediction of survival in pancreatic cancer. J Comput Assist Tomogr. 2016;40:142–51. https://doi.org/10.1097/RCT.0000000000000323.

    Article  PubMed  Google Scholar 

  33. Chaika NV, Gebregiworgis T, Lewallen ME, Purohit V, Radhakrishnan P, Liu X, et al. MUC1 mucin stabilizes and activates hypoxia-inducible factor 1 alpha to regulate metabolism in pancreatic cancer. Proc Natl Acad Sci U S A. 2012;109:13787–92. https://doi.org/10.1073/pnas.1203339109.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Li N, Li Y, Li Z, Huang C, Yang Y, Lang M, et al. Hypoxia inducible factor 1 (HIF-1) recruits macrophage to activate pancreatic Stellate cells in pancreatic Ductal Adenocarcinoma. Int J Mol Sci. 2016;17 https://doi.org/10.3390/ijms17060799.

  35. Hoffmann AC, Mori R, Vallbohmer D, Brabender J, Klein E, Drebber U, et al. High expression of HIF1a is a predictor of clinical outcome in patients with pancreatic ductal adenocarcinomas and correlated to PDGFA, VEGF, and bFGF. Neoplasia. 2008;10:674–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Bali MA, Metens T, Denolin V, Delhaye M, Demetter P, Closset J, et al. Tumoral and nontumoral pancreas: correlation between quantitative dynamic contrast-enhanced MR imaging and histopathologic parameters. Radiology. 2011;261:456–66. https://doi.org/10.1148/radiol.11103515.

    Article  PubMed  Google Scholar 

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Funding

The study is funded by National Taiwan University Hospital, Taipei, Taiwan: A1 project No. NTUH103-A124; Ministry of Science and Technology (MOST): No. 104–2314-B-002-080-MY3

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Correspondence to Tiffany Ting-Fang Shih.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Chen, BB., Tien, YW., Chang, MC. et al. Multiparametric PET/MR imaging biomarkers are associated with overall survival in patients with pancreatic cancer. Eur J Nucl Med Mol Imaging 45, 1205–1217 (2018). https://doi.org/10.1007/s00259-018-3960-0

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