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PET-MRI of the Pancreas and Kidneys

  • Nuclear Imaging (B Franc, Section Editor)
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Abstract

Purpose of Review

PET-MRI is a novel imaging modality which can non-invasively provide both morphological and functional information about benign and malignant lesions. In this article, recent updates in the applications of PET-MRI in pancreatic and renal cancers are reviewed.

Recent Findings

Multiparametric imaging has raised great interest in the oncology field by revealing various functional characteristics within tumors. For example, we can quantify tumor perfusion by dynamic contrast-enhanced MRI or arterial spin labeling MRI, tumor cellularity by diffusion-weighted image, tumor metabolites by MR spectroscopy, tumor metabolism by FDG-PET, and tumor heterogeneity by radiomics. Recent studies have demonstrated that these imaging biomarkers were correlated with tumor aggressiveness, treatment response, and prognosis.

Summary

Combination of these imaging biomarkers from “one-stop shop” PET-MRI has great potential to more accurately characterize and monitor tumor behavior in the clinic to deliver individualized treatment plans.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. Herrmann K, Kohan A, Gaeta M, Rubbert C, Vercher-Conejero J, Paspulati R, et al. PET/MRI: applications in clinical imaging. Curr Radiol Rep. 2013;1(3):161–76. doi:10.1007/s40134-013-0021-0.

    Article  Google Scholar 

  2. Ryan DP, Hong TS, Bardeesy N. Pancreatic adenocarcinoma. N Engl J Med. 2014;371(22):2140–1. doi:10.1056/NEJMc1412266.

    PubMed  Google Scholar 

  3. National Cancer Institute: Previous version: SEER cancer statistics review. 1975–2012. https://www.seercancergov/archive/csr/1975_2012/.

  4. Wang XY, Yang F, ** C, Fu DL. Utility of PET/CT in diagnosis, staging, assessment of resectability and metabolic response of pancreatic cancer. World J Gastroenterol. 2014;20(42):15580–9. doi:10.3748/wjg.v20.i42.15580.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Huellner MW, Appenzeller P, Kuhn FP, Husmann L, Pietsch CM, Burger IA, et al. Whole-Body nonenhanced PET/MR versus PET/CT in the staging and restaging of cancers: preliminary observations. Radiology. 2014;273(3):859–69. doi:10.1148/radiol.14140090.

    Article  PubMed  Google Scholar 

  6. Tatsumi M, Isohashi K, Onishi H, Hori M, Kim T, Higuchi I, et al. 18F-FDG PET/MRI fusion in characterizing pancreatic tumors: comparison to PET/CT. Int J Clin Oncol. 2011;16(4):408–15. doi:10.1007/s10147-011-0202-x.

    Article  PubMed  Google Scholar 

  7. Nagamachi S, Nishii R, Wakamatsu H, Mizutani Y, Kiyohara S, Fujita S, et al. The usefulness of (18)F-FDG PET/MRI fusion image in diagnosing pancreatic tumor: comparison with (18)F-FDG PET/CT. Ann Nucl Med. 2013;27(6):554–63. doi:10.1007/s12149-013-0719-3.

    Article  PubMed  Google Scholar 

  8. • 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(1):149–59. doi:10.1148/radiol.2016152798. A prospective study comparing whole body FDG PET-MRI with PET/CT plus contrast-enhanced multidetector CT demonstrated similar diagnostic performance in the preoperative evaluation of the resectability and staging of pancreatic tumors, thereby shortening the work-up period for the determination of the therapeutic strategy.

  9. Nishida T, Yoshitomi H, Takano S, Kagawa S, Shimizu H, Ohtsuka M, et al. Low stromal area and high stromal microvessel density predict poor prognosis in pancreatic cancer. Pancreas. 2016;45(4):593–600. doi:10.1097/MPA.0000000000000499.

    Article  CAS  PubMed  Google Scholar 

  10. 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(1):185–96. doi:10.1148/radiol.12120111.

    Article  PubMed  Google Scholar 

  11. 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. Eur J Radiol. 2012;81(8):e917–22. doi:10.1016/j.ejrad.2012.05.011.

    Article  PubMed  Google Scholar 

  12. Liu K, **e P, Peng W, Zhou Z. Assessment of dynamic contrast-enhanced magnetic resonance imaging in the differentiation of pancreatic ductal adenocarcinoma from other pancreatic solid lesions. J Comput Assist Tomogr. 2014;38(5):681–6. doi:10.1097/RCT.0000000000000120.

    Article  PubMed  Google Scholar 

  13. Liu K, **e 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 Tomogr. 2015;39(1):13–8. doi:10.1097/RCT.0000000000000171.

    Article  PubMed  Google Scholar 

  14. 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(6):737–60. doi:10.1016/j.mri.2014.08.014.

    Article  PubMed  Google Scholar 

  15. 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(2):441–9. doi:10.1148/radiol.10091733.

    Article  PubMed  Google Scholar 

  16. Tang MY, Zhang XM, Chen TW, Huang XH. Various diffusion magnetic resonance imaging techniques for pancreatic cancer. World J Radiol. 2015;7(12):424–37. doi:10.4329/wjr.v7.i12.424.

    PubMed  PubMed Central  Google Scholar 

  17. 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(1):45–63. doi:10.1148/radiol.14130778.

    Article  PubMed  Google Scholar 

  18. 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(10):319–28. doi:10.4329/wjr.v7.i10.319.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Park MJ, Kim YK, Choi SY, Rhim H, Lee WJ, Choi D. Preoperative detection of small pancreatic carcinoma: value of adding diffusion-weighted imaging to conventional MR imaging for improving confidence level. Radiology. 2014;273(2):433–43. doi:10.1148/radiol.14132563.

    Article  PubMed  Google Scholar 

  20. Hong BZ, Li XF, Lin JQ. Differential diagnosis of pancreatic cancer by single-shot echo-planar imaging diffusion-weighted imaging. World J Gastroenterol. 2015;21(20):6374–80. doi:10.3748/wjg.v21.i20.6374.

    Article  PubMed  PubMed Central  Google Scholar 

  21. 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(5):709–16. doi:10.1097/RCT.0000000000000434.

    Article  PubMed  Google Scholar 

  22. 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 Magn Reson Imaging. 2015;. doi:10.1002/jmri.24939.

    PubMed  Google Scholar 

  23. Nishiofuku H, Tanaka T, Marugami N, Sho M, Akahori T, Nakajima Y, et al. Increased tumour ADC value during chemotherapy predicts improved survival in unresectable pancreatic cancer. Eur Radiol. 2016;26(6):1835–42. doi:10.1007/s00330-015-3999-2.

    Article  PubMed  Google Scholar 

  24. Niu X, Das SK, Bhetuwal A, **ao Y, Sun F, Zeng L, et al. Value of diffusion-weighted imaging in distinguishing pancreatic carcinoma from mass-forming chronic pancreatitis: a meta-analysis. Chin Med J. 2014;127(19):3477–82.

    PubMed  Google Scholar 

  25. Iima M, Le Bihan D. Clinical intravoxel incoherent motion and diffusion MR imaging: past, present, and future. Radiology. 2016;278(1):13–32. doi:10.1148/radiol.2015150244.

    Article  PubMed  Google Scholar 

  26. Kang KM, Lee JM, Yoon JH, Kiefer B, Han JK, Choi BI. Intravoxel incoherent motion diffusion-weighted MR imaging for characterization of focal pancreatic lesions. Radiology. 2014;270(2):444–53. doi:10.1148/radiol.13122712.

    Article  PubMed  Google Scholar 

  27. Concia M, Sprinkart AM, Penner AH, Brossart P, Gieseke J, Schild HH, et al. Diffusion-weighted magnetic resonance imaging of the pancreas: diagnostic benefit from an intravoxel incoherent motion model-based 3 b-value analysis. Invest Radiol. 2014;49(2):93–100. doi:10.1097/RLI.0b013e3182a71cc3.

    Article  PubMed  Google Scholar 

  28. Kim B, Lee SS, Sung YS, Cheong H, Byun JH, Kim HJ, et al. Intravoxel incoherent motion diffusion-weighted imaging of the pancreas: characterization of benign and malignant pancreatic pathologies. J Magn Reson Imaging. 2017;45(1):260–9. doi:10.1002/jmri.25334.

    Article  PubMed  Google Scholar 

  29. Klau M, Mayer P, Bergmann F, Maier-Hein K, Hase J, Hackert T, et al. Correlation of histological vessel characteristics and diffusion-weighted imaging intravoxel incoherent motion-derived parameters in pancreatic ductal adenocarcinomas and pancreatic neuroendocrine tumors. Invest Radiol. 2015;50(11):792–7. doi:10.1097/RLI.0000000000000187.

    Article  CAS  PubMed  Google Scholar 

  30. Li X, Zhuang L, Zhang X, Wang J, Chen T, Li L, et al. Preliminary study of MR diffusion tensor imaging of pancreas for the diagnosis of acute pancreatitis. PLoS ONE. 2016;11(9):e0160115. doi:10.1371/journal.pone.0160115.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Nissan N, Golan T, Furman-Haran E, Apter S, Inbar Y, Ariche A, et al. Diffusion tensor magnetic resonance imaging of the pancreas. PLoS ONE. 2014;9(12):e115783. doi:10.1371/journal.pone.0115783.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Fukukura Y, Takumi K, Kamimura K, Shindo T, Kumagae Y, Tateyama A, et al. Pancreatic adenocarcinoma: variability of diffusion-weighted MR imaging findings. Radiology. 2012;263(3):732–40. doi:10.1148/radiol.12111222.

    Article  PubMed  Google Scholar 

  33. Hedayati V, Tunariu N, Collins D, Koh D-M. Diffusion-weighted MR imaging in oncology. Curr Radiol Rep. 2014;2(5):44. doi:10.1007/s40134-014-0044-1.

    Article  Google Scholar 

  34. Kartalis N, Manikis GC, Loizou L, Albiin N, Zollner FG, Del Chiaro M, et al. Diffusion-weighted MR imaging of pancreatic cancer: a comparison of mono-exponential, bi-exponential and non-Gaussian kurtosis models. Eur J Radiol Open. 2016;3:79–85. doi:10.1016/j.ejro.2016.04.002.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Noda Y, Kanematsu M, Goshima S, Horikawa Y, Takeda J, Kondo H, et al. Diffusion kurtosis imaging of the pancreas for the assessment of HbA1c levels. J Magn Reson Imaging. 2016;43(1):159–65. doi:10.1002/jmri.24982.

    Article  PubMed  Google Scholar 

  36. Tesiram YA, Lerner M, Stewart C, Njoku C, Brackett DJ. Utility of nuclear magnetic resonance spectroscopy for pancreatic cancer studies. Pancreas. 2012;41(3):474–80. doi:10.1097/MPA.0b013e31822a708c.

    Article  PubMed  Google Scholar 

  37. Akai H, Kiryu S, Matsuda I, Satou J, Takao H, Tajima T, et al. Detection of hepatocellular carcinoma by Gd-EOB-DTPA-enhanced liver MRI: comparison with triple phase 64 detector row helical CT. Eur J Radiol. 2011;80(2):310–5. doi:10.1016/j.ejrad.2010.07.026.

    Article  PubMed  Google Scholar 

  38. Yao X, Zeng M, Wang H, Fei S, Rao S, Ji Y. Metabolite detection of pancreatic carcinoma by in vivo proton MR spectroscopy at 3T: initial results. Radiol Med. 2012;117(5):780–8. doi:10.1007/s11547-011-0757-7.

    Article  CAS  PubMed  Google Scholar 

  39. Cho SG, Lee DH, Lee KY, Ji H, Lee KH, Ros PR, et al. Differentiation of chronic focal pancreatitis from pancreatic carcinoma by in vivo proton magnetic resonance spectroscopy. J Comput Assist Tomogr. 2005;29(2):163–9.

    Article  PubMed  Google Scholar 

  40. Serrao EM, Kettunen MI, Rodrigues TB, Dzien P, Wright AJ, Gopinathan A, et al. MRI with hyperpolarised [1-13C]pyruvate detects advanced pancreatic preneoplasia prior to invasive disease in a mouse model. Gut. 2016;65(3):465–75. doi:10.1136/gutjnl-2015-310114.

    Article  CAS  PubMed  Google Scholar 

  41. Ying H, Kimmelman AC, Lyssiotis CA, Hua S, Chu GC, Fletcher-Sananikone E, et al. Oncogenic Kras maintains pancreatic tumors through regulation of anabolic glucose metabolism. Cell. 2012;149(3):656–70. doi:10.1016/j.cell.2012.01.058.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. 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(1):142–51. doi:10.1097/RCT.0000000000000323.

    Article  PubMed  Google Scholar 

  43. Im HJ, Oo S, Jung W, Jang JY, Kim SW, Cheon GJ, et al. Prognostic value of metabolic and volumetric parameters of preoperative FDG-PET/CT in patients with resectable pancreatic cancer. Medicine. 2016;95(19):e3686. doi:10.1097/MD.0000000000003686.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Chong JU, Hwang HK, Lee JH, Yun M, Kang CM, Lee WJ. Clinically determined type of 18F-fluoro-2-deoxyglucose uptake as an alternative prognostic marker in resectable pancreatic cancer. PLoS ONE. 2017;12(2):e0172606. doi:10.1371/journal.pone.0172606.

    Article  PubMed  PubMed Central  Google Scholar 

  45. 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 Cancer Res. 2009;15(17):5511–7. doi:10.1158/1078-0432.CCR-09-0414.

    Article  CAS  PubMed  Google Scholar 

  46. 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(1):13–5. doi:10.2217/fon.09.151.

    Article  CAS  PubMed  Google Scholar 

  47. Luo F, Liu X, Yan N, Li S, Cao G, Cheng Q, et al. Hypoxia-inducible transcription factor-1alpha promotes hypoxia-induced A549 apoptosis via a mechanism that involves the glycolysis pathway. BMC Cancer. 2006;6:26. doi:10.1186/1471-2407-6-26.

    Article  PubMed  PubMed Central  Google Scholar 

  48. 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 USA. 2012;109(34):13787–92. doi:10.1073/pnas.1203339109.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. New M, Van Acker T, Long JS, Sakamaki JI, Ryan KM, Tooze SA. Molecular pathways controlling autophagy in pancreatic cancer. Front Oncol. 2017;7:28. doi:10.3389/fonc.2017.00028.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Shen G, Hu S, Kuang A. Correlation between apparent diffusion coefficients (ADC) on diffusion-weighted MRI and standardized uptake value (SUV) on FDG-PET/CT in patients with cancer: a meta-analysis. J Nucl Med. 2016;57(supplement 2):1887.

    Google Scholar 

  51. 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(9):1034–41. doi:10.1177/0284185114549825.

    Article  PubMed  Google Scholar 

  52. 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. 2013;201(5):1115–9. doi:10.2214/AJR.13.11304.

    Article  PubMed  Google Scholar 

  53. • 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(10):1753–64. doi:10.1007/s00259-016-3356-y. Various imaging parameters from multiparametric PET-MRI were shown to be correlated with tumor aggressiveness and clinical stage in pancreatic and periampullary cancers. MTV/ADC min ratio was associated with relapse-free survival. Combined imaging biomarkers from PET-MRI have great potential to better predict survival outcome than those from PET or MRI alone.

  54. Notohamiprodjo M, Reiser MF, Sourbron SP. Diffusion and perfusion of the kidney. Eur J Radiol. 2010;76(3):337–47. doi:10.1016/j.ejrad.2010.05.033.

    Article  PubMed  Google Scholar 

  55. Wu Y, Kwon YS, Labib M, Foran DJ, Singer EA. Magnetic resonance imaging as a biomarker for renal cell carcinoma. Dis Markers. 2015;2015:648495. doi:10.1155/2015/648495.

    PubMed  PubMed Central  Google Scholar 

  56. Sikka A, Adam SZ, Wood C, Hoff F, Harmath CB, Miller FH. Magnetic resonance imaging of pancreatic metastases from renal cell carcinoma. Clin Imaging. 2015;39(6):945–53. doi:10.1016/j.clinimag.2015.07.012.

    Article  PubMed  Google Scholar 

  57. Braunagel M, Radler E, Ingrisch M, Staehler M, Schmid-Tannwald C, Rist C, et al. Dynamic contrast-enhanced magnetic resonance imaging measurements in renal cell carcinoma: effect of region of interest size and positioning on interobserver and intraobserver variability. Invest Radiol. 2015;50(1):57–66. doi:10.1097/RLI.0000000000000096.

    Article  PubMed  Google Scholar 

  58. Hotker AM, Mazaheri Y, Wibmer A, Karlo CA, Zheng J, Moskowitz CS, et al. Differentiation of clear cell renal cell carcinoma from other renal cortical tumors by use of a quantitative multiparametric MRI approach. AJR. 2017;208(3):W85–91. doi:10.2214/AJR.16.16652.

    Article  PubMed  Google Scholar 

  59. Zhang Y, Kapur P, Yuan Q, ** Y, Carvo I, Signoretti S, et al. Tumor vascularity in renal masses: correlation of arterial spin-labeled and dynamic contrast-enhanced magnetic resonance imaging assessments. Clin Genitourin Cancer. 2016;14(1):e25–36. doi:10.1016/j.clgc.2015.08.007.

    Article  PubMed  Google Scholar 

  60. Yuan Q, Kapur P, Zhang Y, ** Y, Carvo I, Signoretti S, et al. Intratumor heterogeneity of perfusion and diffusion in clear-cell renal cell carcinoma: correlation with tumor cellularity. Clin Genitourin Cancer. 2016;14(6):e585–94. doi:10.1016/j.clgc.2016.04.007.

    Article  PubMed  Google Scholar 

  61. Cornelis F, Tricaud E, Lasserre AS, Petitpierre F, Bernhard JC, Le Bras Y, et al. Multiparametric magnetic resonance imaging for the differentiation of low and high grade clear cell renal carcinoma. Eur Radiol. 2015;25(1):24–31. doi:10.1007/s00330-014-3380-x.

    Article  CAS  PubMed  Google Scholar 

  62. Pohlmann A, Arakelyan K, Hentschel J, Cantow K, Flemming B, Ladwig M, et al. Detailing the relation between renal T2* and renal tissue pO2 using an integrated approach of parametric magnetic resonance imaging and invasive physiological measurements. Invest Radiol. 2014;49(8):547–60. doi:10.1097/RLI.0000000000000054.

    Article  CAS  PubMed  Google Scholar 

  63. Zhang YD, Wu CJ, Wang Q, Zhang J, Wang XN, Liu XS, et al. Comparison of utility of histogram apparent diffusion coefficient and R2* for differentiation of low-grade from high-grade clear cell renal cell carcinoma. AJR. 2015;205(2):W193–201. doi:10.2214/AJR.14.13802.

    Article  PubMed  Google Scholar 

  64. Wu GY, Suo ST, Lu Q, Zhang J, Zhu WQ, Xu JR. The value of blood oxygenation level-dependent (BOLD) MR imaging in differentiation of renal solid mass and grading of renal cell carcinoma (RCC): analysis based on the largest cross-sectional area versus the entire whole tumour. PLoS ONE. 2015;10(4):e0123431. doi:10.1371/journal.pone.0123431.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Choi YA, Kim CK, Park SY, Cho SW, Park BK. Subtype differentiation of renal cell carcinoma using diffusion-weighted and blood oxygenation level-dependent MRI. AJR. 2014;203(1):W78–84. doi:10.2214/AJR.13.11551.

    Article  PubMed  Google Scholar 

  66. Ozulker T, Ozulker F, Ozbek E, Ozpacaci T. A prospective diagnostic accuracy study of F-18 fluorodeoxyglucose-positron emission tomography/computed tomography in the evaluation of indeterminate renal masses. Nucl Med Commun. 2011;32(4):265–72. doi:10.1097/MNM.0b013e3283442e3b.

    Article  PubMed  Google Scholar 

  67. Wang HY, Ding HJ, Chen JH, Chao CH, Lu YY, Lin WY, et al. Meta-analysis of the diagnostic performance of [18F]FDG-PET and PET/CT in renal cell carcinoma. Cancer Imaging. 2012;12:464–74. doi:10.1102/1470-7330.2012.0042.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Ma H, Shen G, Liu B, Yang Y, Ren P, Kuang A. Diagnostic performance of 18F-FDG PET or PET/CT in restaging renal cell carcinoma: a systematic review and meta-analysis. Nucl Med Commun. 2017;38(2):156–63. doi:10.1097/MNM.0000000000000618.

    Article  PubMed  Google Scholar 

  69. Nakhoda Z, Torigian DA, Saboury B, Hofheinz F, Alavi A. Assessment of the diagnostic performance of (18)F-FDG-PET/CT for detection and characterization of solid renal malignancies. Hell J Nucl Med. 2013;16(1):19–24. doi:10.1967/s002449910067.

    PubMed  Google Scholar 

  70. Takahashi M, Kume H, Koyama K, Nakagawa T, Fujimura T, Morikawa T, et al. Preoperative evaluation of renal cell carcinoma by using 18F-FDG PET/CT. Clin Nucl Med. 2015;40(12):936–40. doi:10.1097/RLU.0000000000000875.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Nakajima R, Abe K, Kondo T, Tanabe K, Sakai S. Clinical role of early dynamic FDG-PET/CT for the evaluation of renal cell carcinoma. Eur Radiol. 2016;26(6):1852–62. doi:10.1007/s00330-015-4026-3.

    Article  PubMed  Google Scholar 

  72. Ferda J, Ferdova E, Hora M, Hes O, Finek J, Topolcan O, et al. 18F-FDG-PET/CT in potentially advanced renal cell carcinoma: a role in treatment decisions and prognosis estimation. Anticancer Res. 2013;33(6):2665–72.

    CAS  PubMed  Google Scholar 

  73. Alongi P, Picchio M, Zattoni F, Spallino M, Gianolli L, Saladini G, et al. Recurrent renal cell carcinoma: clinical and prognostic value of FDG PET/CT. Eur J Nucl Med Mol Imaging. 2016;43(3):464–73. doi:10.1007/s00259-015-3159-6.

    Article  CAS  PubMed  Google Scholar 

  74. Namura K, Minamimoto R, Yao M, Makiyama K, Murakami T, Sano F, et al. Impact of maximum standardized uptake value (SUVmax) evaluated by 18-Fluoro-2-deoxy-d-glucose positron emission tomography/computed tomography (18F-FDG-PET/CT) on survival for patients with advanced renal cell carcinoma: a preliminary report. BMC Cancer. 2010;10:667. doi:10.1186/1471-2407-10-667.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Nakaigawa N, Kondo K, Tateishi U, Minamimoto R, Kaneta T, Namura K, et al. FDG PET/CT as a prognostic biomarker in the era of molecular-targeting therapies: max SUVmax predicts survival of patients with advanced renal cell carcinoma. BMC Cancer. 2016;16:67. doi:10.1186/s12885-016-2097-4.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Ueno D, Yao M, Tateishi U, Minamimoto R, Makiyama K, Hayashi N, et al. Early assessment by FDG-PET/CT of patients with advanced renal cell carcinoma treated with tyrosine kinase inhibitors is predictive of disease course. BMC Cancer. 2012;12:162. doi:10.1186/1471-2407-12-162.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Chen JL, Appelbaum DE, Kocherginsky M, Cowey CL, Rathmell WK, McDermott DF, et al. FDG-PET as a predictive biomarker for therapy with everolimus in metastatic renal cell cancer. Cancer Med. 2013;2(4):545–52. doi:10.1002/cam4.102.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Kierans AS, Rusinek H, Lee A, Shaikh MB, Triolo M, Huang WC, et al. Textural differences in apparent diffusion coefficient between low- and high-stage clear cell renal cell carcinoma. AJR. 2014;203(6):W637–44. doi:10.2214/AJR.14.12570.

    Article  PubMed  Google Scholar 

  79. Doshi AM, Ream JM, Kierans AS, Bilbily M, Rusinek H, Huang WC, et al. Use of MRI in differentiation of papillary renal cell carcinoma subtypes: qualitative and quantitative analysis. AJR. 2016;206(3):566–72. doi:10.2214/AJR.15.15004.

    Article  PubMed  Google Scholar 

  80. • Yin Q, Hung SC, Wang L, Lin W, Fielding JR, Rathmell WK, et al. Associations between tumor vascularity, vascular endothelial growth factor expression and PET/MRI radiomic signatures in primary clear-cell-renal-cell-carcinoma: proof-of-concept study. Sci Rep. 2017;7:43356. doi:10.1038/srep43356. This study utilized radiomics analysis to correlate with angiogenesis of primary clear cell RCCs using PET/MRI. They highlighted the significant strong correlations between radiomic features and MVD, and also demonstrated that the spatiotemporal features extracted from DCE-MRI provided stronger radiomic correlation to MVD than the textural features extracted from Dixon sequences and FDG PET. PET/MRI had higher radiomics correlation to MVD than solely utilizing PET or MRI alone.

  81. Brooks SA, Khandani AH, Fielding JR, Lin W, Sills T, Lee Y, et al. Alternate metabolic programs define regional variation of relevant biological features in renal cell carcinoma progression. Clin Cancer Res. 2016;22(12):2950–9. doi:10.1158/1078-0432.CCR-15-2115.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Antunes J, Viswanath S, Rusu M, Valls L, Hoimes C, Avril N, et al. Radiomics analysis on FLT-PET/MRI for characterization of early treatment response in renal cell carcinoma: a proof-of-concept study. Transl Oncol. 2016;9(2):155–62. doi:10.1016/j.tranon.2016.01.008.

    Article  PubMed  PubMed Central  Google Scholar 

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  1. Department of Medical Imaging and Radiology, National Taiwan University College of Medicine and Hospital, No 7,Chung-Shan South Rd, Taipei, 10016, Taiwan, ROC

    Bang-Bin Chen

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Chen, BB. PET-MRI of the Pancreas and Kidneys. Curr Radiol Rep 5, 33 (2017). https://doi.org/10.1007/s40134-017-0229-5

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