Abdominal Radiology

, Volume 44, Issue 12, pp 4057–4062 | Cite as

The value of 18F-FDG PET/CT and carbohydrate antigen 19-9 in predicting lymph node micrometastases of pancreatic cancer

  • Siyang Wang
  • Hongcheng ShiEmail author
  • Feixing Yang
  • Xinyu Teng
  • Bo Jiang



This study aimed to assess the value of 18F-FDG PET/CT and carbohydrate antigen 19-9 (CA 19-9) levels in predicting lymph node micrometastases in patients with pancreatic cancer.

Patients and methods

A total of 160 patients with pancreatic carcinoma were included in the study from 2012 to 2017. All patients underwent surgical treatment and PET/CT scans as well as tests to measure CA 19-9 levels before surgery. The PET/CT scans were evaluated by 2 nuclear medicine physicians who were blinded to the clinical information and were compared to the postsurgical pathological findings. Logistic regression analysis was performed to determine the variables that could predict lymph node micrometastases. Receiver operating characteristic (ROC) curves were utilized to find the best cutoff value of the variables related to predicting lymph node micrometastases.


The maximum standardized uptake value (SUVmax) of the primary tumor and CA 19-9 level were potent predictors for determining the lymph node status. The best SUVmax and CA 19-9 cutoff values for predicting lymph node micrometastases were 7.05 (sensitivity = 71.2%, specificity = 76.6%) and 240.55 U/ml (sensitivity = 62.1%, specificity = 79.8%), respectively.


Patients with pancreatic cancer with a tumor SUVmax ≥ 7.05 or a CA 19-9 value ≥ 240.55 are likely to have lymph node micrometastases.


Pancreatic cancer Lymph nodal micrometastases 18F-FDG PET/CT CA19-9 


Compliance with ethical standards

Conflict of interest

The authors have declared that there are no conflicts of interest.


  1. 1.
    Siegel RL, Miller KD, Jemal A. (2015) Cancer statistics, 2015. CA CANCER J CLIN 65:5-29CrossRefGoogle Scholar
  2. 2.
    Roland CL, Yang AD, Katz MH, Chatterjee D, Wang H, Lin H, Vauthey JN, et al. (2015) Neoadjuvant therapy is associated with a reduced lymph node ratio in patients with potentially resectable pancreatic cancer. ANN SURG ONCOL 22:1168-1175CrossRefGoogle Scholar
  3. 3.
    Fink DM, Steele MM, Hollingsworth MA. (2016) The lymphatic system and pancreatic cancer. CANCER LETT 381:217-236CrossRefGoogle Scholar
  4. 4.
    Xiao Z, Luo G, Liu C, Wu C, Liu L, Liu Z, Ni Q, et al. (2014) Molecular mechanism underlying lymphatic metastasis in pancreatic cancer. BIOMED RES INT 2014:925845PubMedPubMedCentralGoogle Scholar
  5. 5.
    Artinyan A, Anaya DA, McKenzie S, Ellenhorn JD, Kim J. (2011) Neoadjuvant therapy is associated with improved survival in resectable pancreatic adenocarcinoma. CANCER-AM CANCER SOC 117:2044-2049Google Scholar
  6. 6.
    Mokdad AA, Minter RM, Zhu H, Augustine MM, Porembka MR, Wang SC, Yopp AC, et al. (2016) Neoadjuvant Therapy Followed by Resection Versus Upfront Resection for Resectable Pancreatic Cancer: A Propensity Score Matched Analysis. J CLIN ONCOL. 35:515-522CrossRefGoogle Scholar
  7. 7.
    Sun H, Zhou J, Liu K, Shen T, Wang X, Wang X. (2019) Pancreatic neuroendocrine tumors: MR imaging features preoperatively predict lymph node metastasis. ABDOM RADIOL (NY) 44:1000-1009CrossRefGoogle Scholar
  8. 8.
    Abdel RA, Elkammary S, Elmorsy AS, Elshafey M, Elhadedy T. (2011) Characterization of mediastinal lymphadenopathy with diffusion-weighted imaging. MAGN RESON IMAGING 29:167-172CrossRefGoogle Scholar
  9. 9.
    Abdel RA, Gaballa G. (2011) Role of perfusion magnetic resonance imaging in cervical lymphadenopathy. J COMPUT ASSIST TOMOGR 35:21-25CrossRefGoogle Scholar
  10. 10.
    Lee JH, Han SS, Hong EK, Cho HJ, Joo J, Park EY, Woo SM, et al. (2019) Predicting lymph node metastasis in pancreatobiliary cancer with magnetic resonance imaging: A prospective analysis. EUR J RADIOL 116:1-7CrossRefGoogle Scholar
  11. 11.
    Kim R, Prithviraj G, Kothari N, Springett G, Malafa M, Hodul P, Kim J, et al. (2015) PET/CT Fusion Scan Prevents Futile Laparotomy in Early Stage Pancreatic Cancer. CLIN NUCL MED 40:e501-e505CrossRefGoogle Scholar
  12. 12.
    Rijkers AP, Valkema R, Duivenvoorden HJ, van Eijck CH. (2014) Usefulness of F-18-fluorodeoxyglucose positron emission tomography to confirm suspected pancreatic cancer: a meta-analysis. EUR J SURG ONCOL 40:794-804CrossRefGoogle Scholar
  13. 13.
    Zhang J, Zuo CJ, Jia NY, Wang JH, Hu SP, Yu ZF, Zheng Y, et al. (2015) Cross-modality PET/CT and contrast-enhanced CT imaging for pancreatic cancer. WORLD J GASTROENTEROL 21:2988-2996CrossRefGoogle Scholar
  14. 14.
    Jiang XT, Tao HQ, Zou SC. (2004) Detection of serum tumor markers in the diagnosis and treatment of patients with pancreatic cancer. HEPATOBILIARY PANCREAT DIS INT 3:464-468PubMedGoogle Scholar
  15. 15.
    Wu L, Huang P, Wang F, Li D, Xie E, Zhang Y, Pan S. (2015) Relationship between serum CA19-9 and CEA levels and prognosis of pancreatic cancer. ANN TRANSL MED 3:328PubMedPubMedCentralGoogle Scholar
  16. 16.
    Boeck S, Stieber P, Holdenrieder S, Wilkowski R, Heinemann V. (2006) Prognostic and therapeutic significance of carbohydrate antigen 19-9 as tumor marker in patients with pancreatic cancer. ONCOLOGY 70:255-264CrossRefGoogle Scholar
  17. 17.
    Nakagawa T, Yamada M, Suzuki Y. (2008) 18F-FDG uptake in reactive neck lymph nodes of oral cancer: relationship to lymphoid follicles. J NUCL MED 49:1053-1059CrossRefGoogle Scholar
  18. 18.
    Riediger H, Keck T, Wellner U, Zur HA, Adam U, Hopt UT, Makowiec F. (2009) The lymph node ratio is the strongest prognostic factor after resection of pancreatic cancer. J GASTROINTEST SURG 13:1337-1344CrossRefGoogle Scholar
  19. 19.
    La Torre M, Cavallini M, Ramacciato G, Cosenza G, Rossi DMS, Nigri G, Ferri M, et al. (2011) Role of the lymph node ratio in pancreatic ductal adenocarcinoma. Impact on patient stratification and prognosis. J SURG ONCOL 104:629-633CrossRefGoogle Scholar
  20. 20.
    Razek A, Elfar E, Abubacker S. (2019) Interobserver agreement of computed tomography reporting standards for chronic pancreatitis. ABDOM RADIOL (NY) 44:2459-2465CrossRefGoogle Scholar
  21. 21.
    Im HJ, Oo S, Jung W, Jang JY, Kim SW, Cheon GJ, Kang KW, et al. (2016) Prognostic Value of Metabolic and Volumetric Parameters of Preoperative FDG-PET/CT in Patients With Resectable Pancreatic Cancer. MEDICINE (BALTIMORE) 95:e3686CrossRefGoogle Scholar
  22. 22.
    Barugola G, Partelli S, Marcucci S, Sartori N, Capelli P, Bassi C, Pederzoli P, et al. (2009) Resectable pancreatic cancer: who really benefits from resection? ANN SURG ONCOL 16:3316-3322CrossRefGoogle Scholar
  23. 23.
    Ferrone CR, Finkelstein DM, Thayer SP, Muzikansky A, Fernandez-delCastillo C, Warshaw AL. (2006) Perioperative CA19-9 levels can predict stage and survival in patients with resectable pancreatic adenocarcinoma. J CLIN ONCOL 24:2897-2902CrossRefGoogle Scholar
  24. 24.
    Crippa S, Salgarello M, Laiti S, Partelli S, Castelli P, Spinelli AE, Tamburrino D, et al. (2014) The role of (18)fluoro-deoxyglucose positron emission tomography/computed tomography in resectable pancreatic cancer. Dig Liver Dis 46:744-749CrossRefGoogle Scholar
  25. 25.
    Asagi A, Ohta K, Nasu J, Tanada M, Nadano S, Nishimura R, Teramoto N, et al. (2013) Utility of contrast-enhanced FDG-PET/CT in the clinical management of pancreatic cancer: impact on diagnosis, staging, evaluation of treatment response, and detection of recurrence. PANCREAS 42:11-19CrossRefGoogle Scholar
  26. 26.
    Kim MJ, Lee KH, Lee KT, Lee JK, Ku BH, Oh CR, Heo JS, et al. (2012) The value of positron emission tomography/computed tomography for evaluating metastatic disease in patients with pancreatic cancer. PANCREAS 41:897-903CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Nuclear Medicine, Zhongshan HospitalFudan UniversityShanghaiChina
  2. 2.Nuclear Medicine Institute of Fudan UniversityShanghaiChina
  3. 3.Shanghai Institute of Medical ImagingShanghaiChina
  4. 4.Beijing Center for Disease Prevention and ControlBeijingChina

Personalised recommendations