Baseline 18F-FDG PET image-derived parameters for therapy response prediction in oesophageal cancer

  • Mathieu Hatt
  • Dimitris Visvikis
  • Olivier Pradier
  • Catherine Cheze-le Rest
Original Article



The objectives of this study were to investigate the predictive value of tumour measurements on 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) positron emission tomography (PET) pretreatment scan regarding therapy response in oesophageal cancer and to evaluate the impact of tumour delineation strategies.


Fifty patients with oesophageal cancer treated with concomitant radiochemotherapy between 2004 and 2008 were retrospectively considered and classified as complete, partial or non-responders (including stable and progressive disease) according to Response Evaluation Criteria in Solid Tumors (RECIST). The classification of partial and complete responders was confirmed by biopsy. Tumours were delineated on the 18F-FDG pretreatment scan using an adaptive threshold and the automatic fuzzy locally adaptive Bayesian (FLAB) methodologies. Several parameters were then extracted: maximum and peak standardized uptake value (SUV), tumour longitudinal length (TL) and volume (TV), SUVmean, and total lesion glycolysis (TLG = TV × SUVmean). The correlation between each parameter and response was investigated using Kruskal-Wallis tests, and receiver-operating characteristic methodology was used to assess performance of the parameters to differentiate patients.


Whereas commonly used parameters such as SUV measurements were not significant predictive factors of the response, parameters related to tumour functional spatial extent (TL, TV, TLG) allowed significant differentiation of all three groups of patients, independently of the delineation strategy, and could identify complete and non-responders with sensitivity above 75% and specificity above 85%. A systematic although not statistically significant trend was observed regarding the hierarchy of the delineation methodologies and the parameters considered, with slightly higher predictive value obtained with FLAB over adaptive thresholding, and TLG over TV and TL.


TLG is a promising predictive factor of concomitant radiochemotherapy response with statistically higher predictive value than SUV measurements in advanced oesophageal cancer.


Oesophageal cancer Response to therapy PET scan Tumour volume Total lesion glycolysis 



This work was partly funded by ANR (French National Research Agency) under the contract ANR-08-ETEC-005-01.

Conflicts of interest



  1. 1.
    Parkin DM, Bray F, Ferlay J, Pisani P. Global cancer statistics, 2002. CA Cancer J Clin 2005;55(2):74–108.PubMedCrossRefGoogle Scholar
  2. 2.
    Hayat MJ, Howlader N, Reichman ME, Edwards BK. Cancer statistics, trends, and multiple primary cancer analyses from the Surveillance, Epidemiology, and End Results (SEER) Program. Oncologist 2007;12(1):20–37.PubMedCrossRefGoogle Scholar
  3. 3.
    Gebski V, Burmeister B, Smithers BM, Foo K, Zalcberg J, Simes J, et al. Survival benefits from neoadjuvant chemoradiotherapy or chemotherapy in oesophageal carcinoma: a meta-analysis. Lancet Oncol 2007;8(3):226–34.PubMedCrossRefGoogle Scholar
  4. 4.
    Kelsen DP, Winter KA, Gunderson LL, Mortimer J, Estes NC, Haller DG, et al. Long-term results of RTOG trial 8911 (USA Intergroup 113): a random assignment trial comparison of chemotherapy followed by surgery compared with surgery alone for esophageal cancer. J Clin Oncol 2007;25(24):3719–25.PubMedCrossRefGoogle Scholar
  5. 5.
    Chirieac LR, Swisher SG, Ajani JA, Komaki RR, Correa AM, Morris JS, et al. Posttherapy pathologic stage predicts survival in patients with esophageal carcinoma receiving preoperative chemoradiation. Cancer 2005;103(7):1347–55.PubMedCrossRefGoogle Scholar
  6. 6.
    Stahl M, Wilke H, Stuschke M, Walz MK, Fink U, Molls M, et al. Clinical response to induction chemotherapy predicts local control and long-term survival in multimodal treatment of patients with locally advanced esophageal cancer. J Cancer Res Clin Oncol 2005;131(1):67–72.PubMedCrossRefGoogle Scholar
  7. 7.
    Dragovich T, Campen C. Anti-EGFR-targeted therapy for esophageal and gastric cancers: an evolving concept. J Oncol 2009;2009:804108.PubMedGoogle Scholar
  8. 8.
    Makino T, Yamasaki M, Miyata H, Yoshioka S, Takiguchi S, Fujiwara Y, et al. p53 Mutation status predicts pathological response to chemoradiotherapy in locally advanced esophageal cancer. Ann Surg Oncol 2010;17(3):804–11.PubMedCrossRefGoogle Scholar
  9. 9.
    Lee JM, Yang SY, Yang PW, Shun CT, Wu MT, Hsu CH, et al. Polymorphism in epidermal growth factor receptor intron 1 predicts prognosis of patients with esophageal cancer after chemoradiation and surgery. Ann Surg Oncol 2011. [Epub ahead of print].Google Scholar
  10. 10.
    van Westreenen HL, Westerterp M, Bossuyt PM, Pruim J, Sloof GW, van Lanschot JJ, et al. Systematic review of the staging performance of 18F-fluorodeoxyglucose positron emission tomography in esophageal cancer. J Clin Oncol 2004;22(18):3805–12.PubMedCrossRefGoogle Scholar
  11. 11.
    Krause BJ, Herrmann K, Wieder H, zum Büschenfelde CM. 18F-FDG PET and 18F-FDG PET/CT for assessing response to therapy in esophageal cancer. J Nucl Med 2009;50 Suppl 1:89S–96S.PubMedCrossRefGoogle Scholar
  12. 12.
    Kwee RM. Prediction of tumor response to neoadjuvant therapy in patients with esophageal cancer with use of 18F FDG PET: a systematic review. Radiology 2010;254(3):707–17.PubMedCrossRefGoogle Scholar
  13. 13.
    Ott K, Weber WA, Lordick F, Becker K, Busch R, Herrmann K, et al. Metabolic imaging predicts response, survival, and recurrence in adenocarcinomas of the esophagogastric junction. J Clin Oncol 2006;24(29):4692–8.PubMedCrossRefGoogle Scholar
  14. 14.
    Therasse P, Arbuck SG, Eisenhauer EA, Wanders J, Kaplan RS, Rubinstein L, et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst 2000;92(3):205–16.PubMedCrossRefGoogle Scholar
  15. 15.
    Larson SM, Erdi Y, Akhurst T, Mazumdar M, Macapinlac HA, Finn RD, et al. Tumor treatment response based on visual and quantitative changes in global tumor glycolysis using PET-FDG imaging. The visual response score and the change in total lesion glycolysis. Clin Positron Imaging 1999;2(3):159–71.PubMedCrossRefGoogle Scholar
  16. 16.
    Hatt M, Cheze le Rest C, Descourt P, Dekker A, De Ruysscher D, Oellers M, et al. Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int J Radiat Oncol Biol Phys 2010;77(1):301–8.PubMedCrossRefGoogle Scholar
  17. 17.
    Hatt M, Cheze le Rest C, Turzo A, Roux C, Visvikis D. A fuzzy locally adaptive Bayesian segmentation approach for volume determination in PET. IEEE Trans Med Imaging 2009;28(6):881–93.PubMedCrossRefGoogle Scholar
  18. 18.
    Schaefer A, Kremp S, Hellwig D, Rübe C, Kirsch CM, Nestle U. A contrast-oriented algorithm for FDG-PET-based delineation of tumour volumes for the radiotherapy of lung cancer: derivation from phantom measurements and validation in patient data. Eur J Nucl Med Mol Imaging 2008;35(11):1989–99.PubMedCrossRefGoogle Scholar
  19. 19.
    Kruskal W, Wallis W. Use of ranks in one-criterion variance analysis. J Am Stat Assoc 1952;47(260):583–621.CrossRefGoogle Scholar
  20. 20.
    Metz CE. Basic principles of ROC analysis. Semin Nucl Med 1978;8(4):283–98.PubMedCrossRefGoogle Scholar
  21. 21.
    Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med 2009;50 Suppl 1:122S–50S.PubMedCrossRefGoogle Scholar
  22. 22.
    Hofman MS, Hicks RJ. Restaging: should we percist without pattern recognition? J Nucl Med 2010;51(12):1830–2.PubMedCrossRefGoogle Scholar
  23. 23.
    Hatt M, Visvikis D, Albarghach NM, Tixier F, Pradier O, Cheze-le Rest. Prognostic value of (18)F-FDG PET image-based parameters in oesophageal cancer and impact of tumour delineation methodology. Eur J Nucl Med Mol Imaging 2011. [Epub ahead of print].Google Scholar
  24. 24.
    Lucignani G, Larson SM. Doctor, what does my future hold? The prognostic value of FDG-PET in solid tumours. Eur J Nucl Med Mol Imaging 2010;37(5):1032–8.PubMedCrossRefGoogle Scholar
  25. 25.
    Levine EA, Farmer MR, Clark P, Mishra G, Ho C, Geisinger KR, et al. Predictive value of 18-fluoro-deoxy-glucose-positron emission tomography (18F-FDG-PET) in the identification of responders to chemoradiation therapy for the treatment of locally advanced esophageal cancer. Ann Surg 2006;243(4):472–8.PubMedCrossRefGoogle Scholar
  26. 26.
    Rizk NP, Tang L, Adusumilli PS, Bains MS, Akhurst TJ, Ilson D, et al. Predictive value of initial PET-SUVmax in patients with locally advanced esophageal and gastroesophageal junction adenocarcinoma. J Thorac Oncol 2009;4(7):875–9.PubMedCrossRefGoogle Scholar
  27. 27.
    Makino T, Miyata H, Yamasaki M, Fujiwara Y, Takiguchi S, Nakajima K, et al. Utility of response evaluation to neo-adjuvant chemotherapy by (18)F-fluorodeoxyglucose-positron emission tomography in locally advanced esophageal squamous cell carcinoma. Surgery 2010;148(5):908–18.PubMedCrossRefGoogle Scholar
  28. 28.
    Kato H, Fukuchi M, Miyazaki T, Nakajima M, Tanaka N, Inose T, et al. Prediction of response to definitive chemoradiotherapy in esophageal cancer using positron emission tomography. Anticancer Res 2007;27(4C):2627–33.PubMedGoogle Scholar
  29. 29.
    Yendamuri S, Swisher SG, Correa AM, Hofstetter W, Alani JA, Francis A, et al. Esophageal tumor length is independently associated with long-term survival. Cancer 2009;115(3):508–16.PubMedCrossRefGoogle Scholar
  30. 30.
    Sillah K, Williams LR, Laasch HU, Saleem A, Watkins G, Pritchard SA, et al. Computed tomography overestimation of esophageal tumor length: implications for radiotherapy planning. World J Gastrointest Oncol 2010;2(4):197–204.PubMedCrossRefGoogle Scholar
  31. 31.
    Zhong X, Yu J, Zhang B, Mu D, Zhang W, Li D, et al. Using 18F-fluorodeoxyglucose positron emission tomography to estimate the length of gross tumor in patients with squamous cell carcinoma of the esophagus. Int J Radiat Oncol Biol Phys 2009;73(1):136–41.PubMedCrossRefGoogle Scholar
  32. 32.
    Mamede M, Abreu-E-Lima P, Oliva MR, Nosé V, Mamon H, Gerbaudo VH. FDG-PET/CT tumor segmentation-derived indices of metabolic activity to assess response to neoadjuvant therapy and progression-free survival in esophageal cancer: correlation with histopathology results. Am J Clin Oncol 2007;30(4):377–88.PubMedCrossRefGoogle Scholar
  33. 33.
    Roedl JB, Harisinghani MG, Colen RR, Fischman AJ, Blake MA, Mathisen DJ, et al. Assessment of treatment response and recurrence in esophageal carcinoma based on tumor length and standardized uptake value on positron emission tomography-computed tomography. Ann Thorac Surg 2008;86(4):1131–8.PubMedCrossRefGoogle Scholar
  34. 34.
    Hong TS, Killoran JH, Marmede M, Mamon HJ. Impact of manual and automated interpretation of fused PET/CT data on esophageal target definitions in radiation planning. Int J Radiat Oncol Biol Phys 2008;72(5):1612–8.PubMedCrossRefGoogle Scholar
  35. 35.
    Arslan N, Miller TR, Dehdashti F, Battafarano RJ, Siegel BA. Evaluation of response to neoadjuvant therapy by quantitative 2-deoxy-2-[18F]fluoro-D-glucose with positron emission tomography in patients with esophageal cancer. Mol Imaging Biol 2002;4(4):301–10.PubMedCrossRefGoogle Scholar
  36. 36.
    Roedl JB, Colen RR, Holalkere NS, Fischman AJ, Choi NC, Blake MA. Adenocarcinomas of the esophagus: response to chemoradiotherapy is associated with decrease of metabolic tumor volume as measured on PET-CT. Comparison to histopathologic and clinical response evaluation. Radiother Oncol 2008;89(3):278–86.PubMedCrossRefGoogle Scholar
  37. 37.
    Lee HY, Hyun SH, Lee KS, Kim BT, Kim J, Shim YM, et al. Volume-based parameter of (18)F-FDG PET/CT in malignant pleural mesothelioma: prediction of therapeutic response and prognostic implications. Ann Surg Oncol 2010;17(10):2787–94.PubMedCrossRefGoogle Scholar
  38. 38.
    Cazaentre T, Morschhauser F, Vermandel M, Betrouni N, Prangère T, Steinling M, et al. Pre-therapy 18F-FDG PET quantitative parameters help in predicting the response to radioimmunotherapy in non-Hodgkin lymphoma. Eur J Nucl Med Mol Imaging 2010;37(3):494–504.PubMedCrossRefGoogle Scholar
  39. 39.
    Hatt M, Cheze Le Rest C, Albarghach N, Pradier O, Visvikis D. PET functional volume delineation: a robustness and repeatability study. Eur J Nucl Med Mol Imaging 2011;38(4):663–72.PubMedCrossRefGoogle Scholar
  40. 40.
    Hatt M, Cheze-Le Rest C, Aboagye EO, Kenny LM, Rosso L, Turkheimer FE, et al. Reproducibility of 18F-FDG and 3′-deoxy-3′-18F-fluorothymidine PET tumor volume measurements. J Nucl Med 2010;51(9):1368–76.PubMedCrossRefGoogle Scholar
  41. 41.
    Hyun SH, Choi JY, Shim YM, Kim K, Lee SJ, Cho YS, et al. Prognostic value of metabolic tumor volume measured by 18F-fluorodeoxyglucose positron emission tomography in patients with esophageal carcinoma. Ann Surg Oncol 2010;17(1):115–22.PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Mathieu Hatt
    • 1
  • Dimitris Visvikis
    • 1
  • Olivier Pradier
    • 1
    • 2
  • Catherine Cheze-le Rest
    • 1
  1. 1.LaTIM, INSERM U650CHU MorvanBrestFrance
  2. 2.Department of RadiotherapyCHU MorvanBrestFrance

Personalised recommendations