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Intratumoral heterogeneity of 18F-FDG uptake predicts survival in patients with pancreatic ductal adenocarcinoma

  • Seung Hyup Hyun
  • Ho Seong Kim
  • Seong Ho Choi
  • Dong Wook Choi
  • Jong Kyun Lee
  • Kwang Hyuck Lee
  • Joon Oh Park
  • Kyung-Han Lee
  • Byung-Tae Kim
  • Joon Young Choi
Original Article

Abstract

Purpose

To assess whether intratumoral heterogeneity measured by 18F-FDG PET texture analysis has potential as a prognostic imaging biomarker in patients with pancreatic ductal adenocarcinoma (PDAC).

Methods

We evaluated a cohort of 137 patients with newly diagnosed PDAC who underwent pretreatment 18F-FDG PET/CT from January 2008 to December 2010. First-order (histogram indices) and higher-order (grey-level run length, difference, size zone matrices) textural features of primary tumours were extracted by PET texture analysis. Conventional PET parameters including metabolic tumour volume (MTV), total lesion glycolysis (TLG), and standardized uptake value (SUV) were also measured. To assess and compare the predictive performance of imaging biomarkers, time-dependent receiver operating characteristic (ROC) curves for censored survival data and areas under the ROC curve (AUC) at 2 years after diagnosis were used. Associations between imaging biomarkers and overall survival were assessed using Cox proportional hazards regression models.

Results

The best imaging biomarker for overall survival prediction was first-order entropy (AUC = 0.720), followed by TLG (AUC = 0.697), MTV (AUC = 0.692), and maximum SUV (AUC = 0.625). After adjusting for age, sex, clinical stage, tumour size and serum CA19-9 level, multivariable Cox analysis demonstrated that higher entropy (hazard ratio, HR, 5.59; P = 0.028) was independently associated with worse survival, whereas TLG (HR 0.98; P = 0.875) was not an independent prognostic factor.

Conclusion

Intratumoral heterogeneity of 18F-FDG uptake measured by PET texture analysis is an independent predictor of survival along with tumour stage and serum CA19-9 level in patients with PDAC. In addition, first-order entropy as a measure of intratumoral metabolic heterogeneity is a better quantitative imaging biomarker of prognosis than conventional PET parameters.

Keywords

18F-FDG PET/CT Intratumoral heterogeneity Texture analysis Prognosis Pancreatic cancer 

Notes

Acknowledgments

This study was supported by a grant from the National R&D Program for Cancer Control, Ministry of Health & Welfare, Republic of Korea (grant number 1120150).

Compliance with ethical standards

Funding

This study was funded by National R&D Program for Cancer Control, Ministry of Health & Welfare, Republic of Korea (grant number 1120150).

Conflicts of interest

None.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the principles of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. For this type of study formal consent is not required.

Informed consent

Written informed consent was waived.

Supplementary material

259_2016_3316_MOESM1_ESM.docx (16 kb)
Supplementary Table 1 (DOCX 16 kb)

References

  1. 1.
    Conlon KC, Klimstra DS, Brennan MF. Long-term survival after curative resection for pancreatic ductal adenocarcinoma. Clinicopathologic analysis of 5-year survivors. Ann Surg. 1996;223:273–9.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Osayi SN, Bloomston M, Schmidt CM, Ellison EC, Muscarella P. Biomarkers as predictors of recurrence following curative resection for pancreatic ductal adenocarcinoma: a review. Biomed Res Int. 2014;2014:468959.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Dibble EH, Karantanis D, Mercier G, Peller PJ, Kachnic LA, Subramaniam RM. PET/CT of cancer patients: part 1, pancreatic neoplasms. AJR Am J Roentgenol. 2012;199:952–67.CrossRefPubMedGoogle Scholar
  4. 4.
    Wang Z, Chen JQ, Liu JL, Qin XG, Huang Y. FDG-PET in diagnosis, staging and prognosis of pancreatic carcinoma: a meta-analysis. World J Gastroenterol. 2013;19:4808–17.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Lee JW, Kang CM, Choi HJ, Lee WJ, Song SY, Lee JH, et al. Prognostic value of metabolic tumor volume and total lesion glycolysis on preoperative 18F-FDG PET/CT in patients with pancreatic cancer. J Nucl Med. 2014;55:898–904.CrossRefPubMedGoogle Scholar
  6. 6.
    Hyun SH, Ahn HK, Kim H, Ahn MJ, Park K, Ahn YC, et al. Volume-based assessment by (18)F-FDG PET/CT predicts survival in patients with stage III non-small-cell lung cancer. Eur J Nucl Med Mol Imaging. 2014;41:50–8.CrossRefPubMedGoogle Scholar
  7. 7.
    Kim J, Hong J, Kim SG, Hwang KH, Kim M, Ahn HK, et al. Prognostic value of metabolic tumor volume estimated by (18)F-FDG positron emission tomography/computed tomography in patients with diffuse large B-cell lymphoma of stage II or III disease. Nucl Med Mol Imaging. 2014;48:187–95.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Hyun SH, Ahn HK, Park YH, Im YH, Kil WH, Lee JE, et al. Volume-based metabolic tumor response to neoadjuvant chemotherapy is associated with an increased risk of recurrence in breast cancer. Radiology. 2015;275:235–44.CrossRefPubMedGoogle Scholar
  9. 9.
    Kim HS, Choi JY, Choi DW, Lim HY, Lee JH, Hong SP, et al. Prognostic value of volume-based metabolic parameters measured by (18)F-FDG PET/CT of pancreatic neuroendocrine tumors. Nucl Med Mol Imaging. 2014;48:180–6.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366:883–92.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Rahim MK, Kim SE, So H, Kim HJ, Cheon GJ, Lee ES, et al. Recent trends in PET image interpretations using volumetric and texture-based quantification methods in nuclear oncology. Nucl Med Mol Imaging. 2014;48:1–15.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Cheng NM, Fang YH, Lee LY, Chang JT, Tsan DL, Ng SH, et al. Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer. Eur J Nucl Med Mol Imaging. 2015;42:419–28.CrossRefPubMedGoogle Scholar
  13. 13.
    Cook GJ, O’Brien ME, Siddique M, Chicklore S, Loi HY, Sharma B, et al. Non-small cell lung cancer treated with erlotinib: heterogeneity of 18F-FDG uptake at PET – association with treatment response and prognosis. Radiology. 2015;276:883–93.CrossRefPubMedGoogle Scholar
  14. 14.
    Hatt M, Majdoub M, Vallieres M, Tixier F, Le Rest CC, Groheux D, et al. 18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort. J Nucl Med. 2015;56:38–44.CrossRefPubMedGoogle Scholar
  15. 15.
    Shah B, Srivastava N, Hirsch AE, Mercier G, Subramaniam RM. Intra-reader reliability of FDG PET volumetric tumor parameters: effects of primary tumor size and segmentation methods. Ann Nucl Med. 2012;26:707–14.CrossRefPubMedGoogle Scholar
  16. 16.
    Werner-Wasik M, Nelson AD, Choi W, Arai Y, Faulhaber PF, Kang P, et al. What is the best way to contour lung tumors on PET scans? Multiobserver validation of a gradient-based method using a NSCLC digital PET phantom. Int J Radiat Oncol Biol Phys. 2012;82:1164–71.CrossRefPubMedGoogle Scholar
  17. 17.
    Fang YH, Lin CY, Shih MJ, Wang HM, Ho TY, Liao CT, et al. Development and evaluation of an open-source software package “CGITA” for quantifying tumor heterogeneity with molecular images. Biomed Res Int. 2014;2014:248505.PubMedPubMedCentralGoogle Scholar
  18. 18.
    Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJ. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging. 2013;40:133–40.CrossRefPubMedGoogle Scholar
  19. 19.
    Heagerty PJ, Lumley T, Pepe MS. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics. 2000;56:337–44.CrossRefPubMedGoogle Scholar
  20. 20.
    Lausen B, Schumacher M. Maximally selected rank statistics. Biometrics. 1992;43:73–85.CrossRefGoogle Scholar
  21. 21.
    Pyka T, Bundschuh RA, Andratschke N, Mayer B, Specht HM, Papp L, et al. Textural features in pre-treatment [F18]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLC patients receiving primary stereotactic radiation therapy. Radiat Oncol. 2015;10:100.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Cook GJ, Yip C, Siddique M, Goh V, Chicklore S, Roy A, et al. Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med. 2013;54:19–26.CrossRefPubMedGoogle Scholar
  23. 23.
    Orlhac F, Soussan M, Maisonobe JA, Garcia CA, Vanderlinden B, Buvat I. Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis. J Nucl Med. 2014;55:414–22.CrossRefPubMedGoogle Scholar
  24. 24.
    Yan J, Chu-Shern JL, Loi HY, Khor LK, Sinha AK, Quek ST, et al. Impact of image reconstruction settings on texture features in 18F-FDG PET. J Nucl Med. 2015;56:1667–73.CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Seung Hyup Hyun
    • 1
  • Ho Seong Kim
    • 1
  • Seong Ho Choi
    • 2
  • Dong Wook Choi
    • 2
  • Jong Kyun Lee
    • 3
  • Kwang Hyuck Lee
    • 3
  • Joon Oh Park
    • 3
  • Kyung-Han Lee
    • 1
  • Byung-Tae Kim
    • 1
  • Joon Young Choi
    • 1
  1. 1.Department of Nuclear Medicine, Samsung Medical CenterSungkyunkwan University School of MedicineSeoulRepublic of Korea
  2. 2.Department of Surgery, Samsung Medical CenterSungkyunkwan University School of MedicineSeoulRepublic of Korea
  3. 3.Department of Medicine, Samsung Medical CenterSungkyunkwan University School of MedicineSeoulRepublic of Korea

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