Skip to main content
Log in

Usefulness of texture analysis for grading pancreatic neuroendocrine tumors on contrast-enhanced computed tomography and apparent diffusion coefficient maps

  • Original Article
  • Published:
Japanese Journal of Radiology Aims and scope Submit manuscript

Abstract

Purpose

To determine whether texture analysis of contrast-enhanced computed tomography (CECT) and apparent diffusion coefficient (ADC) maps could predict tumor grade (G1 vs G2–3) in patients with pancreatic neuroendocrine tumor (PNET).

Materials and methods

Thirty-three PNETs (22 G1 and 11 G2–3) were retrospectively reviewed. Fifty features were individually extracted from the arterial and portal venous phases of CECT and ADC maps by two radiologists. Diagnostic performance was assessed by receiver operating characteristic curves while inter-observer agreement was determined by calculating intraclass correlation coefficients (ICCs).

Results

G2–G3 tumors were significantly larger than G1. Seventeen features significantly differed among the two readers on univariate analysis, with ICCs > 0.6; the largest area under the curve (AUC) for features of each CECT phase and ADC map was log-sigma 1.0 joint-energy = 0.855 for the arterial phase, log-sigma 1.5 kurtosis = 0.860 for the portal venous phase, and log-sigma 1.0 correlation = 0.847 for the ADC map. The log-sigma 1.5 kurtosis of the portal venous phase showed the largest AUC in the CECT and ADC map, and its sensitivity, specificity, and accuracy were 95.5%, 72.7%, and 87.9%, respectively.

Conclusion

Texture analysis may aid in differentiating between G1 and G2–3 PNET.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Ansari NA, Ramalho M, Semelka RC, Buonocore V, Gigli S, Maccioni F. Role of magnetic resonance imaging in the detection and characterization of solid pancreatic nodules: an update. World J Radiol. 2015;7:361–74.

    Article  Google Scholar 

  2. Lewis RB, Lattin GE Jr, Paal E. Pancreatic endocrine tumors: radiologic-clinicopathologic correlation. Radiographics. 2010;30:1445–644.

    Article  Google Scholar 

  3. Lloyd RV, Osamura RY, Klöppel G, Rosai J. WHO classification of tumours of endocrine organs. 4th ed. Lyon: International Agency for Research on Cancer; 2017. p. 209–240.

    Google Scholar 

  4. Singhi AD, Klimstra DS. Well-differentiated pancreatic neuroendocrine tumours (PanNETs) and poorly differentiated pancreatic neuroendocrine carcinomas (PanNECs): concepts, issues and a practical diagnostic approach to high-grade (G3) cases. Histopathology. 2018;72:168–77.

    Article  Google Scholar 

  5. Amin MB, Edge SB, Greene F, Byrd DR, Brookland RK, Washington MK, et al. AJCC cancer staging manual. 8th ed. New York: Springer; 2017.

    Book  Google Scholar 

  6. Plöckinger U, Rindi G, Arnold R, Eriksson B, Krenning EP, de Herder WW, et al. European Neuroendocrine Tumour Society Guidelines for the diagnosis and treatment of neuroendocrine gastrointestinal tumours: a consensus statement on behalf of the European Neuroendocrine Tumour Society (ENETS). Neuroendocrinology. 2004;80:394–424.

    Article  Google Scholar 

  7. Sahani DV, Bonaffini PA, Fernández-Del Castillo C, Blake MA. Gastroenteropancreatic neuroenodocrine tumors: role of imaging in diagnosis and management. Radiology. 2013;266:38–61.

    Article  Google Scholar 

  8. Yang G, Ji M, Chen J, Chen R, Chen Y, Fu D, et al. Surgery management for sporadic small (≤2 cm), non-functioning pancreatic neuroendocrine tumors: a consensus statement by the Chinese Study Group for Neuroendocrine Tumors (CSNET). Int J Oncol. 2017;50:567–74.

    Article  Google Scholar 

  9. Smith JK, Ng SC, Hill JS, Simmmons JP, Arous EJ, Shah SA, et al. Complications after pancreatectomy for neuroendocrine tumors: a national study. J Surg Res. 2010;163:63–8.

    Article  Google Scholar 

  10. Sallinen VJ, Le Large TYS, Tieftrunk E, Galeev S, Kovalenko Z, Haugvik SP, et al. Prognosis of sporadic resected small (≤2 cm) nonfunctional pancreatic neuroendocrine tumors—a multi-institutional study. HPB. 2018;20:251–9.

    Article  Google Scholar 

  11. Rodallec M, Vilgrain V, Couvelard A, Rufat P, O'Toole D, Sauvanet A, et al. Endocrine pancreatic tumours and helical CT: contrast enhancement is correlated with microvascular density, histoprognostic factors and survival. Pancreatology. 2006;6:77–85.

    Article  CAS  Google Scholar 

  12. Manfredi R, Bonatti M, Mantovani W, Graziani R, Segala D, Capelli P, et al. Non-hyperfunctioning neuroendocrine tumors of the pancreas: MR imaging appearance and correlation with their biological behaviour. Eur Radiol. 2013;23:3029–39.

    Article  Google Scholar 

  13. Lotfalizadeh E, Ronot M, Wagner M, Cros J, Couvelard A, Vullierme MP, et al. Prediction of pancreatic neuroendocrine tumour grade with MR imaging features: added value of diffusion-weighted imaging. Eur Radiol. 2016;27:1748–59.

    Article  Google Scholar 

  14. Wang Y, Chen ZE, Yaghmai V, Nikolaidis P, McCarthy RJ, Merrick L, et al. Diffusion-weighted MR imaging in pancreatic endocrine tumors correlated with histopathologic characteristics. J Magn Reson Imaging. 2011;33:1071–9.

    Article  Google Scholar 

  15. Toshima F, Inoue D, Komori T, Yoshida K, Yoneda N, Minami T, et al. Is the combination of MR and CT findings useful in determining the tumor grade of pancreatic neuroendocrine tumors? Jpn J Radiol. 2017;35:242–53.

    Article  CAS  Google Scholar 

  16. Park HJ, Kim HJ, Kim KW, Kim SY, Choi SH, You M-W, et al. Comparison between neuroendocrine carcinomas and well-differentiated neuroendocrine tumors of the pancreas using dynamic enhanced CT. Eur Radiol. 2020. https://doi.org/10.1007/s00330-020-06867-w.

    Article  PubMed  Google Scholar 

  17. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Perry JP. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics. 2017;37:1483–503.

    Article  Google Scholar 

  18. Yun G, Kim YH, Lee YJ, Kim B, Hwang J-H, Choi DJ. Tumor heterogeneity of pancreas head cancer assessed by CT texture analysis: association with survival outcomes after curative resection. Sci Rep. 2018;8:7226.

    Article  Google Scholar 

  19. Canellas R, Burk KS, Parakh A, Sahani D. Prediction of pancreatic neuroendocrine tumor grade based on CT features and texture analysis. AJR Am J Roentgenol. 2018;210:341–6.

    Article  Google Scholar 

  20. Choi TW, Kim JH, Yu MH, Park SJ, Han JK. Pancreatic neuroendocrine tumor: prediction of the tumor grade using CT findings and computerized texture analysis. Acta Radiol. 2018;59:383–92.

    Article  CAS  Google Scholar 

  21. Pereira JA, Rosado E, Bali M, Metens T, Chao SL. Pancreatic neuroendocrine tumors: correlation between histogram analysis of apparent diffusion coefficient maps and tumor grade. Abdom Imaging. 2015;40:3122–8.

    Article  Google Scholar 

  22. De Robertis R, Maris B, Cardobi N, Martini PT, Gobbo S, Capelli P, et al. Can histogram analysis of MR image predict aggressiveness in pancreatic neuroendocrine tumors? Eur Radiol. 2018;28:2582–91.

    Article  Google Scholar 

  23. Guo CG, Ren S, Chen X, Wang QD, Xiao WB, Zhang JF, et al. Pancreatic neuroendocrine tumor: prediction of the tumor grade using magnetic resonance imaging findings and texture analysis with 3-T magnetic resonance. Cancer Manag Res. 2019;11:1933–44.

    Article  Google Scholar 

  24. D'Onofrio M, Ciaravino V, Cardobi N, De Robertis R, Cingarlini S, Landoni L, et al. CT enhancement and 3D texture analysis of pancreatic neuroendocrine neoplasms. Sci Rep. 2019;9:2176.

    Article  Google Scholar 

  25. Liu Y, Liu S, Qu F, Qian L, Cheng R, Ye Z. Tumor heterogeneity assessed by texture analysis on contrast-enhanced CT in lung adenocarcinoma: association with pathologic grade. Oncotarget. 2017;8:53664–74.

    Article  Google Scholar 

  26. Al-Hawary MM, Francis IR, Chari ST, Fishman EK, Hough DM, Lu DS, et al. Pancreatic ductal adenocarcinoma radiology reporting template: consensus statement of the Society of Abdominal Radiology and the American Pancreatic Association. Radiology. 2014;270:248–60.

    Article  Google Scholar 

  27. Ducreux M, Cuhna AS, Caramella C, Hollebecque A, Burtin P, Goéré D, et al. Cancer of the pancreas: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2015;26(Suppl 5):v56–v68.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Soumei Nozu (Siemens Japan K. K., Customer Service Division) for providing information about detailed MR parameters.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kazuyoshi Ohki.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ohki, K., Igarashi, T., Ashida, H. et al. Usefulness of texture analysis for grading pancreatic neuroendocrine tumors on contrast-enhanced computed tomography and apparent diffusion coefficient maps. Jpn J Radiol 39, 66–75 (2021). https://doi.org/10.1007/s11604-020-01038-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11604-020-01038-9

Keywords

Navigation