Advertisement

Gastrointestinal stromal tumor risk classification: spectral CT quantitative parameters

  • Xueling Zhang
  • Liangcai Bai
  • Dan Wang
  • Xiaoyu Huang
  • Jinyan Wei
  • Wenjuan Zhang
  • Zhuoli Zhang
  • Junlin ZhouEmail author
Hollow Organ GI
  • 30 Downloads

Abstract

Purpose

To examine the value of spectral CT quantitative parameters in gastrointestinal stromal tumor (GIST) risk classification.

Materials and methods

This retrospective study was approved by the institutional review board. The requirement for informed consent was signed. The authors evaluated 86 patients (30 high risk, 22 medium risk, 28 low risk, and 6 very low risk; mean age: 59 years [range 19–83 years]) with pathologically confirmed GIST who underwent plain and triple-phase contrast-enhanced CT with spectral CT imaging mode from March 2015 through September 2017, with manual follow-up. Quantitative parameters including the CT value of 70 keV monochromatic images, the slope of spectral curves, and the normalized iodine concentration (NIC) and water (iodine) concentrations were measured and calculated, and conducted a power analysis of the above data.

Results

(1) The CT values at 70 keV of the high-risk group were higher than the intermediate and low groups in each of the enhanced phases (P ≤ 0.001), no significant differences in the intermediate-risk and low-risk groups were noted (P = 0.874, 0.871, 0.831, respectively). (2) The slope of the spectral curve of the high-risk group was higher than those of the intermediate and low groups in each of the enhanced phases (P ≤ 0.001), and there were no significant differences between the intermediate- and low-risk groups (P = 0.069, 0.466, 0.840, respectively). (3) The NIC of the high-risk group significantly differed from the lower risk groups (P ≤ 0.001). There was also no significant difference observed between the intermediate- and low-risk groups (P = 0.671, 0.457, 0.833, respectively). (4) The power analysis results show that only the low-risk group with delay period is 0.530, the rest groups are all greater than 0.999.

Conclusion

Dual-energy spectral CT with quantitative analysis may help to increase the accuracy in differentiating the pathological risk classification of GIST between high risk and non-high risk, preoperatively. There were limitations for distinguishing the intermediate- and low-risk groups.

Keywords

Gastrointestinal stromal tumor Spectral CT Quantitative parameters Risk classification 

Notes

Acknowledgements

This research was supported by the Talent Innovation and Entrepreneurship Project of Lanzhou: Project Name—Application value and technological innovation of dual-energy spectral CT in thoracic and abdominal tumor (No. 2016-RC-58).

Compliance with ethical standards

Conflict of interest

The authors declare no potential conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    H. Joensuu. Risk stratification of patients diagnosed with gastrointestinal stromal tumor[J]. Hum Pathol, 2008,39(10):1411-9.CrossRefGoogle Scholar
  2. 2.
    A. C. O'Neill, A. B. Shinagare, V. Kurra, et al. Assessment of metastatic risk of gastric GIST based on treatment-naive CT features[J]. Eur J Surg Oncol, 2016,42(8):1222-8.CrossRefGoogle Scholar
  3. 3.
    S. H. Tirumani, A. D. Baheti, H. Tirumani, et al. Update on Gastrointestinal Stromal Tumors for Radiologists[J]. Korean J Radiol, 2017,18(1):84-93.CrossRefGoogle Scholar
  4. 4.
    A. Dimitrakopoulou-Strauss, U. Ronellenfitsch, C. Cheng, et al. Imaging therapy response of gastrointestinal stromal tumors (GIST) with FDG PET, CT and MRI: a systematic review[J]. Clin Transl Imaging, 2017,5(3):183-197.CrossRefGoogle Scholar
  5. 5.
    A. B. Shinagare, I. K. Ip, R. Lacson, et al. Gastrointestinal stromal tumor: optimizing the use of cross-sectional chest imaging during follow-up[J]. Radiology, 2015,274(2):395-404.CrossRefGoogle Scholar
  6. 6.
    Z. Pan, L. Pang, B. Ding, et al. Gastric cancer staging with dual energy spectral CT imaging[J]. PLoS One, 2013,8(2):e53651.CrossRefGoogle Scholar
  7. 7.
    L. Consolino, D. L. Longo, M. Sciortino, et al. Assessing tumor vascularization as a potential biomarker of imatinib resistance in gastrointestinal stromal tumors by dynamic contrast-enhanced magnetic resonance imaging[J]. Gastric Cancer, 2017,20(4):629-639.CrossRefGoogle Scholar
  8. 8.
    Y. Kamiyama, R. Aihara, T. Nakabayashi, et al. 18F-fluorodeoxyglucose positron emission tomography: useful technique for predicting malignant potential of gastrointestinal stromal tumors[J]. World J Surg, 2005,29(11):1429-35.CrossRefGoogle Scholar
  9. 9.
    M. H. Yu, J. M. Lee, J. H. Baek, et al. MRI features of gastrointestinal stromal tumors[J]. AJR Am J Roentgenol, 2014,203(5):980-91.CrossRefGoogle Scholar
  10. 10.
    A. Nowain, H. Bhakta, S. Pais, et al. Gastrointestinal stromal tumors: clinical profile, pathogenesis, treatment strategies and prognosis[J]. J Gastroenterol Hepatol, 2005,20(6):818-24.CrossRefGoogle Scholar
  11. 11.
    M. Karcaaltincaba, A. Aktas. Dual-energy CT revisited with multidetector CT: review of principles and clinical applications[J]. Diagn Interv Radiol, 2011,17(3):181-94.Google Scholar
  12. 12.
    C. H. McCollough, S. Leng, L. Yu, et al. Dual- and Multi-Energy CT: Principles, Technical Approaches, and Clinical Applications[J]. Radiology, 2015,276(3):637-53.CrossRefGoogle Scholar
  13. 13.
    C. L. Corless, J. A. Fletcher,M. C. Heinrich. Biology of gastrointestinal stromal tumors[J]. J Clin Oncol, 2004,22(18):3813-25.CrossRefGoogle Scholar
  14. 14.
    M. Jinzaki, A. Tanimoto, M. Mukai, et al. Double-phase helical CT of small renal parenchymal neoplasms: correlation with pathologic findings and tumor angiogenesis[J]. J Comput Assist Tomogr, 2000,24(6):835-42.CrossRefGoogle Scholar
  15. 15.
    D. M. McDonald,P. Baluk. Significance of blood vessel leakiness in cancer[J]. Cancer Res, 2002,62(18):5381-5.Google Scholar
  16. 16.
    V. E. Reuter. The pathology of renal epithelial neoplasms[J]. Semin Oncol, 2006,33(5):534-43.CrossRefGoogle Scholar
  17. 17.
    A. Graser, T. R. Johnson, H. Chandarana, et al. Dual energy CT: preliminary observations and potential clinical applications in the abdomen[J]. Eur Radiol, 2009,19(1):13-23.CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Xueling Zhang
    • 1
  • Liangcai Bai
    • 1
  • Dan Wang
    • 1
  • Xiaoyu Huang
    • 1
  • Jinyan Wei
    • 1
  • Wenjuan Zhang
    • 1
  • Zhuoli Zhang
    • 2
  • Junlin Zhou
    • 1
    • 3
    Email author
  1. 1.Department of RadiologyLanzhou University Second HospitalLanzhouPeople’s Republic of China
  2. 2.Department of RadiologyNorthwestern UniversityChicagoUSA
  3. 3.LanzhouPeople’s Republic of China

Personalised recommendations