European Radiology

, Volume 26, Issue 9, pp 3086–3093 | Cite as

Predictive features of CT for risk stratifications in patients with primary gastrointestinal stromal tumour

  • Cuiping Zhou
  • Xiaohui Duan
  • Xiang Zhang
  • Huijun Hu
  • Dongye Wang
  • Jun ShenEmail author



To determine the predictive CT imaging features for risk stratifications in patients with primary gastrointestinal stromal tumours (GISTs).

Materials and methods

One hundred and twenty-nine patients with histologically confirmed primary GISTs (diameter >2 cm) were enrolled. CT imaging features were reviewed. Tumour risk stratifications were determined according to the 2008 NIH criteria where GISTs were classified into four categories according to the tumour size, location, mitosis count, and tumour rupture. The association between risk stratifications and CT features was analyzed using univariate analysis, followed by multinomial logistic regression and receiver operating characteristic (ROC) curve analysis.


CT imaging features including tumour margin, size, shape, tumour growth pattern, direct organ invasion, necrosis, enlarged vessels feeding or draining the mass (EVFDM), lymphadenopathy, and contrast enhancement pattern were associated with the risk stratifications, as determined by univariate analysis (P < 0.05). Only lesion size, growth pattern and EVFDM remained independent risk factors in multinomial logistic regression analysis (OR = 3.480–100.384). ROC curve analysis showed that the area under curve of the obtained multinomial logistic regression model was 0.806 (95 % CI: 0.727–0.885).


CT features including lesion size, tumour growth pattern, and EVFDM were predictors of the risk stratifications for GIST.

Key Points

CT features were of predictive value for risk stratification of GISTs.

Tumour size, growth patterns, and EVFDM were risk predictors of GISTs.

Large size, mixed growth pattern, or EVFDM indicated high risk GIST.


Abdominal neoplasm Gastrointestinal stromal tumour, primary Prognosis Computed tomography Risk factors 



The scientific guarantor of this publication is Jun Shen. The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. This study has received funding by the Program for New Century Excellent Talents in University of China (No:NCET-11-0538) and Elite Young Scholars Program of Sun Yat-Sen Memorial Hospital (No:J201403). One of the authors has significant statistical expertise. No complex statistical methods were necessary for this paper. Institutional Review Board approval was obtained. Written informed consent was waived by the Institutional Review Board. Methodology: retrospective, diagnostic or prognostic study, multicenter study.


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Copyright information

© European Society of Radiology 2015

Authors and Affiliations

  • Cuiping Zhou
    • 1
  • Xiaohui Duan
    • 2
  • Xiang Zhang
    • 1
  • Huijun Hu
    • 2
  • Dongye Wang
    • 2
  • Jun Shen
    • 2
    Email author
  1. 1.Department of RadiologyThe Huizhou Central municipal HospitalHuizhouChina
  2. 2.Department of Radiology, Sun Yat-Sen Memorial HospitalSunYat-Sen UniversityGuangzhouChina

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