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European Radiology

, Volume 29, Issue 5, pp 2481–2489 | Cite as

The roles of CT and EUS in the preoperative evaluation of gastric gastrointestinal stromal tumors larger than 2 cm

  • Tao ChenEmail author
  • Lili Xu
  • Xiaoyu Dong
  • Yue Li
  • Jiang Yu
  • Wei XiongEmail author
  • Guoxin Li
Gastrointestinal
  • 184 Downloads

Abstract

Objective

This study aimed to investigate the endoscopic ultrasound (EUS) and computed tomography (CT) features of gastric gastrointestinal stromal tumors (GISTs) for assessing potential malignancy and prognosis.

Methods

Fifty consecutive patients with primary gastric GISTs larger than 2 cm were retrospectively enrolled in this study. The association of CT and EUS features with malignancy was analyzed using univariate and stepwise logistic regression method. The agreement between EUS/CT lesion size and pathologic tumor size was analyzed by calculating the intraclass correlation coefficient (ICC) value, and the association of imaging features with mitotic counts was further analyzed using univariate analysis. The Kaplan-Meier method and Cox proportional hazards models were used to assess the value of imaging features for predicting the prognosis of GIST patients.

Results

Tumor size > 5 cm and an exophytic/mixed growth pattern on CT as well as tumor size > 5 cm and the presence of cystic spaces on EUS were independent predictors of highly malignant GISTs (all p < 0.05). The ICC values of CT/EUS lesion size relative to pathologic tumor size showed very good reliability (0.853 for EUS and 0.831 for CT). Only tumor shape and growth pattern on CT were significant for predicting mitotic index (both p < 0.05). Direct organ invasion on CT (p = 0.036; hazard ratio [HR] = 11.891) and serosal invasion on EUS (p = 0.015; HR = 8.223) were independent adverse prognostic factors.

Conclusions

CT features may be more useful than EUS features for predicting tumor mitotic index. In addition, preoperative imaging features can help predict the prognosis of gastric GISTs.

Key Points

Both CT and EUS features can be used for risk stratification of gastric GISTs larger than 2 cm.

• CT features performed better than EUS features for predicting tumor mitotic index.

• Preoperative imaging features can help predict the prognosis of gastric GISTs.

Keywords

Gastrointestinal stromal tumors Prognosis Stomach neoplasms Endosonography Tomography, x-ray computed 

Abbreviations

CI

Confidence interval

EUS

Endoscopic ultrasonography

GIST

Gastrointestinal stromal tumor

HR

Hazard ratio

ICC

Intraclass correlation coefficient

NIH

National Institutes of Health

OR

Odds ratio

RFS

Recurrence-free survival

SMT

Submucosal tumor

TKI

Tyrosine kinase inhibitor

Notes

Acknowledgements

We thank Xixi Zhao and Zelong Han for their contributions.

Funding

This study has received funding by the State’s Key Project of Research and Development Plan (2017YFC0108300 and 2017YFC0108303).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Tao Chen.

Conflict of interest

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.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2018_5945_MOESM1_ESM.doc (52 kb)
ESM 1 (DOC 52 kb)

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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of General Surgery, Nanfang Hospital, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive SurgerySouthern Medical UniversityGuangzhouChina
  2. 2.Medical Image Center, Nanfang HospitalSouthern Medical UniversityGuangzhouChina
  3. 3.Department of Digestive Endoscopy, Nanfang HospitalSouthern Medical UniversityGuangzhouChina

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