Prognostic value of computed tomography radiomics features in patients with gastric cancer following curative resection

  • Wuchao Li
  • Liwen Zhang
  • Chong Tian
  • Hui Song
  • Mengjie Fang
  • Chaoen Hu
  • Yali Zang
  • Ying Cao
  • Shiyuan Dai
  • Fang Wang
  • Di Dong
  • Rongpin WangEmail author
  • Jie TianEmail author



The present study aimed to investigate the clinical prognostic significance of radiomics signature (R-signature) in patients with gastric cancer who had undergone radical resection.


A total of 181 patients with gastric cancer who had undergone radical resection were enrolled in this retrospective study. The association between the R-signature and overall survival (OS) was assessed in the primary cohort and verified in the validation cohort. Furthermore, the performance of a radiomics nomogram integrating the R-signature and significant clinicopathological risk factors was evaluated.


The R-signature, which consisted of six imaging features, stratified patients with gastric cancer who had undergone radical resection into two prognostic risk groups in both cohorts. The radiomics nomogram incorporating R-signature and significant clinicopathological risk factors (T stage, N stage, and differentiation) exhibited significant prognostic superiority over clinical nomogram and R-signature alone (Harrell concordance index, 0.82 vs 0.71 and 0.82 vs 0.74, respectively, p < 0.001 in both analyses). All calibration curves showed remarkable consistency between predicted and actual survival, and decision curve analysis verified the usefulness of the radiomics nomogram for clinical practice.


The R-signature could be used to stratify patients with gastric cancer following radical resection into high- and low-risk groups. Furthermore, the radiomics nomogram provided better predictive accuracy than other predictive models and might aid clinicians with therapeutic decision-making and patient counseling.

Key Points

Radiomics can stratify the gastric cancer patients following radical resection into high- and low-risk groups.

Radiomics can improve the prognostic value of TNM staging system.

Radiomics may facilitate personalized treatment of gastric cancer patients.


Multidetector computed tomography Stomach neoplasms Survival 



Akaike information criterion


Computed tomography


Hazard ratio


Interclass correlation coefficients


Least absolute shrinkage and selection operator


Overall survival


Radiomics scores


Radiomics signature


Region of interest





This work was supported by the National Natural Science Foundation of China (Nos. 81360565, 61661010, 81227901, 81771924, 81501616, 61231004, 81671851, and 81527805), the National Key R&D Program of China (2017YFA0205200, 2017YFC1308700, 2017YFC1309100), the Science and Technology Service Network Initiative of the Chinese Academy of Sciences (KFJ-SW-STS-160), the Beijing Municipal Science and Technology Commission (Z161100002616022), the Guizhou Provincial Department of Science and Technology and Guizhou Provincial People’s Hospital United Foundation (QKHLHZ[2015]7115), the Guizhou Provincial People’s Hospital Doctoral Foundation (GZSYBS[2015]02), the Science and Technology Foundation of Guizhou Province (QKHJC[2016]1096), the Technology and Innovation Foundation for the Returned Overseas Chinese Scholars (QRXMZZ(2016)03), and the Guizhou Science and Technology Department Key Project (QKF[2017]25).

Compliance with ethical standards


The scientific guarantor of this publication is Jie Tian.

Conflict of interest

The authors declare that they have no conflict of interest.

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.


• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2018_5861_MOESM1_ESM.docx (7.7 mb)
ESM 1 (DOCX 7872 kb)


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Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of RadiologyGuizhou Provincial People’s HospitalGuiyangChina
  2. 2.CAS Key Laboratory of Molecular Imaging, Institute of AutomationChinese Academy of SciencesBeijingChina
  3. 3.Guizhou Provincial Key Laboratory of Intelligent Medical Image Analysis and Precision DiagnosisGuizhou Provincial People’s HospitalGuiyangChina
  4. 4.School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
  5. 5.Department of PathologyGuizhou Provincial People’s HospitalGuiyangChina
  6. 6.Department of Medical Records and StatisticsGuizhou Provincial People’s HospitalGuiyangChina
  7. 7.Department of General SurgeryGuizhou Provincial People’s HospitalGuiyangChina
  8. 8.Beijing Advanced Innovation Center for Big Data-Based Precision MedicineBeihang UniversityBeijingChina

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