European Radiology

, Volume 30, Issue 1, pp 239–246 | Cite as

Radiomics analysis using contrast-enhanced CT for preoperative prediction of occult peritoneal metastasis in advanced gastric cancer

  • Shunli Liu
  • Jian He
  • Song Liu
  • Changfeng Ji
  • Wenxian Guan
  • Ling Chen
  • Yue Guan
  • Xiaofeng YangEmail author
  • Zhengyang ZhouEmail author



To evaluate the predictive value of CT radiomics features derived from the primary tumor in discriminating occult peritoneal metastasis (PM) in advanced gastric cancer (AGC).


Preoperative CT images of 233 patients with AGC were retrospectively analyzed. The region of interest (ROI) was manually drawn along the margin of the lesion on the largest slice of venous CT images, and a total of 539 quantified features were extracted automatically. The intra-class correlation coefficient (ICC) and the absolute correlation coefficient (ACC) were calculated for selecting influential features. A multivariate logistic regression model was constructed based on the training cohort, and the testing cohort validated the reliability of the model. Additionally, another model based on the preoperative clinic-pathological features was also developed. The comparison of the diagnostic performance between the two models was performed using ROC analysis and the Akaike information criterion (AIC) value.


Six radiomics features (ID_Energy, LoG(0.5)_Energy, Compactness2, Max Diameter, Orientation, and Surface Area Density) differed significantly between AGCs with and without PM and performed well in distinguishing AGCs with PM from those without PM in the primary cohort (AUC = 0.618–0.658). The radiomics model showed a higher AUC value than each single radiomics feature in the primary cohort (0.741 vs. 0.618–0.658) and similar diagnosis performance in the validation cohort. The radiomics model showed slightly worse diagnostic efficacy than the clinic-pathological model (AUC, 0.724 vs. 0.762).


Venous CT radiomics analysis based on the primary tumor provided valuable information for predicting occult PM in AGCs.

Key Points

Venous CT radiomics analysis provided valuable information for predicting occult peritoneal metastases in advanced gastric cancer.

CT-based T stage was an independent predictive factor of occult peritoneal metastases in advanced gastric cancer.

A radiomics model showed slightly worse diagnostic efficacy than a clinic-pathological model.


Stomach neoplasms Multidetector computed tomography Peritoneum Diagnosis Neoplasm metastasis 



Absolute correlation coefficient


Advanced gastric cancer


Akaike information criterion


Area under the curve


Hounsfield unit


Intra-class correlation coefficient


Peritoneal metastasis


Receiver operating characteristic


Regions of interest



This study has received funding by the National Natural Science Foundation of China (ID: 81501441, 81601463, 81871410), Social Development Foundation of Jiangsu Province (BE2015605), Natural Science Foundation of Jiangsu Province (ID: BK20150109), Jiangsu Province Health and Family Planning Commission Youth Scientific Research Project (ID: Q201508), Six Talent Peaks Project of Jiangsu Province (ID: 2015-WSN-079), and Jiangsu Provincial Medical Youth Talent (ID: QNRC2016040).

Compliance with ethical standards


The scientific guarantor of this publication is Zhengyang Zhou, MD.

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.


• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2019_6368_MOESM1_ESM.docx (352 kb)
ESM 1 (DOCX 351 kb)


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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Shunli Liu
    • 1
  • Jian He
    • 1
  • Song Liu
    • 1
  • Changfeng Ji
    • 1
  • Wenxian Guan
    • 2
  • Ling Chen
    • 3
  • Yue Guan
    • 4
  • Xiaofeng Yang
    • 5
    Email author
  • Zhengyang Zhou
    • 1
    Email author
  1. 1.Department of Radiology, Nanjing Drum Tower HospitalThe Affiliated Hospital of Nanjing University Medical SchoolNanjingChina
  2. 2.Department of Gastrointestinal Surgery, Nanjing Drum Tower HospitalThe Affiliated Hospital of Nanjing University Medical SchoolNanjingChina
  3. 3.Department of Pathology, Nanjing Drum Tower HospitalThe Affiliated Hospital of Nanjing University Medical SchoolNanjingChina
  4. 4.School of Electronic Science and EngineeringNanjing UniversityNanjingChina
  5. 5.Department of Radiation Oncology and Winship Cancer InstituteEmory UniversityAtlantaUSA

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