Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer



To build a dual-energy CT (DECT)–based deep learning radiomics nomogram for lymph node metastasis (LNM) prediction in gastric cancer.

Materials and methods

Preoperative DECT images were retrospectively collected from 204 pathologically confirmed cases of gastric adenocarcinoma (mean age, 58 years; range, 28–81 years; 157 men [mean age, 60 years; range, 28–81 years] and 47 women [mean age, 54 years; range, 28–79 years]) between November 2011 and October 2018, They were divided into training (n = 136) and test (n = 68) sets. Radiomics features were extracted from monochromatic images at arterial phase (AP) and venous phase (VP). Clinical information, CT parameters, and follow-up data were collected. A radiomics nomogram for LNM prediction was built using deep learning approach and evaluated in test set using ROC analysis. Its prognostic performance was determined with Harrell’s concordance index (C-index) based on patients’ outcomes.


The dual-energy CT radiomics signature was associated with LNM in two sets (Mann-Whitney U test, p < 0.001) and an achieved area under the ROC curve (AUC) of 0.71 for AP and 0.76 for VP in test set. The nomogram incorporated the two radiomics signatures and CT-reported lymph node status exhibited AUCs of 0.84 in the training set and 0.82 in the test set. The C-indices of the nomogram for progression-free survival and overall survival prediction were 0.64 (p = 0.004) and 0.67 (p = 0.002).


The DECT-based deep learning radiomics nomogram showed good performance in predicting LNM in gastric cancer. Furthermore, it was significantly associated with patients’ prognosis.

Key Points

• This study investigated the value of deep learning dual-energy CT–based radiomics in predicting lymph node metastasis in gastric cancer.

• The dual-energy CT–based radiomics nomogram outweighed the single-energy model and the clinical model.

• The nomogram also exhibited a significant prognostic ability for patient survival and enriched radiomics studies.

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Arterial phase


Area under the receiver operating characteristic curve


Confidence interval


Gastric cancer


Gemstone spectral imaging


Iodine concentration


Lymph node metastasis


Material deposition


Overall survival


Progression-free survival


Venous phase


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This study has received funding by National Natural Science Foundation of China (81271573, 91959130, 0, 81971776, 81771924).

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Corresponding author

Correspondence to Jianbo Gao.

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The scientific guarantor of this publication is Jianbo Gao.

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 not required for this study because this is a retrospective diagnostic study. Written informed consent was waived by the Institutional Review Board of Zhengzhou University.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

A total of 73 of the 204 patients have been previously reported. This prior article dealt with the potential of IC value for the prediction of lymph node metastasis in gastric cancer by multivariate logistic analysis, whereas in this manuscript, we report on the additional predictive value of a deep learning spectral CT-based radiomics model for the LNM prediction in gastric cancer.


• retrospective

• diagnostic or prognostic study

• performed at one institution

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Li, J., Dong, D., Fang, M. et al. Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer. Eur Radiol 30, 2324–2333 (2020). https://doi.org/10.1007/s00330-019-06621-x

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  • Gastric cancer
  • Tomography, X-ray computed
  • Lymph node
  • Radiomics
  • Deep learning