CT radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer
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To investigate the role of computed tomography (CT) radiomics for the preoperative prediction of lymph node (LN) metastasis in gastric cancer.
Materials and methods
This retrospective study included 247 consecutive patients (training cohort, 197 patients; test cohort, 50 patients) with surgically proven gastric cancer. Dedicated radiomics prototype software was used to segment lesions on preoperative arterial phase (AP) CT images and extract features. A radiomics model was constructed to predict the LN metastasis by using a random forest (RF) algorithm. Finally, a nomogram was built incorporating the radiomics scores and selected clinical predictors. Receiver operating characteristic (ROC) curves were used to validate the capability of the radiomics model and nomogram on both the training and test cohorts.
The radiomics model showed a favorable discriminatory ability in the training cohort with an area under the curve (AUC) of 0.844 (95% CI, 0.759 to 0.909), which was confirmed in the test cohort with an AUC of 0.837 (95% CI, 0.705 to 0.926). The nomogram consisted of radiomics scores and the CT-reported LN status showed excellent discrimination in the training and test cohorts with AUCs of 0.886 (95% CI, 0.808 to 0.941) and 0.881 (95% CI, 0.759 to 0.956), respectively.
The CT-based radiomics nomogram holds promise for use as a noninvasive tool in the individual prediction of LN metastasis in gastric cancer.
• CT radiomics showed a favorable performance for the prediction of LN metastasis in gastric cancer.
• Radiomics model outperformed the routine CT in predicting LN metastasis in gastric cancer.
• The radiomics nomogram holds potential in the individualized prediction of LN metastasis in gastric cancer.
KeywordsGastric cancer Radiomics Nomogram
Area under the curve
Classification and regression tree
Decision curve analysis
Digital Imaging and Communications in Medicine
Intraclass correlation coefficient
Gray-level co-occurrence matrix
Gray-level run-length matrix
Magnetic resonance imaging
Receiver operating characteristics
Volume of interest
We would like to appreciate our co-author Yang Yu from the Siemens Healthineers for assisting in radiomics model construction and statistical analysis. The authors also acknowledge Wei Han from the Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College, for his kind help for statistical analysis.
The study was funded by National Public Welfare Basic Scientific Research Program of the Chinese Academy of Medical Sciences (Grant Nos. 2018PT32003 and 2017PT32004).
Compliance with ethical standards
The scientific guarantor of this publication is Prof. Zheng-yu Jin.
Conflict of interest
One of the co-authors, Yang Yu, is an employee of Siemens Healthineers. The other 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
Our co-author Yang Yu and Wei Han from the Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College, kindly provided statistical advice for this manuscript.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
• diagnostic or prognostic study
• performed at one institution
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