Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma
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To determine the value of radiomics in predicting lymph node (LN) metastasis in resectable esophageal squamous cell carcinoma (ESCC) patients.
Data of 230 consecutive patients were retrospectively analyzed (154 in the training set and 76 in the test set). A total of 1576 radiomics features were extracted from arterial-phase CT images of the whole primary tumor. LASSO logistic regression was performed to choose the key features and construct a radiomics signature. A radiomics nomogram incorporating this signature was developed on the basis of multivariable analysis in the training set. Nomogram performance was determined and validated with respect to its discrimination, calibration and reclassification. Clinical usefulness was estimated by decision curve analysis.
The radiomics signature including five features was significantly associated with LN metastasis. The radiomics nomogram, which incorporated the signature and CT-reported LN status (i.e. size criteria), distinguished LN metastasis with an area under curve (AUC) of 0.758 in the training set, and performance was similar in the test set (AUC 0.773). Discrimination of the radiomics nomogram exceeded that of size criteria alone in both the training set (p <0.001) and the test set (p=0.005). Integrated discrimination improvement (IDI) and categorical net reclassification improvement (NRI) showed significant improvement in prognostic value when the radiomics signature was added to size criteria in the test set (IDI 17.3%; p<0.001; categorical NRI 52.3%; p<0.001). Decision curve analysis supported that the radiomics nomogram is superior to size criteria.
The radiomics nomogram provides individualized risk estimation of LN metastasis in ESCC patients and outperforms size criteria.
• A radiomics nomogram was built and validated to predict LN metastasis in resectable ESCC.
• The radiomics nomogram outperformed size criteria.
• Radiomics helps to unravel intratumor heterogeneity and can serve as a novel biomarker for determination of LN status in resectable ESCC.
KeywordsEsophageal squamous cell carcinoma Lymphatic metastasis Diagnostic imaging Nomograms Precision medicine
American Joint Committee on Cancer
Area under curve
Esophageal squamous cell carcinoma
Gray level co-occurrence matrix
Gray level run length matrix
Gray level size zone matrix
Intra-class correlation coefficient
Integrated discrimination improvement
Least absolute shrinkage and selection operator
Net reclassification improvement
Receiver operator characteristic
Volume of interest
This study has received funding by the National Key Research and Development Plan of China (grant number: 2017YFC1309100), National Natural Scientific Foundation of China (grant number: 81771912 and U1301258) and Science and Technology Planning Project of Guangdong Province (grant number: 2017B020227012)
Compliance with ethical standards
The scientific guarantor of this publication is Changhong Liang.
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.
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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