Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-01

AUTHORS

Xianzheng Tan, Zelan Ma, Lifen Yan, Weitao Ye, Zaiyi Liu, Changhong Liang

ABSTRACT

OBJECTIVES: To determine the value of radiomics in predicting lymph node (LN) metastasis in resectable esophageal squamous cell carcinoma (ESCC) patients. METHODS: 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. RESULTS: 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. CONCLUSIONS: The radiomics nomogram provides individualized risk estimation of LN metastasis in ESCC patients and outperforms size criteria. KEY POINTS: • 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. More... »

PAGES

1-9

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URI

http://scigraph.springernature.com/pub.10.1007/s00330-018-5581-1

DOI

http://dx.doi.org/10.1007/s00330-018-5581-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1104994133

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29922924


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41 schema:description OBJECTIVES: To determine the value of radiomics in predicting lymph node (LN) metastasis in resectable esophageal squamous cell carcinoma (ESCC) patients. METHODS: 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. RESULTS: 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. CONCLUSIONS: The radiomics nomogram provides individualized risk estimation of LN metastasis in ESCC patients and outperforms size criteria. KEY POINTS: • 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.
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