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Br J Cancer. 2013 Sep 3;109(5):1109-16. doi: 10.1038/bjc.2013.379. Epub 2013 Aug 13.

Support vector machine-based nomogram predicts postoperative distant metastasis for patients with oesophageal squamous cell carcinoma.

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  • 1Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou City, Guangdong Province 510060, China.



We aim to develop effective models for predicting postoperative distant metastasis for oesophageal squamous cell carcinoma (OSCC) for the purpose of guiding tailored therapy.


We used data from two centres to establish training (n=319) and validation (n=164) cohorts. All patients underwent curative surgical treatment. The clinicopathological features and 23 immunomarkers detected by immunohistochemistry were involved for variable selection. We constructed eight support vector machine (SVM)-based nomograms (SVM1-SVM4 and SVM1'-SVM4'). The nomogram constructed with the training cohort was tested further with the validation cohort.


The outcome of the SVM1 model in predicting postoperative distant metastasis was as follows: sensitivity, 44.7%; specificity, 90.9%; positive predictive value, 81.0%; negative predictive value, 65.6%; and overall accuracy, 69.5%. The corresponding outcome of the SVM2 model was as follows: 44.7%, 92.1%, 82.9%, 65.9%, and 70.1%, respectively. The corresponding outcome of the SVM3 model was as follows: 55.3%, 93.2%, 87.5%, 70.7%, and 75.6%, respectively. The SVM4 model was the most effective nomogram in prediction, and the corresponding outcome was as follows: 56.6%, 97.7%, 95.6%, 72.3%, and 78.7%, respectively.Similar results were observed in SVM1', SVM2', SVM3', and SVM4', respectively.


The SVM-based models integrating clinicopathological features and molecular markers as variables are helpful in selecting the patients of OSCC with high risk of postoperative distant metastasis.

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