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. 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

H X Yang  1 , W Feng, J C Wei, T S Zeng, Z D Li, L J Zhang, P Lin, R Z Luo, J H He, J H Fu
Affiliations

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

H X Yang et al. Br J Cancer. .

Abstract

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

Methods: 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.

Results: 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.

Conclusion: 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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Figures

Figure 1
Figure 1
Representative figures on IHC staining for 23 molecular markers ( × 200, for IHC staining).
Figure 2
Figure 2
ROC curves for receptors for 12 immunomarkers, pathological T category, pathological N category, cell differentiation, tumour length, and the SVM-based models using (A) the training cohort (n=319), (B) the validation cohort (n=164), (C) the mixed training cohort (n=322), and (D) the mixed validation cohort (n=161).
See this image and copyright information in PMC

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