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

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.