Yang HX, Feng W, Wei JC, Zeng TS, Li ZD, Zhang LJ, Lin P, Luo RZ, He JH, Fu JH. Support vector machine-based nomogram predicts postoperative distant metastasis for patients with oesophageal squamous cell carcinoma.
Br J Cancer 2013;
109:1109-16. [PMID:
23942069 PMCID:
PMC3778272 DOI:
10.1038/bjc.2013.379]
[Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2013] [Revised: 04/25/2013] [Accepted: 06/24/2013] [Indexed: 12/18/2022] Open
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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