Machine Learning Algorithms for Predicting Treatment Outcomes of Oropharyngeal Cancer After Surgery

Abstract

Background and Objectives : This study analyzed data from patients who were diagnosed with human papilloma virus (HPV)-associated oropharyngeal (OPC) and treated surgically to construct a machine learning survival prediction model.

Subjects and Method : We retrospectively analyzed the clinico-pathological data of 203 patients with HPV-associated oropharyngeal squamous cell carcinoma (OPSCC) from 2007 to 2015.

Results : In the Cox proportional hazard (CPH) model, the c-index values for the training set and the test set were 0.81 and 0.59, respectively. The univariate analysis showed that contralateral lymph nodes (LNs) metastasis, lymphovascular invasion, pN, stage, surgical margin status, histologic grade, pT, and the number of metastatic LNs had significant correlations with survival. Contrastively, the multivariate analysis showed pT and histologic grade to have significant correlation with survival. In the random survival forest model, the c-index values for the training set and the test set were 0.83 and 0.87, respectively. In the DeepSurv model, the cindex values for the training set and the test set were 0.75 and 0.83. Among the three models mentioned above, Random Survival Forest and DeepSurv showed the best performance for predicting the survival of HPV-associated OPSCC patients.

Conclusion : We confirmed that a survival prediction model using machine learning and deep learning algorithms showed reasonable survival estimates for HPV-associated OPSCC patients.