Survival outcomes, such as overall survival or recurrence-free survival, are called right-censored because for many patients the event has not yet occurred at the last follow-up time. With an increased number of available features and relatively small number of patients and even smaller number of events, dimensionality reduction is needed to reduce the sparsity of the data and make standard approaches such as Cox Proportional Hazards (Cox) model effective. Clustering is used to identify similar groups within the data and can be thought as a dimensionality reduction technique when the cluster label is used in the analysis. Our goal is to identify similar groups of patients that exhibit the same response to treatment or expected outcomes in order to improve the prediction accuracy for new patients.
In this thesis, we explore different ways of leveraging clustering for improved prognosis for head and neck cancer patients. To circumvent the right-censoring of survival outcomes, we use the residuals from a Cox as the dependent variable for guiding clustering of the data. We propose two approaches. The first one, Supervised Scaled Clustering (SSC), uses the residuals to scale the features using a regression model before clustering the patients using K-medians and consensus clustering. The second one, Supervised Domain Clustering (SDC), considers groups of features and uses the residuals to learn the most suitable dissimilarity for clustering. Cluster labels are then used as covariates within a Cox model and/or other survival models. A rigorous experimental evaluation summarizes, compares and contrasts different metrics for model comparison and performance evaluation. Results show that our approaches find significantly discriminative groupings w.r.t. to the outcomes, and can serve as a feature extraction method that can improve performance while considerably reducing the dimensionality of the original feature space.