Interval Kernels for Combining Biometric Measurements from Multiple Prostate Samples per Patient in Prognostic Models with Transductive Semi-Supervised Support Vector Regression

Prostate cancer is the most prevalent form of cancer and second most common form of cancer deaths among men in the United States. Physicians work with patients to make difficult treatment decisions. They are often aided by a multitude of prognostic models to assess risk and predict outcomes. These survival analysis models may analyze features characterizing biomolecular and histomorphic properties of the solid tumor sample. Commonly, multiple samples per patient are analyzed and features are combined in some way to generate a single result, such as by taking the median measurement. Recently, support vector regression approaches paired with a semi-supervised transduction framework have proven powerful in building such prognostic models. Separately, there has been work presenting the use of interval kernels to capture the range of measurements across multiple samples as an “interval” via the Hausdorff distance within a kernel based support vector approach. This paper presents the first results in exploring a combination of these two concepts. Namely, using an interval kernel based support vector approach within the aforementioned transduction framework to build prognostic models leveraging information from multiple tumor samples per patient. The results show that interval kernels yield more accurate prognostic models, and the semi-supervised transduction framework further improves performance. This suggests that this novel combination of unique recent advances can help build better prognostic models and improve the treatment of prostate cancer.