Prediction of Time to Tumor Recurrence in Ovarian Cancer: Comparison of Three Sparse Regression Methods

摘要

Ovarian cancer is the most fatal gynecological malignancy among women. Making a reliable prediction of time to tumor recurrence would be a valuable contribution to post-surgery follow-up care. In this paper we study three well-known data sets, known as TCGA, Tothill and Yoshihara, and compare three sparse regression methods, two of which (LASSO and EN) are well-known and the third (CLOT) is from our laboratory. It is established that the three data sets are very different from each other. Therefore a two-stage predictor is built, whereby each test sample is first assigned to the most likely data set and then the corresponding predictor is used. The weighted concordance of each regression method is computed to compare the methods and select the best one. CLOT uses a biomarker panel of 103 genes and achieves a concordance index of 0.7829, which is higher than that achieved by the other two methods.