In the presence of high dimensional predictors, it is challenging to develop reliable regression models that can be used to accurately predict future outcomes.Further complications arise when the outcome of interest is an event time which is often not fully observed due to censoring.In this paper, we develop robust prediction models for event time outcomes by regularizing the Gehans estimator for the AFT model with LASSO penalty.Unlike existing methods based on the inverse probability wcighting and the Buckley and James estimator, the proposed approach does not require additional assumptions about the censoring and always yields a solution that is convergent.Furthermore, the proposed estimator leads to a stable regression model for prediction even if the AFT model fails to hold.To facilitate the adaptive selection of the tuning parameter, we detail an efficient numerical algorithm for obtaining the entire regnlarization path.The proposed procedures are applied to a breast cancer dataset to derive a reliable regression model for predicting patient survival based on a set of clinical prognostic factors and gene signatures.Finite sample performances of the procedures are evaluated through a simulation study.