Precision medicine is formalized through the identification of individualized treatment rules (ITRs) that maximize a clinical outcome. When the type of outcome is time-to-event, the correct handling of censoring is crucial for estimating reliable optimal ITRs. In this paper, we extend the jackknife estimator of the value function to allow for right-censored data for a binary treatment. The jackknife estimator or leave-one-out-cross-validation (Jiang et al., 2020) approach was proposed to estimate the value function and select optimal ITRs using existing machine learning methods. We address the issue of censoring by introducing an inverse probability of censoring weighted (IPCW) adjustment in the expression of the jackknife estimator of the value function. We prove the consistency of the newly proposed estimator under minimal assumptions. Furthermore, using a Z-test, we compare the optimal ITR learned with a precision medicine model with the zero-order model. Through simulation studies, we show the asymptotic properties and the performance of our proposed estimator under different censoring rates.