We will develop a general extended hazard spatial survival model to predict geographical effects and geo- graphically varying effects in cancer survival. The proposed model can include the proportional hazards spatial survival model and the accelerated failure time spatial survival model as its special cases. Furthermore, the new model can correctly identify the geographical effects and geographically varying effects in cancer survival. The performance of the proposed model will be evaluated by a comprehensive simulation study. To demonstrate the usage of the proposed model, we will apply the proposed method to analyze prostate cancer within Louisiana from the Surveillance, Epidemiology, and End Results program, and prostate cancer data set from South Carolina Central Cancer Registry (SCCCR). The software development will solve the computational issue in practice and will enable the practitioners and researchers apply the proposed method easily.