With the ongoing advancements in cancer drug development, a subset of patients can live quite long, or are even considered cured in certain cancer types. Additionally, nonproportional hazards, such as delayed treatment effects and crossing hazards, are commonly observed in cancer clinical trials with immunotherapy. To address these challenges, various cure models have been proposed to integrate the cure rate into trial designs and accommodate delayed treatment effects. In this article, we introduce a unified approach for calculating sample sizes, taking into account different cure rate models and nonproportional hazards. Our approach supports both the traditional weighted logrank test and the Maxcombo test, which demonstrates robust performance under nonproportional hazards. Furthermore, we assess the accuracy of our sample size estimation through Monte Carlo simulations across various scenarios and compare our method with existing approaches. Several illustrative examples are provided to demonstrate the proposed method.