Using local MRI and whole-body PET/CT imaging, we integrate local and global deep features, incorporate clinical data, and utilize a multi-modal model to predict overall patient survival. Our primary focus is on exploring the fusion of PET/CT and MRI image features, utilizing attention mechanisms, and understanding the correlation of deep features across different modalities. We also emphasize the effective integration of attention-based multi-modal deep features with clinical information. Building upon prognostic predictions, we employ a reverse-search strategy to identify the optimal treatment regimen. By employing a reinforcement learning approach to enhance treatment decision-making, our predictive model functions as both a reward and penalty mechanism, enabling the realization of personalized treatment strategies. This includes employing exhaustive methods to refine treatment choices, such as drug types, dosages, and administration schedules, and leveraging reinforcement learning for dynamically adapting treatment plans.