For metastatic esophagogastric cancer (EGC), treatments aim to extend survival time, manage symptoms, and enhance the quality of life . However, determining the best treatments for patients with EGC is challenging due to patients’ variability. Personalised treatments supported by predictive models enable tailoring treatment process to individuals. Even so, traditional predictive models often neglect the interaction between treatments, limiting their utility in comprehensive planning. State-of-the-art Predictive Process Monitoring shows promising results in predicting the outcome of the treatment process but often lacks transparency. This paper investigates the potential of supporting healthcare experts in personalising the EGC treatment process, using eXplainable Predictive Process Monitoring methods. A real-world case study among 7,090 patients identifies expert needs for helpful explanations and discusses the capabilities and limitations of existing methods, suggesting future research directions. Our findings demonstrate high-quality explanations with strong fidelity, providing insights validated by expert knowledge. While the resulting explanations are not always actionable, experts acknowledged their value for exploratory analysis.