In this case study, records about prostate cancer patients, provided by the Cancer Registry of Rhineland-Palatinate, Germany, are analyzed. The dataset is comparatively large and cases are rather complete, as they contain events gathered not only from one institution (e. g., a single hospital), but from multiple institutions along the end-to-end patient journey. The analysis, which aims at getting insights on prostate cancer treatment pathways and contributing to state-of-the-art research in the Process Mining for Healthcare (PM4H) field, is powered by methods and techniques from the process mining domain. Therefore, dealing with a process mining project, the \(PM^2\) method was followed with the recommended phases in collaboration with the Cancer Registry of Rhineland-Palatinate, Germany. The initial analysis of \(\sim \) 12k cases ( \(\sim \) 90k events) recorded during 2018–2022 and considering only a small number of potential available data attributes already led to barely comprehensible spaghetti models, emphasizing the need for different views of granularity and complexity. This case study also provides results on the regular treatment pathways (such as surgery, or therapies).