By using systems pathology, it might be possible to provide a predictive, personalized therapeutic recommendation for patients with prostate cancer. Systems pathology integrates quantitative data and information from many sources to generate a reliable prediction of the expected natural course of the disease and response to different therapeutic options. In other words, through the integration of relatively large data sets and the use of knowledge engineering, systems pathology aims at predicting the future behavior of tumors and their interaction with the host. In this Review, we introduce the methods used in systems pathology and summarize a recent study providing the first evidence of a concept for this strategy. The results show that systems pathology can provide a personalized prediction of the risk of recurrence after prostatectomy for cancer.