Radiology reports are unstructured free text documents that describe abnormalities in patients that are visible via imaging modalities such as X-ray. The number of imaging examinations performed in clinical care is enormous, and mining large repositories of radiology reports connected with clinical data such as patient outcomes could enable epidemiological studies, such as correlating the frequency of infections to the presence or length of time medical devices are present in patients. We developed a natural language processing (NLP) system to recognize device mentions in radiology reports and information about their state (insertion or removal) to enable epidemiological research. We tested our system using a reference standard of reports that were annotated to indicate this information. Our system performed with high accuracy (recall and precision of 97% and 99% for device mentions and 91-96% for device insertion status). Our methods are generalizable to other types of radiology reports as well as to other information extraction tasks and could provide the foundation for tools that enable epidemiological research exploration based on mining radiology reports.