We coded 700 radiology reports from 373 women using an unmodified deployment of the Cancer Text Information Extraction System (caTIES), a publicly-available tool using natural language processing techniques. We were moderately successfully using caTIES for case ascertainment, successfully identifying 9/11 of a random sample of cancer case (sensitivity 82%) and 5/100 controls (specificity 95%) We are currently developing a classification scheme to assess clinical risk of ovarian cancer and identifying required extensions to caTIES algorithms.