Extracting tumour prognostic factors from a diverse electronic record dataset in genito-urinary oncology

Abstract

Objectives: To implement a system for unsupervised extraction of tumor stage and prognostic data in patients with genitourinary cancers using clinicopathological and radiology text. Methods: A corpus of 1054 electronic notes (clinician notes, radiology reports and pathology reports) was annotated for tumor stage, prostate specific antigen (PSA) and Gleason grade. Annotations from five clinicians were reconciled to form a gold standard dataset. A training dataset of 386 documents was sequestered. The Medtex algorithm was adapted using the training dataset. Results: Adapted Medtex equaled or exceeded human performance in most annotations, except for implicit M stage (F-measure of 0.69 vs 0.84)..