Expanding the Secondary Use of Prostate Cancer Real World Data: Automated Classifiers for Clinical and Pathological Stage - Bozkurt, Selen; Magnani, Christopher J.; Seneviratne, Martin G.; Brooks, James D.; Hernandez-Boussard, Tina (Frontiers in Digital Health, 2022)
Background: Explicit documentation of stage is an endorsed quality metric by the National Quality Forum. Unfortunately, clinical and pathological cancer staging is inconsistently recorded within clinical narratives but can be derived from text in the Electronic Health Record (EHR). To address this need, we developed a Natural Language Processing (NLP) solution for extraction of clinical and pathological TNM stages from the clinical notes in prostate cancer patients. Methods: A randomly selected sample of patients were manually annotated for stage to establish the ground truth for training and validating the NLP methods. For each patient, a vector representation of clinical text was used to train a machine learning model alongside a rule-based model and compared with the ground truth. Results: A total of 5,461 prostate cancer patients were identified in the CDW and over 30% were missing stage information. 33-36% of patients had missing clinical stage the models accurately imputed stage in 21%-32%. 21% of had missing pathological stage and with NLP 71% of missing T stages and 56% of missing N stages were imputed. For both clinical and pathological T and N stages, the rule-based NLP approach out-performed the ML approach. For clinical M stage the ML approach out-performed the rule-based model. Conclusions: We developed an NLP pipeline to successfully extract clinical and pathological staging information from clinical narratives. These methods can serve as a model for augmenting clinical and pathological stage reporting in cancer registries and EHRs to enhance the secondary use of these data.