title: A natural language processing program effectively extracts key pathologic findings from radical prostatectomy reports.
abstract: OBJECTIVE
  Natural language processing (NLP) software programs have been widely developed to transform complex free text into simplified organized data. Potential applications in the field of medicine include automated report summaries, physician alerts, patient repositories, electronic medical record (EMR) billing, and quality metric reports. Despite these prospects and the recent widespread adoption of EMR, NLP has been relatively underutilized. The objective of this study was to evaluate the performance of an internally developed NLP program in extracting select pathologic findings from radical prostatectomy specimen reports in the EMR.
  
  
  METHODS
  An NLP program was generated by a software engineer to extract key variables from prostatectomy reports in the EMR within our healthcare system, which included the TNM stage, Gleason grade, presence of a tertiary Gleason pattern, histologic subtype, size of dominant tumor nodule, seminal vesicle invasion (SVI), perineural invasion (PNI), angiolymphatic invasion (ALI), extracapsular extension (ECE), and surgical margin status (SMS). The program was validated by comparing NLP results to a gold standard compiled by two blinded manual reviewers for 100 random pathology reports.
  
  
  RESULTS
  NLP demonstrated 100% accuracy for identifying the Gleason grade, presence of a tertiary Gleason pattern, SVI, ALI, and ECE. It also demonstrated near-perfect accuracy for extracting histologic subtype (99.0%), PNI (98.9%), TNM stage (98.0%), SMS (97.0%), and dominant tumor size (95.7%). The overall accuracy of NLP was 98.7%. NLP generated a result in <1 second, whereas the manual reviewers averaged 3.2 minutes per report.
  
  
  CONCLUSIONS
  This novel program demonstrated high accuracy and efficiency identifying key pathologic details from the prostatectomy report within an EMR system. NLP has the potential to assist urologists by summarizing and highlighting relevant information from verbose pathology reports. It may also facilitate future urologic research through the rapid and automated creation of large databases.
authors.affiliation: 1 Department of Urology, Kaiser Permanente Los Angeles Medical Center , Los Angeles, California.
authors.forename: Brian J
authors.initials: BJ
authors.lastname: Kim
authors.forename: Madhur
authors.initials: M
authors.lastname: Merchant
authors.forename: Chengyi
authors.initials: C
authors.lastname: Zheng
authors.forename: Anil A
authors.initials: AA
authors.lastname: Thomas
authors.forename: Richard
authors.initials: R
authors.lastname: Contreras
authors.forename: Steven J
authors.initials: SJ
authors.lastname: Jacobsen
authors.forename: Gary W
authors.initials: GW
authors.lastname: Chien
mesh_terms: D000230:Adenocarcinoma / Q000473:pathology*; D003625:Data Collection; D016208:Databases, Factual; D006801:Humans; D008297:Male; D009323:Natural Language Processing; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D010336:Pathology; D011467:Prostate / Q000473:pathology*; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000473:pathology*; D058028:Research Report; D047368:Tumor Burden